Big data has clearly reached a certain level of maturity on the technology hype cycle.

Basically, that means many initial and early investments in business intelligence, customer intelligence, advanced analytics or other big data capabilities have yet to yield the kind of breakthrough performance gains that were promised.

You could say that the potential value and the ROI on these investments were overhyped. Or you could point out that the culprit in many cases was a failure to develop a sufficient data strategy to guide activities and prioritize investments in these areas.

We’ve previously explained what a data strategy is, and how having one helps your company treat data as an asset. Now we’ve identified several key indicators that implementing a data strategy – or overhauling your existing one – would help your organization get more value from its data.

Seven Signs That Your Company Needs a Data Strategy

1. It’s not clear what data is worth or how to use it.

From an accounting standpoint, many companies are struggling with the “big mystery” about what their data is worth.

On a tactical basis, much of the value of data comes from knowing how to apply it to seize market opportunities – which is exactly what a data strategy helps define.

2. Serious questions exist about data quality or security.

That uncertainty leads to a lack of trust in data. The “multiple versions of the truth” problem remains an issue though clearly there is plenty of technology to ensure all data capture is clean, well categorized and secure.

Data strategies can help ensure data quality and security standards are applied broadly to all data, not just a few subsets.

3. There is poor or limited access to data.

This can result from having an insufficient toolset for accessing data. For example, business analysts or managers may be forced to decipher poorly designed reports pulled directly from data warehouses.

Limited data accessibility can also result from restrictive governance models. These may ensure a certain level of data cleanliness, but they won’t help users make better decisions or manage performance more effectively because actionable data is stashed away under IT’s lock and key.

4. There is no ability to apply advanced analytics broadly across the enterprise.

If analytics tools are only available to the finance team or a single marketing unit, opportunities that could be uncovered through broad-based, cross-enterprise and multi-dimensional visibility will go undiscovered.

5. You lack executive support for data initiatives and analytics.

This symptom indicating that your company badly needs a data strategy can take many forms. Some signs include insufficient resource allocation and/or poor coordination of data and analytics programs across business units and functions. There may also be general skepticism regarding the use of data in critical strategic and operational decisions (see point six below).

6. Your organization doesn’t have an “analytics” or “data-driven” culture.

Many organizations are going through a culture change, which may pit highly experienced, senior executives used to making “gut” calls against younger executives who rely on data broadly.

These cultural issues are why clear communication and change management are likely to be necessary components of a highly effective data strategy.

7. There is an overemphasis on technology and implementing the next new thing or technology du jour.

Too often, companies have taken a “just do something” approach to their investments in big data capabilities. Many organizations look to the latest technology to solve business problems that can only be fully and adequately addressed with comprehensive strategic plans.

No one should feel bad if all or most of these symptoms apply. In our experience, most organizations need a data strategy. At a minimum, their data strategies need updating or expanding.

The good news is that investing time and resources in developing an appropriate data strategy can lay the foundation for success that will reverse the hype cycle and get you pointed on the data value train.

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