Mind the Gap Between Data and Analytics
If you’ve ever been to London, you are probably familiar with the announcements on the London Underground to “mind the gap” between the trains and the platform. I suggest we also need to mind the gap between data and analytics. These worlds are often disconnected in organizations and, as a result, it limits their effectiveness and agility.
Part of the issue is that data and analytics tasks are often handled by different teams. Data engineering tasks are more likely to be handled by IT or technical resources while analytics are typically performed by line-of-business analysts. As I’ve written previously, our research shows that this separation can hinder the success of analytics efforts. Data-related tasks are still a big obstacle to analytics. Organizations report that data preparation and reviewing data quality are more time-consuming than the analytics themselves.
We see separation in governance activities also, with data governance often stopping short of, and separate from, analytics governance. The weak link in many organizations’ data governance processes may, in fact, be analytics governance which I’ve discussed here. Similarly, organizations need to complete the last mile of their Data Operations (DataOps) processes with Analytic Operations (AnalyticOps). Organizations can’t be agile with respect to changes in their data and analytics processes if they’ve only adopted DataOps and not AnalyticOps.
Our research helps us identify some of the best practices for dealing with these issues. First, I would suggest an awareness of the issue is the best place to start. This awareness is predicated upon the fact that the whole point of data and analytics processes is to improve the performance of the organization. It is important for data and analytics leaders to put aside parochial interests and explore how the organization can best use the data it collects and what obstacles exist.
There are five areas of best practices to consider:
Organizations whose data and analytics efforts are led by business intelligence and data warehousing (BI/DW) teams, along with those using cross-functional teams, tend to have the highest levels of satisfaction with their results.
