What Is Databricks and Does Your Business Need It?

Introduction

You have probably heard the word “Databricks” in a board meeting, an investor update, or a job posting for a data engineer, and nodded along without being entirely sure what it does.

You are not alone. Databricks has grown into one of the most talked about names in business technology, recently reaching a valuation of 188 billion US dollars. But most of what gets written about it is aimed at engineers, not the people actually deciding whether to spend money on it.

This post explains what Databricks is in plain language, the real business problem it solves, and how to work out if your company is at the stage where it makes sense.

What Databricks Actually Is

At its simplest, Databricks is a platform that brings all of a company’s data into one place and makes it usable, both for people asking questions and for AI tools built on top of it.

Most growing businesses collect data in dozens of separate systems: a sales tool, a support desk, spreadsheets, website analytics, maybe sensors on factory equipment. Each one holds part of the picture, but none of them talk to each other properly.

Databricks calls itself a “lakehouse” platform, which is really just two older ideas combined. A data lake is a place where you can dump raw information cheaply, in whatever format it arrives in. A data warehouse is a more organised, cleaned up version that is easier to search and report on. Databricks tries to give you the low cost of a lake with the usability of a warehouse, in a single system.

It sits in the same category as tools like Snowflake and Microsoft Fabric. What has set Databricks apart recently is how directly it has built AI features into that same platform, rather than treating AI as a separate add-on.

The Problem It Solves

The reason platforms like this exist is not really about storage. It is about a much more common business problem: most companies are trying to use AI on data that is not ready for it.

A recent study from IDC and NetApp found that 84 percent of businesses are struggling to get value from AI because their data storage was not built with AI in mind. Separately, a 2026 survey found that 60 percent of business leaders are unsure whether their organisation is actually ready to get real value from AI, and only around a third of US companies feel their data setup could support AI at scale.

This is what causes AI projects to quietly stall. A team gets excited about building something useful, then spends months just trying to clean up and connect data that is scattered across ten different tools before any AI model can even be trained on it. Many projects never make it past this stage.

Fragmented data also creates a compliance headache. If nobody can say for certain where a piece of data came from or how it has been used, that becomes a real risk once regulators or auditors start asking questions, particularly in finance, healthcare, or any regulated industry.

How It Works in Practice

Once a company connects its various systems to Databricks, the platform cleans up and organises that data automatically, rather than requiring someone to do this by hand every time a report is needed.

The more interesting shift for non-technical teams is a feature called Genie. Instead of needing a data analyst to write a query, someone in operations or marketing can type a plain English question, such as “which region had the highest returns last quarter”, and get an answer with the supporting numbers and charts, without writing a single line of code.

Databricks has also opened up a way for businesses to build their own small internal tools, such as an inventory tracker or an approval workflow, directly on top of their existing data, without a lengthy custom development project. More than 5,000 companies are already using this feature and have collectively built over 150,000 of these internal applications, six times more than the year before.

A Real-World Example

Energy company Axpo offers a useful example of what this looks like in practice, beyond the marketing language. Facing security data at a scale their older systems could not handle, the company rebuilt its security monitoring on a unified data platform. From there, it expanded to run more than a dozen AI-based tools across engineering, procurement, trading, and customer-facing teams, all built on the same governed foundation of data.

The pattern is consistent across most of these projects. It rarely starts with “let’s buy an AI tool.” It starts with “let’s fix the mess our data is in,” and the AI capability follows naturally once that foundation exists.

Does Your Business Actually Need It?

Not every company is at the stage where a platform like this makes sense. A useful way to check is to ask a few honest questions.

• Is your data spread across five or more disconnected tools that do not share information automatically?
• Do different teams sometimes report different numbers for the same thing, because they are each pulling from a separate source?
• Are you planning to build AI features into your product or operations in the next year?
• Would a compliance audit be difficult right now because nobody can trace exactly where your data comes from?

If you answered yes to two or more of these, it is probably time to look seriously at data unification, whether that is Databricks or one of its competitors. Businesses that make this move often report cost savings in the range of 30 to 40 percent, largely from cutting duplicated tools and reducing the manual work of pulling reports together by hand.

If your business is still small, with data mostly living in one or two systems, this is likely a case of solving a problem you do not have yet. A simpler setup will serve you better until the complexity actually arrives.

Key Takeaways

• Databricks is a platform that unifies scattered business data into one governed, searchable system, and layers AI tools on top of it.
• Most AI projects fail not because the AI is bad, but because the underlying data was never properly organised.
• Features like plain English querying mean these tools are no longer only useful to technical teams.
• The decision to adopt a platform like this should follow from a genuine data fragmentation problem, not from wanting to be seen using the latest tool.

Conclusion

Databricks is not really about the brand name. It is about a much older business problem: scattered, inconsistent data slowing everyone down, made more urgent now that AI depends on that same data being usable.

Working out whether your business has outgrown its current data setup is often the harder part, well before any platform decision gets made. This is a stage Nexgits has walked through with businesses more than once, helping teams see clearly whether their data is holding back their AI plans before recommending any particular tool or platform.