Whilst self-service data analytics has many benefits, there are also a number of problems, most notably the lack of reusability of datasets. The cost of low quality data is staggering, around $3.1 trillion according to IBM, and according to their studies, analysts spend around half their time finding and correcting bad data. This is a waste of time and opportunity, and stops big data form being as useful as it could potentially be.
The problem lies in the fact that there are too many one-off projects in terms of data acquisition, it takes them too long to receive and clean good data, and it isn’t saved. In other words, no one knows what anyone else has done. This means that other organisations waste time trying to recreate and replicate previously available datasets, rather than being able to delve into an already existing goldmine of data.
The solution is to approach data in a way that is more akin to social media. By socialising data acquisition, and integrating traditional approaches to self-service data with processes already common to social media platforms, we end up with high quality, reusable data sets. This methods provides operational repeatability, and means that the overall data acquisition process becomes quicker, easier and more efficient.
By being more collaborative, we ensure that good quality, trustworthy data is easily available to all. Bad data needs to be filtered for the useful stuff, and this is yet another time consuming process. Having a reservoir of independently reviewed, useful datasets solves this problem. For data analysts, this inevitably leads to an increase in productivity, as they no longer have to spend inordinate amounts of time trying to recreate old datasets. A more collaborative culture at a cross organisational level will contribute to better business results