As someone who works at a company that conducts surveys, I know that collecting more information is pointless if you don’t have the capabilities to analyze it effectively. If people don’t take this conclusion to heart, they will be deeply disappointed by whatever “big data” or predictive analytic solution they purchase for their company.
Luckily, the Corporate Executive Board (CEB) recently published a report that frames this issue effectively: Overcoming the Insight Deficit: Big Judgement in an Era of Big Data. Even though they might not want to admit it, this is a good follow-up for executives who read McKinsey’s Big Data: The next frontier for innovation, competition, and productivity and want to take some specific action. This posting is a summary of their report, with commentary by moi interspersed.
Without a high level of data competency, companies can’t take advantage of the information they already have, let alone the information deluge that is about to come.
CEB creates a compelling case that employees need to improve their ability to find and analyze relevant information to make better decisions. To help executives achieve this goal, they’ve created an “Insight IQ” index that benchmarks the “analytic maturity” of the company. In general, they measure this in terms of 1) information attainability, 2) information usefulness, and 3) employee capability. Unfortunately, CEB doesn’t reveal how they actual calculate the index. For a report about data, it is odd that they don’t actually detail how they came up with numbers. That said, the main value of the report, without the consequent benchmarking service that is being sold is in highlighted actions that can be taken. Here are examples:
- Develop more “informed skeptics” by educating employees on the limitations of data and help them improve their critical thinking. They also note that formal training on analytic tools should focus on techniques rather than the functionality of specific tools. Based on a recent Meet-up I attended, I also agree with their assessment that coaching skills are critically important for consultants or new hires. In fact, interpersonal skills are really important because IT and hardcore data analysts are much less effective if they don’t have the “anthropological” skills to work with business leaders.
- Challenge Biases and Assumptions: Similar to what a good futurist does in strategic planning sessions, the entire company, as a group, should be willing to challenge assumptions about what data is important. From personal experience, I know that executives don’t communicate effectively about the data they want to use to make actual decisions.
- Improve Quality and Sharing of Data: A core problem is maintaining clean data that is accessible to analysts in multiple business units. This is a core issue that requires executive leadership because otherwise IT departments and other fiefs will cause problems.
- Make information usable by providing a greater selection of analytic tools. This recommendation was one of my takeaways after listening to a Focus roundtable on Self-Service BI. I like to say this is the basis of the open data movement: standardize the format of data and make it accessible to people regardless of the tools they use to analyze it. Some people might be Excel whizzes, others might be SAS jockeys, and still others might be writing interactive dashboards with tools like Tableau. The important thing is that the data is sound and the methods are well applied. In that regard, the way the data is visualized, aggregated, and filtered is really important. However, since people have different needs, it is fool’s errand to try to create one über tool to use the data.