Information is a currency in modern business and strategic decisions that are tied to it, can make or break the enterprise.
The relative significance of efficient customer data management cannot be overstated. There has been a sustained surge in demand for business intelligence (BI) tools for years now, and this is a testament to the competitive business landscape, where the ability to access timely and relevant information is essential for good decision-making.
At the center of some, is the Business Intelligence Competency Center (BICC). For some businesses, the BICC is a pivotal entity that ensures organizations can deploy BI solutions with sustainability, efficiency, and strategic precision.
Often a BICC operates as a driving force, establishing policies, governance structures, methodologies, and support models that foster just-in-time decision-making. The mandate of the BICC extends beyond mere functionality, encompassing the transformation of insights into tangible assets, thereby providing a competitive advantage and fueling business growth. Thatâs the theory at least.
We think of the BICC as a cross-functional team within an organization that focuses on promoting and implementing best practices in business intelligence, data management, and analytics. The BICC develops the overall strategic plan and priorities for Business Intelligence (BI) and defines the requirements including data quality and governance, and fulfils the role of promoting the use of BI. Organizations with a BICC claim to have seen increased usage of Business Intelligence, increased business user satisfaction, better understanding of the value of BI, increased decision-making speed, and decreased staff and software costs.
Often there is a significant role played by data warehouses in managing business information. Organizations increasingly recognize that technology as an enabler means more effective information management. Recognition of this underscores the need for a centralized approach to information management that integrates policies, technologies, programs, and people to deliver BI efficiently.
BICCs are a growing trend in industry and are made up of both technical and business experts, they signal a departure from the conventional integration of BI with IT. Instead, a more holistic approach is adopted, where BI experts equipped with technical, analytical, and business skills collaborate with the business to align knowledge strategies with broader business objectives. In some organizations, the head of the BICC is often even part of C-level management resulting in an expectation of higher returns on investment for BI projects.
BICCs can be multifaceted and contribute significantly to the effective integration of BI into organizational processes. From conducting assessments for strategic services to actually implementing BI software, administering systems, providing training, mentoring users, and overseeing development activities, the BICC ensures that every facet of BI is optimized. This optimization, in turn, leads to a better understanding of the value of BI, increased user satisfaction, faster and more accurate decision-making, adaptability to changes, and enhanced collaboration between IT and business entities.
One of the key dependencies for many BICCs is accurate and complete Customer Master Data and systems like the Pretectum CMDM can be of assistance here. A CMDM system will often play a pivotal role in streamlining the process of referencing and analyzing customer data across the organization. The integration of CMDM within any BICC framework ensures that customer data is accessible, timely, of good quality and relevance and s highly suitable to be leveraged for strategic decision-making. Pretectum CMDM, with its advanced capabilities, facilitates the creation of a unified and accurate view of customer information, enabling organizations to not only enhance customer experiences and drive targeted marketing initiatives but also elevate the quality of the output of the BICC.
Integration of the Pretectum CMDM into the BICC ecosystem supports a new dimension of data insights and reporting by unifying the customer data in such a way that organizations can derive deeper insights into customer characteristics, behaviour, preferences, and trends. Such a comprehensive understanding enables businesses to tailor products and services, adjust tactically and strategically and also personalize customer interactions, and ultimately build longer-lasting customer relationships.
The centralized control and standardization of customer data provide greater assurances about customer master data quality, integrity, and consistency across the organization. This, in turn, leads to more reliable and accurate BI outcomes, providing decision-makers with a solid foundation for their strategic initiatives.
Integration of a Customer Master Data Management system like Pretectum CMDM within the broader framework of a Business Intelligence Competency Center elevates the capabilities of those organizations looking to customer data to advance BICC capabilities. Such an approach not only ensures more precise and efficient management of customer data but also amplifies the impact of BI initiatives. Modern organizations must navigate what is now a very dynamic business environment, the fusing of the BICC and CMDM systems holds great potential for unlocking the great capabilities and insights from customer data, driving innovation, and staying ahead of competitors.
Federated customer data governance is an approach to customer data management that allows organizations to implement data governance policies and controls in a decentralized manner across multiple domains or business units. This is an intrinsic characteristic of the Pretectum CMDM approach to Customer Master Data Management (CMDM)
Key aspects of federated data governance are the establishment of governance authorities within each data domain or business unit to define the rules, policies, and standards specific to that business or data domain. These domain-specific governance authorities work collaboratively to ensure alignment with overall organizational goals and data governance requirements. Where appropriate, enterprise or federated business data rules and structures are established and leveraged to influence and control the data creation and management processes.
Federated customer data governance supports the balance between decentralized data ownership/management and centralized data governance, allowing the business domains to work autonomously within their own defined interoperability standards, connecting to their business unit-specific data sources and sharing data internally.
This approach is essential for implementing a successful data mesh architecture, where data is treated as a product and managed in a decentralized manner. The key benefit of federated data governance for customer master data management is that it allows organizations to scale data governance practices across a complex, distributed data landscape while maintaining agility and business and data domain-specific requirements.
Federated data governance facilitates the centralization of data governance, data quality, and data lifecycle management across an organization.
There are several key steps for implementing a successful customer master data management program that need to be considered:
An organization should begin with an âas-isâ state analysis and stakeholder engagement. Understand the vision and key drivers, assess the current state, and document all the âpain pointsâ and goals of the different data stakeholders.
Selecting tools that are contextually appropriate for the organization is an important decision point. Any tooling that is considered should offer a ready-to-run platform for business leaders to easily carry out their customer master data governance, allowing easy access and updating of master data, and the support of seamless integration with any, and all, third-party and internal systems as required.
Approaches to Customer MDM
The adoption of new or different tooling has to be with one goal in mind, namely the establishment of better data governance by integrating business operations, data collection, and data optimization requirements. This goes a long way toward ensuring the business runs smoothly and effectively, all the while, complying with privacy, data handling, and regulatory policies; in accordance with local, regional, and international law.
It should also be recognized and acknowledged, that maintaining a customer-centric approach is a concept that, just like customer data itself, is constantly evolving. Any customer master data management solution should be composable, adaptable, and evolve with the compliance, integration, and business needs of the organization, seamlessly. Only through the best possible customer data can an organization hope to ultimately build and maintain strong customer relationships.
The user experiences within the tools and applications should also be supportive of employees and employee tasking while also helping in the handling and safe access and sharing of specific customer data to accelerate digital transformation, meet business needs, and ultimately support the organizationâs pursuit of exceeding customer expectations.
Role-based-Access-Controls (RBAC) are an important control element in ensuring that only the right people have access to the right data and platform functionality. Extensive auditing and logging is an important aspect that needs to be in place here also.
Any platform under consideration should also continuously maintain the customer master data to ensure accurate and up-to-date information, avoid discrepancies in the customer master, and maintain the highest possible data quality.
By considering all of these aspects, organizations can better implement an effective customer master data management program that delivers trusted, high-quality data to drive operational efficiency, improve customer experience, and enable better business decisions.
At the intersection of optimal business operations and the discipline of appropriately aligned data governance principled master data management lies Customer Master Data. The practice includes dimensions that define the needs of the business including contact information, customer vitalstatics and pretty much any data attribute that the business needs to leverage for a perfectly harmonized customer relationship.
Thatâs the dream, unfortunately, the reality is that for many organizations, their data governance practice is mired in conflicting interests of largely divergent stakeholders. There is also the challenge of inter-divisional competition of who owns the customer, and the proverbial data silos that arise from divergent divisional needs.
Some business applications, designed with specific and often narrow objectives in mind, operate within a confined scope of customer data requirements. These applications might be tailored for singular functions such as order processing, billing management, or customer support. In such instances, the focus is primarily on the immediate and specific needs of the application, and the depth of customer data required is limited to the operational necessities of that particular function. While this makes for efficient data processing at the business unit level, it retards opportunities for the whole organization which suffers from the lack of a single identity for the customer with all the salient attributes that make for personalized long-lasting and loyal relationships.
Recognizing the indispensability of a comprehensive customer master, some organizations will embark on a comprehensive rethink of their customer master data management practice. Doing so is a strategic decision and as such, requires a strategic approach to constructing a single, authoritative source of truth for the customer master data information asset accompanied by improved integrations and change management.
Practice not technology
Modern-day Customer Master Data Management also isnât about the technology as much as it is a realignment of business principles around the most appropriate way to handle the customer and customer data, especially these days in the face of so many emerging and established privacy and consumer protective regulations.
Consider if you will, the fact that how and what you store and nurture as a customer data repository reflects the true essence of your companyâs identity. Store it incomplete, haphazardly and with duplicates and youâre relating a narrative that suggests that you simply donât care too much about data quality and the integrity of the customer master.
Think of the customer master as a reservoir of knowledge that if established properly, can deliver insights, smooth transaction processing, hone personalization and convey confidence and integrity in your teamâs engagement with the customer. All this can be done on demand, providing a foundation for robust operational and financial structures. Depending on your industry and the relative intimacy of the relationship with the customer, your business may tap into that reservoir and find previously unexplored areas of opportunity and relationship sustainment.
If youâre in finance or sales, it is easy to see customer data management as a ballet of numbers, for marketing. logistics, service and support it might be other business intricacies like past engagements, previous purchases, warranties, returns and the like. For some, it may even just be about the legitimacy and legalities associated with the customer and their data.
Data governance is the systematic management, control, and oversight of customer-related information within a given organization.
Data governance involves the establishment and enforcement of policies, procedures, and standards to ensure the accuracy, integrity, and security of customer data throughout its lifecycle. The primary goal is to enhance the quality of customer information, facilitate compliance with regulations, and support reliable decision-making processes across the organization.
In his domain, this includes defining roles and responsibilities, implementing data quality measures, and establishing protocols for data access, usage, and privacy.
Some fundamentals
Meticulous management of data quality entails a systematic and detailed approach to ensuring the accuracy, consistency, and reliability of data within an organization. It involves implementing rigorous processes and practices to identify, rectify, and prevent errors, inconsistencies, and redundancies in the data.
The objective is to cultivate a dataset that serves as a trustworthy foundation for decision-making processes, minimizing the risk of misinformation and supporting the organizationâs overall goals. This involves continuous monitoring, validation, and improvement efforts to uphold a high standard of data quality throughout its existence.
Security and privacy in the context of customer master data involve systematically implementing measures to protect sensitive customer information from unauthorized access, misuse, and breaches.
This would encompass the establishment and enforcement of policies, procedures, and controls to safeguard customer data against potential threats. The primary goal is to ensure the confidentiality and integrity of customer information, aligning with relevant data protection regulations.
Security and privacy measures also include access controls, encryption, authentication protocols, and ongoing monitoring to detect and respond to any potential security risks. The objective is to create a robust framework that instils confidence in customers, mitigates risks, and upholds the organizationâs commitment to data protection.
Data lifecycle management (DLCM) is an integral component of data governance and involves a systematic and comprehensive approach to handling customer data from its creation or acquisition through various stages of utilization, storage, and eventual disposition or archival.
This essential process ensures that data is managed efficiently and in alignment with the organizational objectives and legal obligations of the organization. A DLCM framework includes the formulation of policies, procedures, and standards to govern the handling of data at each stage.
The primary goal of DLCM is to optimize the utility of data while also addressing issues related to data storage, access, and compliance. It requires organizations to define clear retention policies, in particular, specify how long data should be retained based on its value and regulatory requirements. DCLM also involves establishing protocols for secure data disposal or archival once it has fulfilled its purpose.
Executing a DLCM practice well, involves continuous monitoring, assessment, and adaptation of policies to align with changing business needs and regulatory landscapes. This structured approach ensures that data remains a valuable asset throughout its journey within the organization and is managed with efficiency, cost-effectiveness, and compliance in mind.
Thinking about the people
At the heart of any data governance program are people who may or may not be explicitly tagged as the data governance stewards. These are individuals or teams entrusted with the responsibility of maintaining data quality, upholding governance policies and serving as the data owners and people âin the knowâ about all things about the data. They are the data domain experts.
Data stewards navigate the vast seas of data, ensuring that each byte is accounted for and that each dataset aligns with the broader goals of the organization. They are the custodians of the data practice.
A more explicit definition would have it, that a data steward is an individual or team responsible for overseeing the management, quality, and governance of data within the organization.
Duties include ensuring data accuracy, defining and enforcing data policies, and maintaining the integrity of data assets. Data stewards play a crucial role in facilitating communication between business units and IT, acting as custodians of data quality and providing expertise on data-related matters.
Their responsibilities encompass data profiling, monitoring, and resolving data issues, as well as collaborating with other stakeholders to establish and adhere to data governance policies. The role requires accountability for the reliability and usability of data across the organization.
Metadata matters
The descriptive information about the customer data, data that provides context, structure, and understanding of its characteristics, is metadata. Such information includes details about the origin, usage, format, and relationships of data. In any data governance program, metadata plays a crucial role in enhancing data discoverability, usability, and overall management.
For customer master data management, metadata associated with customer data would include information about data sources, data quality, and any transformations or processes applied to the data. It helps in maintaining a comprehensive understanding of customer data, ensuring its accuracy and facilitating effective data governance.
For data governance, metadata serves as a bridge between stakeholders and systems. It facilitates collaboration by offering a common language for business users, data stewards, and IT professionals. Stakeholders leverage the metadata to comprehend the meaning and lineage of the customer data, converging on a shared understanding for everyone across the organization. Metadata also enhances the interoperability of systems by providing a standardized framework for data exchange and integration, promoting consistency and coherence in the data landscape.
No respected data governance program is launched, adopted and survives without data governance and management policies. Data Governance policies define who can access specific data, how it can be used, and under what circumstances. These policies form a framework that prescribes how to prevent unauthorized access and ensures responsible data utilization as well as other behaviours and measures that serve to protect the integrity of the customer master.
A data governance council or committee overseeing and steering the program is helpful but not essential. Comprising representatives from various business units and the IT realm, this body ensures that data governance aligns with organizational objectives, and its impact is felt across the entire enterprise.
Fostering a culture of data awareness and responsibility becomes a crucial act in this governance play. Communication and training programs under the aegis of a data governance program are the conduits through which employees grasp the importance of data governance, the program aims to develop an understanding of their roles in maintaining data quality and integrity.
Culturally a data governance program requires a major shift where each employee becomes informed and empowered as a guardian of the data they interact with, hopefully thereby recognizing its intrinsic value.
Continuous improvement in data governance is another essential trait of a data governance program which is sustained through a dynamic and iterative process that prioritizes refinement, adaptability, and ongoing assessment.
Continuous improvement involves regular evaluations of data quality, security protocols, and adherence to established policies.
Organizations that foster a culture of feedback, with data stewards and relevant stakeholders providing insights into the efficacy of existing practices are the most successful.
Insights from continuous improvement initiatives guide adjustments to data governance policies and procedures, ensuring they align with evolving business needs and industry standards. Implementing feedback loops, periodic audits, and staying attuned to technological advancements in data management contribute to the ongoing enhancement of data governance strategies.
This commitment to continuous improvement not only safeguards the integrity of customer master data but also enables the organization to respond effectively to changes in the data landscape, maintaining a robust and adaptive foundation for strategic decision-making.
Effective risk management within customer master data management involves implementing robust processes to identify, assess, and mitigate potential risks associated with the handling of customer information. This includes ensuring the accuracy, completeness, and security of customer data to prevent errors, fraud, and unauthorized access.
A comprehensive risk management approach would also involve regular audits and monitoring to detect anomalies or irregularities in customer data, as well as establishing clear protocols for data governance and compliance with relevant regulations such as data protection laws.
By proactively addressing risks related to customer master data, organizations can enhance data quality, build trust with customers, and safeguard sensitive information, ultimately fostering a more resilient and secure customer data management environment.
Evaluating a prospective source of truth
The criteria for selecting the right home for your CMDM initiative will revolve around the accuracy and integrity of data. Whatever you choose for CMDM it must incorporate robust validation mechanisms and quality checks to uphold the sanctity of customer data, preventing errors and discrepancies that might reverberate through the entire organizational structure.
Integration capabilities will likely play a crucial role in the CMDM selection process, whether it be in support of Customer Relationship Management (CRM), Enterprise Resource Planning (ERP), or other systems. Such integration will ensure a unified and consistent view of the customer data, eliminating silos and fostering a panoramic perspective across the enterprise.
Scalability becomes the next checkpoint in the CMDM evaluation. Will your choice accommodate a likely ever-growing number of occupants? A CMDM solution must exhibit scalability to handle an expanding volume of customer data. If your business landscape is dynamic, then the chosen system should gracefully scale to meet the demands of your expanding enterprise without compromising performance.
Security measures are non-negotiable when dealing with customer data. The selected CMDM home should have robust security, actively defending against unauthorized access, monitoring for data breaches, and proactively looking out for cyber threats. For customer data, sanctity and confidentiality are paramount, you must make security a top priority for your CMDM abode.
Quite naturally, user-friendliness and the proverbial UXD (User Experience and Design) is often a pivotal criterion in any selection process. The experience should be intuitive and provide a user-friendly interface that supports employeesâ easy navigation and interaction with the customer data. Such a system would foster user adoption through its design and navigational simplicity; enhance productivity and ensure that the benefits of CMDM permeate throughout the organizational structure.
Data governance should be centre stage. CMDM home must shelter and govern the data within its confines. A CMDM that comprehensively supports your data governance framework is imperative. You will want to be able to outline and enforce policies, standards, and processes for the entire lifecycle of customer data. This ensures internal consistency and compliance with external regulatory requirements, safeguarding the organization against legal ramifications.
Flexibility and customization emerge as key facets in this selection saga. Every organization has unique preferences and requirements. Your choice of CMDM solution should mirror this diversity, offering flexibility and customization options that align with specific business processes and evolving data management needs. The home for your customer data should not be an entirely rigid structure but rather an adaptable space that flexes with the unique rhythm of the organization it serves.
AI and Machine Learning Integration bring a futuristic dimension to the CMDM narrative. The idea of CMDM solutions leveraging AI and machine learning suggests opportunities to plumb the depths of the data with advanced data matching, deduplication, and predictive analytics. Such an infusion of intelligence would enhance the accuracy and utility of the customer master and provide insights that transcend traditional data management boundaries.
We believe that the Pretectum CMDM will address all of these expectations and provide you with some surprising additional ones. Contact us today to learn more.
You might have more recently heard of âcomposableâ solutions, this composability refers to the flexibility and modularity of systems, allowing organizations to adapt, customize, and integrate them into their existing technology landscape efficiently.
The concept of composable solutions has been largely in the shadows for the past decade, with its roots tracing back to the evolution of modular and service-oriented architectures in software development. However, it is gaining more prominence in the context of enterprise systems descriptions.
In the 2010âs there was a notable shift towards more flexible and agile approaches to software design and integration within enterprises. This shift was driven by factors such as the increasing complexity of business requirements, the rise of cloud computing, the growing demand for scalability and interoperability, and the emergence of microservices architecture. Itâs fair to say that the term started gaining traction notably around the mid-2010s and has since become a key aspect of discussions surrounding modern enterprise software architecture and integration strategies.
For master data management and customer master data management in particular, a composable approach involves breaking down data management processes into modular components that can be easily assembled or reconfigured to meet specific data governance and data quality requirements.
Composable CMDM solutions allow organizations to adapt to evolving data landscapes and support various varied demands of organizations about customer master data management, including ensuring data accuracy, consistency, and compliance. Additionally, these solutions enable organizations to scale more effectively and integrate seamlessly with existing technology ecosystems.
Overall, composable solutions represent a significant paradigm shift in enterprise systems architecture, offering organizations the flexibility and agility needed to navigate the complexities of modern business environments.
Pretectum CMDM aligns with the concept of the composable solution by offering a flexible, scalable, and interoperable platform that supports the modular and service-oriented architecture businesses are increasingly adopting.
The platformâs design allows for seamless integration with various software applications, facilitating smooth data flow across different departments and systems.
This integration capability is crucial for promoting collaboration, enhancing productivity, and enabling a more agile response to customer demands. Furthermore, Pretectum CMDMâs ability to scale both vertically and horizontally accommodates the growing volume and complexity of data, ensuring that businesses can rely on it as a foundational data management solution that evolves with their needs.
By automating data integration, cleansing, and standardization processes, Pretectum CMDM reduces manual effort and human error, supporting the principles of composable solutions where efficiency and adaptability are key.
Pretectum CMDM vs monolithic solutions
Older monolithic Customer Master Data Management (CMDM) architectures have all components of the CMDM tightly integrated into a single, cohesive application. In this architecture, all functionalities, such as data storage, data processing, data governance, and user interfaces, are bundled together within a single application or platform.
Traditional stacks with their tightly integrated components are difficult to separate or modify. Changes often require extensive reconfiguration or redevelopment of the entire system. Such platforms struggle with adapting to change due to their tightly coupled nature. Upgrades or changes often involve significant downtime and risk of system instability.
Integrating these traditional stacks with newer technologies or external systems can be challenging and may require custom development efforts. Interoperability issues are common, leading to data silos and inefficiencies. Scaling the traditional stacks often involves scaling the entire system, which can be costly and inefficient.
Vertical scaling may lead to performance bottlenecks, while horizontal scaling can be complex and disruptive. Automation capabilities in traditional stacks may also be limited, leading to manual intervention in repetitive tasks and increased risk of errors.
The Pretectum CMDM, with its composable architecture, offers benefits in terms of flexibility and modularity, adaptability to change, integration and interoperability, scalability, automation, and efficiency to all shapes and sizes of organizations.
Pretectum CMDM employs a modular architecture, which allows organizations to break down data management processes into smaller, reusable components. This modularity enables greater flexibility in configuring the CMDM solution to meet specific business requirements. An organization can choose which parts of the platform they want to use, based on their needs. Part of this is also covered by the deployment approaches for CMDM. Adding or removing components as necessary gives the organization many options and a great deal of flexibility. This flexibility ensures that the CMDM solution can evolve alongside the changing business landscape and evolving data governance requirements.
With the composable architecture, Pretectum CMDM supports high adaptability to changes in business requirements, technology advancements, and regulatory frameworks. Organizations can easily take advantage of new functionality as it becomes available or switch approaches to individual components or discrete functionality with minimal disruption. This adaptability enables organizations to respond quickly to emerging trends, regulatory updates, or shifts in customer demands, ensuring that the CMDM solution remains relevant and effective over time.
Seamless integration with existing systems and technologies is essential with all modern systems, the promotion of interoperability across the organizationâs data landscape is emphasized by support for meshed customer data management. The modularity of the platform allows for easy integration with department or division or business unit-specific software applications, databases, and third-party services.
By facilitating data flow across different departments and systems, Pretectum CMDM promotes collaboration, enhances productivity, and ensures consistent data across the organization.
Pretectum CMDMâs composable architecture enables both vertical and horizontal scalability, allowing organizations to scale their CMDM solution to accommodate growing data volumes, user loads, or business expansion. Vertical scaling involves adding resources such as CPU, memory, or storage with minimal impact â this is achieved as a result of the SaaS architecture of the platform. Horizontal scaling involves adding more instances of components to distribute the workload, this is not a problem for the platform because it is built multi-tenant from the bottom up and makes use of on-demand compute resources. This scalability ensures that the platform services the needs of your organization and many others, as required.
Automation is a key feature of Pretectum CMDM, streamlining integration, loading, standardization, quality assessment and deduplication, and other data management processes. By automating repetitive tasks, the Pretectum CMDM reduces manual effort and human error, improving your teamsâ overall efficiency. Automated workflows and business rules also help drive improved data quality, consistency, and compliance, supporting the principles of composable solutions where efficiency and adaptability are paramount.
Setting precise objectives is an indispensable factor in the successful implementation of Customer Master Data Management (CMDM).
The fundamental threat to a burgeoning CMDM program lies in its initiation with unclear or ambiguous business objectives. Although overarching goals such as enhancing data quality, supporting informed decision-making, achieving a unified truth, or obtaining a 360-degree customer view might seem logically sound, they often lack the specificity required for the effective execution of a CMDM program.
Gartner, a leading research and advisory company, highlights four key reasons for Master Data Management (MDM) program failures, among them insufficient executive sponsorship, inadequate adjustment of business processes, a lack of validation, and the potential pitfalls of an âall at onceâ or âbig-bangâ implementation strategy. These pitfalls underscore the critical importance of a carefully structured and well-defined approach in implementing CMDM initiatives.
One notable aspect contributing to failure might be the absence of a structured framework to measure the value of data management for an organization, particularly within the domain of customer data. Without well-defined objectives, CMDM initiatives often struggle to progress beyond their initial stages or may fail outright during implementation.
To mitigate the risk of CMDM failure and ensure the success of the program, it is imperative to follow a systematic approach. The first step involves defining measurable business outcomes related specifically to customer data. The litmus test for these objectives lies in the ability to articulate CMDM outcomes in non-technical terms that resonate with both business and IT stakeholders. If an organization cannot express its objectives without relying on technical jargon, it raises a red flag, indicating the need for re-evaluation.
A helpful technique in this regard is to encourage organizations to state their objectives without using the word âdataâ Instead, the focus should be on articulating business objectives related to customer data that CMDM aims to address.
Increasing customer retention rates: Achieved by reducing customer service response times to a specific duration, for example.
Augmenting cross-sell opportunities: Achieved through a more personalized enhancement of the customer experience. This might be another.
Improving CSAT scores: As a lagging indicator through more accurate and timely responses to customer interactions.
By steering away from technical language and concentrating on specific business outcomes linked to customer data, organizations can ensure that CMDM objectives are clear, understandable, and relevant to all stakeholders.
Understanding the core motivations behind CMDM initiatives is paramount. Whether the objective is to increase customer loyalty, optimize marketing strategies, or personalize customer interactions, there must be a compelling business reason underpinning CMDM efforts. Organizations need to document these customer-centric business challenges and connect them to the âwhatâ and âhowâ of the CMDM project.
Failure to establish this connection can lead to confusion and a loss of focus. To prevent this, organizations must emphasize the value of CMDM by demonstrating its ability to drive customer-centric outcomes, such as personalized marketing campaigns, improved customer service, or enhanced customer loyalty programs. Identifying specific quick wins related to customer data is crucial to showcasing the tangible value of the CMDM program.
Beyond traditional Return on Investment (ROI) studies, CMDM initiatives focused on customer data require a roadmap that outlines the core business problem and provides a detailed plan to address it. This roadmap should encompass stakeholder engagement and commitment strategies, ensuring that the CMDM program progresses smoothly from conception to implementation, specifically in the domain of customer master data.
An effective âStrategic Outcomes Blueprintâ (SOB) is instrumental in identifying quick wins related to customer data that prioritize business outcomes, thereby highlighting the value of the CMDM program.
A âStrategic Outcomes Blueprintâ should include:
A clear description of the customer-centric business opportunity, such as increasing customer lifetime value or improving customer retention rates.
Prioritized initiatives and resource allocation focusing on customer data management.
Key performance indicators specific to customer data quality, customer satisfaction, or customer engagement.
Quantification of projected ROI related to customer-centric outcomes.
By creating a compelling business case through the SOB, organizations can think big while starting small, focusing on targeted problem-solving related to customer data and demonstrating the immediate value of the CMDM program.
Itâs crucial to recognize that CMDM in the context of customer master data is not a one-time project but a continuous journey. By tying CMDM implementations to real-world business challenges specific to customer data and showcasing their value through quick wins, organizations can establish CMDM as an ongoing initiative. Celebrating achievements and sharing insights derived from clean, trusted customer data helps maintain momentum and enthusiasm among stakeholders.
Furthermore, CMDM programs related to customer data often involve multi-domain challenges, such as customer relationships, product preferences, and service histories. By mastering one customer data domain at a time and celebrating successes, organizations can expand their CMDM efforts gradually, addressing various aspects of customer interactions. This incremental approach enables businesses to build expertise, tackle specific challenges related to customer data, and continuously demonstrate value to stakeholders.
Any successful CMDM program focused on customer master data necessitates clear and customer-centric objectives, active collaboration between business and IT teams, a deep understanding of underlying customer-centric business challenges, and a well-defined roadmap specific to customer data management.
By following these steps, organizations can steer clear of potential pitfalls, reduce risks, and ensure that CMDM initiatives focused on customer data deliver meaningful and measurable results. Implementation of CMDM in the context of customer master data is not merely a project; itâs a continuous journey toward customer data excellence, personalized customer experiences, and sustainable growth in todayâs customer-centric business landscape.
Competition is fierce and customer choices are abundant, businesses may find solace and success in the art of cultivating meaningful customer relationships.
The art, often referred to as Customer Relationship Management (CRM), has become more than just a marketing strategy; itâs the cornerstone upon which businesses thrive and flourish.
Building Bridges Beyond the Transaction
At its essence, CRM isnât just software; itâs a philosophical way of doing business that prioritizes people over profits; the practice of understanding and managing relationships and interactions with customers, with the goal of creating mutual benefits.
CRM extends beyond the initial customer purchase, it focuses on enhancing customer satisfaction, loyalty, and retention. Sustaining a long-term relationship, and maximizing customer lifetime value (CLV).
Something Pretectum tags as being encapsulated in the hashtag #loyaltyisupforgrabs.
In this competitive market, where attracting a new customer is a costly endeavor, CRM shifts the focus to the existing customers. You do this by promptly resolving issues, offering excellent and consistent customer service, and keeping customers updated about products and promotions. Through these activities, businesses can foster loyalty and drive repeat sales.
Retention, Loyalty, and Satisfaction
There is a triad associated with CRM, doing it well offers numerous advantages for businesses. It reputedly costs 5 times more money to acquire a new customer than to get an existing customer to make a purchase!
Firstly, it enhances customer retention rates, ensuring that customers keep returning, thus stabilizing revenue streams.
Secondly, it fortifies customer loyalty, making it difficult for competitors to lure customers away.
Lastly, it amplifies customer satisfaction, a metric vital in the age of digital influence where a dissatisfied customer can tarnish a brandâs reputation across mediums like social media platforms.
Good CRM is about Facts and Figures
The impact of CRM on businesses is substantiated by compelling data.
Increasing customer retention by a mere 5% is suggested as having the potential to surge profits by 25% to 95%.
For 73% of customers, the quality of their experience is often the linchpin of their purchasing decisions.
According to Zippia, following a poor customer experience, up to 89% of consumers have switched to a competitor. On average, customers will tell 16 people about a negative experience, while they will only tell 9 people about a positive one
Learning with machines
Artificial Intelligence (AI) is the next step towards a novel and more capable management of customer relationship management, CRM âis the outcome of the continuing evolution and integration of marketing ideas and newly available data, technologies, and organizational formsâ (Boulding et al., 2005).
AI algorithms can analyze vast amounts of customer data in real time. By processing this data, an integrated form of AI can identify patterns, trends, and correlations that human analysts might miss.
Predictive analytics powered by AI alongside CRM can forecast customer behavior, helping businesses anticipate needs and preferences. This proactive approach allows for targeted marketing, personalized product recommendations, and strategic decision-making.
Chatbots and virtual assistants are capable of handling routine customer inquiries and tasks 24/7 especially when AI is instrumented. These chatbots are able to engage customers in natural language conversations, answer frequently asked questions, and assist with basic problem-solving. By automating these interactions, businesses can improve response times and enhance customer satisfaction.
Algorithms analyze customer data to create highly personalized experiences. By understanding individual preferences and behaviors, these AI can recommend products or services tailored to the unique characteristics of the customer. Personalized marketing messages and offers significantly improve customer engagement, leading to higher conversion rates and customer loyalty.
AI-powered sentiment analysis tools assess customer sentiments based on their interactions with a company, through emails, social media, or customer service calls. Understanding customer emotions and feedback in real-time allows businesses to respond promptly to negative experiences, mitigating potential issues and preserving customer relationships.
This can also analyze sales patterns and customer interactions to identify high-value leads. The algorithms can score leads based on their likelihood to convert, allowing sales teams to prioritize their efforts effectively. They can also analyze sales conversations, providing insights to improve sales techniques and close rates.
Automating various CRM processes, such as data entry, task assignments, and follow-up reminders. Automation reduces the administrative burden on employees, allowing them to focus on more strategic tasks and meaningful customer interactions.
Detection of unusual patterns or behaviors in customer transactions has long been a quest for all types of organizations, using AI to perform the analysis helps businesses identify potential fraudulent activities. By flagging suspicious activities in real time, businesses can take immediate action to protect both customers and their own interests.
Top Tips for Lasting Connections
Implementing CRM effectively doesnât have to be daunting. Consider these practical tips to guide your business.
Continuously collect and update customer profiles, and use preferences for personalized recommendations and promotions, especially through the use of a zero or first-party data strategy with a customer master data management system like the Pretectum CMDM at the core.
Focus on quality, ensure your products or services meet the highest standards to prevent dissatisfaction and negative reviews â drive quality in the customer data too! Pretectumâs data quality is integral to the CMDM journey.
Train and empower your staff to provide exceptional customer service, encouraging personalized interactions that make customers feel valued. The best way to do this is to make your CMDM solution central to your various systems in a syndicated or hub-and-spoke deployment.
Maintain regular communication with customers through various channels, fostering a sense of community and excitement. Maintaining top-notch customer contact information is key, getting customers to verify it regularly, can help.
Encourage customer feedback, promptly address issues to show customers their opinions matter, and improve your offerings accordingly. Here you can also add that feedback to the CMDM as a part of your Customer master history.
Implement loyalty programs, discounts, and special merchandise to reward customers, enhancing their sense of belonging.
Greet customers warmly, making them feel acknowledged, and always express gratitude for their patronage. Part of the key here is knowing your customers. How exactly you achieve a certain state of KYC hinges on how far you integrate solutions like CMDM with your other systems like POS and eCommerce.
Explore CMDM systems like Pretectum CMDM, which streamlines processes, manages customer data, provides valuable insights, and facilitates personalized experiences.
In essence, CRM is the heart of a successful business. By investing in relationships, understanding customer needs, and leveraging modern tools, businesses can create a customer-centric ecosystem where loyalty thrives, and profits follow suit. Itâs more than just a strategy; itâs a commitment to building lasting connections that stand the test of time, ensuring business growth in a competitive world.
Data Mesh seems to be all the rage in data governance circles and although it is a relatively new concept in data architecture it aims to address the challenges of managing and scaling data in large organizations.
The concept was coined by Zhamak Dehghani, a principal consultant at ThoughtWorks, In Dehghaniâs concept, Data Mesh proposes a decentralized approach to managing data at scale, making it more accessible and manageable for different teams within an organization.
Data Mesh might be considered groundbreaking because it decentralizes data management, empowering individual domain teams to own and operate their data as data products.
By distributing responsibility, it enhances scalability, agility, and collaboration. This approach optimizes resource utilization, improves data quality, fosters innovation, and ensures compliance, addressing the challenges of modern data operations and enabling organizations to harness the full potential of their data in a rapidly evolving digital landscape.
Traditionally, in many organizations, data is treated as a centralized, monolithic entity. Data engineers and data teams build large, centralized data lakes or data warehouses to store all the data. However, this approach can lead to bottlenecks, where a central team has to manage and process data requests from various parts of the organization. This centralized approach may be inefficient and difficult to scale as the volume and complexity of data increase.
Now, some of us might be thinking, sounds just like decentralized data management â right? Nothing new here, letâs move on. This idea would sell the real power of Data Mesh short though.
Both decentralized data management and Data Mesh involve distributing data-related tasks across different teams, the key distinction lies in the approach and principles employed.
Decentralized data management, in a general sense, implies distributing tasks without specifying a structured methodology. It might lack clear guidelines on ownership, interfaces, or data product-oriented thinking.
In contrast, Data Mesh provides a specific set of principles and practices that guide how data should be decentralized. It introduces a well-defined framework, emphasizing domain-oriented ownership, treating data as a product, and implementing self-serve infrastructure, among other principles.
These specific guidelines ensure that data is not just spread out across teams but is also managed cohesively, ensuring accessibility, quality, and innovation. So, while both concepts involve decentralization, Data Mesh offers a more structured and systematic approach to achieve more effective decentralized data management within organizations.
Data Mesh is not a technology in itself; though you will find âData Meshâ vendors in the market. Rather, itâs a conceptual framework and set of principles for managing and scaling data within organizations. Data Mesh provides guidelines on how to structure data teams, processes, and architecture, emphasizing concepts like domain-oriented ownership, data as a product, and self-serve infrastructure.
Organizations implementing the concept of a Data Mesh typically use a variety of existing technologies to enable the principles outlined in the framework. These technologies can include data lakes, data warehouses, data cataloging tools, ETL (Extract, Transform, Load) processes, microservices architectures, and various data processing and analysis tools. The choice of specific technologies depends on the organizationâs needs, existing infrastructure, and the preferences of individual teams within the organization.
Your Pretectum CMDM can play a crucial role in supporting the Data Mesh concept in various ways. It does this by ensuring consistent and accurate customer data across various domains within your organization along with disciplined ways to collect and manage the customer data.
The Pretectum CMDM centralizes customer data from different sources, ensuring consistency and eliminating duplicates. In a Data Mesh model, where different domain teams and business areas manage their data, having a consistent customer view is vital. The CMDM maintains a single, accurate version of customer data, promoting uniformity across domains.
Approaches to Customer MDM
Pretectum helps you to enforce data quality standards and governance policies. Your teams are able to validate, cleanse, and enrich customer data, ensuring that all the data domains within the Data Mesh adhere to the same quality standards. This consistency is essential in a decentralized environment, preventing data discrepancies and ensuring reliable insights.
Pretectum facilitates collaboration between domains. When different teams within the Data Mesh need to share customer-related data, the centralized CMDM system ensures they are using the same standardized data, fostering seamless collaboration and reducing miscommunication.
CMDM systems are designed to handle large volumes of data efficiently. In a Data Mesh setup where data volumes can be substantial, having a robust system like the Pretectum CMDM ensures scalability and optimal performance, supporting the decentralized processing needs of various business areas.
The customer MDM comes with built-in security and compliance features. Ensuring that customer data is handled securely and compliantly is critical. The Pretectum CMDM systems help enforce access controls, data encryption, and compliance with regulations, which is particularly important when multiple domain teams are involved in data processing.
The Pretectum CMDM can adapt to your evolving business needs. As your organization and its Data Mesh strategy grow, the CMDM can accommodate changes in data structures, relationships, and business rules. This flexibility is valuable when different domains within the Data Mesh need to modify your data requirements over time.
By providing a centralized, reliable, and consistent source of customer data, a Customer Master Data Management system supports the core principles of Data Mesh, enabling different domain teams to work independently while ensuring your organization has access to high-quality, standardized customer information when needed.
Do you know what your customers purchased from your company last month?
Can you predict what they will purchase next month?
If you have some difficulty answering these questions, your company may be missing out on additional opportunities. Most companies witness an uplift in transactional conversion rates from customization of the user experience. Customers want, and expect, a customized and relevant experience when working with your company, and whether or not you can deliver this depends on how much your company knows about them.
The extent to which your company knows your customers depends on the content and quality of your customer MDM. Customizing the customer experience becomes difficult if the data you would use lives in many disconnected systems and departments that do not or cannot interact with each other directly. A customer-centric element of master data management, customer MDM is the focal point of driving new business opportunities by allowing companies to find, merge, and link customer data across the organization.
Customer MDM makes it easy to link and integrate systems like CRM and ERP and provides a single, centralized view of your customers in a holistic way. Companies that understand the substantial benefits of connecting the data they hold in the master data management platform can understand the business value that could be attained from appropriately wide access to customer data. Customer master data management tools can assist in harnessing and boosting CRM functions like sales, marketing, customer service, and e-commerce.
The CRM leaders who avoid customer master data management land up with flawed results that could upset customers. An integrated CRM strategy enabled by customer master data management links the entire customer journey from sales and marketing to customer service and e-commerce to provide you with a 100% overview of customer history and communication.
To have a holistic customer data view, you need to integrate operational master data for every customer. The data must then be cleansed, verified for duplicates, and maintained. Automating this process through data quality operations that match and connect the data, provides you with a set of correct and reliable records.
Marketing teams run campaigns with a better chance of success by targeting contacts on a more personal and relevant level. Sales teams generate offers and promotions based on historical engagement, delivering better predictability of response and outcomes.
Customer MDM makes it all possible by providing a single point of entry for customer data accessible enterprise-wide. This can improve the customer experience by reducing the time your service teams spend finding customer records and digging into customer problems.
A broad view of your companyâs customer data makes the segmentation process simple and more accurate, as you have insights gained from their integrated historical behavior and purchasing patterns. You gain a comprehensive understanding of how your customers are expected to act, enabling you to generate products and services that they actually want and increase the likelihood of them purchasing from you.
As you get a complete and holistic overview of customer activity across your company, customer interaction patterns may begin to surface. Eventually, companies can appropriately cross-sell and up-sell more products and services by providing incentives based on previous patterns. Most consumers appreciate emails from retailers suggesting promotions, products, and services aligned with past behavior.
Companies can create new products and develop more targeted marketing campaigns based on what they currently know about their customersâ behavior. And, of course, they have the insights to deliver a lot more customized service to individuals with centralized offers and promotions. Companies can identify customers who may start looking elsewhere and hopefully retain them by offering customized incentives to stay with you.
Taking a customer-centric approach to master data creation and service delivery enables your organization to fulfill the actual needs of your customers and add value. Consider the sources of insight and how you can leverage these to best effect.
Contact us to learn more about you could leverage the Pretectum CMDM to unlock more value from your Customer master data.
Most of us will have heard of the Industrial Revolution. However, some readers may not realise that the Industrial Revolution talked about in school commonly references what is widely considered the âFirst Industrial Revolutionâ.
The first Industrial Revolution was signified by the introduction of machines to replace handmade production methods. The 1st was also fueled by steam power and water power. In the west, the era spans colonial America all the way through the ascent of Queen Victoria to the British throne. It most affected western economies, textile and clothing manufacturing, shipping, transportation, mining and metalwork industries, agricultural methods, and societal culture.
Fast forward two hundred and fifty years and we are described as being in the fourth industrial revolution (4IR), or as some term it âIndustry 4.0â. In parallel, we can also think of Enterprise 4.0.
Per a Wikipedia definition, this revolution is defined by a number of technological developments in virtual reality and augmented reality facilitated by high-speed data exchanges. In addition, new human-machine interaction modes are more commonplace such as touch interfaces and gesture-based interfaces that use camera and audio technology, robotics and 3D printing.
The core trend in commerce and industry is automation and data exchange across both manufacturing technologies and process technologies. These include the proverbial Internet of Things (IoT), cyber-physical systems (CPS) more commonly understood as Virtual Reality (VR) and Augmented Reality (AR), moving workloads to âthe cloudâ via cloud computing, cognitive computing, and artificial intelligence (AI).
It is more generally acknowledged that computing technologies still struggle to replace the deep domain expertise but computer technologies donât forget and so, can often be more efficient in performing repetitive functions. When this robust performance of repetitive tasks is combined with machine learning and appropriate computing power, some very complex tasks can be accomplished. We can already see this tremendous potential in the very devices that inhabit our pockets and purses.
The Fourth Industrial Revolution is thus viewed by some as signifying the early beginning of what some consider an âimagination ageâ, a period beyond the Information Age where creativity and imagination become the primary creators of economic value.
W. Michael Cox and Richard Alm described this at some length in the 2017 Annual Report of the William J. OâNeil Center for Global Markets and Freedom, SMU Cox School of Business. Cox and Alm went so far as to describe it as âAmericaâs Fourth Wave of Economic Progressâ with most of the Ageâs employment being services related. âAmericans wonât be going back to the farms and factories in any significant numbers.â
The implications for customer and consumer data
While we may be a way off from an era where we no longer need to carry the devices we have today. Battery and data-hungry devices like mobile phones help us to stay connected and engage with one another. But, it is conceivable that such a technological shift that supports us regaining our humanity and discarding these devices in their current form is not too many years far away. What this might look like, is already visible in the futuristic ideas of cinema and television.
Kevin Parikh, Chairman and CEO of Avasant in a presentation at OWS21 describes modern-day society as being in a current era of âdigital singularityâ. He concludes this based on a convergence of ubiquitous personal technologies and the overall human experience. We can likely all attest to this being probably mostly true. We stayed connected with colleagues, friends and family through Zoom, Whatsapp, Facebook Messenger and Facetime during periods of pandemic isolation. Weâve stayed away from the office and continued to interact with colleagues using these same methods.
Working remotely and in some cases becoming what some would call âdigital nomadsâ doesnât seem particularly exotic today but it was novel and interesting a quarter of a century ago as I promoted it through a teleworker and telecottage association.
Developing an optimal consumer customer experience in this present-day singularity requires a deeper understanding of the consumer, something that can only be reasonably achieved through consumer consent and consumer buy-in to the idea that you might use their information for only good objectives. After all, personal information in the U.S. alone is a multibillion-dollar-a-year industry per Sarah Myers West in her research article Data Capitalism: Redefining the Logics of Surveillance and Privacy.
This is something that we are already seeing as essential as regional interest groups and authorities crack down on the previously unbridled use and abuse of personally identifiable data stitched together often through inferences based on common devices and behavioural patterns accompanied by unique consumer identifiers.
Without critical customer information, the systems that businesses would hope to leverage to unburden consumers from being stuck in the information age and in fact flourishing in the imagination age will never fully materialise. Personalization of interactions and engagement experiences are at the top of the list.
Businesses will need to maximize the network effects that could be achieved through platforms, and control databases that store customer and user data that can in turn drive more control and predictability over the market. Online publisher Tim OâReilly believes that businesses need to âleverage customer self-service and algorithmic data to reach out to the entire web, to the edges and not just the centre, to the long tail and not just the headâ.
A richer more personalized experience starts to become a reality fueled by consumer information. Systems that are able to describe the person will have the advantage. More particularly, systems that make use of ZPD (zero-party data), i.e. data that consumers have willingly and consensually offered up will be the systems that are the most valuable.
Learn how the Pretectum CMDM can help your business keep up with the increased importance of customer MDM with a SaaS software solution designed specifically to address the challenges of Customer Master Data Management.
Japanâs third-largest car manufacturer is Nissan and they are currently improving their operation practices as they aim for better sales output and of course brand awareness.
Nissan is considered an example of one a European car brand that is notable and successful. Founded in 1933 by Engineer Kenjiro Den, Nissan initially produced just motorcycle engines before branching out into automobile production in 1934.
For more than 80 years Nissan has built cars in Japan and in developing markets around Europe (and beyond) selling more than 4M units annually under the Nissan brand â including well-known models like the Nissan Leaf and Nissan X-Trail SUVs.
Nissan is also the owner of the premium Infiniti brand and the heritage brand Datsun which it discontinued on April 22. In addition to cars and trucks, it has in-house performance tuning products and cars labelled Nismo
It is a tough job, focusing on the environment, vehicle and product quality and customer loyalty. The approach has to be multi-pronged and is driven by data.
In the late 1990s, Nissan was Europeâs best-selling Asian car brand thanks to the Micra, Almera and Primera, which were the epitome of Japanese cars in that period ânot very exciting, but extremely reliableâ.
By the early noughties, Nissan tried to bring more frivolous design into their cars with the 2002 Micra K12 and 2002 Primera P12, and as a result, the once faithful customers looking for anonymous transportation stayed away.
Nissan recently referenced a project whose plan is anchored on providing improved consumer service. The new program aimed to make Nissan dealerships a strong competition to outside repair facilities. This is important because once the manufacturer warranty expires, car owners more often than not approach independent repair facilities.
A data Project
Some years earlier, Nissanâs ValĂŠrie Clert and CGIâs Christophe Jeandidier presented some important insights on customer master data to an Informatica World conference audience.
The results that Nissan harvested from taking a good hard look at their data suggested that for marketing organizations at least, they could see improved campaign conversion rates using a âpreferred channelsâ approach. This would in turn lead to 1.5 to 2.5-factor increases in campaign effectiveness. How did they do it? Implementing more rigour around customer master data management.
According to technofunc, the top players in the automotive industry globally, are Toyota, General Motors, Volkswagen, Hyundai, Ford, and Honda. Nissan isnât in that top 6, yet today in Europe Nissan places as No5 according to WheelsInquirer.
The automotive industry is quite competitive at the brand level.
Although dealerships possess some of the localized market power, the retail market in general is still quite competitive. Demand is relatively elastic because consumers have different dealerships and cars to choose from but more importantly, the brands that they chase have different appeals according to brand recognition and perceived status, understood utility, styling and other factors.
Customer loyalty and rewards
As can be seen, the perception vs. the reality also varied depending on regional market differences. Although Nissan has dealerships all over the world the desire is to make the Nissan experience even more fulfilling for customers, hence the launch of a special loyalty program â Nissan: One to One Rewards â Customers earn points for spending money on vehicles, parts and services. Buying a vehicle provides a $250 discount for the next vehicle purchase Enrolled members in the Nissan loyalty program are also eligible for a physical loyalty card.
Every industry has its own challenges and the automotive industry is no different.
In an interview with HervĂŠ Moulin: Alliance Project Leader â Telematics in Finished Vehicle Logistics and Transport Means Specialist in Alliance Logistics Europe â Vehicle Operations, Moulin suggested there would be major challenges in future years.
âCustomers are now changing their behaviour â they are more reliant on the internet and prefer car sharing than going somewhere themselves. Customers now prefer staying in large cities, where they must, understandably, share their transport capabilities. Also, local authorities donât want traditional freight deliveries anymore. Cities are getting increasingly congested and trucks are not welcome in the cities. â
Nissan Europe had been facing a declining customer retention rate and a customer renewal rate lagging behind that of competitors. Shortfalls were driven by poor customer data management leading to poor customer communications and interaction. Personalized communication is viewed as key to sustaining customer loyalty.
A reinvention of the corporate mission
Nissanâs plan as part of the Renault-Nissan-Mitsubishi Motors groups was to move from being considered just a carmaker, to a mobility service provider serving urban markets. Part of this strategy considered introducing an Electric Car-sharing Service something it has actively partnered with auto brand â Renault on delivering. This is in line with urban residents increasingly looking to multi-modal transportation systems to meet their needs.
Renault Group also supported Karhoo, a taxi, ride-hailing and private vehicle hiring service provider and offers a very broad choice of services with a total of more than 1.8 million vehicles as part of a signed partnership agreement with Karhoo. The company is also supporting Yuso through, RCI Bank and Services, a subsidiary of the Renault-Nissan-Mitsubishi Motors group.
While electric vehicles are quiet, cheap to operate, and offer most of the same conveniences available through conventionally-fueled vehicles; they are currently more expensive, have a reduced range of travel and taking longer to refuel. While these negative characteristics present the cars as being less capable, they are still possess the range and charging capabilities to meet the needs of almost every urban commuter, for 95% of their daily usage patterns. Yet perceptions persist for electric vehicles of being unattainable and undesirable. Providing the public with a positive first impression and direct experience with electric vehicles remains a challenge for any automotive manufacturer who is looking to enter the EV market.
James Hallam, PhD â Design Researcher, Design Strategist
All auto manufacturers would suggest that an improved unified view of the customer is needed to support the personalization of customer interaction but the challenges often include over one hundred data sources with silos of customer data, heterogenous data models and a lack of visibility into the end-to-end customer lifecycle. Every country in which Nissan operates for example also has different needs.
In 2021, Nissan laid out a plan to reach 50% electrification by 2030, spending 2 trillion yen ($13.8 billion) over five years. Based on customer demands for a diverse range of exciting vehicles, launching 23 new electrified models, including 15 new EVs, aiming for a 50% electrification mix by the fiscal year 2030. Nissanâs refurbishing infrastructure aims to support a circular economy in energy management, and Nissan aims to fully commercialize its vehicle-to-everything and home battery systems in the mid-2020s. Additionally investing up to 20 billion yen by 2026 towards charging infrastructure. The greatest success in all of these spheres has to include developing an ever closer relationship with the customer.
The Nissan Europe-CGI-Informatica tie-up at one point, saw 16 countries of Nissan Europe with their variability in data, live with the Nissan Customer Database and experiencing the benefits of improved customer insights, of course, a single view of each customer allowing Nissan to communicate in a personalized way too.
Personalization at work
According to Sinch, Nissan used their CRM data with Adobe Campaign to customize personalized Rich SMS campaigns in France and Spain, building long-lasting customer relationships after purchase that resulted in 4.7 times higher engagement, an 80% conversion rate and all off of a base of 200,000 customers targeted in the first 6 months.
Reinforcing its commitment to electric mobility and creating a carbon-neutral society, Nissan also announced a partnership with leading EV charging suppliers, Allego and E.ON, to update and increase the rapid charger offering provided by Nissanâs extensive European dealership network.
Nissan Europe also announced the launch of the GigCX peer-to-peer support. This is for pre-sale queries surrounding the Nissan LEAFÂŽ a leader in mainstream electric vehicles.
The partnership between Nissan Europe and Nissan United, Limitless, Amplify.ai and Facebook Messenger â offers a solution that features a combination of Facebook Messenger, Conversational AI, and seamless hand-off to humans for the peer-to-peer support model, a first for the automotive industry at the time and accelerated with the pandemic-driven digital shift.
Many prospective buyers of electric vehicles are first-time car buyers for whom empathy has become paramount when it comes to answering questions, increasing the importance of personal connection more than ever before.
As you can tell, the data is coming from a lot of places, different systems, different partnerships and in different formats.
The single-customer view
The approach to formulating the best possible golden nominal and single customer view means you need to make some careful choices about the data that you need to retain and curate. In the case of the CGI, Informatica and Nissan projects, this was done by focusing on the accuracy and reliability of the data coming from the different source systems. The analysis allowed source priority scoring so that the team could determine the leading system-of-record for each of the entities in the customer database. Data latency was also a factor with the recent data receiving a higher score.
The combination of both source priority and latency score determines which source data is kept in the Nissan Customer Database.
With so many sources in different formats, converging on a single, unique version of what a customer is, becomes a necessary part of establishing the Nissan Customer Database, ideally with the best possible version of the digital customer.
Advanced clustering and matching techniques identify groups of data that most likely belonged to the same customer, then cleansed the data, and then applied matching algorithms to identify the likelihood of two entities being the same customer.
This all paints a picture of the typical multithreaded nature of a big business pulling on many strings to improve top and bottom-line growth. The most important thing is that we should remember that the customer and customer records are central to the story. Without knowledge and insight, without convergence on a single source of definition for the customer, it is incredibly difficult to know whether all these efforts are being as effective as they possibly can be. It is a journey that never really ends.
Just like the transition from motorcycle engines to urban charging stations and local manufacturer-branded dealerships to maintain the smooth operations of after-sales service and fuel, maintaining customer data has to be considered a continuous activity and one where we believe Pretectum CMDM can help. Contact us today to learn more on how.
Master Data Management is seen as a way for the business to address a number of technical and operational problems that may be strategic and tactical in nature.
Triggering events may be new business acquisitions and the wave of new data that may need to be incorporated in a robust de-duplicated way.
Another triggering event may be more rigorous operational scrutiny in response to new public regulations, changes from private to public accountability or compliance pressures.
Changes in organizational leadership accompanied by a revitalisation of the business vision and business strategy may also be a catalyst.
Whatever the cause, there is often a handful of expectations from the business as to what more data governance, influenced by the implementation of an MDM will deliver. With the customer master in particular there is often the belief that more rigorous deduplication, enhancement and data quality in the customer repository will help in improving the Know-Your-Customer (KYC) situation. The establishment of a â360-degree-viewâ of customer records is seen as a key to supporting KYC.
Software vendors see MDM in particular as a strong software systems-based approach to helping to formulate said views and addressing the needs of business pressures to improve data quality. These solutions focus on technical efficacy without an understanding or a desire to understand the organizational challenges that centralized control and governance of the data have for the day-to-day needs and operational requirements of the business itself.
Further, the approach of many of these solutions is often rooted in data practice theory and built on legacy technology stacks that are robust but aged, and somewhat incompatible with contemporary business-led as opposed to IT-led initiatives.
Expectations
The business should be able to define the terms and descriptions that it has for data elements that it uses. In some applications, this is referred to as a glossary but in reality, this is a collection of descriptions and data classifiers.
The business should be able to define the customer in a single way for all business areas to adhere to where the function is fully centralized. The business should be able to define what a customer record should look like in totality if this is required, especially if this is necessary for integration with systems.
The business should be able to use core elements of the defined customer with extensions by other areas of the business where the approach is looser or decentralized. Every business area may have a lens through which it chooses to see the customer, which may have supplementary attributes or simply a subset of all the elements that the systems require.
Data itself is created or collated centrally
The data is assessed in real-time and in batches as well as recurrent cycles for anomalies relative to the data definitions in place.
The data is identified for potential duplicates
the duplicates can be grouped, consolidated or merged via fully automated, semi-automated or manual methods, according to the needs of the business.
The data definitions can be leveraged by ecosystem applications and systems to ensure that records that are created or amended meet the configuration expectations of the MDM
The data itself is accessible to ecosystem applications and systems in a secure, authenticated and permission-based way through a syndication approach either in real-time, serially, batched/bulk or through continuous integration.
Data design creation and amendment can be established through decisions by a crowdsourced organizational hierarchy of stakeholders and interested parties who can approve/reject the designs.
Data creation and changes can, if necessary, also be established through a similar hierarchy of controllers, administrators, curators and stakeholders.
Where appropriate, the subjects of the data curation system can engage in a secure, authenticated self-servicedata curation approach to evaluate the data held and apply further curation.
Extensive secure and authenticated integrations are available for a wide array of technologies
Extensive reporting is available for compliance and operations on lineage, usage, statistics and data quality.
Learn how the Pretectum CMDM meets these expectations
Itâs debatable as to which customer data you have is your most valuable but without a doubt, the data that is unique to your business is the most important. It is that data which you are able to rely on to make critical business decisions and engage most comprehensively and effectively with your customers that is certainly likely to be valued far above other data.
It helps to provide a definition of what that unique first-party data really is.
First-party data is the data that you are able to obtain from your prospects, customers and audiences. Typically it is given first-hand by form entries, engagement, calls or transactions.
On one hand, you could think of it as the classic Rolodex entry but these days it is more than likely lurking in your ERP, CRM, CDP or POS system.
Your first-party data may also be present in emails, spreadsheets and of course a CMDM like a Pretectum Customer MDM.
Your first-party data typically carries all the essential information that you need to contact, transact and engage with a given customer or prospect. Over time that data may be enhanced through the addition of measures, insights and indicators related to transactional behaviour, preferences, tastes and engagement. Some of these enhancements may be unique to your business and its direct relationship, others might come from annual data refresh or contact update requests. Some may be inferred.
The reason you have this data is to minimize friction in the engagement and transacting with the customer or prospect. You minimize friction best by personalizing the customer experience every time they engage with your brand, message, people, processes and technologies.
But there is a problem with first-party data, a problem that is inherent in almost all data that is not appropriately managed and which devalues the data. That problem is related to the classical six core data quality dimensions.
Accuracy
Completeness
Consistency
Timeliness
Validity
Uniqueness
You can learn more about these on the web but they are just a handful of the 65 dimensions and subdimensions created by DAMA that flex according to the needs of different industries.
Data Quality dimensions were described by Richard Y. Wang and Diane M. Strong in Beyond Accuracy: What Data Quality Means to Data Consumers. They recognized 15 dimensions. DAMA International, a not-for-profit, vendor-independent, global association of technical and business professionals dedicated to advancing the concepts and practices of information and data management developed a more elaborate list containing 65 dimensions and subdimensions.
So the first challenge with ensuring that your data is initially, and then continues to be valuable, is ensuring that the data meets some sort of data quality objective. Defining data quality measures and then measuring the quality of your data is the best way to determine if your data is going to be useful.
Pretectum allows you to manage first-party data quality, by allowing you to define the measures of quality upfront before you even add data to your CMDM. When you eventually load or enter that data, the system then informs you of problems and keeps you aware of records that may not be complete or consistent with defined values. Other mechanisms allow you to verify the data against external reference sets to inform you on accuracy or validity as well as uniqueness. All the while, the latest version of the data is served up to you with the ability to examine the change history of the records over time.
The original version of this article is found here
What do you interpret as a Single Customer View or SCV? You only have to search through the internet, literature, and marketing materials from vendors to realize that the concept of an SCV is a fuzzy-edged and somewhat amorphous thing, just like your actual customer master data perhaps?
One solution vendor describes it as âa method for gathering all the data about your prospects and clients and merging it into a single recordâ, they also might describe it as a â360â, â360 degreeâ or âunifiedâ customer view. But this all assumes that you know the position from which youâre looking at the customer and the business purpose that you have in mind.
The assumption in some instances is that the Single Customer View is composed primarily of first-party data, in other words, data gathered by the many functions of a given business during the course of transactions and various market engagements.
However, when the first-party data is combined with supplementary data from third parties who have anonymized datasets that can be attached to those same customer records by data relationships like physical address, that customer record becomes infinitely more valuable and is able to be categorized for uses that are potentially more useful, valuable and meaningful for different business activities.
The challenge today, across many organizations is that the customer entity record is often not held in just one place. It is often purposefully held in systems that have very specific functions that have customer records either as a focus or necessary side aspect of their function. As a consequence, having and in fact, maintaining a unified view and understanding of the customer is quite challenging.
Consider a bank, for example, that maintains the customer master records for mortgages and bonds, separately from brokerage, savings, credit card, checking, and loan accounts. It is not inconceivable that for regular transaction processing, that same bank, has at least five or more customer master records for ostensibly the same person. In some countries, these can be connected using a national identity number like an SSN, but in many instances, this is not a requirement for the establishment of the account â any legitimate state, province, or government-issued identity document might suffice.
For those organizations and traders that are unregulated the problem might be that the only tenuous relationship that might exist between the marketing, inventory, and billing systems might be an email address, a phone number, or a delivery address depending on the level of integration between systems and the relative maturity of the organizationâs data management practices.
Pretectum seeks to resolve these types of challenges by supporting a number of different approaches to customer MDM in whatever way makes the most sense for the business. These approaches include a hub and spoke deployment where the MDM is a collation point for the customer entity from one or more systems or as a derived golden record master record that can be used as a cross-referencing or search index point for multiple systems to reduce the proliferation of duplicate records.
Ultimately, Pretectumâs CMDM could also serve as your origination system, supporting the establishment and immutable authority on the most complete description of the customer record for transactional and reference purposes and a true golden record that is originated and managed centrally and syndicated to all systems as and when appropriate.
If your choice is to include aggregations and summations of data and external identifiers from other platforms and systems, that is entirely at your discretion. The main thing is that you have the customer master data that you need, continuously curated and fit for your business purposes no matter how diverse those purposes may be.
Answering the question of âwhat is a Customer MDM?â
Across the globe, in all industry segments, data drives business processes, and systems.
The overall organization, its employees, and its customers benefit when this data is shared and accessible across all business units. A unified single point of access for the same customer lists and data used to run the business. On the whole, business data users within the organization generally assume that the customer data that they have access to is consistent across the whole business until they identify anomalies.
The reality though, is that customer data evolves in a more organic and somewhat haphazard way than data management professionals would prefer. This is especially true in larger organizations. Mergers and business acquisitions, projects and initiatives, and other general business activities often result in multiple systems being created, that often perform a similar or exact same function but for a variety of reasons, these redundancies must coexist.
The result is that these conditions inevitably lead to inconsistencies in the overall data structures and the data values between the various systems. This variance leads to increased data management costs and organizational risks.
The general consensus is that both data management and organizational costs and risk can be reduced through the dual strategies of Master Data Management and Reference Data Management.
Master Data Management is about the management of data that relates to organizational entities. These organizational entities are objects like logical financial structures, assets, locations, products, customers, suppliers, and employees.
These same structures provide the necessary context for smoothing of business transactions and transactional and business activity analysis.
Within them, are entities, real-world persons, organizations, places, and things as virtual objects. These same entities are represented by entity instances. In digital forms, they are effectively digital entities but really they are data records. Master Data should represent the authoritative, most accurate data available about key business entities.
When managed well, Master Data entities are trustworthy and can be used by employees in partner engagement with confidence. Surrounding these entities, are business rules that dictate formats, allowable ranges, and characteristics that should be applied to appropriately frame the master data values held.
Common organizational master data may include data that relates to partners that are made up of private individuals, organizations, and their employees. That same data may describe their role, their relationships, and essential attributes that might be useful for engaging with them as an organization.
Typical people-based master data entities are objects like customer, citizen, patient, vendor, supplier, agent, business partner, competitor, employee, or student.
Seeking the truth
When multiple repositories of these entities exist, there are potentially different versions of âthe truthâ and it becomes difficult to work out which one is correct and whether in fact, two or more entities are referring to the same thing.
In order to do so, one must have an understanding of the origins of the data. A defined System of Record (SoR) is often considered an authoritative system where that data is created/captured and maintained in a disciplined and controlled way.
The capture and maintenance are undertaken with defined rules and expected outcomes. Historically this would mean that the Point of Sale system is there to support selling activities, ERP to support make-to-sell or buy-to-sell, and CRM to support selling, service, and support of customers.
For any of these systems to be deemed trustworthy sources, they need to be generally recognized as holding âthe best version of the truthâ in relation to records they hold, based on automated and manual data curation. That trusted source is sometimes also referred to as a Single View. Within that system, the entities are often referred to as Golden Records.
Systems of Reference similarly, are authoritative systems where reliable data is held and maintained to support proper transaction processing and analysis. Those systems of reference may or may not originate their own data.
Historically, Master Data Management (MDM) applications, Data Sharing Hubs, and Data Warehouses have often served as systems of reference.
The challenge is that different systems have different purposes and often no single system describing the same entity, needs to be describing the exact same characteristics of that entity. The question then becomes, can any of these systems truly be âthe single source of truthâ?
Master data management efforts often pursue the consolidation of entities from the many sources that create and contain them and then formulate a composite record that may be incomplete and only a partially accurate representation of all the entities held. For different entity users that can mean that they have less faith in the âgolden recordsâ that the system presents. When this is the situation, the representation may switch from âSingle Sourceâ to âTrusted Sourceâ suggesting that measures are in place to drive consistency, accuracy, and completeness in the entity records with minimal ambiguity and contentiousness.
Gartner defines Master Data Management as âa technology-enabled discipline in which business and IT work together to ensure the uniformity, accuracy, stewardship, semantic consistency, and accountability of the enterpriseâs official shared Master Data assets.â
MDM is therefore a discipline, made up of people, processes, and technology. There is often no specific application solution despite the fact that vendors will often use the acronym to describe their products, systems, and platforms that manage master data but that does not mean that they are effectively managing the master data, simply that they have characteristics that, when used correctly. can assist in proper master data management.
As you can imagine then, when something is described as a Customer MDM, it is a practice that relates to the management of digital customer entities. That practice could be paper-based also but weâre assuming that at scale youâre more interested in digital record-keeping.
The CMDM systems then, are the people processes and technology that support the customer master data management practice. The CMDM platform concept is therefore a composite software application on-premise or in the cloud, that provides metadata and data that relates to the management of the customer entities.
CMDM Platforms and related technologies for Customer Master Data Management are offered by many of the leading global software brands as parts of multidomain MDM like SAP, Oracle, IBM, and Informatica but there are some specialist offerings that you might not have heard like Ataccama, Pretectum, Profisee, Reltio, Riversand, Semarchy and Stibo Systems
Pretectum previously mentioned how some airlines leveraged their loyalty programs to secure loans from various backers. At face value, this tells you that though we as consumers store value in our reward points or air miles, so do the airlines and retailers themselves!
Customer loyalty programs like Airmiles and rewards are forged relationships between brands and customers. This is one of the reasons that when a rewards or loyalty program changes the terms and conditions of the relationship, sometimes you will see a sharp uptick or drop-off in the use or support of that program. Launching a loyalty program is also expensive and complex.
In the US alone, companies spend a staggering $2 billion on loyalty programs every year according to Capgemini.
Typically loyalty programs serve as a way for the brand to offer membership-related exclusive products, promotions, or pricing. The reciprocated offer from the customer is their agreement to sustain the relationship with preference against that product line or service through repeat purchases or brand engagement.
In a nutshell, a loyalty program is another marketing mix element. A part of any marketing strategy Loyalty Programs is designed to encourage customers to sustain their shopping or use the services of a business associated with the brand and program.
Loyalty programs cover most types of commerce, each having different features and rewards. Industry segments that have leveraged broad loyalty programs include financial credit, hospitality & travel, retail and entertainment.
According to Sallie Burnett, a loyalty consultant and Founder of Customer Insight Group. in a Forbes article, one of the most successful loyalty program examples is that ofNordstrom retail stores. Nordstrom customers move up through levels from Member to Insider, Influencer and Ambassador.
Annex Cloud a loyalty experience solution suggests however that not all loyalty programs are a guarantee of success. Annex Cloud cite, a report by Capgemini wherein a high percentage of loyalty programs are considered to be failing.
53% of consumers stated that they abandoned at least one loyalty program within the last year which means businesses are putting money and energy into strategies that arenât being successful. The main reasons vary, from a lack of reward relevance, flexibility, and value (44%) through a lack of a seamless multi-channel experience (33%) to customer service issues (17%).
Pretectum feels that one of the ways to mitigate some of the aspects of customer service and multi-channel interaction is through the convergence on a single-source-of-truth in relation to the customer master. If your sales, service, support and loyalty programs are all reading from the same song-sheet, a centralized customer master data hub, then the ability to service the same message consistently and coherently is greatly improved. This in turn leads to a greater likelihood of retention.
Here are some interesting statistics in relation to loyalty programs and the customer relationship.
82% of companies say retention is cheaper than acquisition.
75% of consumers say they prefer brands that offer rewards.
56% of customers stay loyal to brands that âget themâ
58% of companies use personalization to retain customers
Master Data Management may be positioned as a âsilver bulletâ for the woes of poor customer master data but it doesnât solve for long-standing systemic organizational deficiencies.
Any organization embarking on any kind of Master Data Management (MDM) initiative will need to look long and hard at a number of characteristics of how data is created, described and managed that are independent of the MDM itself if they wish to get the best value from their MDM.
That highly desirable 360º view of the customer, for example, what is it exactly?
Benefits as described by vendors and industry experts are numerous, but perhaps a small handful is the most important and most achievable.
Reducing the costs and risks of customer data ownership
Reducing friction in transacting or engaging with the customer
Improved customer segmentation and targeting lists
All three of these outcomes are however heavily contingent on a number of important behaviours and organizational culture shifts.
How often does this come up as a problem to solve? It may happen more frequently than you think.
Having clean, comprehensive, and consistent data is paramount to the most appropriate customer engagement and interaction. If your business is also an advocate and heavy user of automation, machine learning and artificial intelligence then your technical teams will tell you that the results of their efforts are commensurate not only with their efforts but also with the quality of the data that they are working with.
Without the best possible customer data, your staff and systems are exposed to a partial picture which can result in bad decisions, model bias and skewed results.
The US National Library of Medicine and National Institute of Health (PMC) journal contains an article from May 2013 describing three types of potential data deficiency in any given data set. While the focus in this case by the author, Hyun Kang would be on suitability for studies, this basis is useful for considering customer master data in general.
The three types are Missing Completely at Random (MCAR), Missing at Random (MAR), and MNAR (Missing Not at Random). Each with its own cause and potential solutions.
Weâll look at this through the lens of a customer master data management system. Read more at Pretectum.com
When we hear the words âdigital transformationâ, the first thought that might come into your mind might be the shift from a traditional manual office environment into one that is leveraging all things digital.
That perspective is valid to some extent. Many companies and businesses shift from traditional physical business practices to more modern digital ones to improve not just operational efficiency but also to accentuate their digital presence.
They might do this either by creating a mobile app, revamping or launching their website to support e-commerce or even by ramping up their social media presence. All of these decisions and behaviours fall under the umbrella of digital transformation initiatives.
Ask any hill-walker or mountaineer why they do what they do and the answer you get might be surprising. Sometimes the main reason that they do what they do is for where the journey ultimately takes them.
Do you know why the view from the top is worth the climb? This is because it gives you a single view of everything laid out before you. It is the same appeal of ballooning and even flying for someâŚ
The same is true with Master Data Management. The âhard climbâ to a unified single source of customer data, gives you a single âclear pictureâ of the customer.
There has been an incredible amount of technological change that impacts our everyday lives. The advent of the internet, social media, mobile and more, have changed how customers interact with brands, hence the need for change from brands.
It is the need for that change, that drives effective digital transformation.
Digital transformation can mean different things for different organizations. It can mean launching e-commerce or a mobile app. For some, it could be about improving the web experience.
Assuming you have the executive endorsement, a budget, or a commitment from the business to improve customer data quality, the first thing you will want to do is determine the scope of work that you need to undertake as part of your customer-related data governance project.
In order to achieve effective horizontal integration with all the business units within the organization, the scoping of your MDM needs to gather the requirements of all employees and interest groups that would be expected to work with or be dependent on a new and unified approach customer master data management.
The use of a standardized technical framework will help in framing whether the solution meets the needs of each business area.
You do your readers a huge disservice by conflating CDP and CRM. Yes, both store customer data â as do data lakes, data warehouses, marketing automation, email engines, personalization tools, web content managers, and a host of other systems. Each of those is designed for a specific purpose and stores customer data in a way that fits that purpose.
CRM also has its own purpose â to support sales and service agents when speaking with customers â and is optimized for it. CRMs are notoriously bad at dealing with data that was imported from elsewhere, and with unstructured and semi-structured data types.  Â
Theyâre generally poor at sharing their data with other systems. Â
An observation in the market is that every organization recognizes that it has some sort of data issue that could be improved with the implementation of yet another solution.
There are plenty of vendors whose primary objective is to push their technology or solution without too much concern about whether the solution meets your particular business needs. Suitability is ultimately determined at the time of subscription or software maintenance renewal.
