Knowledge workers are increasingly afloat in a sea of disorganized data spread across a thicket of software-as-a-service (SaaS) applications, messaging threads, and shared drives. Departmental silos, inadequate version controls, and weak access controls make data sprawl a drag on productivity and a security risk.
Why have IT leaders struggled to fix this problem? It turns out that solving information sprawl is more complicated than issuing a memo and buying another platform. The CIO Experts Network — including IT and business decision-makers as well as technologists and influencers — suggests that a solution demands technology and culture change.
For example, Isaac Sacolick (@nyike), president of StarCIO and author of Driving Digital, says information sprawl and tool proliferation are linked: “Organizations leverage hundreds of platforms, SaaS tools, and information repositories to complete daily work. The unfortunate byproduct is that information is buried in different tools.”
Every tool uses its own metadata scheme to categorize information, and complexity increases with the volume of video, audio, and artificial intelligence (AI)–generated content. Low storage costs long ago made it cheaper to keep data than throw it away.
Cycle of frustration
Sacolick believes that the result is that IT is left trying to “centralize the right information, normalize content into useful formats, and deliver search and agentic AI experiences.” It’s an enormous task that grows larger as the amount of data expands.
Many previous attempts to unify information have backfired, causing employees to become cynical about new initiatives, says Will Kelly, a writer focusing on AI and cloud computing. “Information silos often grow in the aftermath of poorly executed knowledge management efforts.”
It isn’t always the technology’s fault, Kelly says. People become frustrated with tools that don’t match the way they work. Inadequate governance and lack of training compound the problem. “Left to fend for themselves, employees build side systems just to get things done.”
Product-focused IT leaders may check the “collaboration tool deployed” box without attending to details such as ownership or long-term vision. “The result? Tools look good on paper but don’t fit how teams actually work,” says Kelly.
Scott Schober, CEO of Berkeley Varitronics Systems (@ScottBVS), notes that SaaS has made it so easy for organizations to deploy new tools that gnarly details such as ensuring that information is current and easy to find get overlooked.
“Everyone is juggling so many platforms these days, and everyone has a ‘magic bullet’ claiming to make work easier,” Schober says. “But more often than not, it just adds another place where information can get lost.” Put another way: Each new tool might solve a small problem but create new ones.
Duct-taped solutions
IT leaders are expected to walk a tightrope, keeping “everything secure and compliant but also making sure data is easy to find,” Schober says. “That’s often impossible without top-down buy-in. IT teams are left duct-taping systems together while employees waste hours just trying to find the right file or message.”
Where information access is a problem, legacy systems are often involved. Aging databases with proprietary data structures “weren’t designed for cross-platform access or integration,” says Chris Selland, partner at TechCXO, a provider of technology executive talent.
Add in departmental fiefdoms, with every team wanting to protect its own turf, and the technical debt mounts up. “Teams are often reluctant to change access policies and workflows they’ve optimized for their specific needs,” Selland says. “The rapid proliferation of SaaS solutions has outpaced data governance frameworks.”
Even when technology exists to bridge the gaps, humans can be the weak link. Gene de Libero, principal at Digital Mindshare, points out that departments often treat their favorite tools like sacred objects. “No team wants to surrender control over how they work.”
Integration nightmares occur when connecting one system breaks three others, “creating an avalanche of technical debt that compounds daily,” de Libero says. Meanwhile, IT must salvage useful insights from decades of patched-up workflows and forgotten SharePoint folders. Context may be stuck in someone’s inbox from 2018, but no one can access it or even know it’s there.
Speaker, adviser, and blogger Arsalan Khan (@ArsalanAKhan) boils the problem down to two root causes: “fragmented systems with incompatible standards and cultural barriers like poor data visibility, shadow IT, and limited cross-departmental collaboration.”
Battling apathy
The technical problems are far easier to fix than the human ones, agrees Vivek Singh, senior vice president of IT and Strategic Planning at PALNAR. Even if the tech works perfectly, resistance, inertia, and apathy frustrate change. Singh says that productivity fixes that don’t directly generate new revenue aren’t seen as priorities: “Without that ROI story, budget and buy-in are hard to come by.”
Even if people can find the information they need, weak governance can thwart its productive use, according to Naga Vadrevu, chief technology officer at Wonderschool, a platform for people seeking and providing childcare. “The challenge today isn’t just access; it’s relevance and freshness of information. Workers don’t just want information but answers in context.”
Kumar Srivastava, chief technology officer at software development platform maker Turing Labs, agrees that data quality control is part of the problem. “It requires constant oversight and review,” he notes. Without a solid recommendation engine and regular cleanup, “managing shifting ownership, quality, and relevancy makes this a very hard problem to solve.”
Traditional data governance practices were designed for a time when all digital data was structured. It is not surprising that these processes have broken down now, given that by many estimates, between 80% and 90% of new data is unstructured.
Technology advances are solving this problem. Intelligent search engines can organize and derive insights from plain text and even multimedia content. Vector embeddings and semantic search convert unstructured text into a form that can be searched for context-aware retrieval. Automated metadata tagging automates indexing for enhanced accessibility. Generative AI brings all these technologies together to enable people to ask questions and get answers with natural language.
These solutions work best when anchored in robust governance practices that not only organize and index data but also make it useful. Adding context promotes collaboration and speeds decision-making. Organizations need to invest in governance with as much enthusiasm as shiny new tools.