Bridge the CxO Gap with a Data-Driven Approach

Bridge the CxO Gap with a Data-Driven Approach

There has been a lot of noise both in the press and in social channels about how to identify the compelling business case to get senior leaders to “buy in” to the investment for improved information management and governance. I’m going to share a secret with you: They are already bought in!

All you have to do is look at the cover of any business journal to find examples of how data (or a lack thereof) has impacted a business. A recent infographic showed that bad data may cost businesses as much 10-25 percent of an organization’s revenue each year, and Gartner surveyed a wide variety of companies and found that, on average, data quality issues cost them an estimated (and staggering) $14.2 million annually.

Any executive knows that, in order to properly run their business, they need to understand their expenses, their revenue, their risk, and their future opportunity – all categories of information – using aggregated data to provide a picture of the current state of the business. In addition, effective information management is crucial to compliance with regulations as well as country- and state-specific laws and regulations about data privacy. Whether it’s for the purposes of regulatory reporting and compliance, decision making, or just ad hoc investigation and analysis, executives already know that it’s important to have accurate, reliable, and auditable data to run their business.

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But if senior leadership are already bought in, then why don’t they behave like they are? And why don’t they approve the budgets for projects that can improve their data quality?

Let me share another secret with you: They think you are already doing it. Because executives have to certify the validity of the data that is distributed externally to customers and investors, they assume that the data are already accurate, current, and controlled. What they may not be aware of is the amount of effort (often manual) that is needed to get the data to the state in which it is trusted. Addressing this is problematic because requesting the funding to automate the manual data sorting and cleaning and improve the process of data sourcing and reporting requires admitting that there are inefficient processes in the first place. Putting together this sort of proposal means honestly assessing the inefficiencies and identifying how improvements will increase productivity and reduce costs in the long term.

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There’s another reason that executives don’t seem to behave like they are bought in to the requirement to better manage and govern data: Executives don’t necessarily see the link between data management and governance and their corporate goals. This is partially our fault as an industry. We need to recognize that there is a business language that is different than a data language. In business-speak, the budgetary process consists of a series of trade-offs and decisions about what investments will enable the organization to meet their corporate goals in the most effective way possible. Therefore, in our data-speak, when we are creating our roadmaps and seeking funding, we need to understand explicitly how the data enables, or impedes, the organization’s ability to meet its corporate objectives and be able to clearly communicate that requirement and dependency. Improving data for data’s sake will never get approved. Improving data to progress a business program and objective will. Information management programs need a business justification to make them worthwhile.;

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