Improve your reporting accuracy with MDM

How can MDM help you? is a series exploring how applying Master Data Management techniques and best practices can affect your bottom-line profit.

Read Eniko’s intro to this series: Apply MDM today to avoid paying a hefty price tomorrow

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Eniko Tucker

More and more organizations are starting to treat their data as corporate assets, reflecting an elevated level of interest in improving financial performance through better master data quality. At Slalom, we’re starting to notice a shift from companies relying on IT resources for upper-management needs to companies staffing up their data management or data analytics business groups to help interpret data and harvest business insights.

As a sales operations manager, or FP&A VP, we understand how important it is for you to have accurate reporting. Your internal and external audit groups and your executives are constantly knocking on your door for more reports and explanations of what they see, essentially demanding more data analytics.

In this article, we’ll look at some of the most common root causes that prohibit companies from benefiting from data analytics, and then recommend an approach that lets companies start small and progressively achieve higher master data management (MDM) maturity—improving consumer confidence in numbers used in financial or operational reporting.

Lost in translation

In order for everyone to better communicate about your company data’s business performance, it is critical to first find a common language everyone can understand and speak. In today’s complex IT application landscape it is not unusual to see dozens, if not hundreds, of disparate applications all trying to capture data with business functional groups and management struggling to make sense of what the data means.

Data silos

Once the critical master data elements have been identified, it can quickly become apparent that data is everywhere, and if you ask multiple people about what the data really means, you’ll get many different answers. It may be a painstaking exercise to figure out the who, what, when, how and why of information assets. When and how does data get created? How does data flow through multiple applications? What transformation may occur before information retires? Who will consume data and how? What is the communication path when data gets updated? Answering these types of questions helps paint a realistic picture of a company’s current state of MDM and DG maturity.

Technology gaps

Companies are often surprised when they learn about the plethora of open-source or freeway technologies that are available to help gain significant efficiencies from mining, analyzing, and improving data quality. Implementing tools for profiling, standardizing, consolidating, cleansing, and enriching data have proven to lead to significant cost savings for many organizations. By investing time in evaluating available applications up front, organizations can find cost-effective solutions that won’t ruin their budgets but will enable them to harvest tangible benefits from better data quality.

A proven methodology to get started with MDM

To shift from spending time gathering data to analyzing it, consider advocating for an investment in information management. Try taking the following steps to get started on an MDM implementation:

  1. Conduct a comprehensive MDM assessment to shed light on areas of your operations where significant improvements and efficiencies can be gained.
  2. Review the maturity of your master data management business and technology environments and capabilities, such as data modeling and analysis, information quality, and performance. We recommend the following steps in your review:
  • Map out your IT landscape.
  • Seek out all definitions and viewpoints and then come to a common agreement about the new meaning of terms. Data does not interpret itself, so tap into best practices of master data management to succeed in breaking down data silos. MDM will drive business value enterprise-wide and significantly improve interaction between business units.
  • Venture out to try different open-source resources before you make a decision to invest in costly solutions.

3. Try focusing on improving the quality of your reference and master data while helping develop strategies for ensuring data integrity and interoperability across enterprise systems.

4. Define a plan that prioritizes releases into an actionable roadmap that will result in improved data quality, better business results, and more strategic decision making.

How much does bad data cost to you? How much are you willing to invest in your master data management program to stop the bleeding? What is it worth to you?

At Slalom, we’ve seen many companies succeed with their master data management initiatives by strategically aligning on data priorities across the company, creating governance and oversight of the management of their data assets, revising their technology and architecture accordingly, and finally successfully transitioning from a siloed environment to a collaborative one.

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