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?

Data-driven decision making (DDDM) is defined as using facts, metrics, and data to guide strategic business decisions that align with your goals, objectives, and initiatives. When organizations realize the full value of their data, that means everyone—whether you’re a business analyst, sales manager, or human resource specialist—is empowered to make better decisions with data, every day. In business, this is seen in many forms. 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

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

Finally, here are 10 practical tips and takeaways for better data driven decision making in business. By the end, you’ll be 110% sold on the importance of making these kinds of decisions.

1. Know your biases

Psychologists 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 Goals

To 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 now

Gathering 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
  1. Determine What Information You Want to Collect
  2. Set a Timeframe for Data Collection
  3. Determine Your Data Collection Method
  4. Collect the Data
  5. Analyze the Data and Implement Your Findings

4. Problem Finding and Framing

Once your strategy and goals are set, you will then need to find the questions in need of an answer, so that you reach these goals. Asking the right data analysis questions helps teams focus on the right data, saving time and money. In the examples earlier in this article, both Walmart and Google had very specific questions, which greatly improved the results. That way, you can focus on the data you really need, and from bluntly collecting everything “just in case” you can move to “collecting this to answer that”.

5. Data Source for solving the problem

Among the data you have gathered, try to focus on your ideal data, that will help you answer the unresolved questions defined at the previous stage. Once it is identified, check if you already have this data collected internally, or if you need to set up a way to collect it or acquire it externally.

6. Understand, Analyze and Draw Insights

That may seem obvious, but we have to mention it: after setting the frame of all the questions to answer and the data collection, you then need to read through it to extract meaningful insights and analytical reports that will lead you to make data driven business decisions. In fact, user feedback is a useful tool for carrying out more in-depth analyses into the customer experience and extracting actionable insights. To do this successfully, it’s important to have context. For example, if you want to improve conversions in the purchasing funnel, understanding why visitors are dropping off is going to be a critical insight. By analyzing the responses in the open comments of your feedback form (within this funnel), you will be able to see why they’re not successful in the checkout and optimize your website accordingly.

7. Don’t be afraid to revisit and re-evaluate

Our brains leap to conclusions and are reluctant to consider alternatives; we are particularly bad at revisiting our first assessments. Verifying data and ensuring you are tracking the right metrics can help you step out of your decision patterns. Relying on team members to have a perspective and to share it can help you see the biases. But do not be afraid to step back and to rethink your decisions. It might feel like a defeat for a moment, but to succeed, it’s a necessary step. Understanding where we might have gone wrong and addressing it right away will produce more positive results than if we are to wait and see what happens. The cost of waiting to see what happens is well documented…

8. Visualization and Storytelling

Digging and gleaning insights is nice, but managing to tell your discoveries and convey your message is better. You have to make sure that your acumen doesn’t remain untapped and dusty, and that it will be used for future decision making. With the help of a great data visualization software, you don’t need to be an IT crack to build and customize a powerful online dashboard that will tell your data story and assist you, your team, and your management to make the right data driven business decisions. For example, you need to have your finances under control at all costs:

9. Decision Making

After you have your problem, your data, your insights, then comes the hard part: decision making. You need to apply the findings you got to the business decisions, but also ensure that your decisions are aligned with the company’s mission and vision, even if the data are contradictory. Set measurable goals to be sure that you are on the right track… and turn data into action!

10. Continue to evolve your data-driven business decisions

This is often overlooked, but it’s incredibly important nonetheless: you should never stop examining, analyzing, and questioning your data driven decisions. In our hyper-connected digital age, we have more access to data than ever before. To extract real value from this wealth of insights, it’s vital to continually refresh and evolve your business goals based on the landscape moving around you. Do you want to learn more about how to use data to inform business decisions and change your career trajectory? Explore how our International Certification in Data Science and Business Analytics, with courses like Business Problem Framing, Data Visualization, Data Storytelling, can help you develop a data mindset.
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