5 Tips on Making Your Business More Data-Driven

September 3, 2018 Off By Hoofer
Businesses and business owners have more instruments and resources to use when it comes to making critical decisions in today’s modern world. Aside from the wealth of software and tools you can now add to your business workflows, there is also more data to process and insights to gather than ever before.

This can be a great advantage or a challenge, depending on how you approach making data-driven decisions. The key to turning a wealth of data into a competitive advantage is good data management. In this article, we are going to go over the five tips on how you can make your business more data-driven.

Understand the Boundaries

Privacy and security are the two most important keywords to keep in mind when you are gathering and storing data, especially when the information you gather is about customers and other parties. There is a growing concern about how businesses are storing information and using data for various purposes; you want your business to be a part of the solution, not the problem.

Privacy is fairly easy to tackle. You know the boundaries that you must not cross, so staying within the boundaries of protecting others’ privacy isn’t difficult. On top of that, you can always ask for consent before gathering information about other parties, particularly about your customers.

Security is a bit trickier to deal with. You need to be extra certain that the data you store is secure in every way. Unless you really know how to maintain a secure database and a reliable set of servers, it is best to lean on database services and dependable service providers.

Collect, But Don’t Hoard

There is a big misconception about data gathering and processing. Many businesses believe they should gather as much data as possible and process the information later. This isn’t the best approach to take since you can easily fall into the trap of hoarding data that you don’t necessarily need.

In this instance, the two keywords to keep in mind are context and relevance. When collecting data, you need to also collect contextual information. More importantly, you need to be able to process data in a contextual way.

For example, a customer’s purchase history is data. Further recognizing the times and instances of those purchases adds context to what would be plain data. The next time that customer contacts you to make a purchase, you can provide a more personalized experience based on his preferences; this is how you process data in a contextual way.

Relevance, on the other hand, helps you separate data from noise. Unless you are certain that the data will be useful to your business, there is no point in storing it for an extended period of time. What you are actually storing is noise, and the more noise you add to your database, the more difficult it will be to get meaningful data for decision-making.

Keep It Simple

Another common misconception about data gathering and processing is about the way the entire process is conducted. You hear big words like big data and Artificial Intelligence or AI being used in various instances. Sure, big data and AI are handy tools to have, but only when they add value to your data processing workflow.

Using AI that knows a lot about your business, you can easily filter the noise that gets accidentally collected. You’ll end up with meaningful data that allows you to make cool and calculated decisions even in the most difficult situations.

On the other hand, AI can overcomplicate your data gathering and analysis process unnecessarily. For smaller data that is best interpreted by humans, the use of AI is actually a noise of its own.

As mentioned before, you want to keep your data management process as simple as it can be. Simplicity leads to efficiency, which then leads to better data-driven operations and the availability of valuable insights at every turn.

Get Help

There is nothing wrong with getting more people involved in the process of collecting data and analyzing the information. Many bigger corporations now maintain dedicated data teams to help support their massive operations. You see positions like Head of Big Data and VP of Information being filled by people from backgrounds like math and statistics.

For small businesses, establishing and maintaining a dedicated data team isn’t always possible. After all, you are dealing with a smaller amount of data; a dedicated team isn’t the most efficient way to go. Instead of investing in an in-house data team, it is more effective to outsource some of the data gathering and analysis to service providers.

Don’t be afraid to get help. You can have expert data analysts without establishing a dedicated team inside the business. In fact, you can have hundreds of specialists looking at different angles, all while keeping your overheads at a minimum.

Review and Refine

Just like how the market is changing, data changes in different ways on a regular basis. You cannot expect a data gathering and analysis system to work indefinitely in a competitive market like today. Even the system you establish may not be accurate, which means tweaks and refinements are needed.

In reality, evaluation is a big part of the process. You want to know whether your data and insights are valuable, and the only way you can do that is by evaluating how the insights are used and how they impact the business. The more often you perform this type of evaluation, the more you will know about the quality of your data workflow.

Evaluation alone isn’t enough. The results of your frequent evaluations are data on their own. You need to use these insights to make data-driven decisions on how to refine the workflow further. The more you improve your data processing prowess, the more you can rely on accurate insights when making strategic decisions.

These tips will help you cover the basics as you venture into the world of data-driven decision-making. It doesn’t take long to appreciate the value of having data and relevant insights every time you need to make a decision that impacts the business in a big way.