10 Tips for Data-Driven Decision Making Excellence
- Data Science
- 16 min read
Decision making shouldn’t be done blindly, especially when it comes to making big ones that will soon yield big results, which let’s say, the success of your company. Big decisions should be based on something big too, and no, not big random guesses which you don’t even have the slightest idea on which track they’ll go. Big Data – that will be your companion throughout carefully crafting a big decision.
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
- 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
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.
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