Focus on quality and integration for best results
Executive summary
Data — not technology — is the true differentiator in digital transformation. Organizations are embracing generative AI and other emerging technologies, but many stumble on a core flaw: insufficient data. Siloed, inconsistent and poorly governed data erodes trust and limits performance. Firms that focus on integration, quality and discoverability aren’t just future-ready — they’re unlocking real-time value. Get your data foundation right and turn tech ambition into measurable outcomes.
Introduction
Tech investments are booming. From predictive analytics to AI-powered decision-making, companies are racing to capitalize on new technological capabilities. Ninety-three percent of executives in Grant Thornton’s Digital Transformation Survey said they plan to increase their spending on technology this year. But despite the spending, many leaders are underwhelmed by the results. The disappointment is often rooted in the data, not the tech tools.
When organizations modernize without addressing their underlying information layer, the whole enterprise suffers. Our survey shows that this suffering is widespread — just 16% of business leaders rated the quality of their data as “excellent” for supporting technology initiatives.
Data is often insufficient because it is siloed, inconsistent or shaped by outdated business logic. Key definitions — such as “customer” or “document date” — might vary across teams. As a result, critical relationships are missed, and duplicate efforts go unnoticed. Even advanced systems return conflicting answers, undermining trust in the investment.
Without a unified view of data — anchored by governance, integration and context — tech can’t deliver on its promise. Data readiness is not back-office work. It’s central to competitiveness.
Even when company leaders understand this, the scope of a data initiative can be overwhelming. But there’s a way to narrow the focus and make the task more manageable. Our survey shows that just 27% of executives say their current technology aligns very effectively with their business strategy. Before launching a data initiative, leaders should ask themselves: What are we trying to achieve?
“We get calls all the time from companies wanting to ‘do a data governance project,’” said Grant Thornton Technology Modernization Partner Tony Dinola. “But when we ask why, the answer is often vague — ‘because our data isn’t clean.’ That’s not enough. You’re not going to overhaul your data just because it’s necessary for AI use cases that are undefined.”
To get the most out of your data, sharpen your focus. Start with the business outcome — better customer segmentation, faster time to market, stronger compliance — and work backward to define the data that matters.
A disciplined focus on the ultimate objectives of technology enablement helps leaders choose the right tools — and develop the right data to support those tools, turning tech investment into business value. Here’s how it’s done.
Prioritize data quality from the start
The simplest measure for the quality of data is whether it helps the organization answer simple business questions. How is an inventory adjustment or pricing change going to affect your revenue? How will changing suppliers affect speed to market?
“If you can’t answer those business questions with your data, then the data might be sufficient to move your operations along, but it’s not helping you develop your strategy,” said Grant Thornton Technology Modernization Managing Director Supreet Singh. “You want data that helps inform business decisions.”
Companies also need to be able to quickly identify and produce data that is needed in another part of the organization. If locating and producing data for an audit request or quality of earnings analysis requires an all-hands-on-deck search mission, your data’s quality and organization are probably lacking.
The answer to data quality issues of this nature requires a disciplined look at the basics of your business objectives and the data that helps you achieve them. At a very basic level, problems with data can be avoided or reduced by defining critical data elements (CDEs) for each of the core domains in the company’s environment. Once CDEs are established and defined, leadership can create crucial data quality rules for each CDE and systematically measure data quality based on those rules and definitions.
This must be an ongoing process, as data quality isn’t a cleanup project — it’s a continuous discipline. Many companies take an ad hoc or reactive approach rather than embedding quality upfront — and even those that do have high-quality data often struggle to integrate it properly.
Break down silos with strategic integration
Siloed data often is a byproduct of growth. Teams adopt tools and develop data that meet their needs, often without integration or the needs of the larger organization in mind.
“Data often passes through the enterprise with inconsistencies that are difficult to reconcile,” Singh said. “Point solutions house data in different data models, using different programming languages — even for something as basic as a document date.”
Is your document date the effective date or the date of signature or something else? This is where variation can create chaos. Without standardized language and widespread availability of data, enterprise-wide strategies become difficult to develop.
Integration should proceed with the future in mind instead of just addressing current needs. For example, application programming interfaces (APIs) can be used to help different systems communicate with each other. But if they’re set up without standard signatures, APIs can couple systems so tightly that a future upgrade or system change can require extensive additional integration work.
“Is it future-proof? Does my infrastructure strategy include data architecture that aligns with my organizational growth needs? Can I scale appropriately for the peaks that occur throughout my business? You need to answer all these questions,” Singh said.
Meanwhile, as silos are broken down and data availability widens, it’s critical to maintain security and privacy for sensitive data. Companies can maintain information security by establishing controls at the enterprise level that manage access. These controls need to carefully consider which data truly needs to be sequestered.
For example, access to payroll or sensitive HR data is generally highly restricted. However, certain types of data can be anonymized and shared broadly throughout the organization without compromising security.
“To calculate revenue per FTE for reporting and strategic purposes, you need to know the number of employees, which is HR data,” Dinola said. “Providing access to data that’s not sensitive permits more meaningful reporting and measurement.”
Reframe data governance as a business enabler
Data governance is often seen as a blocker. But with the right structure, it accelerates progress.
When governance is put in place around a process that becomes standardized, agility can be increased — especially when governance and quality can be established on a case-by-case basis.
“If you wait for enterprise data governance to be established, time will pass you by,” Dinola said.
For best results, define use cases, focus on data quality around that use case, and execute.
Make data discoverable with a modern catalog
Even when the right data exists, it may be hard to find — or trust. A modern, auto-updating catalog bridges the gap by connecting business and technical metadata, clarifying definitions and showing ownership and lineage.
“Especially in large organizations, communication breaks down and there’s just data everywhere,” Singh said. “Having a comprehensive data catalog reduces missteps that can occur when people don’t know everything that’s going on across the entire organization.”
Even when a detailed data catalog is built, though, it’s important to make people aware of it — and to tailor the catalog to the needs of the users.
“You want to make sure the end users have a viewpoint on data architectures,” Singh said. “They need to weigh in on the data structures and how they will consume it, such as trend analysis”
Cultivate a data-driven culture
Technology reveals culture gaps. It takes people to close them. A data-driven mindset starts with transparency, clarity and systems that help people make sense of the output.
“You can’t replace business experience,” Singh said. “But data augments it. It provides a second lens to validate or refine decisions.”
To be data driven, you have to understand and trust the data. This requires establishing transparency in the actual data itself, along with clear expectations regarding performance. Meanwhile, systems need to be put in place to allow the enterprise to understand the output of technology investments and initiatives.
“The direction and needs of the business should be effectively communicated,” Dinola said. “End users need to be educated and trained, and adoption needs to be measured after deployment. Any gaps that are discovered need to be defined and addressed as continuous improvement.”
Data that delivers on strategy
Digital transformation doesn’t start with tech. It starts with a business strategy and with developing the data that enables technology to deliver on that strategy.
Trust in that data is built through a commitment to quality, governance and integration that meets current needs and enables future scalability. Optimum results occur when people are empowered with a full understanding of the organization’s data and are held responsible for adoption and results.
By getting the data right — defining what matters, standardizing how it’s captured and embedding it into workflows — leaders can get the returns they’re seeking from their technology investments.
Contacts:
Managing Director, Technology Modernization Services
Grant Thornton Advisors LLC
Supreet Singh is a seasoned Managing Director with extensive expertise in technology strategy and management.
Houston, Texas
Industries
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This Grant Thornton Advisors LLC content provides information and comments on current issues and developments. It is not a comprehensive analysis of the subject matter covered. It is not, and should not be construed as, accounting, legal, tax, or professional advice provided by Grant Thornton Advisors LLC. All relevant facts and circumstances, including the pertinent authoritative literature, need to be considered to arrive at conclusions that comply with matters addressed in this content.
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