In every industry, companies are amassing more data than ever. But while we have all this data, and it’s becoming more influential than ever, there’s still a big problem at hand: Most of us are not very good at interpreting and making sense of it.
Over the last five to 10 years, the data skills we need have evolved. In the early days of data science, companies wanted skills in SQL, data extraction, information normalization, and technologies like parallel processing, big data analysis, and R (a programming language).
Today, as research from IBM indicates, many of these technologies are embedded into data platforms, so companies are looking for different skills. Not only do business people need to understand the nature of these data systems and how they work, they need to understand how to create sound data governance, privacy, security, and trust.
As AI becomes a more critical component of business, modern professionals have a growing need to understand how to challenge the outputs of algorithms, and not just assume system decisions are always right. Analytics teams are not suffering from a lack of technical skills, but from skills in data-driven problem solving, specifically the skills to:
- Ask the right questions
- Understand which data is relevant and how to test the validity of the data they have
- Interpret the data well, so the results are useful and meaningful
- Test hypotheses using A/B tests to see what results pan out
- Create easy-to-understand visualizations so leaders understand the results
- Tell a story to help decision-makers see the big picture and act on the results of analysis
The cost of not understanding the context of data is huge. Read this blog by Josh Bersin and Marc Zao-Sanders to know where to start improving capability.