In the world of technology, data is key. In order for your business to continue moving forward, you need to understand your audience to better serve users and their needs. But without knowing who your users are, trying to target them is like blindly going about your next steps in the dark and hoping that they resonate. Such a process would be a waste of time and finances with no guaranteed results. This is where data comes into play. By collecting user data—such as demographics, preferences, locations, and patterns—your company can have a firm understanding to base its future plans of action and revenue streams on. But in order to assess its data, first operationalizing it is imperative.
For a quick refresher, or if you are new to the concept of operationalizing your data, let’s go over what it means to operationalize your data and three great ways to do so. In no time, you’ll be able to better understand your audience.
What Does It Mean to Operationalize Your Data?
Businesses collect data on their users and site visitors in order to better understand their reach and what is and isn’t working to draw their audiences in. This data is highly beneficial to increase revenue and improve the customer experience (again, leading to new streams of revenue) but the data is only useful when it’s accessed and then properly utilized. As companies collect data on their users through their websites, this data doesn’t come across as straightforward facts and figures—it actually looks like abstract nonsense without putting it in context.
And we’re talking about a vast amount of raw data to observe; by 2025, the amount of worldwide data collected will be an estimated 163 zettabytes. For context, one zettabyte is equivalent to a trillion gigabytes. It is businesses and other enterprises who are in possession of 60 percent of the 163 zettabytes, and yet a miniscule 3 percent of business professionals feel they can properly access and use all of their data.
The problem comes down to important data not being properly operationalized. To operationalize your data is to transform the abstract data into analytics that can be understood, measured, and utilized. But there is not just one way to go about the data operationalizing process. Here are three ways you can find success turning your data into measurable statistics.
Use Reverse ETL To Unlock Operational Analytics
One excellent way to essentially unlock your stored data as operational analytics that you can use and build off of is through using reverse extract-transform-load, or ETL. Because of the accessibility of the modern data stack, which now allows businesses to acquire and understand their data compared to the traditional data stack, businesses can take how they use their data into their own hands. The MDS lets you move your data from the source to the data warehouse through ETL—but think about it, what good is your data when it simply sits in its destination? This is where Reverse ETL becomes a prime way to operationalize your data. Reverse ETL is the process of taking the data warehouse and actually sending it into an organization’s systems. This way it can be accessed and used so your business is not one of the 97 percent who aren’t able to work from their analytics.
Set Parameters for How Data Is Gathered and When
With the ability to access your data warehouse comes the realization of just how many analytics there are to be examined. Rather than overloading your organization with information, the best way to ensure each department is properly accessing the right data is to set parameters for how the data will be filtered from the warehouse to your teams. You’ll want to specify which analytics get sent to which department and how often they get sent. Also, consider your best method for saving the data, and don’t forget to explore your security options for sensitive information as it’s being filtered out to you.
Increase the Quality of the Data Received by Cleaning and Filtering It
As you’re accessing your data, you have to remember that data errors occur so that not all analytics are high quality and/or consistent. In order to ensure you’re receiving and then working with accurate and high caliber data, incorporate data cleaning into your system. You may want to add this step into your data transformation process to ensure that the data you filter from the warehouse to your teams is not duplicated, erroneous, or wrongly configured.
Why You Can’t Skip Operationalizing Your Data
Operationalizing your data to turn it into processes and statistics you can measure and build off of is highly important; it lets you successfully study your users and then implement your plan of action. Without gathering useful data and understanding each specific element, you won’t ever be able to take informed business steps. When you operationalize your data, you can intentionally move towards implementing new streams of revenue because of your better understanding of your users.