I’ve read and heard a lot about how organizations need ‘data interpreters’ lately. For me, this raises the question of what do analytics departments do today?
I asked that question to a group of senior analytics leaders recently and they said that 80% of their time was spent preparing data, yet the majority believed interpreting results was the number one way they could add value to the business. So, if analysts are spending the majority of their time preparing data, do they actually have the skills to move up to a data interpretation role?
Part of the problem is a lot of analysts are very tool-centric rather than technique-centric. This means that they see their role as using Tableau, Qlik or PowerBI to build content rather than actually analyzing the business.
Mechanical analytics will soon be automated
The problem is that building content is a mechanical task. As automation starts to do more mechanical tasks, the role of the analyst is inevitably going to change. Rather than worrying about whether a chart is pixel perfect, analysts will need to deliver value to the business by interpreting data and helping business users understand what the numbers and trends really mean.
This change will affect how resources are prioritized within the analytics function. Right now mechanical tasks tend to be prioritized. Analysts work through a data preparation process and aren’t required to think too hard about the implications for the business. But when you start to interpret results, the onus of responsibility is much broader. The role of data interpreter is a challenging one.
To make this jump, analytics leaders need to ask some difficult questions about whether their analysts have the skills and tools they need to interpret data. Are their analysts driven by their tool rather than techniques? Are they thinking about the business and understanding the broader implications of the data?
The data interpreter is someone who deeply understands the business. They take the data, tell the business what it means and make recommendations for actions likely to change the results. Data interpreters need a platform that helps them take disparate data sources and deliver them to the business in a coherent narrative that provides the context of what happened, why it happened, and what they should do about it. This is data storytelling and it’s a very different skill from data preparation.
While many businesses are focused on automating mechanical tasks, they also need to be developing the skills of their data interpreters and giving them the tools to do their role effectively. Tools that help them bring together disparate data sources in a long-form narrative that interprets results and provides context to the business are essential. I believe Yellowfin is at the leading edge of delivering a platform that does just this.
By giving analysts the tools to give context to data, analytics departments can start building the skillsets they need for the future of the function.