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.
Existence
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.
Integration
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.
Analytics
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.
Quality
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.
Exit
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.
3 Comments
Good fundamental approach. Thanks for sharing!
Very good points, Jim. I'd agree with all the above, but would go on to add this one: the strategy for data needs to tie in with the larger purpose or aspired direction of the enterprise, for example 'transforming into a fully digital organization by 2020'. This way the data strategy would be nimble enough to assimilate new demands as they unfurl in the future, yet lay down a clear action path for the now.
Excellent point, Ravi. Too often data management, and even data governance, activities are carried out without contextual awareness of the overall and, as you noted, evolving direction of the enterprise as a business entity. This context, and the capability it provides to change as the business changes, is an essential characteristic of a strong data strategy.