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Mar 26 2024
Composable Customer Master Data Management (CMDM)

You might have more recently heard of “composable” 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 2010’s 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. It’s 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 platform’s 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 CMDM’s 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 organization’s 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 CMDM’s 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 – 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 teams’ 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.

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Mar 11 2024
The Customer Master Data Management Top 10 for 2024

There is a logical progression of concepts that build upon each other to articulate the comprehensive benefits of Customer Master Data Management (CMDM).

This starts with the foundational importance of data quality in a solution like the Pretectum CMDM. Data quality serves as the bedrock upon which all subsequent benefits rely. Without accurate and reliable customer data, organizations cannot effectively streamline operations, make informed decisions, or enhance the customer experience. Therefore, it’s crucial to establish data quality as a primary focus of any CMDM program.

CMDM streamlines customer data management operations. By centralizing and unifying customer data, organizations can eliminate inefficiencies associated with managing disparate data sources. This streamlined approach not only reduces operational costs but also lays the groundwork for more effective decision-making and customer engagement.

With operations streamlined, the question then, is how CMDM empowers organizations to make better decisions. By providing comprehensive insights into customer behavior and preferences, CMDM enables decision-makers to formulate more targeted strategies and initiatives. This, in turn, leads to more impactful customer interactions and ultimately drives business success.

Building upon the theme of decision-making, CMDM enhances the customer experience. Organizations can deliver personalized interactions and seamless experiences across all touchpoints by leveraging deep insights into customer data. This not only fosters customer satisfaction but also strengthens brand loyalty and advocacy.

Everything is done in pursuit of driving revenue growth. By optimizing operations, decision-making, and customer experience, CMDM enables organizations to capitalize on revenue opportunities and maximize customer lifetime value. This solidifies the value proposition of CMDM as a strategic imperative for organizations looking to achieve sustainable growth and success in today’s competitive business landscape.

Enhanced Data Quality

Ensuring superior data quality is fundamental for any organization leveraging a Customer Master Data Management (CMDM) solution. It is the cornerstone of all customer-related initiatives, ensuring that every interaction, analysis, and decision is based on accurate and consistent customer information. By meticulously identifying and rectifying discrepancies, purging redundancies, and maintaining data integrity across customer datasets, CMDM guarantees that businesses have a reliable foundation for their customer-centric strategies. This commitment to data quality not only instills trust in customer data but also minimizes the risk of errors, misinformation, and misguided decisions, ultimately leading to more effective customer engagement and sustained business success.

Streamlined Operations

Streamlining operations through Customer Master Data Management (CMDM) is essential for organizations aiming to enhance efficiency and agility in customer-facing activities. By establishing a unified and centralized repository of customer information, CMDM eliminates the complexities and inefficiencies associated with managing disparate customer data sources. This unified approach not only accelerates customer-related processes but also reduces operational costs stemming from data redundancy, manual reconciliation efforts, and inconsistent workflows. With streamlined operations enabled by CMDM, organizations can respond more swiftly to customer needs, deliver personalized experiences, and seize market opportunities, thereby maintaining a competitive edge and driving business growth.

Improved Decision-Making

Enhanced decision-making facilitated by Customer Master Data Management (CMDM) is critical for organizations seeking to optimize customer interactions and drive sustainable growth. By providing decision-makers with comprehensive and accurate insights into customer behavior, preferences, and interactions across various channels, CMDM empowers them to make informed decisions with confidence. This holistic view of customer data enables executives to identify trends, forecast demand, and anticipate customer needs more accurately. As a result, organizations can develop targeted marketing strategies, optimize resource allocation, and deliver personalized experiences that resonate with their customers, ultimately driving customer satisfaction, loyalty, and profitability.

An Ability to Drive New Customer Experiences

Elevating the customer experience through Customer Master Data Management (CMDM) is paramount for businesses aiming to build enduring relationships and foster brand loyalty. Only by consolidating and centralizing customer data from disparate sources, CMDM systems enable organizations to gain a holistic understanding of their customer’s preferences, behaviors, and interactions. Armed with this comprehensive insight, businesses can personalize interactions, tailor products and services, and deliver seamless experiences across touchpoints, thereby enhancing customer satisfaction and fostering long-term loyalty. Moreover, by leveraging CMDM to anticipate and address customer needs proactively, organizations can differentiate themselves in the market and position themselves as trusted advisors, driving customer advocacy and revenue growth.

Increased Revenue

Driving revenue growth through Customer Master Data Management (CMDM) is a strategic imperative for businesses seeking to capitalize on customer insights and market opportunities. By leveraging CMDM to analyze customer data, segment audiences, and target the right customers with personalized offerings, organizations can enhance conversion rates, increase sales performance, and maximize customer lifetime value. Additionally, by delivering consistent and compelling experiences across channels, CMDM helps organizations cultivate customer loyalty and advocacy, driving repeat business and revenue growth.

Customer Benefits

Based on their significance in directly impacting the customer experience and fostering long-term relationships with customers, consider these important customer benefits when you focus on your customer master data management.

Personalization is a key driver of customer satisfaction and loyalty. When businesses understand their customers’ preferences and tailor interactions accordingly, it creates a more engaging and meaningful experience for the customer, ultimately leading to higher satisfaction and repeat business.

Customers expect businesses to have accurate information about them. By ensuring data accuracy, businesses can make informed decisions that directly impact the customer experience. For example, offering relevant products or services based on accurate customer data leads to more positive interactions and increased trust.

Quick and effective customer support is crucial for resolving issues and building trust with customers. By providing support representatives with a holistic view of the customer and any journeys with the customer, an organization can address relationship needs more efficiently, leading to higher satisfaction and loyalty.

Customers appreciate relevant and appropriate suggestions and recommendations at the right and best time to cater to their interests preferences and situations. Leveraging customer data, an organization’s teams and applications can make more precise, targeted, and accurate recommendations, businesses can enhance the shopping experience, increase sales, and build stronger relationships with customers.

I f your organization is in the business of selling goods, or services, or simply having a relationship with consumers; targeted marketing campaigns are more effective in engaging than generic messaging. By segmenting customers based on their characteristics and behaviors, businesses can tailor their marketing efforts to specific audience segments, resulting in higher engagement and conversion rates.

These five benefits directly contribute to a positive customer experience by providing personalized interactions, accurate information, efficient support, relevant recommendations, and targeted marketing efforts. By focusing on these areas, organizations can strengthen their relationships with consumers and audiences and drive long-term loyalty and satisfaction.

The value proposition of a Customer Master Data Management (CMDM) system like the Pretectum CMDM, lies in its ability to holistically enhance the entire customer experience journey.

By ensuring superior data quality, streamlining operations, improving decision-making, enabling new customer experiences, and driving increased revenue, CMDM becomes a strategic imperative for organizations. The system provides personalized interactions, accurate information, efficient support, relevant recommendations, and targeted marketing efforts, ultimately fostering enduring relationships, customer loyalty, and satisfaction in today’s competitive business landscape.

Pretectum CMDM serves as the foundation for businesses seeking sustainable growth and success by leveraging comprehensive customer insights and delivering exceptional experiences across touchpoints; Pretectum CMDM serves up the single customer view, integrates it with your business sources and analytics platforms, and provides your personnel with a unified view of the customer with data that can be as rich and comprehensive as your imagination permits.

Red Button 0 Comments
Feb 28 2024
Successful low-risk Customer Master Data implementation
STrategy and Tactics jigsaw pieces

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 “all at once” or “big-bang” 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 “data” 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 “what” and “how” 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 “Strategic Outcomes Blueprint” (SOB) is instrumental in identifying quick wins related to customer data that prioritize business outcomes, thereby highlighting the value of the CMDM program.

A “Strategic Outcomes Blueprint” 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.

It’s 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; it’s a continuous journey toward customer data excellence, personalized customer experiences, and sustainable growth in today’s customer-centric business landscape.

Red Button 0 Comments
Oct 27 2023
Decoding Data Mesh: A Structured Approach to Decentralized Data Management with Pretectum CMDM

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 Dehghani’s 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 – right? Nothing new here, let’s 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 “Data Mesh” vendors in the market. Rather, it’s 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 organization’s 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
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.

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Sep 23 2022
Master Data Management Business Requirements

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 “360-degree-view” 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-service data 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

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Aug 19 2022
Customer Master Data Management matters

Some reasons why you should care

Reducing business friction – when you don’t have a customer MDM, every business department is responsible for collecting and maintaining master data – marketing, sales, service, support, billing and collections.

The result is that the same master data may be collected multiple times, and worse, the exact same data may be maintained by more than one department.

If it is consumer data that may land your business in a non-compliance situation when dealing with GDPR and privacy laws. 

A customer MDM defines a clear governance process; this means every aspect of the customer master needs to be collected only once. this reduces the number of collection points for the master data and in turn, can reduce the workload for customer data collection for each department. There is less time collecting and verifying and less time needing to be spent on reconciliation.

Having a customer MDM will lead to improved customer data quality. One of the main weaknesses in an unstructured, decentralized data management function is that data quality gets compromised.

Every department holds a version of the truth as they see it. This inevitably leads to reconciliation and consolidation problems and can even introduce issues in the transaction processing process for the customer.

Sales sell, but the credit risk department assesses risk and sets credit thresholds. If the two departments are working off different views of the customer because the customer master is out of synch or never gets reconciled then sales may not sell because they suspect that the customer is not current with their payments or they may oversell to a customer who has a poor likelihood of settlement. A unified customer master that both references will give a better view of the customer.

When the Customer MDM is the one single source of truth it will provide all relevant master data and directly deliver all the benefits of superior data quality. Data quality here isn’t only about the correct data but also the customer data that is the most up-to-date.

The best possible customer record and experience for all, is the result.

When all the customer master data is located centrally, the organization is placed in a better position for compliance and governance assessment. The Customer MDM provides the ability to clearly structure data responsibilities.

This structure tells you who is responsible for definition, creation, amendment, viewing/retrieving, deletion and archiving. Moreover, when the data is found centrally, when there are subject access requests, you only have to go to one place to provide details of what you have and what you know about the customer.

Having the customer MDM as your single source of truth will assist in meeting all the expectations of audit, risk, compliance and the customer themselves. Master data management systems in general represent perfect single-source of truth repositories in that they often provide evidence of origin and sourcing lineage. These exist to support business processes but can also be valuable for compliance reporting.

Make better decisions now that you have all your customer data in one place and can have a comprehensive, and complete view of the customer master. Organization-wide decision processes can rely on the latest set of common customer master data which will help operations, executives, managers and marketers make informed, fact-based decisions.

Actually, this is a very important aspect you have to understand. When it comes to decision making, dynamic data is most often in the spotlight. However, as mentioned in my initial statement, master data is actually the powerhouse that drives your dynamic data.

Automation doesn’t have to be a dirty people-displacing word. In fact, it can be a boon to your business efficiency and effectiveness since data exchanges and data hygiene tasks can be performed automatically.

For most companies, the implementation of a customer MDM will eliminate a lot of highly manual tasks engaged in by multiple participants in the creation and maintenance of customer master data.

You can still collect data in Excel if necessary but as long as those spreadsheets land in the master data system through an automated method, you will still have what you consider a tactically effective mechanism as something complementary to the overall needs that the business

Your centralized, system-based Customer Master Data Management eliminates many operational and logistics issues.

With the Pretectum CMDM, there’s no longer a need for creating and maintaining personal Excel files and isolated databases of customer data. Instead, data governance processes restrict that approach and since people always want the most complete and freshest data, why wouldn’t they go to a system that has all that?

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Jul 11 2022
Customer Master Data Management – what to expect

Defining the approach for Customer Master Data Management (MDM) for your organization means also understanding the boundaries of functionality. Define it right and you will have a goldmine of opportunities.

The term MDM has become progressively ambiguous over the years as vendors have branched out into areas of functionality that are not necessarily aligned with the optimal concept of master data management. This is just as true for the customer MDM as it is for the supplier, the employee and other master data elements.

While different classes of MDM can be defined as providing particular functions, MDM as a broad tool description is probably not that useful to business leaders. The ultimate question has to be, what do you want the Customer MDM to do for you? Knowing the answer to that question will direct you to the right combination of features and functionality to evaluate in a vendor or homegrown solution.

MDM technology and functionality overlap many types of software solutions that have addressed the issue of customer records and customer engagement in the past. These include Customer Data Platforms (CDP), Customer Relationship Management (CRM), and Marketing Automation (MA) platforms and technologies.

CRM and MA systems focus on customer interactions at the account level as well as at the individual contact level. Many CRMs are not purpose-built to be master record management systems, though they will have data mastering as an important part of their architecture. CRMs in particular, focus on sales events and pipeline call-off activities. As CRMs and Marketing Automation systems evolve they will subsume more MDM functionality and they will converge, however, CRMs will remain focused on what they do best, which is to gather and curate the interactions that your organization has with its contracted audiences and prospects. CRM and MA tools typically focus on known users.

CDPs capture anonymous data and verified or authenticated data. CDPs can also collect data on the customer journey and are built for the integration and collection of large amounts of data.  The core functionality of CDPs is focused on building out a unified database of existing and prospective customers, but they can also be used to manage customer engagement, provide analytics, campaign and event attribution and market segmentation, as well as enable personalization, message selection and campaign management.

Customer Master Data Management programs seek to have a golden record of the customer but probably should not include interactions from other systems except when those interactions alter the main customer record. While some MDM solutions are aimed at technical audiences, they do not necessarily need to be exclusively for technical teams since many mid-tier organizations don’t necessarily have data stewardship or data governance function. You’re either working in the business or you are in IT.

The view, at Pretectum, is that IT should not own or manage the content of the MDM, they should care for the platform but the ownership of the data and the quality of the data should rest within the business and business users.

Since MDMs come in all shapes and sizes, your needs will be determined by how you choose to frame the challenge and the degree of maturity of supporting processes for customer master data management. A suitable solution will support a litany of pieces of functionality, not all of which you will need.

As an example, if you need a master but you have teams that require personalized structures and attributes for specific tasks, then you should be able to do this the golden thread of the core master record is always accessible and at hand. There will be prerequisites but the system should support flexible usage and appropriate data syndication accompanied by high-quality data and a detailed understanding of what you have at your disposal in terms of the customer master record.

Learn more about the Pretectum CMDM advantage for your organization

    redbuttondata 0 Comments
    Jun 22 2022
    Customer types, patterns, and segments

    Whether you’re in Product or Service management, Sales, or Marketing you know that having an understanding of the customer, analyzing, and keeping track of their behaviour is critical for the effectiveness of your business.

    Customers make thousands of calculated and spur-of-the-moment decisions every waking hour of the day from deciding what they will eat to what they’ll wear. It is easy to think that the many ‘buy’ decisions, in particular, are made without too much thought, particularly the less significant ones.

    Decoding the thought processes behind customer decisions is no mean feat, particularly if you don’t have the data to back up your hypotheses around why customers do certain things.

    The Customer Data Model

    We try to decode the thinking because ultimately it can help us understand how customers arrive at their decisions and choices. In the study of customer behaviours, in particular, you’re looking at the processes customers apply to choose, use and dispose of your products and services and this includes factoring in their emotional, mental, and behavioural responses. Behaviour analytics examines the customer through the analytical lenses of psychology, biology, chemistry, and economic practices. For marketers, these analyses help in understanding what influences the acquire, buy and discard decisions. This in turn leads to a rehoning of the message, branding, positioning, promotions, advertising, and even what is ultimately formulated as a product or service.

    Only through an in-depth understanding of the customer can businesses hope how best to decide on products and service offerings and how adequately they fill gaps in the market and are needed and wanted.

    Revealing the customer

    There are three categories of factors that are considered an influence customer behaviour:

    • Personal: the customer’s interests and opinions as influenced by their age, gender, ethnicity, and culture.
    • Psychological: the customer’s reaction to particular kinds of messaging, which is dependent on their personal perceptions, attitudes, and mentality.
    • Social: factors such as family, friends, education level, social media, income, and socioeconomic status all have an influence on customer behaviour.

    In addition, there are four principal types of “buy” behaviour outcomes amongst customers; complex, dissonance-reducing, habitual, and variety or variability buy behaviours.

    Complex buying behaviour is typically associated with infrequent big-ticket product or service purchases. Such buy activities are complex in that the customers of engaging in a great deal of research before committing to the investment. Examples are purchases of high-end luxury goods, houses, cars, and holidays.

    Dissonance-reducing buy behaviour sees the customer encountering difficulties in determining the differences between specific products or brands. This ‘Dissonance’ often occurs when the customer fears making a bad choice. Clothing purchases may manifest this but equally, this can be present when buying appliances or electronic goods. Habitual buying behaviour.

    Habitual purchases typically involve very little preference, bias, or influence in the product or brand category because the risk is relatively low and there is no major emotional or cognitive investment. While grocery brands will often suggest that this implies brand loyalty, the reality is that in many cases the customer chooses based on past positive experiences which may be tied to price, look, feel, scent, taste, or overall experience. The repeat purchases are habitual rather than carefully thought through.

    Variety and variability in purchase behaviour are typically directly associated with past frustration, disappointment, or a bad experience with a previous purchase decision.

    The highly configurable Pretectum CMDM schema


    Pretectum believes that for you to have the best effect in terms of influence on the subscription to your services or the purchase of your products, you need to have the best possible understanding of your customer through the customer master data repository. This can help to inform your business in relation to how to target customers, what their purchasing power is like, their likes and dislikes and who influences their decision-making processes.

    The Pretectum CMDM enables you to decide the aspects that you need to maintain within the customer master, from an understanding of buying patterns to what they are buying, where, and when they buy, and how they pay.

    Consider what you store about your customers today and consider what you could store with the Pretectum CMDM to help your business in getting closer to your customers and being able to more appropriately understand and provide them with the goods and services that they want.

    Red Button 0 Comments
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    Jun 3 2022
    Social Shopping and Social Commerce

    Social shopping or S Commerce is a type of eCommerce that seeks to involve people with similar tastes in an online shopping experience. Sites like Pinterest claim to differentiate because on the one hand they offer the ability to pin items of interest but on the other hand they allow targeted marketing and click-through referral opportunities through image relationships. Arguably TikTok and Snapchat are also powerful channels for brands to launch their eCommerce campaigns. In fact, perhaps more than 50% of Snap’s business is in Direct Response advertising.

    Many social shopping sites are similar in feel and design to social curation site Pinterest. E-commerce experts suggest this is not coincidental, that the approach is catering to a new generation of shoppers who enjoy and expect a “Facebook experience” where users like and share as part of their online life and are seen to do this.

    Social shopping sites, like Kaboodle and ShopStyle, offer recommendations to members in the same way that you’ll see on Amazon Etsy and eBay.

    The concept of Social Shopping makes presentations personal, by providing members with the ability to create personal boards, preferences and lists. For these sites, stickiness comes in the concept of community and the opportunity to engage in a dialogue with friends and peers. The goal is to build community by encouraging members to talk about products and preferences and make suggestions directly to their friends and social contacts as they might if they were shopping together in actual bricks and mortar stores.

    Social commerce is that segment of eCommerce where sellers can actually sell and not just market their products directly through the social media platform, they can also browse goods catalogues and make those direct purchases.

    Unlike the more limited, social media marketing, true social commerce gives the customer the option to perform a direct checkout and settlement.

    Right now this is a $90Bn market, so what are the implications for customer data and your business?

    Social commerce is a subset of eCommerce makes it easy to measure and evaluate the performance of your ad spend with the various platforms. The social media platforms have built-in eCommerce metrics for impressions, engagement and reach. you can see the number of clicks, the number of views and the level of engagement and perhaps even the response sentiment. All these capabilities come at a price which erodes your potential customer lifetime value. With the average consumer spending around $400 – $500 per annum on social commerce, there are the many costs that are loaded up by the platforms to consider, from the ad spend through the commerce fees and charge and pay commission.

    In reality, social commerce is there for shoppers, not businesses. Since the entire process is focused on the particulars of the Social Media platform and if it includes checkout you lose website traffic and the opportunity to harvest some important customer characteristics.

    Further, unless you build customer contacts from your shipping or Logistics Execution System (LES), it is questionable who owns the actual customer. The more social platforms take over the buying process, the more they take power away from your business. The less data you have the less personalised the experience you can offer both online and offline.

    If those same platforms also sell the customer data they have to others, the further the cut into brand allegiance with shoppers potentially being redirected to competitors including the platform itself if it decides to branch out into retail.

    Contact us to learn more about Pretectum’s Customer Master Data Management system.

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      Red Button 0 Comments
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      Apr 28 2022
      Data Validation is the first step in the journey to Customer Data Governance

      It’s 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

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        Apr 5 2022
        Single Customer View

        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 “a method for gathering all the data about your prospects and clients and merging it into a single record“, they also might describe it as a ‘360’, ‘360 degree’ or ‘unified’ customer view. But this all assumes that you know the position from which you’re 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 – 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 organization’s 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, Pretectum’s 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.

          Red Button 0 Comments
          Mar 28 2022
          What is a CMDM platform?

          Answering the question of “what is a Customer MDM?”

          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 ‘the truth’ 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 “the best version of the truth” 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 “the single source of truth”?

          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 “golden records” that the system presents. When this is the situation, the representation may switch from “Single Source” to “Trusted Source” 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 “a technology-enabled discipline in which business and IT work together to ensure the uniformity, accuracy, stewardship, semantic consistency, and accountability of the enterprise’s official shared Master Data assets.”

          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 we’re assuming that at scale you’re 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

          The original version of this article was posted as What is a CMDM platform?

          Red Button 0 Comments
          Mar 11 2022
          CUSTOMER MDM

          Data quality and consistency should not be neglected, market research suggests that poor quality, data redundancy, and data inconsistency are what most businesses struggle with on a daily basis. Customer data says Pretectum, is amongst the most common of the data that represent challenges to marketing, sales, service, support and billing, and collections functions. There has never been a better time to implement a more disciplined approach to customer data management than today!

          Customer Master data management (“CMDM”) itself is the convergence of people process and technology to maintain a disciplined approach to the definition, creation, maintenance, and distribution of customer data. Customers can be consumers or businesses and when you consider consumer customers these encompass those that visit a brick and mortar stores and locations as well as those that your business engages with via eCommerce or via phone, mail, or home visits.

          The discipline of master data management as a whole is an element of data governance which in turn is an element of business information management.

          Although MDM has historically been the exclusive domain of specialized data groups and IT.

          The Pretectum C-MDM platform view is that master data management and Customer MDM in particular, as an area of data management, cannot be realistically managed and controlled by technologists and IT.

          Instead, we view it as something that should be controlled and managed by business data managers and stakeholders

          Any Customer MDM seeks to ensure there is uniformity, accuracy, semantic consistency, controls, and accountability of the organizational customer master data. There should be the expectation that the data is to be used by multiple groups with different needs and expectations in relation to that data.

          A typical set of traits in your customer master data management system should provide infrastructure, methods, and processes in support of the following key activities:

          • Business Area partitioning
          • Multi-user role and team definition
          • Metadata definitions of one or more customer master object(s)
          • Metadata definitions of the key attributes and what are acceptable data types and values
          • Definition of a semantic data model and classifiers
          • Definitions and mechanisms to support data governance procedures
          • The collection of master data in a central repository via manual entry & automation
          • Data maintenance processes including data quality and statistical reporting
          • Provision of the master data to systems and stakeholders manually & via automation

          What about machine learning and artificial intelligence?

          While the use of artificial intelligence and machine learning is advantageous and can prove to be accelerators in the implementation and adoption of any MDM, these are technical approaches to ensuring the efficacy of data governance and data management where the processes policies and procedures are clearly defined.

          The presence of AI and ML should not be mistakenly considered a prerequisite for MDM particularly if there is a low level of organization data governance maturity or where the business is nascent in it’s data governance program.

          Pretectum C-MDM comes with AI and ML but it is not necessarily technology that is suited for every kind of scenario. The Pretectum C-MDM provides businesses with a single source of the core customer data that you define, in one or more ways, for use across the entire organization. This centrally trustable and governed data repository helps businesses to gain valuable insights and engage in better decision-making.

          What do I get from implementing MDM?

          As a business, implementing a master data management program in support of the customer master will bring a great many hidden and obvious business benefits, among these your business should expect:

          • Reduced or eliminated poor quality data and duplicates which leads to improved speed in identifying customers and maximizing your understanding of those customers
          • More effective customer prioritization and reduced friction and the likelihood of mistaken identity when interacting with customers.
          • Improve confidence in customer interaction opportunities and a likely higher yield from targeted customer event-based activities.
          • Improved decision-making across all divisions and improved value from existing and new data assets

          To achieve the best results, in considering an MDM for the customer, it is essential to assess your overall business goals and examine your business data priorities in particular. Your customer master may not be your main priority, the main priority may lie in inventory management, your product catalog, or some other area of your business.

          If you identify that you have a high degree of issues with your customer data, consider what your business is missing in terms of opportunities and determine what you consider would be success criteria for any initiative that you would implement in pursuit of improved data governance and in particular, the implementation of a Customer Master Data Management program.

          Pretectum C-MDM is a power platform focused on the customer.

          From instantiation through onboarding, data maintenance, and data syndication; businesses will adopt and get desirable results from leveraging Pretectum C-MDM in improving the overall performance of the data governance program using the platform’s customer data specialism.

          Contact us to learn more about how we can help with your customer master data management ( Customer MDM )

            redbuttondata 0 Comments
            Mar 9 2022
            CONSUMER LOYALTY AND CUSTOMER MASTER DATA

            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 of Nordstrom 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 aren’t 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 “get them”
            • 58% of companies use personalization to retain customers 

              redbuttondata 0 Comments
              Feb 28 2022
              GenZ’s surprising love for a brand

              Pew Research Center has been studying the Millennial generation for years but in 2018 decided there was a difference between Millennials and the next generation.

              They decided to use 1996 as the last birth year for Millennials for their future work. Anyone born between 1981 and 1996 was considered a Millennial, and anyone born from 1997 onward is part of a new generation.

              They hesitated at first to give this next generation a name but settled on Generation Z. In the interceding years, Gen Z has taken hold in popular culture and journalism. You will find it referenced in Merriam-Webster, Oxford, and Urban Dictionary as the generation that follows Millennials.

              This is a generation with which you have an 8-second window to either perform or perish.

              Sarwant Singh – Forbes

              As cited in a 2019 article, “generational cutoff points aren’t an exact science. They should be viewed primarily as tools”, tools for analysis and classification of people, like your customers!

              Why do we bring this up? Well, the answer is simple, Gen Z represents a significant customer market and they are a generation that has been raised on the internet and social media.

              Most of them earn their own income and even if they haven’t quite flown the coop, their parents and family members likely support them financially with more robust purchasing power than prior generations of youth.

              In China, for example, they have naturally become the main force that drives China’s consumer market.

              As members of Gen Z take center stage in the consumer market, they are influencing the survivorship of brands and they aren’t necessarily going for the mainstream or traditional brands, and China, in particular, not even necessarily chasing global or foreign brands.

              This represents a significant opportunity for newcomers, niche brands, and brands that choose to distinguish themselves from the rest of the pack.

              “Broadly speaking, Gen Zers are ethnically diverse, socially aware, and environmentally conscious” according to Singh. Authenticity and transparency are key and they prefer direct autonomy and control in their decision-making as opposed to being ‘sold to’. This implies a general distrust of big established brands, and favor for brands that focus on the individual. Purchase decisions are therefore based on peer reviews, accessible product information, and ratings – decision potentially by consensus. Who has all that? Actually, the heritage brands do, they just need to keep the volume and velocity of good ratings up.

              Where global brands sometimes fail

              One has to acknowledge that tastes change over time, and with them, brand preferences. Just ten years ago, if you were considering the purchase of a Tesla car say, you would have raised some eyebrows, today, not so much.

              The eyebrows would likely have been raised by those born in an era that was dominated by global brands like Ford, Volkswagen, Hyundai, Nissan, Toyota, GM, Mazda, and the like. Other brands like Mercedes-Benz, BMW, Porsche, Ferrari, Lamborghini, Bentley, and Rolls Royce would have been regarded as premium brands. You’ll find a lot of sentiment out there about these brands because many of them have been multi-generationally familiar to us.

              An article in the SCMP suggests â€œYour choice between, say, an Audi Q7, a BMW X5, a Porsche Cayenne or a Mercedes GLE (now) becomes more a matter of your brand preference than of significant product differences. The choice of options and materials is pretty much the same.” Engines used to be a differentiator, for example, but that difference may be moot with all-electric engines, and though the heritage brands are late to the game, they may still have their brand and their history with the past customers as the ultimate trump card.

              Studies undertaken in 2020 and 2021 by YPulse in North America according to Inc, noted that young people were already thinking differently about mobility for example. Gen Z’ers are looking at alternatives to ride-share and public transport in favor of their own rides. 3.4% no longer want to ride public transport and 56% say they want safer transportation, options that they obviously feel ridesharing and public transport don’t necessarily offer.

              E&Ys 2020 Mobility Consumer Index found that 45% of all first-time car buyers are Millennials, another powerhouse group with dollars to spend. Intuitively, one would expect the more ‘environment woke’ Gen Z and Millennial consumers would be interested in only all-electric vehicles like those offered initially by just Tesla and a few others, the analysis doesn’t necessarily support this viewpoint though.

              Based on market research performed by Hedges & Company in 2018, it turns out the average Tesla owner is a 54-year-old white man making over $140,000 with no children; so what would it take to change that demographic to more Millennials and Gen Z?

              According to Inc, what matters to these generations is not the flashy features that get Boomers and Gen Xers excited. Ypulse found that the younger buyers are more interested in comfort, reliability, and fuel efficiency – in that order, facets that are offered perhaps by Tesla but which traditional brands offer too, at a lower or comparable price point. Remember too, that peer reviews get factored in too!

              Keeping their product lines aligned with their heritage and yet still seeking to appeal to the potentially more environmentally conscious Gen Z it is interesting then, that a heritage brand like Ford has “gassed” its business up with new and compelling offerings in the hybrid and full EV spaces. They’ve recognized the need and played their brand advantage but they’ve done it with data to back up their position.

              What this is demonstrative of, is a well-established brand responding relatively quickly and introducing horizontal differentiation within the brand vertical that Ford is. The F-150 Lightning for the utilitarian persona perhaps, the Mustang Mach-E for those looking for something with wholly visual and performance appeal, and the E-Transit for the commercial sector. Three distinctive models for likely entirely different markets.

              It is suggested that the Gen Z consumer distinguishes themself from other generations by being perhaps more emotional in terms of needs. There’s an element of their outward appearance and overt visible behavior, combined with a need to be distinctive and unique in the face of a great deal of societal homogeneity.

              It is perhaps that reason that singles out their relative disinterest in the Tesla brand. Theirs is a personalized aesthetic perhaps, and the older more established brands like Ford, GM, Nissan, and Toyota et al may actually have a better shot at servicing this aesthetic as a result of having offered personalized customization in the past, while at the same time being ecologically and environmentally responsible. It is also about availability. Do I buy something I can drive off the lot today or do I wait? Time will tell. There’s still a relative scarcity factor a long waitlist for Tesla and that doesn’t help.

              So what made Ford, take the ambitious objective of all-electric engineering on at the time that it did? According to Forbes contributor Dale Buss, “Ford futurist”, Sheryl Connelly looked at a global survey of thousands of consumers and articulated in Looking Further with Ford Trends Report that off-planet travel for leisure and entertainment Gen Z; and disinterest in next generations of humanity characterized Gen Z. This is backed up by 81% of adults saying climate change is a worry for future generations and at least 40% of Canadian women, for example, cited as having concerns about climate change as a reason for not wanting to have children.

              You could read that as potentially a story that suggests previous generations have ruined the planet and we need to look for alternatives – something not dissimilar to the message in the apocalyptic black comedy film “Don’t Look Up“. It’s conceivable that Gen Z believes that there are well-progressed invisible plans to colonize other planets and not everyone knows or is “in” on those plans.

              You can read more on Connelly’s opinion and discoveries here.

              Now you might ask, what does this all have to do with Customer Master Data and MDM in particular? Pretectum’s view is that there’s actually quite a lot. Companies that embrace authenticity will find that Gen Z customers will be their best brand ambassadors. There’s no better way to demonstrate authenticity than to be environmentally and socially responsible and yet support uniqueness, individualization, and personalization.

              Accommodating individual preferences was ironically the aspect of Henry Ford’s pushback with the Model T that he is well known for. “Any customer can have a car painted any color that he wants, so long as it is black.” The Model T only came in black because the production line required compromise so that efficiency and improved quality could be achieved; modern vehicle production doesn’t have to be so rigid, and in fact, make-to-order (MTO) is increasingly commonplace today even at Ford.

              Ford’s goal is to have MTO factory orders account for upwards of a quarter of vehicle sales and consequently, they can reduce overall finished vehicle inventory in US dealerships from a historic average of 75 days to a targeted range of 50-to-60 days.

              So, if your business is in automotive for example, you likely have a generation or in fact, multiple generations of buyers of your vehicles. If your customers have had a good past buying and driving experience, they may even have bought multiple vehicles from you.

              Those Gen Z’ers that chase a Ford likely grew up in a household where a Ford was owned. There may even be a degree of unintentional brand loyalty among them. As an auto manufacturer, the chances are, you know who those past buyers were, but more importantly, if you’ve afforded them credit, insurance, warranty, extended warranty, and even service and support you know heaps about them from all that interaction.

              To maintain those relationships with past customers, and to benefit from the data you have collected in the past you need to consider that you can harvest unique insight that newcomers cannot. You can start to leverage the adequacy of responses to surveys for example. You have to be engaged with those past and present customers and keep them interested in actually engaging with you again and one of the best ways is through personalized communications which are driven by detailed and appropriate customer master data. You can only really do that if the data is aligned with your business needs and the intentions that you have in mind for your outreach programs.

              This is where Pretectum as a customer master data management platform provider (CMDM) can be of help. 

                Uli LOKSHIN 0 Comments
                Nov 30 2021
                Customer Fundamentals – time to take a big step

                Master Data Management may be positioned as a “silver bullet” for the woes of poor customer master data but it doesn’t 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.

                Continue reading at Pretectum.com

                Red Button 0 Comments
                Nov 30 2021
                Some customer data is missing
                The incomplete person

                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.

                We’ll look at this through the lens of a customer master data management system. Read more at Pretectum.com

                Red Button 0 Comments
                Nov 4 2021
                The Hype About Data-Driven Decision-Making Is Not Being Matched By Reality

                This study commissioned from research firm Forrester found that 78% of enterprises report gaps in their data management that prevent them taking full advantage of their data and 64 percent said they find it challenging to actually move CRM data to platforms where it could be valuably used. 

                Half of large enterprises worldwide (47%) feel they cannot rely on their CRM data to provide a single source of truth regarding customer data.

                For the past few years CRM platforms have been stressing data, data, data.

                They’re using AI (Artificial Intelligence) as a marketing tool to lure companies into the enticing world of being able to predict what customers and prospects will do in the future based on their prior behavior. But the reality is not living up to the hype. 

                Why?

                Read more

                Juwel Tan 0 Comments
                Nov 2 2021
                The case for C-MDM in your existing systems landscape

                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 some…

                The same is true with Master Data Management. The “hard climb” to a unified single source of customer data, gives you a single ‘clear picture’ of the customer.

                Read more

                Uli LOKSHIN 0 Comments
                Oct 26 2021
                Digital transformation depends on effective customer data management

                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.

                Read more

                Uli LOKSHIN 0 Comments
                Oct 19 2021
                Scoping a Master Data Management requirement

                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.

                Read more

                Uli LOKSHIN 0 Comments
                Oct 13 2021
                David Raab of the CDP Institute says CDP and CRM should not be confused.

                You do your readers a huge disservice by conflating CDP and CRM.  Yes, both store customer data – 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 – to support sales and service agents when speaking with customers – 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.   

                They’re generally poor at sharing their data with other systems.   

                read more

                Juwel Tan 0 Comments
                Oct 11 2021
                Tactical vs. Strategic Master Data Management

                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.

                read more

                Uli LOKSHIN 0 Comments
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