Is Data Driven Decision Making Right for Your Brand?

If your brand has ever used the results of a survey to test the market before launching a new product or service, you have used data to make that decision. Intuition, gut-feelings, predictions, and guessing once merely guided the decision-making process. Today, data holds the power to help companies determine the direction that can lead to success. 

Data-driven decision making is about more than collecting survey information or analyzing test results from a new product launch. It’s also about placing a high importance on the collection of data, using it effectively and insightfully, and then taking that information and using it to make better decisions. 

Yet several roadblocks keep businesses from embracing the practice. Using too much data, not analyzing it properly, or allowing it to control the process, may leave you in the dark. Consider the following strategies before making a decision with the mountains of data available at your fingertips. 

Don’t Forget the Data

Studies show that businesses who have embraced a data driven culture are more financially successful, stay ahead of the competition, and remain on the cutting edge for disruptive innovation. But many companies still forego the use of data when making business decisions. In fact, nearly one-third of companies around the world are not using data-driven decision-making, and many are still misusing the process. 

A large number of companies have been slow to embrace this shift for various reasons. Intuitive decision-making has dominated the landscape for decades. Either businesses do not yet understand the value of data, or often lack the organizational skills to handle massive amounts of it. 

Don’t Use the Wrong Data

Using too much data or the wrong data is a common problem. More data does not necessarily equal better results. The quality of collected data is often more important than the quantity. 

Key performance indicators, or KPI’s, should be at the forefront of your data collection goals. By paying attention to the number of people who visit your site and actually make a purchase, rather than those who simply like and view products, your business will see long-term results focused on outcomes, which track the entire customer experience. 

That is not to say that likes don’t matter. Consider Amazon, who uses product recommendations to drive 35% of their sales. 

Don’t Fail to See the Big Picture

When data is siloed, innovation may come to a halt. Keeping data stored separately according to different departments can restrict access and reduce communication among teams. Make sure all teams have access to data by using cloud-based tools.

The way data is analyzed and interpreted also plays a key role in your ability to see the big picture. Look for patterns and take advantage of data visualization techniques. Working in teams can help avoid bias and encourage collaboration. 

Actionable insights can also be realized through analyzing and processing data, and by establishing regular communication through teams, 

Don’t Forget the Human Touch 

Your data should tell a story. By using collected and analyzed data and translating it into an easy-to-understand model, your internal and external audience will be able to relate to your company or product, and find meaning, and value in your message. Think compelling videos, a photographic journey, or a simple chart or graph to convey a story using data. Spotify wrapped is perhaps the most notable example of effective data storytelling because it synthesizes its consumers’ data, creates fun and aesthetically-pleasing slides and appeals to the music they like. 

Implementing data-decision making into your business model is a powerful tool which can be used logically, and creatively, to boost engagement with your brand, drive sales, and innovation.  

Tech research and consulting firm Gartner predicts that data-based stories will be used more than any other technique by 2025, including those with audio visuals. What that means is that sharing data stories should be a top data decision-making approach.