What the Data Analyst Wishes the Business Understood About Master Data

December 15, 2020 0 comments

Data Analyst

Every C-level executive now talks about the value of a data-driven business and culture. That’s all well and good; however, many fails to understand that a comprehensive data management plan often sets the foundation for delivering early business value. The result is they don’t always actively promote the use of, or changes in, how the company manages data. Specifically, data that is widely shared logically across the business and that tends to change slowly, because they can’t see the end results for themselves.

In the absence of a master data plan, there’s no real incentive for line-of-business leaders to cooperate on eliminating data silos. There are also no consequences specific to them when they fail to do so. That hurts the ability for data analysts to make top-level customer or vendor data consistent across diverse enterprise business processes, applications and data platforms.

Continually having to manually resolve errors in master data for entities that live across functions and that have different levels of granularity is both time-consuming and resource-draining. Worse, data instability decreases the efficiency of reporting, analytics, and decision-making, and can also erode the performance of core business processes.

Data management, then, continues to take a back seat to almost everything else that business leaders think is important to do in order to achieve enterprise-scale digital transformation. But the fact is that well-managed and well-governed master data is a prerequisite to achieve

enterprise-scale digital business transformation. Since, by definition, all applications rely on master data, its integrity must be assured.

What many heads of business haven’t fully recognized is that if database analysts aren’t given the support and resources to stabilize master data across different operational systems and analytics platforms, the ability to execute collaborative business opportunities is in jeopardy. The same holds true for cross-brand promotions, as there is an increased likelihood of making ill-informed decisions based on inaccurate reports, and inaccurate informational assets.

 

How Will the Dynamic Change?

A data analyst, and even a data manager, generally doesn’t have access high enough up in the business chain to make a direct case about why it’s important to change the priorities, and eventually the culture, of the organization so that everyone plays a part in making data great. They aren’t empowered on their own to force business stakeholders to invest in efforts to define and manage an organization’s critical data on the journey to having a single point of reference. These efforts must encompass business processes, governance policies, and technology to provide a trusted data foundation across the business.

But such a shift may well only occur if C-level leaders find themselves face-to-face with a major situation that reveals that something is rotten in the state of data. The C-Suite may have been historically shielded from the challenges that data analysts routinely face when trying to reconcile, link, synchronize, and publish master data across different processes, systems, and analytics. But the following incidents could at last make it plain that data management isn’t an IT problem, but rather a foundational business enabler:

  • Declining Revenue & Market Share: A leader in its industry for decades, with a healthy position, an organization realizes its market share has been diminishing over the past year. This consistent, if slow, decline has started to make executive management and the Board of Directors nervous as they ponder the root issue. Are their competitors are doing a better job with marketing, or are converting net new and/or incremental sales at a more rapid pace? Or are they moving more efficiently and at a speedier clip because they can rely on analyzing trusted data, where the dots are all connected across enterprise systems and processes using clean and consistent master data? The reality is that clean and accurate data is the key enabler for all of these strategies, and is mirrored by Forrester who says, all, insights-driven businesses grow on average more than 30 percent annually and are on track to earn $1.8 trillion by 2021.
  • Merger and Acquisition Complications: It takes a long time just to identify, plan, implement, and close an M&A deal that’s designed to help the company gain a competitive advantage or diversify its services. Once executed, executives want to see ROI come sooner than later. But if data and semantics are inconsistent across multiple internal domains within the new enterprise, things are off to a bad start. Moreover, the lack of data coordination within one organization will make it that much more difficult to converge data and systems with the other. With the foresight to manage data consolidation carefully and proactively, sales and profitability will be additionally stymied by the inability to quickly create reliable reports and analytics.

Any business facing such concerns – will hopefully cause its top executives to realize that building a data-driven culture starts at the top and works its way down the business line. Data analysts will be the first to cheer the revolution, followed closely by the balance sheet.

 

About the Author:

Bill O’Kane is vice president and MDM strategist at Profisee, a pioneer in master data management (MDM) solutions. To learn more, visit www.profisee.com or follow the company on Twitter @Profisee.

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