Data Driven Decision Making Series - Part 1

Why should leaders acquire Data Driven Decision Making skills?

December 21, 2021

The ability to make decisions is the hallmark of a leader. It is even more important for them to have the ability to make decisions that are consistent and that drive outcomes. As more and more businesses correlate relevant customer, market, operational, and financial data, the ability to use this data in an actionable way is becoming more paramount to survive in a competitive landscape.

Data-Driven Decision making, or “DDDM” as it is commonly known in academic circles, is the term commonly used for a company having the ability to rely on their data analytics for making decisions. Fundamentally, data driven decision making means working towards key business goals by leveraging verified and analyzed data rather than shooting in the dark.

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:

Managers trained in data analytics are more likely to rise to higher positions and contribute to their organisation’s growth since traditional management strategies are short-sighted and lack quantitative backing. Entrepreneurs and business leaders can also leverage data analytics skills to formulate a clear vision for their businesses, which could help improve their efficiency and attract more potential investors. With data analytics talent in short supply, most companies end up relying on external, often expensive, resources to handle data. This can be countered if managers and business leaders get trained in data analytics. Through this, companies can cut costs while also improving the quality of their decisions. Tech companies have especially been at the forefront of attracting executives with analytical skills. Since 2014, tech recruiters have particularly hired executives with math and statistical skills, looking to harness their data-driven abilities to solve real-world business issues.

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.