The Corporate Executive Board (CEB) offers comprehensive data analysis, research and advisory services that align to executive leadership roles and empower clients to focus efforts, move quickly, and address emerging and recurring business challenges with confidence.
CEB developed the Insight IQ which evaluated over 5,000 employees at 22 global companies, it assesses the ability to find and analyze relevant information.
The found that employees can be categorized into 3 categories with regards to this.
- 19% – Visceral decision makers –> the narcissist
- 38% – Informed skeptics –> the team player
- 43% – Unquestioning empiricists –> the yes man
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Informed skeptics is the preferred one. Analysis shows that only 38% of employees fall into this group and out of only 50% of managers. These people performed 24% better than other functions including effectiveness, productivity, employee engagement, and market-share growth.
In a general consensus, finance, supply chain and HR domains have business needs clearly defined, thus making it easier to perform big data analysis. For other domains, anthropological skills and behavioral understanding can help with refining data to specific needs in meeting business goals.
To reap the benefits of big data trend, companies must provide two things; 1.) training workers to increase their data literacy and more efficiently incorporate information into decision making 2.) giving employees the right tools.
More importantly, training mustn’t be some sort of one-off workshops. In retrospect, ongoing coaching is often more effective.
Let’s take one company for example; Tiffany. It holds year-round workshops that teach employees to use broad categories of information.
The underlying knowledge-base which holds everything together is math’s Statistics. The fundamental application is to understand that not all numbers are created equal – some are more reliable than others. While the fundamental challenge is having the ability to understand the factors and calculations behind the numbers and learn to think critically about the accuracy, sample sizes, biases, and quality of their data.
For more information, refer to Good Data Won’t Guarantee Good Decisions
