Crafting a mature Master Data Management (MDM) strategy is challenging, and obtaining cultural buy-in across various business units often necessitates a herculean effort to educate stakeholders on the benefits of high-quality data, grow communication channels, and instill accountability.

87% of executives recognize that a robust MDM program is essential for success. Investment in this area prevents data silos and inconsistencies, boosts operational efficiency and customer satisfaction, and augments the value of new software integration. Even with leadership support, cultural challenges pose the most significant obstacle to implementing mature MDM programs. [1, 2]

A strong culture is essential to maintaining data quality after a comprehensive overhaul. While investing in the overhaul itself is worthwhile, it is the organization's culture that will determine how well those improvements are sustained. 

Attaining comprehensive data quality across the entire company transcends merely selecting the correct software tool or a back-office task relegated to the IT department. Data quality must be a foundational aspect of company culture, requiring conscious dedication from every employee. Without a robust data culture, organizations remain oblivious to risks until they silently turn into crises, resulting in substantial damage or missed opportunities:

• NBCUniversal estimated that tens of millions of dollars in potential revenue were lost due to an overlooked analytics error triggered by including an ampersand in a show title. [3]

• The local government of Brent, London, UK, implemented an MDM strategy to create a unified view of its 350,000 residents. It realized savings of £800,000 in council tax discount fraud and £150,000 in parking fee recovery. [4]

When Harvard Business Review asked business leaders to identify the challenges with implementing a robust MDM strategy, 45% of respondents cited culture as the top organizational issue, outweighing a lack of skilled resources or technical difficulty [1]. Nicola Askham of the Data Management Association says, “The mark of a poor data culture is people don’t think about data at all, or they think of it as a negative, where it isn’t central to their job.” 

A strong MDM program leads to operational efficiencies and improved customer satisfaction

Leaders of organizations understand the potential of being able to trust the quality of data with which they make decisions and their organization uses to operate. Creating a shared, trusted, single view of customer data across marketing, sales, and service functions enhances customer engagement, consistency, and accuracy in interactions [7]. Better operational efficiency can improve lead times to launch new products and provide a competitive advantage. Synchronization of product and location data throughout the supply chain optimizes inventory management, reduces errors, and enhances overall efficiency.

Scholars Journal of Engineering and Technology & Harvard Business Review Analytic Services highlight material successes after implementing an MDM program:

• Procter & Gamble (P&G) streamlined product information and supply chain management. Precision and centralization ensured global consistency and compliance. 

• Coca-Cola improved its ability to maintain brand standards by federating data management across its vast network of bottling partners while allowing for regional marketing customization.

• General Electric (GE) leveraged a dual system of centralized control for key product information while enabling individual business units’ oversight of less fundamental product attributes specific to them.

• Wal-Mart centralized product and consumer data, resulting in accuracy improvements in inventory control and better customer satisfaction.

• Simplot will maximize value of an ERP migration by creating a business-led MDM team and avoiding migration of poor-quality data into the new system. [1, 2]

Fostering a data-centric culture will unlock potential, drive efficiency, and maximize software integrations in today's data-driven landscape. “Improving data quality should be a top priority for all business leaders. Data quality management is an organizational responsibility. Good-quality data propels business growth in revenues and profits, reduces expenses (OPEX and CAPEX), and cost of goods sold (COGS), and mitigates risk, thereby protecting the business.” - Prashanth H. Southekal, PhD.

How to create a culture of clean data

Fostering a culture of understanding and commitment to high-quality data, starting from the C-Suite and extending throughout the organization, is the bedrock of long-term success. According to a 2022 study by Dresner Advisory Services [6], the presence of formal data leadership (e.g., a C-level data/analytics officer or competency group) within an organization significantly increased the likelihood of support for a data literacy program. 46% of organizations established a program after formal leadership was in place for a year or longer. In comparison, only 19% of surveyed organizations reported having data literacy programs without formal leadership to promote them.

Source: Dresner Advisory Services

Executives building a culture of clean data should leverage the Define-Assess-Realize-Sustain (DARS) framework. This sequential process helps achieve high-quality data and preserves it through a cohesive culture.

1. Define data quality, including its characteristics or dimensions. 

   
   

2. Assess the current state and perform root-cause analysis to identify the source of the problem(s). 

   
   

3. Realize by following industry best practices throughout the data lifecycle. 

   
   

4. Sustain the benefits from the previous iterations for ongoing success. [8]

During the Realize phase, organizations should prioritize developing and enhancing their data culture and literacy as one of the ten best practices for achieving data quality [8].

1. Cultivate a service culture that consistently adds value and builds trust with stakeholders. To ensure reliable service, prioritize data quality as it provides a dependable frame of reference for service levels.

   
   

2. Drive continuous performance improvement by measuring and enhancing service performance. Measurement creates visibility, allowing you to identify areas for improvement. Focusing on continuous improvement ensures that service quality remains reliable and efficient.

   
   

3. Prioritize consensus culture over a hierarchical one. In a consensus culture, decisions are driven by insights derived from data. Unlike hierarchical decision-making, which relies primarily on titles, positions, and seniority, a consensus approach ensures that data guides decision-making.

   
   

4. Build data literacy programs with training that covers technical, organizational, and personal needs. Technical aspects include data management across the lifecycle, including security and storage. Promote collaboration and data sharing to prevent organizational silos. Address individual needs to clarify data capabilities and limitations. 

   
   

5. Leverage descriptive analytics to interpret historical data and understand past business performance. Basic questions like sales figures, top products, and key performance indicators (KPIs) enhance data literacy through hands-on experience.

Cultivate change

Realizing the benefits of a robust MDM strategy that acknowledges cultural challenges and cultivates its adoption and growth requires frequent assessment and adjustments supported by clear and direct communication. 

• Lead the charge with continuous, informed dialogue between top decision-makers and data leaders.

• Frame the conversation from the standpoint of how the business will benefit without relying on jargon that colleagues may not resonate with.

• Iterate through building an MDM strategy with a phased approach, adding new scope after initial successes are realized and appreciated.

  • Create a service culture that places stakeholder trust and value first.

  • Measure and evaluate performance for continuous improvement.

  • Emphasize a democratized culture of consensus via insights gleaned from data.

• Ensure colleagues understand the complexity of keeping data clean across systems or organizational units; illustrate with examples.

• Promote data literacy programs to explain the value and importance of quality data.

Organizations face hurdles in building mature MDM programs. Human behavior often creates barriers that outweigh IT concerns. Data culture is the top organizational issue (45%), outweighing a lack of skilled resources or difficulty implementing MDM software. When everyone embraces the impact of data quality, MDM initiatives succeed through collaboration between departments and ownership of data within separate domains.

About us: mXa, on the 20+ year foundation of Method360, was founded to intentionally serve fast-growth companies and the unique challenges they face. We understand that inorganic and organic growth provokes change, ambiguity, and uncertainty that can deeply burden the organizations involved. By seeking to understand the human element in M&A and fast growth environments, mXa embraces a unique, contrarian approach in advising clients that seeks to realize maximum value for them in alignment with business objectives.

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References:

1. Harvard Business Review Analytic Services survey, June 2021

2. Strategies for Master Data Management Integration and Their Benefits

3. Fast-track Digital Transformation with Master Data Management

4. Data culture: Where organization meets analytics | McKinsey

5. How to Get Started With Master Data Management (gartner.com)

6. McKinsey 2019 Global Data Transformation Survey

7. A Leader's Guide to Building a Data-Driven Culture

8. Dresner Advisory Services Research Insights (altair.com)