Lead with Data-Driven Decisions
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.
What is Data-Driven Decision Making?
- 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
Data-Driven Decision Making Examples
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.
10 Tips to For An Enhanced Data Driven Decision Making
1. Know your biasesPsychologists Daniel Kahneman, Paul Slovic, and Amos Tversky introduced the concept of psychological bias in the early 1970s. They published their findings in their 1982 book, “Judgment Under Uncertainty.” They explained that psychological bias – also known as cognitive bias – is the tendency to make decisions or take action in an illogical way. For example, you might subconsciously make selective use of data, or you might feel pressured to make a decision by powerful colleagues. Psychological bias is the opposite of common sense and clear, measured judgment. It can lead to missed opportunities and poor decision making. Imagine that you’re researching a potential product. You think that the market is growing, and, as part of your research, you find information that supports this belief. As a result, you decide that the product will do well, and you launch it, backed by a major marketing campaign. However, the product fails. The market hasn’t expanded, so there are fewer customers than you expected. You can’t sell enough of your products to cover their costs, and you make a loss. In this scenario, your decision was affected by confirmation bias. With this, you interpret market information in a way that confirms your preconceptions – instead of seeing it objectively – and you make wrong decisions as a result. Confirmation bias is one of many psychological biases to which we’re all susceptible when we make decisions. In this article, we’ll look at common types of bias, and we’ll outline what you can do to avoid them. Tips for overcoming a biased behavior
- Simple Awareness – Everyone is biased, but being aware that bias exists can affect your decision making can help limit their impact.
- Collaboration – Your colleagues can help keep you in check since it is easier to see biases in others than in yourself. Bounce decisions off other people and be aware of biased behavior in the boardroom.
- Seeking out Conflicting Information – Ask the right questions to yourself and others to recognize your biases and remove them from your decision process.
2. Define the GoalsTo get the most out of your data teams, companies should define their objectives before beginning their analysis. Set a strategy to avoid following the hype instead of the needs of your business and define clear Key Performance Indicators (KPIs). Although there are various KPI examples you could choose from, don’t overdo it and concentrate on the most important ones within your industry.
3. Gather data nowGathering the right data is as crucial as asking the right questions. When it comes to data businesses collect about their customers, primary data is also typically first-party data. First-party data is the information you gather directly from your audience. It could include data you gathered from online properties, data in your customer relationship management system or non-online data you collect from your customers through surveys and various other sources. First-party data differs from second-party and third-party data. Second-party data is the first-party data of another company. You can purchase second-party data directly from the organization that collected it or buy it in a private marketplace. Third-party data is information a company has pulled together from numerous sources. How to Collect Data in 5 Steps
- Determine What Information You Want to Collect
- Set a Timeframe for Data Collection
- Determine Your Data Collection Method
- Collect the Data
- Analyze the Data and Implement Your Findings