Nearly 85% of respondents realize that “data” is a cornerstone of business success. However, only a third are satisfied with their enterprise data quality and realize that more than 40% of their organizational data is unusable. If data is so precious, why are we failing to make meaningful progress?
HFS Research, in partnership with Syniti, reached out to more than 300 leaders across the global 2000 enterprises to learn what enterprises can do to improve data management and realize their strategic ambitions.
The good news is that enterprise leaders are serious about data. The downside is enterprises are struggling to make progress.
- Data is precious. 85% of enterprise leaders agree that effective data management has a significant impact on their organizations, driving the top line, bottom line, and shareholder value.
- However, enterprises are drowning in data debt. More than 40% of organizational data is bad and unusable, which creates an opportunity cost of 25%–35% across organizational goals.
- There is poor alignment between data quality and business outcomes. Less than a third of leaders are completely satisfied with the ability of their enterprise data to meet business objectives.
- Enterprises are failing to scale their data strategies. Only one in four respondents have fully implemented an active company-wide data management strategy.
- The boom in AI tools is driving the need to fix bad data. Improving operational data availability to better integrate AI tools into business processes is emerging as the top challenge to implementing AI technologies.
This research unpacks five strategic tenets to effectively unleash the transformative power of data and realize significant business benefits:
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Embrace a data first culture. Realize that data isn’t just IT’s problem; it’s a core business issue. The strategic goal for data management is to drive successful transformations, foster innovation, and create a competitive advantage, all while supporting the seamless “OneOffice” experience1. Still, business leaders are taking a back seat in setting key data objectives. Consequently, data remains siloed across departments, and IT versus business expectations remain misaligned.
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Data and AI have a chicken-and-egg relationship; you need to address both together. Building a solid data foundation emerges as the number one initiative to leverage AI capabilities more effectively. This includes rigorous data cleansing, continuous maintenance, and robust governance. Insufficient emphasis on data governance hampers success, and this trend needs more attention and immediate action to ensure data management practices effectively support advanced AI initiatives.
At the same time, increased use of AI and ML for data management is among the top three improvement areas respondents would like to see in their data management practices.
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Measure the impact of bad data; it’s critical to reducing your data debt. Less than 40% of organizations have methods and metrics in place to quantify the impact of bad data on their operations. Identifying the root causes of bad data and effectively measuring its impact are essential in turning data from a liability into a strategic asset.
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Give data management the respect and talent it deserves. To unlock the true potential of your data, your organization must fundamentally redefine the approach to data management and services. Often, data management is treated as a line item to fill with a resource rather than a critical function that demands a unique blend of technical, business, and industry expertise to be successful. Organizations need professionals who can interpret data within a business context to drive strategic decisions and generate better outcomes.
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It’s business data services, not professional services; you need to focus on outcomes, not effort. Data is a precious commodity, with nearly 90% of enterprises relying on third-party providers for data projects. Yet, these relationships are typically effort-based rather than results-driven. Traditional service providers often fall short, offering standardized solutions that fail to address the complex needs of modern data initiatives. A new category of specialist data providers is emerging to address this needs gap, proving that effective data management requires targeted skills and innovative approaches. Services must be redefined to focus on data outcomes, where the value delivered by data dictates the behaviors of both customers and suppliers. Let’s stop shoving data under the umbrella of professional services; this is about business data services where the value of data outcomes dictates customer and supplier behaviors.
Enterprise data management requires a fresh perspective and unconventional thinking. Despite its critical importance, the “same old, same old” approach has not propelled the data needle forward.
1HFS defines OneOffice as where the organization’s people, intelligence, processes, and infrastructure come together as one integrated unit, with one set of unified business outcomes tied to exceeding customer expectations
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