Toward a mature data operation

SaplingWith analytics technology in its infancy, few organizations have worked out how to exploit the potential of big data to maximum effect. Continued self-assessment is the key to data maturity, and those enterprises closest to this goal are those with the confidence to appraise their progress regularly and honestly.

“Data analytics is rather like sex,” says one CIO, with a wry smile. “All of us assume that everyone else is getting more of it than we are.”

The truth about big data and analytics is that very few businesses have reached maturity – hardly surprising, given that these are nascent technologies.

The good news is that this means it is not too late to avoid being left behind. On the other hand, there is absolutely no room for complacency – those organizations that don’t do the work now will rapidly find themselves at a commercial disadvantage.

One characteristic shared by leading enterprises, across various fields, is a determination to be self-critical. Through a process of almost constant self-evaluation, these businesses are able to drive continuous improvement.

This is the approach that will yield the best results as organizations seek to achieve more with their data. Regular and honest assessments of the enterprise’s big data and analytics capabilities represent the best chance of reaching maturity more quickly.

It may help to think about the business as progressing along a scale, graduating from one group to another as it acquires new skills and experiences. You can think of five types of organizations on this scale:

  1. Analytically impaired
    These businesses have few data skills and the data they hold is of poor quality. Their analytical endeavours are backward-looking and basic.
  2. Localized analytics
    These businesses mostly use analytics for reporting, and are therefore failing to gain competitive edge from their data. No actionable insights are generated.
  3. Aspirational analytics
    These organizations have begun to acquire business intelligence tools, but as yet have few governance structures that enable them to exploit their newfound capabilities on an enterprise-wide basis. Few insights are generated, and translating them into commercial advantage is difficult.
  4. Analytically advanced
    These businesses are more mature, with high-quality data and a culture of analytics embedded across the entire company. Quantifiable results are already being achieved.
  5. Analytically competitive
    These enterprises are the digital leaders. They use internal and external data, and employ statistical analysis and predictive modeling in order to continuously generate insights on which the entire business can act quickly.
    In practice, there are very few enterprises that are “analytically competitive,” though many organizations are now making rapid progress. The key to reaching the pinnacle lies in the honesty of the self-evaluation process.

Vital questions include: have we set analytical objectives? Have we developed analytical techniques? Do we possess analytical skills? Do we have the right technologies? Do we have a data-ready culture throughout the company? Is our leadership committed to analytics? Have we sought to mitigate the risks in exploiting our data?

If your CIO can answer “yes” with confidence to all of these questions, give them a pat on the back – and then immediately follow up with some searching supplementary queries.

To assess the maturity of your business’s big data and analytics capability, consult our Ready for takeoff assessment tool.


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