In the realm of data enterprise, master data management (MDM) is a strategic, intricate process, akin to a game of chess. If you have had the chance to watch one of my favourite shows, ‘The Queen’s Gambit,’ you may have noticed a reflection of what MDM professionals do daily. Much like Beth Harmon, the chess prodigy in the series who strategically and methodically makes her move to win the U.S. Open Championship, MDM professionals exhibit strategic thinking, management of multiple data points, and precise decision-making.
To that extent, MDM serves as a single source of truth for an organisation’s data. By harmonising data from disparate domains of the business, MDM fosters a unified view essential for enhancing customer success across various industries — from retail and healthcare all the way to energy and agriculture, among others. Equipped with precise and dependable master data, businesses are empowered to anticipate customer needs, optimise service delivery, and tailor offerings, thus driving customer satisfaction and loyalty in competitive markets. As such, MDM plays a crucial role in organisations with diverse systems and data sources, creating a structured approach to managing critical data, which are at the root of business decisions.
Furthermore, as businesses increasingly invest in cutting-edge technologies like generative AI, the importance of MDM becomes even more pronounced. The quality of outcomes derived from such technologies hinges on the integrity of the data they operate on. MDM therefore, helps unlock the full potential of emerging technologies, safeguarding against data inconsistencies and driving innovation forward.
However, while precise and usable data is essential for an organisation’s growth, the question arises: “Why is a single point of reference crucial in data management?” The answer lies in achieving data consistency, accuracy, and enabling informed decision making with MDM.
Managing your pieces
In MDM, strategic planning plays a vital role in shaping short- and long-term business goals. However, this planning hinges on the availability of accurate, high-quality data. According to Gartner, every year, poor data quality costs organisations an average of US$12.9 million.
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What does poor data quality look like? For one, data is often siloed, creating barriers for multiple parts of an organisation when it comes to accessing and sharing information. Moreover, poor data quality introduces inaccurate, outdated records, and duplicate data points.
MDM serves as the linchpin in ensuring the accuracy of critical data, such as customer information, product data, and financial records. It provides a single source of truth for core data that helps various departments within an organisation ensure that they are working with the same set of data. This uniformity, in turn, generates many benefits for a business, including improved business performance.
For example, an international utilities, operations and maintenance provider with over 30,000 staff members sought to integrate an acquired company into their IT landscape. The provider wanted to propel business outcomes using better quality data sets with HR and payroll data being the primary area of focus. By implementing a unified platform, the organisation was able to access dashboards that showed the clear status of progress in business-critical projects.
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Having a single set of accurate data also allows for improved decision-making. Organisations like Zespri, the world’s largest marketer and distributor of kiwifruit, have seen the fruits of a unified source of data. Prior to implementing an integrated platform, the company relied on manual tracking and inputting data into spreadsheets. This wasted time and led to inaccuracies across the organisation. Using a single point of reference, the company was able to make more well-informed decisions.
Extracting full potential from generative AI
Lastly, master data management also enables experts to quickly adapt to ever-changing business needs, such as the adoption of new technological advancements such as generative AI. A recent report by Gartner predicts that generative AI will fuel many innovations. Another report by McKinsey found that across 16 different business functions, there are at least 63 potential use cases where generative AI can address specific business challenges in ways that produce one or more measurable outcomes.
Many companies are now looking to generative AI to stay ahead of the competition and address specific challenges. However, using it to solely get ahead of your competitors isn’t a good enough reason. Furthermore, speeding up the learning process isn’t going to get you ahead of the curve either. Harnessing generative AI requires a thoughtful, concerted approach rooted in data. In order for generative AI to solve specific challenges, it must address all of the data points within a business to make accurate forecasts and predictions. Otherwise, new technologies will do more harm than good, leading to costly mistakes and delays. Thus, organisations must establish a proper data foundation to truly reap the advantages of their AI investments.
Endgame
Similar to the endgame in chess requiring precision and cautious execution, master data management’s endgame needs continuous maintenance and improvement of data quality, accuracy, and usability. It is rooted in a data-first ethos, elevating data to the forefront of all organisational activities. To secure the winning position, data must not only be managed but also harnessed as a valuable, strategic asset.
Gary Chua is the managing director of Asia Pacific and Japan at Syniti
