“By 2023, data literacy will become an explicit and necessary driver of business value, demonstrated by its formal inclusion in over 80% of data and analytics strategies and change management programs.”
From: 10 Ways CDOs Can Succeed in Forging a Data-Driven Organization, © 2020 Gartner
In the decade and a half since Clive Humby coined the now over-used phrase “data is the new oil”, we’ve seen an exponential growth in the amount and type of data that companies manage. Effective enterprises, large and small, now understand that to remain competitive, they must treat their data as a valuable asset, putting in place good data management practices to ensure safe, secure and good quality information.
Despite this awareness, however, most organisations still struggle to achieve a level of data quality that is sufficient for their needs. So why are my reports still inaccurate? Why is our data not up-to-date? Why can’t we trust our data the way we would like to?
The answer is simple: it’s likely that most people in the organisation simply don’t “get it”.
I’m sure that top-level executives in your organisation understand the critical importance of good data. After all, they are the ones accountable for making accurate decisions, for running efficient processes and for ensuring compliance. The trouble is, they’re not the people that actually handle the raw data. They’re not the ones responsible for data collection, recording, cleansing, cataloguing, security. The rest of the workforce are the true custodians of your data, but in our experience most organisations don’t do anything to ensure this wider cohort is data literate.
The problem with this is that if you don’t apply good data management principles at source, at the ‘get-go’ as it were, you end up collecting poor quality raw data. You build up “data debt” – data quality issues that will take much more effort to clean up at a later date, before you can actually trust and use the data to make accurate decisions and run your business effectively. And the individuals responsible for data “at source” are usually the operators – the doers – the people that aren’t particularly interested in reporting and management; they are just trying to get their job done as efficiently as they can.
A couple of examples will illustrate the point: