Ask any data scientist how they feel about the plethora of data available to them and you will hear a common theme: overall, it’s great that they now have access to all of the data they could ever need, but this data is completely useless unless they can gain insight from it and transform it into something actionable. This is something even the best data scientists struggle with.
The wealth of information available to data scientists is only going to increase as technology becomes more advanced. Having software that can identify and predict trends is crucial for how brands interact with their customers. Additionally, smart analytics can provide the next best action to take with a customer based on previous interactions and behavior. This elevates data from just a set of numbers and charts to something truly powerful. In order to do this, design needs to be a critical factor. When it comes to analytics and design, the two need to be working hand-in-hand.
Having access to the right data and turning it into insightful action is certainly important, but there’s more to it than meets the eye. To start, the data needs to be presented to data scientists in a visually pleasing way so they can correctly interpret it. Then, the analyses need to be presented to customers in well-designed way to improve their experiences.
Amazon is a great example of a company making analytics visually pleasing to the customer. Consider this: if you buy a high-end blender on Amazon, other related items instantly appear on your screen, such as a set of cocktail glasses, a special cleaning tool, or a smoothie recipe book. This is an example of data being visually placed in front of you in an easy-to-understand, aesthetically pleasing way that you find helpful. The analysis of the purchase is the first step, but actually enabling a person to add that to their cart in real-time with a one-click process turns this insight into a pleasant, helpful experience.
Another use of data analysis is being able to identify past trends to predict future purchases. Let’s say you bought a refrigerator three months ago. When you come back to the manufacturer’s site, it offers you a pack of water filters for the refrigerator at a discounted price. Why? Because the application knows your purchases, and realizes the time has come to replace your water filter. By using that data and making the experience easy, you are much more likely to purchase it and feel good about the manufacturer. This is how data, insight, design, and experiences all work together.
Analytics needs design to transform data into a great experience for the customer, and also an easy-to-interpret experience for data scientists. If you don’t have this relationship between analytics and design, then you just have data that can’t be used to achieve business goals, or a useless, great design. Invest in design, because it lends itself to the successful progression of your analytic and big data initiatives.