With data now impacting nearly every business activity, there should no longer be any doubt that data needs to be managed as a strategic corporate asset. This post examines the top five characteristics of a strong data strategy.
As I previously blogged, in today’s fast-moving business world now often takes priority over later. This means operational and tactical priorities often trump strategy. Some organizations use this as an excuse for why a formal data strategy does not exist. But organizations that are too focused on today cannot capitalize on tomorrow’s opportunities. So the first and foremost characteristic of a strong data strategy is that it exists.
It's essential to evaluate the value of individual data sources, but the real value of data is revealed by the extent to which it can be integrated. Traditional data sources like data warehouses and master data management must be integrated with new sources, such as those emanating from the Internet of Things. The thing to ask about any new data is how it connects with existing data. A strong data strategy acknowledges the importance of integration.
In the past, analytics was mostly a function that occurred after data was prepared. Now, analytics plays an essential role in evaluating data – before it’s prepared – to determine its applicability to specific business problems. Analytics in this context acts as an advanced filter enabling prioritization of the most valuable data. A strong data strategy embraces a more expansive use of analytics, especially its use as a pre-process to evaluate raw data before the organization invests significant resources (time, money, people) in preparing and integrating data.
Big data has increased the volume but not necessarily the value of enterprise data. This is why data quality is so important, without which the data lakes more organizations have become so enamored with will become much more reminiscent of data swamps. Nonetheless, it’s also important to realize that how much quality data needs cannot be defined in general terms applicable to all data sources and business uses. A strong data strategy defines quality in more specific terms and reflects the reality that data quality standards will, and must, vary.
Without question organizations now collect, store, process, manage, analyze and govern more data than ever before. However, many do so without questioning whether all that data is all that useful – or still being used. The fact is data has an expiration date, after which it should at least be archived, or possibly even deleted. A strong data strategy includes an exit strategy for data (i.e., processes for removing unused or unnecessary data).
What strategically say you?
Does your organization have a formal, well-defined data strategy? If so, what characteristics does it possess? Please share your perspective and experience by posting a comment below.