How Data Fabrics Build Trust for Data and Analytics Success

Companies have access to more data than ever before – but it doesn’t mean everyone in an organization trusts the reliability of that data and resulting analytics. A company may be filled with data engineers and analysts with tremendous individual virtuosity when it comes to dissecting and utilizing data, but that doesn’t necessarily create organization-wide trust of data.

Companies need a way to manage their data that systematically and consistently ensures the analytics come from reliable datasets that everyone can have faith in. The only way to do this is to build in trust as part of a company’s data architecture. Such an architecture also means the data produces better, more actionable answers.

A recent Gartner® report, Predicts 2022: Data and Analytics Strategies Build Trust and Accelerate Decision Making, discusses the key elements important for building trust and accelerating decision making. According to Gartner, Inc., “As Figure 1 shows, D&A leaders and their current D&A strategies need to support and scale measurable business impact by applying trusted D&A to the organizations decision-making capabilities.”[i]

Through years of working with enterprises across a variety of industries, it’s become clear that using a robust data fabric, which ensures all data is integrated into a single place, is critical to building trust across these areas. That data fabric does not necessarily have to extend across the entire business — individual departments or divisions can implement their own data fabrics. While these individual data fabrics may foster data silos, they also ensure data trust within segments of the business, allowing for collaboration on larger, more substantial problems. This trust and collaboration are vital because these problems cannot be undertaken if companies do not have the right processes and architectures in place first.

Read on to learn how data fabrics can help organizations make significant strides in these key areas so they can deliver business value and get ahead of the competition.

In today’s enterprises, data can and should be used to automate processes, including decision-making. For instance, a bank can use automated decision-making to churn through customers’ credit and financial history and decide who is worthy of receiving a loan. But for automation to be successful and for users to trust the results, companies must understand how the automated systems arrive at decisions based on the available data.

The right approach to automation is a layered one. First, set up the tools correctly that will use the data, then establish strong data pipelines to process the data. From there, analytics can be automated to analyze the data, which produces insights and answers for human users more quickly. If this occurs, then the automation leads to standardized and predictable answers based on standardized data. This builds trust because users know the automated processes are producing reliable results based on solid data sources.

Ultimately, governance is a people, process, and technology problem. The goal of governance is to ensure a company knows what every user is doing with the available corporate resources (whether that is data or staff) at any given time.

In a disconnected governance structure, data usage is a black box. In this model, a company has no idea at any given time who is using specific resources, or what they are using those resources for. This leaves companies vulnerable in terms of security, privacy, and compliance. It can also create significant inefficiencies, where multiple analysts are working on the same problem and creating duplicate datasets in the process.

Connected governance, comparatively, is the opposite: a white or clear box.