Quality Data the Key Factor in Business Digital Transformation

"Data is the oil of the 21st century." This phrase is often repeated in various settings — conferences, courses, podcasts, studies, and media outlets — to underscore one point: a new era has arrived in the business world.

Whether cliché or not, the statement holds some truth. Through a simple allegory (referring to the financial and industrial power still attributed to the so-called black gold), we can grasp the economic importance that digital information already has. It is the essential element needed to harness the technological innovations (artificial intelligence, big data, cloud computing, data centers, machine learning, the Internet of Things, among others) that are driving the transformation of the business environment.

However, the phrase overlooks a crucial issue. In this new context, an organization that possesses and generates vast amounts of digital data (abundant information about operations, consumers, and processes) does not automatically guarantee business success. A "data reservoir" — if we wish to extend the analogy to the oil sector — is not an asset that inherently brings benefits.

The real competitive advantage lies elsewhere: in the quality of the data the company works with. As in other areas of life, before focusing on quantity, it’s essential to prioritize quality.

The Major Barrier: Poor Information

Having an enormous amount of data might lead a company to assume it meets the basic requirement for leveraging technological innovations, which use data to — among other goals — improve decision-making, identify new business opportunities, or increase operational efficiency.

However, if the company wants to meet such expectations, it must have quality information: data that is accurate, complete, up-to-date, consistent, and relevant. If this is not addressed, and the company uses poor-quality data in innovative solutions (incorrect, outdated, irrelevant, duplicated, inconsistent data), the results will always be disappointing. Without optimal data, even the most powerful AI or machine learning technologies cannot make a significant difference in a business's trajectory. In fact, for companies, low-quality data is a significant obstacle to their digital transformation initiatives.

One source indicates that poor-quality corporate information is one of the main reasons why 30% of generative AI projects fail to advance beyond the testing phase. Additionally, 56% of companies, according to another study, believe that bad data reduces the accuracy of their AI models. In light of these findings, it’s not surprising that 66% of companies do not fully trust the quality of the data they use in their decision-making processes, which today are supported by analytics or big data systems.

A Quality-Centric Approach

In today's technology market, organizations can find tools specialized in correcting — "cleaning" — deficient business data. However, in the face of the challenge posed by data quality, the long-term solution must go beyond this option.

Organizations need to analyze their information ecosystem by examining instances, processes, roles, and contexts (such as regulations) that influence the management and use of data. The goal of this exploration is to identify situations where the information may be compromising its quality.

This analysis should address questions such as:

• Location: Where is the information within the company structure? How is it stored? Are there corporate instances that exchange data? What information is shared and through what means?

• Users: Which employees have access to the data? How do they use the information and for what business purposes? What kinds of tools do they use to retrieve and process it?

• Controls: Are there mechanisms or processes in place to monitor data quality? How can we confirm that the information is useful for business objectives? How do we ensure that corporate decisions are based on accurate, complete, up-to-date, consistent, and relevant data?

• Relevance: Are the innovations that support decision-making receiving the right data, data that is relevant to the assigned business purpose? Is the information they receive sufficient?

• Regulation and Governance: What regulatory and governance frameworks affect the information the company uses? How is compliance with these criteria ensured? Is there a corporate entity responsible for this critical task?

By answering these questions, an organization can identify the corporate spaces where better practices and strategies are needed to safeguard data quality. A vision focused on ensuring the utility of information from the source will always be a more efficient option than investing resources and time in correcting data.

Taking steps to ensure the quality of information also brings an important reminder: data is the cornerstone of a business's digital transformation. A company that underestimates the data challenges and focuses all its attention on other aspects — specialized applications, data centers, cloud deployments — may end up in an unfortunate situation: having a universe of information but being unable to do anything meaningful with it.