Data Driven Decision Making Series - Part 1
Why should leaders acquire Data Driven Decision Making skills?
For example, a company might:
- Collect survey responses to identify products, services, and features their customers would like
- Conduct user testing to observe how customers are inclined to use their product or services and to identify potential issues that should be resolved prior to a full release
- Launch a new product or service in a test market in order to test the waters and understand how a product might perform in the market
- Analyze shifts in demographic data to determine business opportunities or threats
Companies across the world have cited the presence of unstructured data as one of their biggest problems. In the recent past, business leaders have expressed an increasing desire for objective and strategic decision-making to drive their businesses, with 83% of the CEOs interviewed by IDC Technologies agreeing that they want their companies to be more data-driven. The same study by IDC has also revealed that no more than 35% of the surveyed employees believe that their actions are data-driven or that they possess the requisite skills to interpret data for managerial decisions. This heavy demand coinciding with a shortage of data-driven management professionals has led to a surge in opportunities for job growth for managers trained in data analytics.
Increasing Demand for Data-driven Management:
Successful Implementation of Data-Driven Decision:
1. Leadership Development at Google
Google maintains a heavy focus on what it refers to as “people analytics.” As part of one of its well-known people analytics initiatives, Project Oxygen, Google mined data from more than 10,000 performance reviews and compared the data with employee retention rates. Google used the information to identify common behaviors of high-performing managers and created training programs to develop these competencies. These efforts boosted median favorability scores for managers from 83 percent to 88 percent.
2. Driving Sales at Amazon
Amazon uses data to decide which products they should recommend to customers based on their prior purchases and patterns in search behavior. Rather than blindly suggesting a product, Amazon uses data analytics and machine learning to drive its recommendation engine. McKinsey estimated that, in 2017, 35 percent of Amazon’s consumer purchases could be tied back to the company’s recommendation system.
3. Real Estate Decisions at Starbucks
After hundreds of Starbucks locations were closed in 2008, then-CEO Howard Schultz promised that the company would take a more analytical approach to identifying future store locations. Starbucks now partners with a location-analytics company to pinpoint ideal store locations using data like demographics and traffic patterns. The organization also considers input from its regional teams before making decisions. Starbucks uses this data to determine the likelihood of success for a particular location before taking on a new investment.