Tag: Data Governance

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Federated Customer Master Data Management

a street with a line of street names on the side of it

Federated customer data governance is an approach to customer data management that allows organizations to implement data governance policies and controls in a decentralized manner across multiple domains or business units. This is an intrinsic characteristic of the Pretectum CMDM approach to Customer Master Data Management (CMDM)

Key aspects of federated data governance are the establishment of governance authorities within each data domain or business unit to define the rules, policies, and standards specific to that business or data domain. These domain-specific governance authorities work collaboratively to ensure alignment with overall organizational goals and data governance requirements. Where appropriate, enterprise or federated business data rules and structures are established and leveraged to influence and control the data creation and management processes.

Federated customer data governance supports the balance between decentralized data ownership/management and centralized data governance, allowing the business domains to work autonomously within their own defined interoperability standards, connecting to their business unit-specific data sources and sharing data internally.

This approach is essential for implementing a successful data mesh architecture, where data is treated as a product and managed in a decentralized manner. The key benefit of federated data governance for customer master data management is that it allows organizations to scale data governance practices across a complex, distributed data landscape while maintaining agility and business and data domain-specific requirements.

Customer MDM Maturity Model

Federated data governance facilitates the centralization of data governance, data quality, and data lifecycle management across an organization.

There are several key steps for implementing a successful customer master data management program that need to be considered:

An organization should begin with an ‘as-is’ state analysis and stakeholder engagement. Understand the vision and key drivers, assess the current state, and document all the “pain points” and goals of the different data stakeholders.

Selecting tools that are contextually appropriate for the organization is an important decision point. Any tooling that is considered should offer a ready-to-run platform for business leaders to easily carry out their customer master data governance, allowing easy access and updating of master data, and the support of seamless integration with any, and all, third-party and internal systems as required.

Approaches to Customer MDM
Approaches to Customer MDM

The adoption of new or different tooling has to be with one goal in mind, namely the establishment of better data governance by integrating business operations, data collection, and data optimization requirements. This goes a long way toward ensuring the business runs smoothly and effectively, all the while, complying with privacy, data handling, and regulatory policies; in accordance with local, regional, and international law.

It should also be recognized and acknowledged, that maintaining a customer-centric approach is a concept that, just like customer data itself, is constantly evolving. Any customer master data management solution should be composable, adaptable, and evolve with the compliance, integration, and business needs of the organization, seamlessly. Only through the best possible customer data can an organization hope to ultimately build and maintain strong customer relationships.

The user experiences within the tools and applications should also be supportive of employees and employee tasking while also helping in the handling and safe access and sharing of specific customer data to accelerate digital transformation, meet business needs, and ultimately support the organization’s pursuit of exceeding customer expectations.

Role-based-Access-Controls (RBAC) are an important control element in ensuring that only the right people have access to the right data and platform functionality. Extensive auditing and logging is an important aspect that needs to be in place here also.

Any platform under consideration should also continuously maintain the customer master data to ensure accurate and up-to-date information, avoid discrepancies in the customer master, and maintain the highest possible data quality.

By considering all of these aspects, organizations can better implement an effective customer master data management program that delivers trusted, high-quality data to drive operational efficiency, improve customer experience, and enable better business decisions.

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Avoiding pitfalls in MDM implementation

In the complex landscape of enterprise-level Master Data Management (MDM) implementation, several critical challenges often obstruct the path to success.

One of the primary hurdles lies in horizontal coordination, where ensuring effective collaboration across numerous lines of business becomes a pivotal but challenging “people” issue.

The lack of synchronized systemic change management compounds the problem, requiring businesses to navigate alterations in both processes and master data perspectives, constituting a significant “process” challenge.

Another stumbling block is the quest for consistency in meaning, necessitating simplified procedures to resolve semantic discrepancies—an intricate “knowledge management” challenge.

Furthermore, the establishment of standardized business rules using MDM becomes a crucial “technology” issue, demanding meticulous management and enforcement of agreed data standards.

Challenges Rooted in People Dynamics

Horizontal coordination is fundamentally a “people” issue, it poses significant challenges in the context of MDM and Customer MDM (CMDM). Ensuring effective collaboration across diverse lines of business is essential for cohesive data management.

Siloed business and data operations, where each department operates independently, inhibit the seamless flow of information into the hands of those who need it and into the systems that they use.

To address this, fostering a culture of open communication and cross-functional collaboration is vital. It requires breaking down departmental barriers, encouraging knowledge sharing, and instilling a collective understanding of the importance of unified data across the organization. The use of a centralized CMDM solution like the Pretectum CMDM In a hub and spoke implementation for data syndication, can help.

The Intersection of Processes and Data

Coordinated systemic change, a critical “process” challenge, involves aligning alterations in business processes with master data views. Customer MDM necessitates not just refining customer data management structures but also adapting existing processes.

Mere tweaks in the customer data without corresponding changes in processes are insufficient. Organizations must be prepared to reevaluate and potentially re-engineer existing workflows.

This requires a deep dive into data and process workflows, identifying inefficiencies, and aligning them with optimized master data structures, ideally around solutions like the Pretectum CMDM.

The harmonious integration of process and data forms the bedrock of successful Customer MDM implementation and operations.

Knowledge Management

Consistency in meaning, a nuanced “knowledge management” challenge, is crucial for effective customer master data management.

Over time, disparate business terms are used without a common understanding of their definitions. Inconsistency in the use and application can then lead to confusion, especially when integrating data from various sources.

Establishing standardized semantics involves defining clear business terms and implementing robust data governance practices.

A centralized repository of business terms and their definitions fosters a shared understanding, ensuring uniform interpretation of data across the organization.

Standardized Business Rules Unify Data Management Understanding

Standardized business rules, a pivotal “technology” challenge, demand the utilization of MDM to enforce rules based on agreed data standards.

Inconsistencies in rule definitions across departments hinder streamlined operations. Standardization of business terminology and definitions is key to enabling unified data management. This is easily addressed when rule definitions are centralized as they are in the Pretectum CMDM.

Data Insights in the Pretectum CMDM Platform
Data Insights in the Pretectum CMDM Platform

Collaborative efforts between IT and business units are essential to align technological solutions with specific business requirements.

The alignment ensures that the CMDM technology solution serves simply as an enabler, facilitating the enforcement of standardized rules uniformly across the organization.

Proactive Strategies

A common issue faced during CMDM implementation and adoption is the emergence of reactive data management.

Often, issues surface only when they are likely to, or actually impact internal operations or customer experiences.

Reactive approaches are indicative of limited maturity in leveraging data as a strategic enterprise asset. Transitioning to proactive data management involves fostering cross-functional data coordination.

By encouraging a holistic understanding of data’s potential applications, organizations can identify new opportunities and optimize internal operations. Proactive strategies are instrumental in maximizing the benefits of CMDM, transforming data from a passive resource into a proactive catalyst for innovation and growth.

Paving the Way for Strategic Data Utilization

Technical data improvement lies at the heart of CMDM’s transformative potential for customer interaction and management.

Creating a unified view of customer master data compels technologists to refine definitions, document shared customer metadata, and address customer data discrepancies.

This systematic process initiates data quality and data governance, facilitating robust data sharing and reuse. Technical CMDM benefits, such as promoting consistent data use, enhancing reporting accuracy, improving data sharing, standardizing data validation, and ensuring completeness and consistency, empower organizations to make data-driven decisions.

The technical aspects of Customer MDM not only optimize day-to-day operations but also lay the foundation for strategic, data-informed business initiatives.

In essence, Customer MDM implementation accompanied by a customer master data management solution like the Pretectum CMDM transcends technicality; it’s a holistic endeavor that encompasses people, processes, knowledge, and technology.

Addressing these challenges requires a strategic approach, fostering a culture of collaboration, embracing proactive data management, and leveraging technology to unify data management efforts.

By navigating these challenges effectively, organizations can harness the full potential of CMDM, transforming customer data into a strategic asset that fuels innovation, enhances customer experiences, and propels the business toward sustainable growth.

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The Customer Master and Data Governance

woman taking selfie indoors

Customer master data management (CMDM) and customer data governance are co-dependent disciplines. Successful CMDM is not possible with at least a few key data governance elements.

While the CMDM may be understood as principally the technology, the actual data governance is the people, processes and technology.

With Pretectum’s CMDM we feel that the three are inextricably linked and so do not see it as being possible to pick up a solution like CMDM without data governance.

The reverse may be true. You may have data governance without master data management but then the effectiveness of your data governance policies and procedures have to be called into question.

We, at Pretectum, also feel that data management and data governance cannot realistically be the responsibility of IT. We feel that data management and data governance has to be a collection of technologies, policies, procedures and participants that are squarely found in the realm of the business with IT functioning in a janitorial role, implementing the technology bits and having the business or subject matter experts implementing the policies and procedures and driving adoption.

You can envisage this being something where there are possibly subject-matter experts in IT and the business who apply their skills and knowledge to the data based on the data management and data governance definitions of the business, the Critical Data Assets (CDA) and data handling scenarios for the business.

Unless selling data is actually your business, housing data governance and data management within technology groups make data management an operational overhead rather than a data asset. This, in turn, makes the customer master data management system a silo which may hold near and dear all the good solid data management principles that make sense and are aligned with best in class, but which don’t adequately have the “buy-in” of business leaders and their down-lines.

It is likely you already have a great deal of customer data already but you would do well to clearly define what kind of customer data you already have and identify where it is located.

A data catalogue might help with this and tracking where that data is, what it is and why you need it is a good first step.

Your approach to this could be as simple as creating an inventory and identifying who is responsible for each repository. Again, the assignment of responsibility shouldn’t be IT it should be someone in the business who has skin in the game if that data is bad or the system is down. or not working as expected. This approaches moves toward ensuring that you only collect the data that you actually need, driving toward easier and more manageable use.

To work this out, start by identifying business goals and objectives. What do you expect to achieve by tracking and having that customer data in the first place? Once you know this, identify the commonalities between data sources that can drive you towards customer master data harmonization.

We already suggested that you have an inventory of systems; these will likely be varied, coming from internal and external sources, structured, semi-structured and unstructured. It is critical that in building the best possible view of the customer, you consider your CRM, CDP, eCommerce and ERP systems as well as POS, Service and Support systems as potential sources of data. Pretectum can help with bringing all the sources to a unified front end.

Though there are cautions against having too much customer data, oftentimes, if you have a legitimate purpose and reason for having the data and obtained it through appropriate channels with consent, then collecting too much data is the way to initially start, you can pare this back at some later stage. Obtaining customer data consent and self-service curation is also something Pretectum can help with.

The next part of the journey is performing data cleansing, enrichment, and deduplication rituals. Pretectum can help initially with data quality issue identification but you have to make the hard decision as to whether you want to change records in situ at the source or you want to do this post hoc. Setting up correction regimes is something could do pretty much anywhere depending on the nature of the problems. This is also dependent on your implementation approach for customer master data management.

A sound definition of what constitutes good customer master data is checked and verified by the Pretectum CMDM as an integral part of the data handling. This approach to customer master data management gives you a sense of the level of data quality continuously. While it can be used to intercept data quality issues at the time of data creation it can also be usd periodically to provide you with a data quality scorecard to help you understand your organization’s journey toward error and inconsistency-free data. The highest possible quality of data is essential for making good decisions about customer relationships.

Data governance is a cornerstone for appropriate and optimized customer master data and it should provide a long-term framework for the best decision-making. For it to be most effective though, there needs to be a degree of organizational master data management maturity that sees clear roles, responsibilities, and accountabilities.

The precise nature of your governance structure will depend on the size and complexity of your organization, but it should include a steering committee, data stewards, and data owners. Your data governance steering committee is responsible for setting the organizational strategy and goals for customer master data management. Data stewards own the management and maintenance of the data, while the data owners are accountable for ensuring the data is accurate and current.

With your customer data converging and flowing into the Pretectum SaaS platform as a central repository, your organization is afforded a single source of truth for customer data, thereby making it easier to keep track of customer master data changes, providing customer master data quality assurances and the highest possible security all while making necessary customer master data information available to those who need it.

Pretectum’s CMDM for your customer master data allows your business to benefit from some of the best in class data management best practices which go a long way toward ensuring your customer data is accurate, reliable, up to date and fit for purpose leading to improved customer service personalization, sales and marketing effectiveness, and meaningful opportunities for organizational growth.