How Data Fabrics Build Trust for Data and Analytics Success

julesmentenFebruary 26, 2022

By Chad Smykay

Businesses have access to more data than ever before—but that doesn’t mean everyone in an organization trusts the reliability of that data and the analytics that come from it. A company can be full of data engineers and analysts with tremendous individual virtuosity when it comes to analyzing and using data, but that doesn’t necessarily inspire company-wide trust in data.

Businesses need a way to manage their data that systematically and consistently ensures analytics come from reliable records that everyone can trust. The only way to do this is to build trust as part of an organization’s data architecture. Such an architecture also means the data will provide better, more actionable answers.

A recent Gartner® report, Predicted 2022: Data and analytics strategies build trust and accelerate decision-making, discusses the key elements important to building trust and accelerating decision-making. According to Gartner, Inc., “As Figure 1 shows, D&A leaders and their current D&A strategies must support measurable business impact and scale by applying trusted D&A to the organization’s decision-making capabilities.” ¹

Figure 1. Build trust and accelerate decision-making

Through years of working with companies from a variety of industries, it has become clear that using a robust data structure ensures everything Data is integrated in a single place, is critical to building trust in these areas. This data fabric does not necessarily have to span the entire enterprise—individual departments or departments can implement their own data fabrics. While these individual data fabrics can foster data silos, they also ensure data trust within segments of the enterprise and enable collaboration on larger, broader issues. This trust and collaboration is critical as these issues cannot be addressed unless organizations have the right processes and architecture in place first.

Read on to learn how Data fabrics can help organizations Make significant strides in these key areas to deliver business value and stay ahead of the competition.

Decision Automation

In today’s business, data can and should be used to automate processes, including decision making. For example, a bank can use automated decision-making to sift through customers’ credit and financial histories and decide who is worthy of obtaining credit. However, for automation to be successful and for users to trust the results, organizations need to understand how the automated systems make decisions based on the available data.

The right automation approach is a multi-layered one. First, properly set up the tools that use the data, and then set up strong data pipelines to process the data. From there, analytics can be automated to analyze the data, providing faster insights and answers for human users. When this happens, automation leads to standardized and predictable responses based on standardized data. This inspires trust because users know that the automated processes will provide reliable results based on solid data sources.

Networked administration

Ultimately, governance is a problem of people, process and technology. The goal of governance is to ensure that a company knows at all times what each user is doing with the available company resources (whether data or human).

In a segregated governance structure, data usage is a black box. In this model, at no point does a company have any idea 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 when multiple analysts work on the same problem, creating duplicate records.

Connected governance is comparatively the opposite: a white or transparent box. With this model, organizations have full visibility into who is using specific records and why they are using those records. With connected governance, the entire history of resource usage is available to everyone involved in a given project. Over time, connected governance may incorporate zero-trust principles, allowing users or applications to access only the data they are authorized to use.

Connected governance increases trust because users know the data they have access to and are confident they can use it the way they need it. They also gain confidence in the datasets themselves, as they can see who worked on each data source and how it’s been used up to that point.

data transmission

With the high turnover of data engineers in many organizations, companies must avoid data architectures that restrict data sharing, as projects should not die just because a single analyst or team leaves the company. Correct data transmission means insights are shared between individuals and across the organization in a consistent and continuous manner. This helps companies drive discoveries and allows users to continue working on projects started in the past.

There are multiple ways to achieve this type of data sharing, ranging from automated to manual processes. Data fabrics help organizations support data sharing across multiple disparate data types, whether they reside in a database, spreadsheet, file, or real-time event stream. The data fabric architecture, as mentioned earlier, does this by ensuring that all data is integrated in a single place. The result: It becomes easier to share results, gain insights, and then make decisions based on what you’ve learned. This can be done by developing models that users trust and can be shared and reused over time.

data basis

A strong data foundation provides a data infrastructure that ensures a standardized process for ingesting, ingesting, creating, securing, managing, cleansing and consuming data throughout its lifecycle. This foundation combines the data management and data lifecycle processes with the actual hardware and software that support them. A data fabric is compatible with almost any tool used to build this data foundation.

The more solid a company’s database is, the better and more reliable the organization’s data is. Data is becoming more transparent, from where it comes from to where it is used, meaning it’s easy to spot when something goes wrong. This inspires trust as users have an efficient and reliable data system that allows them to increase the speed at which they can use data to make decisions.

Build trust to deliver business value

Establishing enterprise-wide trust in data with a data structure cannot be achieved overnight—especially for large organizations. Organizations should take it one step at a time over time, focusing on business value and building on each incremental success to establish the foundational architecture and processes that enable true trust. As with any relationship, trust grows over time.

To learn more about how data fabrics solve common data science and analytics challenges for large data-driven companies, visit How to simplify your approach to data analysis

Gartner, Predicted 2022: Data and analytics strategies build trust and accelerate decision makingJoerg Heizenberg etc. December 2, 2021
GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the US and internationally and is used herein with permission. All rights reserved.
¹Prediction 2022: Data and analytics strategies build trust and accelerate decision-making.
Posted on December 2, 2021, by Joerg Heizenberg, Lydia Clougherty Jones, Ted Friedman, Andrew White, Saul Judah, Gareth Herschel, Rita Salam, Ehtisham Zaidi, Svetlana Sicular