By Jordan Morrow, Global Head of Data Literacy, Qlik
Adapted from his presentation at the Southern Africa Qlik Summit 2018
Data literacy will touch everything you are doing within your data initiatives. If you’re not comfortable with it, it’s because you’re not speaking the data literacy language.
The Definition of Data Literacy
There are four characteristics that make up the data literacy definition:
- Read – the ability to read data. Data comes in many different forms; Twitter feeds, algorithms, simplified visuals… Are you able to look at the data and comprehend it? Not everyone needs to be a data scientist, you may not need to know the algorithm, but can you gather information from the data. We live in a world where data is no longer binary – it is not numbers, it’s text and images. To be data literate, you must be able to read the data differently. Takes it beyond just reporting and into analysis.
- Work with data – this might mean building a business intelligence visual dashboard or receiving a visual that you have to work with. Working with data has many different facets. Take a look at those that work with you, help data flow more readily through your organisation, and use data analytics for efficiency.
- Analyse – analysis is the key piece of this definition. It goes beyond reporting to get real insights that decisions can be made from. It is one thing to have the skill and not act on it. You want data driven decisions. We want to get away from rule of thumb and actually use data. We need to get away from just reporting.
- Argue with data – when you express a position on something, you want to have data to back up your argument. We can no longer go on “gut feel”, we need to go with insights as to why we are making suggestions/decisions. We want everyone to become skeptical with data – not cynical, just skeptical. While we don’t want to turn people off of data, we want you to interrogate your data – then more answers can come. It is important, however, not to get caught in analysis paralysis. If you ask too many questions, you’ll bog yourself down.
Two Key Elements
There are two overarching elements critical to this definition:
- The language of data – having conversations about data can matter greatly. We need to be able to talk about data initiatives and the things done. Having people fluent in that language is key to data literacy.
- Decision making power – use data to make strategic decisions.