Federated customer data governance is an approach to customer data management that allows organizations to implement data governance policies and controls in a decentralized manner across multiple domains or business units. This is an intrinsic characteristic of the Pretectum CMDM approach to Customer Master Data Management (CMDM)
Key aspects of federated data governance are the establishment of governance authorities within each data domain or business unit to define the rules, policies, and standards specific to that business or data domain. These domain-specific governance authorities work collaboratively to ensure alignment with overall organizational goals and data governance requirements. Where appropriate, enterprise or federated business data rules and structures are established and leveraged to influence and control the data creation and management processes.
Federated customer data governance supports the balance between decentralized data ownership/management and centralized data governance, allowing the business domains to work autonomously within their own defined interoperability standards, connecting to their business unit-specific data sources and sharing data internally.
This approach is essential for implementing a successful data mesh architecture, where data is treated as a product and managed in a decentralized manner. The key benefit of federated data governance for customer master data management is that it allows organizations to scale data governance practices across a complex, distributed data landscape while maintaining agility and business and data domain-specific requirements.
Federated data governance facilitates the centralization of data governance, data quality, and data lifecycle management across an organization.
There are several key steps for implementing a successful customer master data management program that need to be considered:
An organization should begin with an ‘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
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.
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.
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.
“Thick data” refers to qualitative information that provides insights into the context, emotions, and human experiences associated with a particular phenomenon.
Thick Data is often contrasted with “big data,” which typically consists of large volumes of quantitative and structured data. While big data is valuable for statistical analysis and identifying patterns, thick data adds depth and richness by offering a more nuanced understanding of the social and cultural factors influencing a situation.
Thick data is usually collected through methods such as interviews, ethnographic studies, participant observations, and open-ended surveys. These approaches allow researchers to gather subjective and context-specific information that may be challenging to capture through quantitative means alone. The combination of big data and thick data is often seen as a holistic approach to gaining comprehensive insights into complex phenomena.
“Thin data” is a term that might be used to describe a small amount of information or data points that may lack depth or context. Unlike big data, which involves massive datasets with diverse and complex information, thin data typically refers to limited and less detailed datasets. This Thin Data may not provide a comprehensive understanding of a particular subject, and its analysis may be more straightforward compared to the intricate analysis required for big data.
In some cases, the distinction between thick data and thin data is made to highlight the depth of qualitative insights (thick data) versus the limited and often quantitative nature of some datasets (thin data). The Thin Data might be more readily available but may not capture the full complexity or richness of a situation. It’s essential to consider the quality, relevance, and depth of data when making decisions or drawing conclusions based on thin data.
It’s easy to say an organization should make customers their north star. Yet the reality is many organizations remain product and service-focused and are far from customer-centric. To tackle the challenge of customer-centricity and improve the overall customer experience, businesses need to execute a strategy that is business culture-aligned and technologically provides an advantage.
The concept of Thick Data gained prominence through a 2016 TEDx Cambridge talk and a compelling Ethnography Matters article by former Nokia researcher Tricia Wang. Thick Data is a derivative of qualitative methods, it offers insights into the emotional and motivational aspects of people, shedding light on their thought processes. This goes beyond mere facts and behaviors, providing crucial context and narrating the stories that “breathe life” into the numbers and statistics associated with the customer.
Big Data emphasizes quantification and numerical analysis, Thick Data reminds us of the human side of business, capturing the nuances that might be overlooked in the proverbial sea of spreadsheets and graphs.
The Duality of Big Data and Thick Data
Big Data and Thick Data, though seemingly at odds, play complementary roles in driving optimal business decisions. Big Data analytics focuses on incremental improvements, optimizing existing systems based on data-generated insights. In contrast, Thick Data analytics ventures into the realm of change, challenging the status quo and uncovering transformative opportunities, albeit on a smaller scale.
The sweet spot lies in integrating both types of data, especially in understanding the customer experience. Big Data reveals what customers are doing and where improvements can be made, while Thick Data uncovers the reasons behind their behaviors and their desires for a different experience.
Customer Master Data Management: A Holistic Approach
With Customer Master Data Management (CMDM), the integration of Big Data and Thick Data can be pivotal. CMDM involves the meticulous process of gathering, curating, analyzing, and syndicating customer data.
The Thick
Thick Data in CMDM can originate from qualitative sources such as customer interviews, feedback sessions, and ethnographic studies, such methods delve into the emotional and motivational aspects of customers, with a deeper understanding of customer preferences, experiences, and expectations. Qualitative insights, for example, might reveal that a customer’s loyalty is tied to personalized interactions rather than just transactional efficiency.
Thick Data in CMDM can be profoundly impactful as it adds a layer of context to the quantitative metrics gathered through CMDM processes, enabling organizations to tailor their strategies to align with the genuine needs and desires of their customers. When CMDM decisions are informed by Thick Data, they are rooted in statistical analysis and reflect a deep understanding of the humanness behind customer records.
The Thin
Thin Data in CMDM more typically comes from quantitative sources such as transactional records, purchase history, and demographic data – effectively your transactional systems like POS, CRM, ERP, or even CDP. Thin Data lacks the depth of qualitative insights but provides essential information for CMDM processes. Pattern analysis in Thin Data might reveal the most popular products among a certain demographic or suggest the frequency of customer interactions.
Thin Data is valuable in CMDM for its efficiency in handling large volumes of information because you provide it in aggregate, not in detail! This Thin Data forms the backbone of the structured datasets in support of the customer record, allowing you to identify potential trends, patterns, and correlations that can contribute to better-informed decision-making.
Relying solely on Thin Data without the enriching context of Thick Data might lead to overlooking the intricacies of customer behavior and preferences.
For leaders steering various Customer Master Data Management initiatives, embracing both Thick and Thin Data is important. The integration of these data allows for a comprehensive understanding of customer behaviors, preferences, and expectations. Creating a customer-centered A.U.R.A.
Awareness through Context-Driven Decision Making Data management leaders who focus on customer analytics should champion a culture where business decisions are not solely based on quantitative metrics but are also influenced by the qualitative context provided by Thick Data thereby understanding the “why” behind customer behaviors. This enables more context-driven decision-making through CMDM.
A Holistic Customer Understanding CMDM processes that incorporate both Thick and Thin Data ensure a more holistic understanding of customers. This holistic single-customer view enables organizations to tailor their approaches, offering not just efficient transactions but emotionally resonant interactions that build lasting customer relationships.
Facilitating Agility in Response to Change While Thin Data facilitates agile responses to incremental improvements, Thick Data equips analytical leaders to identify and respond to transformative opportunities. A balance between these data types empowers organizations to adapt to changing customer expectations and market dynamics.
Advocacy for the Customer through Thick Data Being able to advocate for customers, customer data management leaders can play a pivotal role in CMDM. Thick Data becomes the vehicle to facilitate additional tooling for customer advocacy, allowing leaders to voice the genuine needs and desires of customers, challenging the status quo, and driving meaningful change.
Consider next, the very real impact that the insights from your CMDM might have on your data systems landscape in terms of practices and technologies. These might include all or just some of the following :
Leverage curated customer data to build robust personalization engines, that utilize customer preferences, purchase history, and behavior data to tailor marketing messages, product recommendations, and website content to individual customers. Personalization enhances the overall customer experience by making interactions more relevant and engaging.
The use of historical customer data in predictive analytics can forecast customer behaviors, such as purchasing patterns or potential churn. By understanding these trends, businesses can proactively address customer needs, offer personalized incentives, and prevent issues before they arise.
Consolidated customer data gives a comprehensive view through the integrated data from various touchpoints (sales, marketing, support) into the CMDM to create a holistic customer profile. This 360-degree view ensures that every interaction is informed by a complete understanding of the customer, leading to more meaningful engagements.
The use of customer data to enhance automated support systems.supports AI-driven chatbots or virtual assistants that leverage customer records to provide personalized support. These systems can understand customer history, preferences, and past interactions, delivering efficient and tailored assistance.
Curated customer feedback that gets analyzed can provide insights into customer sentiment along with text mining techniques to extract valuable insights from customer reviews, surveys, and feedback. This information can guide product improvements, service enhancements, and overall business strategy. But you need to bring it all to a relationship with a master, best served by CMDM.
You might want to anticipate and address customer issues before they escalate. If you leverage historical customer data you might be able to identify patterns that precede common issues. Implement proactive measures, such as targeted communication or personalized offers, to prevent problems and enhance the customer experience.
Customer segmentation based on curated data and demographics, behavior, or preferences is almost a “table stakes” use case, with which you can tailor marketing campaigns for each segment, ensuring that promotions and messaging resonate with specific customer groups.
Keeping track of an understanding of the customer journey for strategic marketing by way of a map of the customer journey using data on touchpoints, interactions, and behaviors. can be useful in developing targeted marketing strategies aligned with different stages of the customer lifecycle, fostering long-term relationships.
The use of customer behavior data is especially useful for fraud detection wherein you analyze transaction patterns and historical data to identify anomalies that may indicate fraudulent activities. Implementation of preventive measures to secure customer accounts and transactions can be significant and CMDM can support the provisioning of the data and the associative flagging and behavioral markers.
Naturally, enhancements to compliance and privacy measures are very topical these days and businesses want and need to implement tools and processes to ensure compliance with data protection regulations. This includes robust consent management, data encryption, and regular audits to safeguard customer information and maintain trust. Much of which is automatically provided by the Pretectum CMDM in particular.
