It’s estimated that we create an astonishing 2.5 quintillion bytes of data each day. With so much new data coming in—from sources as varied as social media to IoT—getting an accurate snapshot of what all of this information means for your business all comes down to how well—and how long—you use data models.

Every model you run tells a story. But that story may be constantly changing. To derive the insights you need to make business decisions, your models need to evolve at the same pace as your data.

But how do you know when it’s time to build a new model or rebuild an existing one? Below, we explain what it takes to maintain your data models, so you can be sure your business keeps up with the changing times.

Begin with a Common Data Model

You can bet your competitors are using every tool at their disposal to predict the future behavior of customers and prospects. After all, when you know what your target market is buying or opening—or not buying or opening—you get valuable information you can use to make your marketing messages resonate.

When analyzing current and past information, most industries tend to use the same data models, with businesses customizing their models to handle company-specific information like product offerings, geographies, and/or business focus.

One of the best ways to consolidate your data into a single source of truth across the enterprise is through a Common Data Model (CDM). Made up of many different databases, the CDM offers users a single, unified abstract that provides a consistent representation of the data, including analytics, transitional systems, or ad-hoc queries.

With a CDM, you can easily analyze relevant data—no matter its source—and make meaningful business decisions. And because today’s data is more dynamic than ever, the data models within your CDM must evolve once the scope of their original intended use has changed.

How to Build Your Data Model

Simply put, a data model is a representation of the data you’re analyzing. When you build a model, you need to understand what answers you’re hoping to find within the data. Do you have a theory you wish to test? An objective you want to meet? Once you know your goal, you can map out the scope for the data model and begin compiling the data from sources across your company. (Read Overcoming Data Preparation Challenges to find out more.)

Three steps to follow before you start:

  • Establish buy-in from the key players involved in your inquiry, especially if you are obtaining data from them
  • Create detailed documentation on how you are collecting the data so you can repeat the model again, if necessary
  • Assign a centralized location to store the large files of data being pulled
How to Manage Your Data Model

Today’s digital landscape gives you access to both explicit and implicit consumer data. However, aggregating implicit data, which is not provided but gathered from available data streams, can be a challenge. Consolidating this data across a company often requires key figures from IT, Analytics, Finance, and Marketing to work together to build a strong data model. And the work doesn’t stop there: Continued monitoring is necessary to ensure you gather accurate insights.

Three data model management tasks to take on:

  • Validation of Models: Monitor the model’s performance and watch for serious degradation to determine when you need to rebuild the model.
  • Monitoring Model Evolution: Because the model is an algorithm that needs to be applied to raw data, it’s important to monitor and document the evolution of the model and how it shifts over time.
  • Scoring the Data: You must apply a predictive model to two different datasets to obtain predicted outcomes.

You can read more about Model Management from one of our blog posts from earlier this year.

How to Rebuild Your Data Model

As the market landscape changes, your data model needs to keep up. Thankfully, because models sit on top of databases, rebuilding a data model is much easier than updating the database itself. This kind of update also requires far less internal approval.

One important step to remember:

  • Be sure to delete the existing version of the model tables you are changing. If you attempt to update the model from the database without deleting the old tables first, the data may not compile correctly.
The Beauty of Data Models

With so much data being created every day, information—and the ability to act on it—continues to be critical to your company’s success. If you don’t have the internal resources to build, manage, or rebuild data models, explore technology like Lityx’s comprehensive data solutions that can help you better understand your customers, enrich your existing data, and reach your most qualified prospects. Supplementing your IT group’s or business analyst’s capacity with tech like this will set your company up to take on your competitors with agility.


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