Most of you will already be familiar with Abraham Maslow’s hierarchy of needs. And while Mr Maslow didn’t historically focus his model on data and analytics, I will take the liberty of doing that instead.
The vast majority of organizations today have already started their ascent on the analytics staircase. It all began with paper reports that were distributed manually. These gradually got replaced by static reports, followed by modern analytics tools, and now the latest trend we’re looking at is largely made up of AI- and ML-driven reporting tools, often hosted in the cloud.
The next step in the hierarchy
Up until now, the main weakness within data and analytics has been the technology. However, I would argue that this has changed, with the weakest point now being the consumer of the data. As a result, we have defined the next step in the hierarchy as the need to help users and developers support the technology — in regards to how to use it and understand it in a better way. This is often referred to as data literacy, and in order to reach a good level of data literacy, you need to provide your users with tools that can enrich the metadata surrounding your BI reports.
Metadata and data literacy — what is the connection?
To help users move to the next step in the hierarchy, we need to help them understand, as well as trust, their data. You will never reach full adoption and maximize the value of your analytics solution if your users do not feel that they can trust what they are looking at. There is no difference between analytics and any other shared information — if you don’t trust it, you won’t fully embrace it.
The good thing is that there are many solutions available in today’s market that will help you and your users achieve this. These include combining intuitive visualizations with a data catalog and data lineage tools, enabling your users to see the whole picture and understand how everything in the data landscape is tied together. Empowering users with tools that help them search and find specific KPIs and other relevant metadata components — thereby allowing users to get full insight into how data is defined, where it originates, etc. — will help put the power in the hands of the user. This insight will, in turn, make it possible for the user to feel the trust needed to maximize data literacy.
Basically, data literacy is formed from the trust that is gained through combining data, great visualizations and intuitive access to metadata.