The rate at which data is being generated by business and the world at large is rapidly exceeding the speed at which leaders are equipping themselves to work with data.
There was once a business executive who kept coming home troubled and distracted. His partner asked him one evening, “What’s the matter? Something’s worrying you.”. “There’s so much data”, he replied, “and I can’t understand it or get my head around it.” “You could try hiring a data scientist”, suggested his partner. So he did just that. Six months later he was still coming home troubled, and when his partner inquired again what was up, he replied: “There’s so much data, and I can’t understand what my data scientist is telling me about it”.
With the right process, you can keep it in check
The analytics value lifecycle is a framework that can help with the design and structure of an analytics team
An aspect of mathematics that I believe to be sorely lacking in the business world, and which can lead to seriously erroneous decisions being made on a regular basis: the concept of significance when studying data.
Decision makers in the year 2020 will be facing many more data driven documents and charts than they did 10 or 20 years prior. But are those decision makers any better equipped to make accurate decisions in such a data rich environment?
As I read various articles and advice on ‘storytelling’, I see a dangerous trend that encourages individuals away from a research-based approach and runs the risk of major organizations taking erroneous decisions based on a glossy but often inaccurate version of the facts.
If we mean what we say about diversity, then the movement in diversity statistics in the coming decades must substantially outpace that of recent decades. This is both an immense challenge and an incredible opportunity.