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3 Challenges of Data Adoption

  • Writer: Chelsea Wilkinson
    Chelsea Wilkinson
  • Sep 1, 2021
  • 2 min read

1st of September! How did that happen? But I digress.

Last week, DataDiligence’s Adam Votava spoke at a webinar: 'Prepare your data for adoption from the front line to the C-suite', in partnership with Keboola and ThoughtSpot. He transformed his presentation into a compelling 5-min article; read it!


My hope is that this quick summary piques your interest enough, as the concepts he covers are fundamental to the success of data projects (which Gartner tells us are failing ~80% of the time).


So, don’t be a statistic, address these three challenges and you will be a long way to achieving lasting data impact and truly monetise the insights that sit in your data (as opposed to 'monetise the data' - which is rarely relevant to most businesses).


🌉 Challenge 1: WHAT BUSINESS PROBLEM ARE WE SOLVING?

Time and again, Adam and I have conversations where people have commenced a data project without tightly linking it to solving a business problem. This is a huge mistake; your data project needs to address a material business need (with a KPI), like: INCREASE ARR or HOW DO WE SCALE TO 10 NEW MARKETS or HOW DO WE INCREASE UPSELL? Simply, if you’re addressing a business need your data project matters - its business critical and will gain senior support, budget, capacity, and visibility.


🌉 Challenge 2: ABILITY TO MAKE DATA-DRIVEN DECISIONS

To make decisions based on data, you need to be able to do more than read a chart! We would argue that you also need more than data literacy. Rather, you need to build data fluency. Meaning, people need to be thinking in data and talking in the language of business. It’s a 2-way flow, that requires empathy and curiosity from both data scientists and their business colleagues.


🌉 Challenge 3: DE-MYSTIFY & DEMOCRATISE DATA

Only by lowering the bar and making data accessible will adoption go ‘viral’. To do this, (i) have a product management approach to building data solutions, (ii) ditch the data jargon, (iii) use explainable & ethical AI, and (iv) remember that your data should be reflective of your real world – think ‘digital twin’ – to support the widespread understanding of constraints, limitations and assumptions relating to data, including its quality.


Now, click the link to read the full article! Or follow this link to watch the webinar.


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