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Data Governance Challenges: How They Are Slowing Us Down

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Resolving the Governance Paradox Requires Master Data and a MVP

It’s become widely recognized that a company’s data is one of its most valuable assets. High-ROI ideas and new growth opportunities can be uncovered when companies make sense of the data in front of them. It can reveal the right customers to target for the highest return, new market segments to test, and risky partnerships to avoid.

To organize and use data for the purposes of extracting these valuable insights, most companies have turned to master data management software in recent years. Many companies—both large and small—have eagerly sought a return from data this way but are struggling to make it work.

Companies run to software and hope that a new platform will solve everything.
Malcolm Hawker, Distinguished Architect
 

Enter the Data Governance Paradox

In my most recent whitepaper, The Data Governance Minimum Viable Product (MVP) (see end of this article for your copy), I explain that the inability to quantify data value creates a paradox: Companies acknowledge data governance as a dependency to obtain scale and value from their enterprise data, but because they don't measure the value of their data, they often struggle to justify a material or prolonged investment in governing it.

 

Listen to this podcast where I talk about the governance paradox, Master Data, and more.

 

The Zombie Platform

When companies do attempt to make an investment towards some sort of data governance program, they tend to start at the software level, and this results in the dreaded zombie platform that will continue to run aimlessly as long as the hardware runs, but often with no direction or defined objective output. This usually occurs for two reasons:

  • Companies approach the implementation of software as a one-time event or project, where the funding for the implementation is tied to the purchase of the software and the costs to get it up and running.
  • Companies then fail to ensure that the budget for people to run and maintain these systems is accounted for in subsequent years or budget cycles—especially since the cost for people hits the bottom line immediately.

If you don’t define what a customer is, or how a hierarchy is defined, or how your data is structured—then implementing software to automate the management of customer data or hierarchy data will fail. All software requires rules, and a governance program defines the ‘rules’ for your enterprise data. Without these rules, the greatest software platform in the world won’t solve your problems.

I've found that without people in place to run a data governance program, companies will eventually be left with another zombie software platform that runs and runs, without generating tangible business value. People and software go hand in hand. In an efficient data eco-system, they share a symbiotic relationship.

So, what’s stopping companies from investing in the people to manage data?

In a nutshell, this ‘chicken or the egg’ paradox boils down to one question:

How do you measure the value of data when you don’t invest in that same data's supervision, description, and storage?

This lack of clarity in ROI—causing a slowdown of further investment and thus, progress, in data—is caused by a common sequence of events:

  1. People need policies and procedures in place to confidently use the data they must make important business decisions.
  2. For that to happen, there needs to be a team monitoring it.
  3. For there to be a team, there needs to be internal buy-in that this investment will be worthwhile for the company’s bottom line.

Say all the data in your CRM system disappeared tomorrow. What would the financial impact be on your business?

Most companies don’t know. They can’t answer how much an accurate account is worth, or if they put a new account into their CRM today, how much incremental revenue they’d generate from cross-sells or upsells.

That means, if you can’t put a value on your enterprise data, then it’s hard to justify prolonged investments in governing that data. In my years of working with hundreds of companies at Dun & Bradstreet, I’ve only come across one company that can actually put a value on all of the enterprise data assets.

How can companies break through the paradox and prove ROI opportunity?

In order words, what’s the cost of not managing the data? The cost of poor-quality data in the U.S alone is estimated at $3 trillion a year. And time and time again, public examples related to data losses or data privacy breaches occur, with a focus on data security mistakenly being treated as an afterthought by some companies.

Essentially, the governance paradox makes companies feel stuck. They’re often on their second, third, or even fourth iteration of a project where they are trying gain control of their data and determine an ROI by implementing a software solution, but results fail to materialize. In addition, they’re still unable to accurately generate a single view of their customer.

To get any value out of enterprise data, over time, companies recognize that applying a governance program with people, policies, and procedures is the missing piece. An overarching program becomes the glue that brings the data and people together in a company—building a culture of trust and confidence as their business data becomes more accurate, timely, complete—and ultimately, usable.

Start practical. Take the first step to build an MVP Data Governance Program today.

The biggest mistake companies make in governing data is to try to do everything at once. The best way to improve the likelihood of both governance and MDM success is to start small. Read my blog which discusses A Roadmap to Build Your “MVP” Data Governance Strategy.

We need to tackle one category to start—let’s say, customer data – and establishing data quality standards. This MVP structure is built based on Dun & Bradstreet’s experience supporting hundreds of Master Data initiatives for years.

Three of most common questions companies ask when as they begin a data governance endeavor are:

  1. What are the key components of an enterprise-wide data governance program?
  2. What should my top priorities be in an early stage governance program?
  3. Why is data governance important to the overall success of a Master Data initiative?

I put together a detailed guide on how to start extracting the maximum value from your data through an agile approach to data governance. Download the whitepaper below and be sure to visit dnb.com regularly to access more blogs and podcasts in this series on Master Data and data governance topics.

 

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