The Rise of the Flexible Data Model

Businesses are built on data. Whether a hospital or a grocery store, the modern organization produces oceans of data on a daily basis.

Thus, how a business organizes its data is of critical importance. A data model documents the way data is stored and retrieved, as well as the web of interrelationships and communications between different data elements. Data models also help manage requirements and formats for different business purposes.

Historically, businesses have used framework data models. These legacy systems were designed to help companies manage data needs at a single point in time, and thus don’t respond well when business needs shift. When changes are required, they typically require massive configuration and consulting fees. Their rigidity and expense have contributed to the rise to the flexible data model, which LogicGate uses to underpin its own technology.

Let’s take a closer look.

Why is the flexible data model important?

At LogicGate, we believe flexible data models are critically important for three reasons.

1. Change happens quickly. In modern organizations, the old adage is true: the only constant is change. Business conditions evolve so rapidly today that companies need data infrastructures that can handle the shifting demands. Getting locked into one way of doing something can really hamstring a company as needs and strategic objectives change over time.

Just as the business changes, so too do the risk and compliance functions that monitor it. A flexible data model can change and scale with the company, allowing managers to keep their companies nimble and compliant.

2. No need for a crystal ball. Adopting new technology is like hiring. Forward-thinking companies bring new employees aboard not just to satisfy immediate needs, but also with the expectation that they’ll be able to take on changing and expanding responsibilities as the business evolves—without necessarily knowing what those responsibilities will be. This is why good hiring managers screen candidates for the potential for growth and flexibility.  

The same thinking should be applied to technology solutions. Flexible data models allow a company start a GRC program without having to guess at the future needs of the business. With the legacy framework data models, platform rigidity forced managers to start building for a future “end goal” without necessarily knowing if the completed program would still be useful. When the forecast was wrong—as was often the case—huge amounts of time and resources were wasted.  Flexible data models allow companies to build their program on a small scale and adapt it over time in response to changing needs.

In practice, this means companies can start with one or two GRC applications, and then layer in new solutions over time. For example, a company might start with an ERM application, and decide to add a controls or policies program down the road. Whereas this would necessitate starting over with the old data models, flexible data models allow the new programs to be integrated seamlessly, and they all talk to each other.

3. Permits different approaches. Flexible data models allow for different methods for managing data, even within the same company. For example, the IT department’s Enterprise Risk Management program may use one data model to perform a risk assessment, while the Finance department employs a completely different model to assess its own risks. Meanwhile, the board wants a model that offers a holistic view.

Despite the different models, the results can be rolled up and uniformly presented to the corporate level. Marketing may have a way of tracking and remediating risk that’s different from the way the IT department tracks and remediates risk. A flexible data model allows for their processes to be very different, but roll the information up in such a way that it’s easily communicated to company leadership in a uniform and digestible way.

Industry Implications

While it’s apparent that flexible data model technology can help individual companies, it boasts an even greater potential for the GRC industry at large. At many companies, legacy data models in GRC software have been so long entrenched in the ways of doing business that inflexibility has become the norm, and with it the exorbitant fees and added effort to keep the technology current. Flexible data models have the power to end this expectation, and unlock value for companies by making them more agile and responsive to changing demands.