Data indexing and classification are the foundation of good data hygiene. Once data is properly indexed, businesses know everything about a file—when it was created, who made it, when it was made, how big it is, and more. Add classification into the mix, and suddenly it’s clear what that data is and how it needs to be managed based on regulation and corporate records policies.
The impact? Huge. From regulatory compliance to cost savings, proper data management speeds up retrieval, improves query performance, and lays the groundwork for AI. With the global data classification market expected to grow at an annual rate of 24% from 2024 to 2031, reaching an estimated $9.5 billion valuation, organisations are starting to take notice.
In this piece, we’ll explore four business benefits that show the true power of indexing and classification.
Ensuring regulatory compliance
Imagine a business with minimal data classification and indexing. Data is scattered everywhere—on laptops, in inboxes, on thumb drives, and across servers with no real governance. This is far more common than you think, with Forbes suggesting bad data management practices in organisations may be as high as 33%. In a worst-case scenario, some say the “dark data” these practices create could be as high as 88%. Under these conditions, staying compliant with regulations like GDPR, CCPA, PDPB is impossible.
Regulations demand precise, detailed data retrieval. Businesses have two options: either they go the slow manual route, gradually rolling out a data governance strategy, or they automate the process with third-party tools. The automated approach is where indexing software come in, scanning through files, cracking open underlying formats, pulling out metadata, allowing categorisation of everything—like a magician rifling through and organising a deck of cards. When relevant record classification is applied, you have a powerful source of intelligence about your data.
Once files are categorised, regulatory compliance suddenly becomes much easier. When a company receives a data subject request, it doesn’t get ignored, or fines incurred if the data is unable to be located without the time limit (or indeed at all). If a file contains personal protected information that’s older than the legal retention limit and thus no longer needed, it can be easily found and automatically deleted. And when a ransomware attack occurs, businesses can immediately know and report on what data the malicious actor has touched and set an appropriate response. Reporting on touched/encrypted/stolen data is a key requirement of regulations such as DORA.
Cutting costs with smarter storage
Data indexing is a crucial component in creating smarter storage solutions. By systematically organising and classifying data, businesses can ensure only relevant and frequently accessed (hot) data is stored on primary storage platforms. This approach allows for better tiering, where data is allocated to the most appropriate storage or cloud solution based on its usage patterns and importance.
For example, frequently accessed data can be stored on high-performance, low-latency storage systems, while less critical or aged-out data can either be moved to more cost-effective, high-capacity storage solutions or be permanently deleted. This tiered approach not only optimises storage costs but also enhances overall system performance by ensuring that primary storage platforms are not overwhelmed.
Moreover, data indexing and classification enable businesses to implement cost-effective data lifecycle management policies. By identifying and archiving data that is no longer needed, companies can avoid the unnecessary expansion of storage infrastructure. This proactive management of data helps in avoiding forklift upgrades to primary storage platforms, which can be both costly and disruptive to business continuity.
A recent Forrester Total Economic Impact study found that one of the leading data indexing and classification providers, on average, helped reduce backup and data costs by 66%. These savings come from a confluence of forces, from data duplication reduction to cheaper storage costs. This shift towards cost optimisation is evident as, in 2024, it overtook AI preparation as the top data storage priority for IT leaders. The focus has shifted because good data management doesn’t just support AI—it also saves money.
Advancing sustainability goals
There is often a gap between sustainability targets and the actions being taken to achieve them. Often, this comes down to opportunities for decarbonization being overlooked. Incidentally, Loughborough University has the Digital Carbon Footprint Toolkit, a simple calculator that shows the worst-case scenario of carbon emissions from data and view the carbon emissions of dark data.
Many companies store everything by default, including unnecessary, outdated, and even non-compliant records. Someone, somewhere once decided to keep something for seven years, or ten years, or indefinitely, with no real governance. That’s why the biggest cloud providers store exabytes of data—not because they need to, but because customers don’t manage their own data properly.
From a business perspective, rising energy and storage costs naturally push companies to start cutting back. But without classification and indexing, how do they know what’s safe to delete? The legal team won’t sign off on data removal—because they don’t know what it is.
Sustainability officers, for the most part, aren’t in IT. They focus on things like reducing energy by turning off lights at night, building electric charge points in the company car park or putting monitors in sleep mode. Real impact comes from reducing unnecessary storage and compute. Imagine going to a sustainability officer and saying, “If we effectively manage our data, we could remove petabytes of unnecessary servers and storage. That means fewer storage arrays, fewer servers, less networking, lower power and cooling requirements. We could shut down an entire computer room, an entire floor of a data centre, or even decommission a whole facility.” That’s the kind of difference proper data management makes.
Unlocking AI-driven insights
One of the biggest challenges in preparing data for AI is governance and security concerns (45%), followed by data classification and tagging (41%). This is because businesses are finally starting to realise that AI is only as good as the foundations it’s built upon.
When a business has a solid framework for data engineering that includes clear indexing and classification, it’s far easier to use a generative AI application designed to help businesses query their data using natural language processing—something that third-party providers allow for too. Not only do businesses without proper data management have no basis for AI insights, but they also have to dig through endless files manually to retrieve relevant information.
The reason some of the leading data classification services are powerful is because of retrieval-augmented generation (RAG). This is a bespoke question-answering system that pulls from a company’s actual, classified data rather than scraping random information from the internet. If a business needs to check compliance with a specific regulation over a certain period, it doesn’t return a generic response—it provides source-based insights, showing exactly where the data came from, why it’s classified in a certain way, and how it aligns with regulatory requirements.
Most AI tools don’t provide that level of transparency. When you ask ChatGPT, Alexa or Siri a question, they don’t tell you where their information comes from immediately. Leading enterprise classification and indexing businesses, on the other hand, have to verify their AI-driven insights because compliance is built on a foundation of classified and indexed data, and trust.
Tidying up
Businesses are finally getting their heads around data classification and indexing, mostly because of regulation. GDPR was the first big push but now have more regulations to handle, such as the EU AI Act. Customers have the right to be forgotten, but how can businesses ensure this when they have no idea where their data is? How do they know if a relevant record retention policy on that same data actually trumps GDPR and allows them to retain some of all of it legally?
Organisations are starting to see that the benefits go far beyond compliance. This isn’t just about de-duplication or cost savings; it’s about preparing for enhanced AI-driven insights, security, sustainability, and smarter data management overall. The companies that figure this out now won’t just be ahead of compliance—they’ll be ahead of the competition.
Mark is a DORA-certified compliance officer and seasoned Technology Director with 20 years of experience in leading global teams across technology operations, program management, and financial management. With a background as a former global director in financial services, a CTO, and a mentor to IT communities, he has deep expertise in navigating the regulatory landscape and driving data management strategies. Known for his open and collaborative management style, Mark excels in delivering complex projects on time and within budget, most recently overseeing a £78.1m operating budget and £27m change budget. A proven communicator and strategic leader, he brings out the best in highly technical, culturally diverse teams, fostering a high-performance culture and balancing day-to-day demands with long-term vision.
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