March 2024 – 🔴 Red Button Data

Pretectum

Mar 30 2024

Finding the right home for your customer master

At the intersection of optimal business operations and the discipline of appropriately aligned data governance principled master data management lies Customer Master Data. The practice includes dimensions that define the needs of the business including contact information, customer vitalstatics and pretty much any data attribute that the business needs to leverage for a perfectly harmonized customer relationship.

That’s the dream, unfortunately, the reality is that for many organizations, their data governance practice is mired in conflicting interests of largely divergent stakeholders. There is also the challenge of inter-divisional competition of who owns the customer, and the proverbial data silos that arise from divergent divisional needs.

Some business applications, designed with specific and often narrow objectives in mind, operate within a confined scope of customer data requirements. These applications might be tailored for singular functions such as order processing, billing management, or customer support. In such instances, the focus is primarily on the immediate and specific needs of the application, and the depth of customer data required is limited to the operational necessities of that particular function. While this makes for efficient data processing at the business unit level, it retards opportunities for the whole organization which suffers from the lack of a single identity for the customer with all the salient attributes that make for personalized long-lasting and loyal relationships.

Recognizing the indispensability of a comprehensive customer master, some organizations will embark on a comprehensive rethink of their customer master data management practice. Doing so is a strategic decision and as such, requires a strategic approach to constructing a single, authoritative source of truth for the customer master data information asset accompanied by improved integrations and change management.

Practice not technology

Modern-day Customer Master Data Management also isn’t about the technology as much as it is a realignment of business principles around the most appropriate way to handle the customer and customer data, especially these days in the face of so many emerging and established privacy and consumer protective regulations.

Consider if you will, the fact that how and what you store and nurture as a customer data repository reflects the true essence of your company’s identity. Store it incomplete, haphazardly and with duplicates and you’re relating a narrative that suggests that you simply don’t care too much about data quality and the integrity of the customer master.

Think of the customer master as a reservoir of knowledge that if established properly, can deliver insights, smooth transaction processing, hone personalization and convey confidence and integrity in your team’s engagement with the customer. All this can be done on demand, providing a foundation for robust operational and financial structures. Depending on your industry and the relative intimacy of the relationship with the customer, your business may tap into that reservoir and find previously unexplored areas of opportunity and relationship sustainment.

If you’re in finance or sales, it is easy to see customer data management as a ballet of numbers, for marketing. logistics, service and support it might be other business intricacies like past engagements, previous purchases, warranties, returns and the like. For some, it may even just be about the legitimacy and legalities associated with the customer and their data.

Data governance is the systematic management, control, and oversight of customer-related information within a given organization.

Data governance involves the establishment and enforcement of policies, procedures, and standards to ensure the accuracy, integrity, and security of customer data throughout its lifecycle. The primary goal is to enhance the quality of customer information, facilitate compliance with regulations, and support reliable decision-making processes across the organization.

In his domain, this includes defining roles and responsibilities, implementing data quality measures, and establishing protocols for data access, usage, and privacy.

Some fundamentals

Meticulous management of data quality entails a systematic and detailed approach to ensuring the accuracy, consistency, and reliability of data within an organization. It involves implementing rigorous processes and practices to identify, rectify, and prevent errors, inconsistencies, and redundancies in the data.

The objective is to cultivate a dataset that serves as a trustworthy foundation for decision-making processes, minimizing the risk of misinformation and supporting the organization’s overall goals. This involves continuous monitoring, validation, and improvement efforts to uphold a high standard of data quality throughout its existence.

Security and privacy in the context of customer master data involve systematically implementing measures to protect sensitive customer information from unauthorized access, misuse, and breaches.

This would encompass the establishment and enforcement of policies, procedures, and controls to safeguard customer data against potential threats. The primary goal is to ensure the confidentiality and integrity of customer information, aligning with relevant data protection regulations.

Security and privacy measures also include access controls, encryption, authentication protocols, and ongoing monitoring to detect and respond to any potential security risks. The objective is to create a robust framework that instils confidence in customers, mitigates risks, and upholds the organization’s commitment to data protection.

Data lifecycle management (DLCM) is an integral component of data governance and involves a systematic and comprehensive approach to handling customer data from its creation or acquisition through various stages of utilization, storage, and eventual disposition or archival.

This essential process ensures that data is managed efficiently and in alignment with the organizational objectives and legal obligations of the organization. A DLCM framework includes the formulation of policies, procedures, and standards to govern the handling of data at each stage.

The primary goal of DLCM is to optimize the utility of data while also addressing issues related to data storage, access, and compliance. It requires organizations to define clear retention policies, in particular, specify how long data should be retained based on its value and regulatory requirements. DCLM also involves establishing protocols for secure data disposal or archival once it has fulfilled its purpose.

Executing a DLCM practice well, involves continuous monitoring, assessment, and adaptation of policies to align with changing business needs and regulatory landscapes. This structured approach ensures that data remains a valuable asset throughout its journey within the organization and is managed with efficiency, cost-effectiveness, and compliance in mind.

Thinking about the people

At the heart of any data governance program are people who may or may not be explicitly tagged as the data governance stewards. These are individuals or teams entrusted with the responsibility of maintaining data quality, upholding governance policies and serving as the data owners and people “in the know” about all things about the data. They are the data domain experts.

Data stewards navigate the vast seas of data, ensuring that each byte is accounted for and that each dataset aligns with the broader goals of the organization. They are the custodians of the data practice.

A more explicit definition would have it, that a data steward is an individual or team responsible for overseeing the management, quality, and governance of data within the organization.

Duties include ensuring data accuracy, defining and enforcing data policies, and maintaining the integrity of data assets. Data stewards play a crucial role in facilitating communication between business units and IT, acting as custodians of data quality and providing expertise on data-related matters.

Their responsibilities encompass data profiling, monitoring, and resolving data issues, as well as collaborating with other stakeholders to establish and adhere to data governance policies. The role requires accountability for the reliability and usability of data across the organization.

Metadata matters

The descriptive information about the customer data, data that provides context, structure, and understanding of its characteristics, is metadata. Such information includes details about the origin, usage, format, and relationships of data. In any data governance program, metadata plays a crucial role in enhancing data discoverability, usability, and overall management.

For customer master data management, metadata associated with customer data would include information about data sources, data quality, and any transformations or processes applied to the data. It helps in maintaining a comprehensive understanding of customer data, ensuring its accuracy and facilitating effective data governance.

For data governance, metadata serves as a bridge between stakeholders and systems. It facilitates collaboration by offering a common language for business users, data stewards, and IT professionals. Stakeholders leverage the metadata to comprehend the meaning and lineage of the customer data, converging on a shared understanding for everyone across the organization. Metadata also enhances the interoperability of systems by providing a standardized framework for data exchange and integration, promoting consistency and coherence in the data landscape.

No respected data governance program is launched, adopted and survives without data governance and management policies. Data Governance policies define who can access specific data, how it can be used, and under what circumstances. These policies form a framework that prescribes how to prevent unauthorized access and ensures responsible data utilization as well as other behaviours and measures that serve to protect the integrity of the customer master.

A data governance council or committee overseeing and steering the program is helpful but not essential. Comprising representatives from various business units and the IT realm, this body ensures that data governance aligns with organizational objectives, and its impact is felt across the entire enterprise.

Fostering a culture of data awareness and responsibility becomes a crucial act in this governance play. Communication and training programs under the aegis of a data governance program are the conduits through which employees grasp the importance of data governance, the program aims to develop an understanding of their roles in maintaining data quality and integrity.

Culturally a data governance program requires a major shift where each employee becomes informed and empowered as a guardian of the data they interact with, hopefully thereby recognizing its intrinsic value.

Continuous improvement in data governance is another essential trait of a data governance program which is sustained through a dynamic and iterative process that prioritizes refinement, adaptability, and ongoing assessment.

Continuous improvement involves regular evaluations of data quality, security protocols, and adherence to established policies.

Organizations that foster a culture of feedback, with data stewards and relevant stakeholders providing insights into the efficacy of existing practices are the most successful.

Insights from continuous improvement initiatives guide adjustments to data governance policies and procedures, ensuring they align with evolving business needs and industry standards. Implementing feedback loops, periodic audits, and staying attuned to technological advancements in data management contribute to the ongoing enhancement of data governance strategies.

This commitment to continuous improvement not only safeguards the integrity of customer master data but also enables the organization to respond effectively to changes in the data landscape, maintaining a robust and adaptive foundation for strategic decision-making.

Effective risk management within customer master data management involves implementing robust processes to identify, assess, and mitigate potential risks associated with the handling of customer information. This includes ensuring the accuracy, completeness, and security of customer data to prevent errors, fraud, and unauthorized access.

A comprehensive risk management approach would also involve regular audits and monitoring to detect anomalies or irregularities in customer data, as well as establishing clear protocols for data governance and compliance with relevant regulations such as data protection laws.

By proactively addressing risks related to customer master data, organizations can enhance data quality, build trust with customers, and safeguard sensitive information, ultimately fostering a more resilient and secure customer data management environment.

Foundations of CMDM in the wider organizational systems landscape

Evaluating a prospective source of truth

The criteria for selecting the right home for your CMDM initiative will revolve around the accuracy and integrity of data. Whatever you choose for CMDM it must incorporate robust validation mechanisms and quality checks to uphold the sanctity of customer data, preventing errors and discrepancies that might reverberate through the entire organizational structure.

Integration capabilities will likely play a crucial role in the CMDM selection process, whether it be in support of Customer Relationship Management (CRM), Enterprise Resource Planning (ERP), or other systems. Such integration will ensure a unified and consistent view of the customer data, eliminating silos and fostering a panoramic perspective across the enterprise.

Scalability becomes the next checkpoint in the CMDM evaluation. Will your choice accommodate a likely ever-growing number of occupants? A CMDM solution must exhibit scalability to handle an expanding volume of customer data. If your business landscape is dynamic, then the chosen system should gracefully scale to meet the demands of your expanding enterprise without compromising performance.

Security measures are non-negotiable when dealing with customer data. The selected CMDM home should have robust security, actively defending against unauthorized access, monitoring for data breaches, and proactively looking out for cyber threats. For customer data, sanctity and confidentiality are paramount, you must make security a top priority for your CMDM abode.

Quite naturally, user-friendliness and the proverbial UXD (User Experience and Design) is often a pivotal criterion in any selection process. The experience should be intuitive and provide a user-friendly interface that supports employees’ easy navigation and interaction with the customer data. Such a system would foster user adoption through its design and navigational simplicity; enhance productivity and ensure that the benefits of CMDM permeate throughout the organizational structure.

Data governance should be centre stage. CMDM home must shelter and govern the data within its confines. A CMDM that comprehensively supports your data governance framework is imperative. You will want to be able to outline and enforce policies, standards, and processes for the entire lifecycle of customer data. This ensures internal consistency and compliance with external regulatory requirements, safeguarding the organization against legal ramifications.

Flexibility and customization emerge as key facets in this selection saga. Every organization has unique preferences and requirements. Your choice of CMDM solution should mirror this diversity, offering flexibility and customization options that align with specific business processes and evolving data management needs. The home for your customer data should not be an entirely rigid structure but rather an adaptable space that flexes with the unique rhythm of the organization it serves.

AI and Machine Learning Integration bring a futuristic dimension to the CMDM narrative. The idea of CMDM solutions leveraging AI and machine learning suggests opportunities to plumb the depths of the data with advanced data matching, deduplication, and predictive analytics. Such an infusion of intelligence would enhance the accuracy and utility of the customer master and provide insights that transcend traditional data management boundaries.

We believe that the Pretectum CMDM will address all of these expectations and provide you with some surprising additional ones. Contact us today to learn more.

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Data Architecture Blogs Data Architecture News, Articles, & Education Data Blogs | Information From Enterprise Leaders Data Education data governance Data Governance & Data Quality | News & Articles Data Governance Blogs data protection data security DATAVERSITY world backup day

Mar 29 2024

World Backup Day Is So 2023 – How About World Data Resilience Day?

Instead of celebrating World Backup Day 2024 for accomplishing another year of successful backups, I recommend using it to look forward to a year of testing recovery. Instead of starting data protection strategies by planning backups, organizations should flip their mindset and start by planning recovery: What data needs to be recovered first? What systems […]

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Unveiling the ROI Dilemma: How Data Blind Spots Impact Asset Owners’ Bottom Line

In today’s fast-moving investment world, corporate and insurance asset owners are operating in the dark, hindered by the absence of a standardized industry benchmark for an overall asset performance assessment. Asset owners usually have many other responsibilities beyond managing portfolio strategies, affecting their ability to allocate time to comprehensively evaluate and optimize the performance of […]

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How Artificial Intelligence Will First Find Its Way Into Mental Health

Artificial intelligence (AI) startup Woebot Health made the news recently for some of its disastrously flawed artificial bot responses to text messages that were sent to it mimicking a mental health crisis. Woebot, which raised $90 million in a Series B round, responded that it is not intended for use during crises. Company leadership woefully […]

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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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Pretectum

Mar 26 2024

Is your business powered by customer data?

Omicommerce is a convergence on 1:1 brand : consumer engagement.
There have been several pieces of research and surveys published since the start of 2024 from Mintel, UoM, Capgemini, and Qualtrics.

Mintel’s report identifies five behavior trends shaping consumer markets beyond 2024 and their insights cover topics like sustainability, personalization, and changing consumer expectations.

UoM found that consumer sentiment rose significantly in January 2024, reaching its highest level since July 2021 and year-over-year consumer sentiment also showed a substantial increase.

Capgemini’s “What matters to today’s consumer” survey of 10,000 people, in 10 countries just one vital question “What matters to you?” It found that in its focus on consumer behavior in the consumer product and retail industries, it could come up with strategies for success in innovation and new product development that focus on the impact of emerging technologies and changing consumer expectations.

Qualtrics’ survey was based on responses from over 28,000 consumers across 26 countries. focused on preferences, complex customer journeys, and rising expectations with some valuable insights for businesses aiming to build meaningful connections with consumers.

The combined results reveal some important trends and sentiments amongst consumers to create compelling and positive experiences.

The combined reports emphasize the significance of human connection in customer experiences alongside the rise of AI technologies.

Consumers prioritize human interaction for critical tasks but accept AI for simpler activities. As such successful AI strategies entail understanding customer preferences for human versus digital interactions and leveraging AI to augment these connections.
There has been a resurgence in in-store shopping and continued strength of eCommerce – OmniCommerce and the Phygital. As a result quality customer service and post-purchase experiences foster consumer loyalty more than price competitiveness. Organizations that invest heavily in frontline employee training and service quality stand to gain customer loyalty.
Digital support experiences trail human interactions in satisfaction but present significant improvement opportunities. Enhancing digital experiences is vital for customer retention and loyalty.
Consumers are also inclined to provide less direct feedback, necessitating diverse listening tools beyond traditional surveys. Integrating operational data with feedback from various sources provides insights into customer sentiments and behaviors.
A trend towards direct ordering (D2C) from brands, signaling a shift in purchasing behavior supports the view that there is an increased importance accordingly, of efficient delivery and fulfillment services, particularly in certain retail categories.
Consumer priorities are centered around healthy, sustainable living, influencing purchase decisions. The reports determined that there are four key actions for brands and retailers to capitalize on these trends.

There is accordingly a blurring distinction between online and in-store shopping, with consumers expecting comparable levels of service and experience across both channels. All this underscores the need for organizations to prioritize human connections, improve customer service quality, enhance digital support experiences, and adapt to changing feedback dynamics to meet evolving consumer expectations in 2024 and beyond.

These insights provide valuable guidance for retailers and brands aiming to navigate the evolving consumer landscape, emphasizing a customer-centric approach, data-driven decision-making, ethical data practices, personalization, real-time insights, multichannel engagement, and sustainability.

learn more by visiting
https://www.pretectum.com/is-your-business-powered-by-customer-data/

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Mar 26 2024

Integrating AWS Data Lake and RDS MS SQL: A Guide to Writing and Retrieving Data Securely

Writing data to an AWS data lake and retrieving it to populate an AWS RDS MS SQL database involves several AWS services and a sequence of steps for data transfer and transformation. This process leverages AWS S3 for the data lake storage, AWS Glue for ETL operations, and AWS Lambda for orchestration. Here’s a detailed […]

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Getting Ahead of Shadow Generative AI

Like any new technology, a lot of people are keen to use generative AI to help them in their jobs. Accenture research found that 89% of businesses think that using generative AI to make services feel more human will open up more opportunities for them. This will force change – Accenture also found that 86% […]

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Future-Proof Your Cyber Risk Management with These Top Trends in 2024 (Part II)

As shared in part one of this installment, the global marketplace faces an increasingly destructive cyber risk landscape each year, and 2024 is set to confirm this trend. The cost of data breaches alone is expected to reach $5 trillion, a growth of 11% from 2023. As technology advances, attackers continue to develop new, more sophisticated methods […]

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Strategies for Midsize Enterprises to Overcome Cloud Adoption Challenges

While moving to the cloud is transformative for businesses, the reality is that midsize enterprise CIOs and CDOs must consider a number of challenges associated with cloud adoption. Here are the three most pressing challenges we hear about – and how you can work to solve them.

Leveraging existing data infrastructure investments
Closing technical skills gap
Cloud cost visibility and control

Recommendations

Innovate with secure hybrid cloud solutions
Choose managed services that align with the technical ability of your data team
Maintain cost control with a more streamlined data stack

Innovate With Secure Hybrid Cloud Solutions

There is no denying that cloud is cheaper in the long run. The elimination of CapExcosts enables CIOs to allocate resources strategically, enhance financial predictability, and align IT spending with business goals. This shift toward OpEx-based models is integral to modernizing IT operations and supporting organizational growth and agility in today’s digital economy.

But migrating all workloads to the cloud in a single step carries inherent risks including potential disruptions. Moreover, companies with strict data sovereignty requirements or regulatory obligations may need to retain certain data on-premises due to legal, security, or privacy considerations. Hybrid cloud mitigates these risks by enabling companies to migrate gradually, validate deployments, and address issues iteratively, without impacting critical business operations. It offers a pragmatic approach for midsize enterprises seeking to migrate to the cloud while leveraging their existing data infrastructure investments.

How Actian Hybrid Data Integration Can Help

The Actian Data Platform combines the benefits of on-premises infrastructure with the scalability and elasticity of the cloud for analytic workloads. Facilitating seamless integration between on-premises data sources and the cloud data warehouse, the platform enables companies to build hybrid cloud data pipelines that span both environments. This integration simplifies data movement, storage and analysis, enabling organizations to extend the lifespan of existing assets and deliver a cohesive, unified and resilient data infrastructure. To learn more read the ebook 8 Key Reasons to Consider a Hybrid Data Integration Solution

Choose Managed Services That Align With the Technical Ability of Your Data Team

Cloud brings an array of new opportunities to the table, but the cloud skills gap remains a problem. High demand means there’s fierce market competition for skilled technical workers. Midsize enterprises across industries and geos are struggling to hire and retain top talent in the areas of cloud architecture, operations, security, and governance, which in turn severely delays their cloud adoption, migration, and maturity. This carries the potential greater risk of falling behind competitors.

Bridging this skills gap requires strategic investments in HR and Learning and Development (L&D), but the long-term solution has to go simply beyond upskilling employees. One such answer is managed services that are typically low- or no-code, thus enabling even non-IT users to automate key BI, reporting, and analytic workloads with proper oversight and accountability. Managed solutions are typically designed to handle large volumes of data and scale seamlessly as data volumes grow—perfect for midsize enterprises. They often leverage distributed processing frameworks and cloud infrastructure to ensure high performance and reliability, even with complex data pipelines.

Actian’s Low-Code Solutions

The Actian Data Platform was built for collaboration and governance midsize enterprises demand. The platform comes with more than 200 fully managed pre-built connectors to popular data sources such as databases, cloud storage, APIs, and applications. These connectors eliminate the need for manual coding to interact with different data systems, speeding up the integration process and reducing the likelihood of errors. The platform also includes built-in tools for data transformation, cleansing, and enrichment. Citizen integrators and business analysts can apply various transformations to the data as it flows through the pipeline, such as filtering, aggregating and cleansing, ensuring data quality and reliability—all without code.

Maintain Cost Control with a More Streamlined Data Stack

Midsize enterprises are rethinking their data landscape to reduce cloud modernization complexity and drive clear accountability for costs across their technology stack. This complexity arises due to various factors, including the need to refactor legacy applications, integrate with existing on-premises systems, manage hybrid cloud environments, address security and compliance requirements, and ensure minimal disruption to business operations.

Point solutions, while helpful for specific problems, can lead to increased operational overhead, reduced data quality, and potential points of failure, increasing the risk of data breaches and regulatory violations. Although the cost of entry is low, the ongoing support, maintenance, and interoperability cost of these solutions are almost always high.

A successful journey to cloud requires organizations to adopt a more holistic approach to data management, with a focus on leveraging data across the entire organization’s ecosystem. Data platforms can simplify data infrastructure, thus enabling organizations to migrate and modernize their data systems faster and more effectively in cloud-native environments all while reducing licensing costs and streamlining maintenance and support.

How Actian’s Unified Platform Can Help

The Actian Data Platform can unlock the full potential of the cloud and offers several advantages over multiple point solutions with its centralized and unified environment for managing all aspects of the data journey from collection through to analysis. The platform reduces the learning curve for users, enabling them to derive greater value from their data assets while reducing complexity, improving governance, and driving efficiency and cost savings.

Getting Started

The best way for data teams to get started is with a free trial of the Actian Data Platform. From there, you can load your own data and explore what’s possible within the platform. Alternatively, book a demo to see how Actian can accelerate your journey to the cloud in a governed, scalable, and price-performant way.

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Composable Customer Master Data Management (CMDM)