Building a more data literate future for everyone starts with context, translators and empathy
In an effort to simplify the wonderful world of data, I want to start with a challenge I have with data.
The Problem.
Data’s got a problem. It’s a problem of perception. Many people switch off when they hear the word Data.
It’s such a loaded word and some people avoid it at all costs, thinking if we start entertaining its possibilities, we’ll be led to become robots (not true, yet!). Data is a human made concept. The concept was created in 1640 and new concepts continue to eclipse the previous and the way it’s thrown around businesses hurt rather than help its cause.
The meaning and use of data starts with context. Data can be any and all of the following:
- Describing everything and anything such as media, text, images, results, reports, open data, sensors, IoT, databases, servers and metadata
- Associated with privacy like the use of personally identifiable information (PII) and General Data Protection Regulation or GDPR
- Describing the methods applied to it such as statistics, analytics, machine learning, deep learning, artificial intelligence, big data, data science or data visualisation
- Confusing it with information, insight, business modeling even wisdom
- Related to new concepts such as ‘data as a service’ or ‘data design’
For those in ‘data’, we forget its most everyday use is in the context of mobile phone data (SIM cards). In all these examples, defining the context of the word is so important to having a better conversation and humanising the topic.
The Solution.
Like any job, you start to acquire the words, concepts and techniques to perform your role, it develops into its own world, built on shared language and systems. The way to think about data is to think of learning a second language. Data literacy is similar to regular literacy where its one’s ability to read, write, work with, analyse and argue but instead of just words, doing so with data.
According to Gartner, by 2020, 50% of organizations will lack sufficient AI and data literacy skills to achieve business value. When we live in a world becoming more digitally connected, how do we make sure data is still human and understood?
In any project, you have to start with the desired objective and outcome you want to achieve. Most projects don’t even include data but if you have someone who considers it, you could uncover solutions you’ve never thought of. Once a possible data solution is identified, you work out the methodology and processes to achieve the objective but unless you have a translator, you don’t know what’s possible.
A way forward.
Data needs a rebrand, an ambassador and a bit more empathy. People need to pause and clarify what they really mean when referring to data, to remove scary robotic associations from data and to be more open to possibilities. In February 2018, McKinsey coined ‘data translators’ as people who can articulate and act on data related problems and solutions between business people, data scientists, analysts and engineers and educate everyone in between. These translators need to begin partnerships all around the businesses to bolster their impact, but it’s a two way street.
Communication and relationships need to be bridged, questions need to be open, more human and accessible to different thinkers. We need empathy towards people not familiar with data and vise versa, empathy toward data minded people. While data literacy is having people understand and question statistics, math or data models, level setting on definitions is crucial to starting the conversation.
Having more data literate people, we can ask and answer better questions we didn’t think were possible, think more creatively about the use of data and design solutions that can be powered by data we haven’t even collected and categorized yet.
In the future, or even present if you’re ready, the best translators will spend most of their time defining the most valuable questions then fill the gap of what’s possible.
Sources:
https://www.gartner.com/smarterwithgartner/a-data-and-analytics-leaders-guide-to-data-literacy/
https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/analytics-translator
