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.
“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.
Jardine Cycle & Carriage is a well-known brand in Singapore and Malaysia with a reputation that has been built up over the past 125 years and now serving Singapore, Malaysia Indonesia, Thailand, Vietnam, and Myanmar. As a premier automotive dealership on the Malayan peninsula, they have operated since 1899 offering iconic automotive brands like Mercedes-Benz, Mitsubishi, Chrysler, Jaguar, Kia, and Citroën brands.
As the tides of technology continue to reshape industries, JC&C, with its clientele spanning the affluent and high-affluent segments, has seemingly embarked on a new transformative journey by forging an innovative strategic partnership with ada to integrate AI/NLP technologies and “redefine the automotive experience”.
Malaysian multinational Telecomms conglomerate Axiata’s ada (analytics, data, advertising), is headquartered in Singapore and Malaysia, and partners with leading brands across Asia to drive their digital & data maturity and achieve their business goals. In sharp contrast to JC&C, they’re a relatively young company vested by Axiata in 2014 and supported by renowned regional brands like Mitsui, Sumitomo Corporation and SoftBank Group. They bill themselves as a leader in digital transformation across Asia focused on automated customer service solutions and data-driven marketing strategies. Serving 12 markets, working with a composable CDP that makes use of best in class components and leveraging tools like Databricks, ada complements its unique digital expertise with deep proprietary data of 375 Million consumers served by over fourteen hundred employees associated with just as many commercial clients. ada have been recipients of numerous awards like the Effie Awards with Gold, Silver, and Bronze accolades for innovative campaigns in multiple markets.
In an article from The Edge Malaysia Weekly, dated July 8, 2019, Axiata Group’s digital advertising arm, ada, unveiled a plan to revolutionize the advertising industry through the strategic use of tech data and innovative business models.
At the time, led by CEO Srinivas Gattamneni, ada aimed to cater to the evolving landscape of digital consumers by providing digital marketing, data science, and platform-building solutions. Backed by Axiata Digital, ada aspired to become the agency of the future, blending data science, consulting, and agency services.
The substantial investments and ambitious goals aimed to disrupt business models and focus on value-driven approaches to shift industry norms. Central to ada’s digital strategy is emphasis on data-driven advertising, leveraging deep consumer insights to deliver targeted and impactful campaigns. By harnessing data from various sources and investing in technology, ada sought to maximize advertising ROI and drive business outcomes for clients. ada anticipated the seismic shift towards programmatic and automated ad buying, albeit with the concomitant challenges of bad actors using technologies like bots to drive activity and commit fraud.
Despite being a young player in the industry, ada’s apparent rapid growth and innovative approach continue to signal its potential to disrupt the marketing landscape as a whole and shape digital futures in marketing.
Their collaboration with JC&C of course will not come without challenges, amongst them those related to data privacy and security as well as compliance with regulatory requirements. Transparency in what data is used and how it is used is essential in maintaining customer trust and so the integration of AI requires careful planning and ethical considerations.
As Cycle & Carriage and ADA continue on their collaborative journey, the fruits of their labor appear to becoming manifest. From enhanced customer engagement to streamlined operations, the impact of AI integration is apparently palpable, Cycle & Carriage leverages ADA’s expertise to implement AI-powered chatbots, personalized marketing campaigns, and data-driven insights, driving tangible business outcomes and setting a new standard for digital transformation in the automotive industry.
Not every organization can be a JC&C nor will they be able to afford a relationship with an agency like ada. It might not even be that relevant given they are a regionally focused player, however consider the following.
Let’s be clear, Pretectum CMDM is not a part of the tech stack in use in this example, we present it, because it demonstrates some great possibilities for any organization. What we would like you to consider, is how any composable CDP that incorporates something like the Pretectum Customer Master Data Management (CMDM) system, could be leveraged to support more personalized engagement with customers through various touch points in marketing, sales, service, and support channels for any organization.
By centralizing and standardizing customer data across departments and systems, Pretectum CMDM enables businesses to have a holistic view of each customer.
This approach allows for better organizational decision-making, enhanced customer interactions, and the delivery of more personalized customer experiences. More specifically using Pretectum CMDM could involve the following strategies to enhance customer engagement:
Personalized Marketing Campaigns: By using the centralized repository of customer data in Pretectum CMDM, you can create targeted and personalized marketing campaigns tailored to individual customer preferences and behaviors.
AI-Powered Chatbots: Implementing AI-powered chatbots integrated with CMDM data can provide real-time assistance and personalized responses to customer queries across various channels.
Data-Driven Insights: Use the comprehensive customer data stored in CMDM to derive valuable insights that can drive strategic decision-making in marketing, sales, and service operations.
Enhanced Customer Service: Ensure that all parts of the organization have access to up-to-date, verified, and consented, reliable customer information from the CMDM system, improving customer service interactions and overall satisfaction.
Streamlined Operations: By centralizing and synchronizing customer data, organizations can streamline operations, leading to more efficient processes in marketing, sales, and service functions.
By adopting these kinds of strategies with a CMDM platform like the Pretectum CMDM, organizations can enhance their customer engagement efforts across multiple touch points, ultimately leading to improved customer relationships, increased brand loyalty, and better business outcomes.
