Don’t use your data as a doorstop


Enterprises have access to more data than ever before, data that can provide valuable information about customers, markets, and business processes. Leveraged effectively, data (both structured and unstructured) can be used by enterprises to drive decision-making.

But most organizations fail to use data to inform their strategic decisions or innovation initiatives. A recent poll of more than 300 IT professionals by data management vendor Actian shows that “only 34% of enterprises using data to drive decision-making are using it to drive breakthrough insights and innovations vs. business as usual operational reporting.”

Look, there’s plenty to be said for data being used to monitor and report on enterprise operations, including customer service, manufacturing, shipping and transportation, the supply chain, financials — really, any connected platform, network, or business unit that generates data. This allows IT and department leaders to make adjustments to processes and procedures that can lead to lower costs and greater efficiency. Those incremental improvements, however, do little or nothing to fuel digital transformation. They are the equivalent of the proverbial computer as a doorstop: Sure, it can do that job, yet compared to what it could be doing, it’s being criminally underutilized.

So how can enterprises ensure they are getting strategic and transformational value from their data, and not merely operational, incremental value?

The first step is to create a framework in which data can make strategic sense. What are the organization’s goals regarding digital transformation? What information do decision-makers need to formulate plans and take action intended to enable those strategic goals? This will help you focus on identifying, collecting, and analyzing the strategic data you need, which is vitally important when enterprises are being overwhelmed with data from customers, apps, devices, networks, and systems.

While analytics is about data, it’s also about asking the right questions. Without knowing how to tease out the strategic value hidden in data, enterprises run the risk of having that value slip through their fingertips. This is where data scientists come in. A good data scientist understands algorithm design, data modeling, and predictive models.

I’m greatly simplifying here, but from there it’s a matter of collecting and analyzing data relevant to strategic objectives and innovation efforts, and then presenting it to decision-makers. It doesn’t hurt to have a team of data analysts, though these days enterprises are wise to also encourage a data-literate workforce. You never know who’s going to have that critical insight.

It’s easy to get overwhelmed by data, just as it’s easy to consume low-hanging fruit by using data primarily to tweak operations. But the real payoff comes when data is used to fuel innovation and help enterprise leaders make the major strategic decisions that determine the fate of their organizations. Accomplishing that requires a vision, a plan, and the right skills and resources. Anything less and you’re just collecting doorstops.

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