To be successful, any organisation must be able to measure what it manages. This is especially true when it comes to data – easily one of the most significant assets any digitally driven business has access to. I recently came across this Gartner report that examines the five data and analytics (D&A) KPIs that every executive should track. I found the information greatly insightful and so wanted to take the opportunity to add some of my views.
#1 Track the business impact of D&A
This is a great starting point when it comes to KPIs. Often, executives ask what the business value and ROI of data warehousing, business intelligence (BI), and analytics are? Some companies might consider focusing on how much time is spent extracting and juggling data in spreadsheet and cross-checking the information to produce executive and regulatory reports. However, the most effective way of addressing the ROI question is to examine the full time equivalent (FTE) in terms of the time saved by having automated BI and analytics in place.
There are two other useful measures to keep in mind. Firstly, penetration. This is the number of active users; the percentage of the organisation accessing BI, and the percentage of management information supplied by BI. Secondly, is proliferation. This translates to the number of reports run, and the number of reports accessed. Other measurements include the cost of ownership versus the business value delivered.
The challenge in my opinion is that many BI and analytics projects do not adequately conduct before- and after- analysis to accurately determine the business value delivered through a project.
#2 Monitor time from insight to action
This is a very useful measurement to determine the time lag between when a business event happens and when the information about the event is delivered to the relevant decision-maker to make an informed decision about it.
For instance, it is raining heavily in a major city and the retail chain has run out of raincoats and umbrellas. How long does it take to make the decision to truck in more supplies to meet the demand?
Instead, a better use of analytics would be to look at whether there is significant rain forecast, then determine whether the demand for raincoats and umbrellas would exceed current stock levels. Does the relevant decision-maker get the information in time to ensure that more supplies are trucked in to meet the demand?
#3 Measure data quality
There are so many documented data quality KPIs out there that a person can easily do more than one blog post about each of them. Here are just a few examples of important data related measurements to consider:
- Completeness
- Accuracy
- Validity
- Accessibility
- Timeliness
#4 Track data literacy levels
Fortunately, many companies have embarked on data literacy programmes which is definitely something I encourage. After all, a business cannot expect staff to contribute to improved data quality levels and be measured on it if they are not ‘trained’ on how to do it.
As part of this, it is also important to measure the D&A maturity of the company. Several such frameworks are available. In my experience, I have found that a combination of the aspects considered in these frameworks, particularly tailored to the organisation at hand, works best.
It can also be very interesting to compare the data literacy and maturity levels across business functions. This often provides good indications of what works well and where more attention is needed. If we couple these insights with an alignment to strategic goals, it will showcase the priority areas where data literacy and maturity improvements are needed most.
#5 Quality D&A risk
The Gartner report provides a great description of this aspect: “This KPI quantifies your risk exposure. It consists of the estimated probability of a risk happening, multiplied by the involved cost.”
As is the case with any compliance reporting and data ethics concerns, using analytics too aggressively can result in significant breaches of privacy. We have all heard the insight-driven marketing war stories when it comes to overstepping the boundaries of acceptable handling of private information.
These five KPIs provide a great starting point for business leaders to start managing their data like the asset it is.
