Poor data management is crippling digital transformation
Companies spend billions on digital transformation, expecting data-driven decision-making to become second nature. Yet, many still operate in a fog of fragmented, unreliable data. The problem is the failure to manage the data fueling it. Even the most advanced systems can’t function properly with bad data.
This issue isn’t new, but it remains a top concern for CIOs. The numbers paint a clear picture: in 2023, 95% of hospital data went unused, and only 16% of companies successfully integrated data into their business processes. It’s a massive lost opportunity. Companies sitting on untapped data are losing out on insights that could drive revenue, improve efficiency, and cut costs.
C-suite leaders need to stop thinking of data management as just another IT challenge. It’s a strategic priority. Without reliable, well-structured data, digital transformation efforts are little more than expensive experiments with limited ROI.
AI is stalled by data problems, not technology
Everyone wants AI. Few are ready for it. Generative AI (GenAI) has massive potential, but bad data makes it unreliable. You can’t build strong AI models on weak foundations. If businesses want AI to generate real value, they need to fix their data first.
Chad Anderson, CEO of Gable.ai, puts it bluntly: “GenAI is NOT a pure data science problem. It is equally a DATA problem.” AI doesn’t magically improve bad data—it amplifies its flaws. If the input is wrong, the output will be useless. Yet, too many companies rush into AI deployment without addressing data integrity.
For executives, the takeaway is simple: AI success starts with data quality. Businesses serious about AI need robust data governance before they even think about scaling AI solutions. Otherwise, they’re simply rolling the dice.
Data silos are sabotaging collaboration and decision-making
Most companies claim to be data-driven, but their teams still operate in silos. Sales, marketing, finance, and operations all have their own datasets, and they don’t always align. This creates contradictions, delays, and bad decisions.
The scale of the problem is huge: 75% of companies in 2023 reported that internal collaboration was hindered due to data silos. These disconnected data pools slow down processes and introduce risk. When different departments work with inconsistent data, forecasts become unreliable, compliance gets complicated, and business strategy becomes guesswork.
The fix? Data unification. Organizations need to break down silos by automating data intake and standardizing how data is stored and accessed. When every department operates from the same accurate dataset, collaboration improves, and decision-making speeds up.
Uncontrolled data growth is overwhelming companies
Data volumes are exploding, and most businesses don’t have a clear strategy for handling it. They collect everything, afraid to delete anything. The result? Massive, unmanageable datasets filled with useless noise.
By 2024, businesses generated 361 billion emails daily, sent 16 million texts per minute, and created 378.77 million terabytes of data per day. Most of that isn’t useful. Yet, companies continue to hoard data, hoping it might be valuable someday. That approach isn’t sustainable.
Executives need to focus on data relevance, not only data retention. Instead of collecting everything, organizations should define what actually matters. The key is filtering out unnecessary metadata and redundant information before it clogs up data systems. A clear data strategy will make businesses faster, smarter, and more efficient.
Unstructured data is the biggest data management challenge
Right now, 80% of enterprise data is unstructured—meaning it lacks the necessary metadata, keys, or formatting to be properly used. That’s a massive problem. Unstructured data can’t be easily searched, analyzed, or leveraged for decision-making. It just sits there, taking up space.
Fixing this isn’t easy. Most of the time, it requires human intervention to tag, categorize, and transform unstructured data into something usable. This is why businesses struggle to make sense of customer feedback, email archives, and video content. The lack of structure means most of this data goes to waste.
For decision-makers, the solution is clear: invest in AI-driven data management tools that can process and organize unstructured data at scale. Without structure, businesses are losing valuable insights. Getting this under control should be a top priority for any company that wants to compete in a data-driven world.
Data security is an expensive and ongoing battle
Data breaches are getting more expensive, and companies that don’t prioritize security are playing with fire. IBM estimated that the average cost of a data breach in 2024 was $4.88 million. It’s a reputational disaster.
Most businesses already have security policies in place, but many aren’t enough. Strong encryption, zero-trust frameworks, and regular security audits are necessary. The risk is that third-party data exchanges and cloud storage introduce additional vulnerabilities that need constant monitoring.
For executives, this means security is a boardroom issue. Data governance and cybersecurity need to be integrated into every business decision. Investing in security today is a lot cheaper than dealing with a breach tomorrow.
Final thoughts
Data is the backbone of every major business decision, yet most companies are still drowning in outdated, fragmented, and unreliable information. AI won’t fix this. More storage won’t fix this. The only way forward is a serious commitment to structured, high-quality, and well-managed data.
The companies that solve this first will dominate their industries. They will make faster, smarter decisions, integrate AI seamlessly, and outmaneuver competitors still stuck in data chaos. Those that don’t will spend years chasing insights buried under a mountain of noise.
The data problem isn’t going away. The only question is who will control it—and who will let it control them.
