Last updated on September 10, 2026
CEO Perspective: The Case for Databricks
By Jennifer Moreno
I recently spoke with Analytics8 CEO Tom Munley about what he is seeing as organizations modernize their data environments and prepare for AI. In this article, I share his perspective on what makes Databricks a compelling strategic choice, where clients are seeing its greatest impact, and how organizations can invest in the platform without taking an all-or-nothing approach.
Databricks makes technical capabilities more useful to the business
Databricks initially earned its reputation among data engineers, but in recent years, the company has made a concerted effort to help business users interact with data, analytics, and AI more directly.
“The platform is moving toward the business, rather than asking the business to figure out how to make use of the platform.”
The data analytics industry is full of impressive technology solutions that struggle to survive because business users cannot quickly see its value. Too often, they are left to identify the most valuable use cases themselves and then rely on engineering teams to turn those ideas into ROI.
Databricks creates clearer paths from technical capability to practical application, helping the business recognize and act on data and AI opportunities more quickly.
That approach is obviously resonating with the market. In February, Databricks reported more than 65% year-over-year growth, and the company now serves more than 20,000 customers, including over 60% of the Fortune 500.
AI is all the rage, but engineering efficiency delivers immediate value
AI may be dominating data platform conversations, but it is not always the first benefit clients realize from Databricks. Tom sees speed and engineering efficiency as the most immediate sources of impact.
“Migrating away from legacy platforms is where many of our clients start their Databricks journey. When they can complete a migration in six weeks rather than six months or a year, the next high-value use case comes within reach much faster,” he said.
CareQuest Institute for Oral Health followed this progression, consolidating data from 20 source systems and automating ingestion and transformation on Databricks. The result is a scalable foundation for 13 TB of enterprise data that supports governed analytics, faster research insights and future AI initiatives.
Analytics and AI often represent the next stage of value from a Databricks investment. Once trusted, governed data is on the platform, companies are in a position to experiment and scale.
Whetstone unified data from four ERP environments on Databricks, automated reporting, and eliminated ~1,000 hours of manual work. With that foundation in place, they began applying AI to more advanced use cases, including a pricing model that optimizes decisions based on inventory, costs, and market dynamics.
Organizations shouldn’t justify a platform investment with ambitious AI vision alone. They can begin with measurable improvements to speed, efficiency, and engineering productivity while creating a solid data foundation for more advanced use cases.
Continuous innovation extends Databricks’ value
Companies selecting a data platform are making a decision that could shape their architecture, operating model, and ability to innovate for years to come. So realizing a return depends on choosing a platform that will continue delivering value long after the initial implementation is complete.
That’s where Tom believes Databricks’ durability becomes a major part of its value.
“You don’t want to make a short-sighted decision based on the latest flavor of the month when you’re making a choice with long-term implications for the business,” Tom said.
“Databricks gives customers a way to take advantage of what comes next without reengineering every time the market changes.”
Databricks’ pace of innovation is unusual in our industry. Many technology companies assemble broad portfolios of products but leave customers responsible for connecting separate underlying platforms. In contrast, Databricks continually adds capabilities through both internal development and acquisitions, and then deliberately integrates them into the platform, helping customers understand what new features may add more value to their roadmap.
While Databricks’ fast release cycle can sometimes make it difficult for users to keep up, it also means they gain access to new functionality as the market evolves – extending the value of the investment without the need to purchase and integrate separate technologies.
Databricks isn’t only for large enterprises
Databricks is sometimes perceived as a platform reserved for large enterprises with extensive data engineering teams and large budgets. But Tom does not see revenue or company size as the best way to determine fit.
More important are what the company wants to accomplish, how its data needs are expected to grow, and how intentionally it will handle consumption.
Because Databricks uses a consumption-based model, organizations can make deliberate choices about which workloads belong on the platform. Not every historical dataset needs to be migrated: data unlikely to create future value may be better left in lower-cost archival, leaving more resources for the use cases that matter most.
Databricks allows smaller and even pre-revenue companies to start at a manageable spend and scale as their needs grow.
There’s no need to build for the long-term vision at the onset of a Databricks implementation. Companies can start with a focused set of workloads and expand as the business and its data needs grow.
That said, cost management is still necessary. Workloads need to be designed well, monitored, and optimized. Databricks has usage and cost tracking capabilities that give visibility into which workloads are driving consumption and ease to scale up and down as needed.
Platform consolidation doesn’t have to be all or nothing
We see enterprise technology strategies follow a cyclical pattern: organizations consolidate systems to reduce complexity, then add specialized tools as new needs emerge. Over time, that creates a fragmented environment, and then there is a renewed push back toward consolidation.
Databricks’ vision is to bring data and AI capabilities together on one platform. But this decision depends on the company’s current architecture and business priorities.
“We’re not interested in putting all client data on the platform on day one,” Tom said.
“We’re going to identify the highest-value use cases first, integrate the corresponding data sources, and deliver a quick win to the business… then iterate from there.”
This is a more practical way to approach consolidation.
A focused first use case gives an opportunity to build skills, refine governance, and demonstrate value before expanding on Databricks.
Additional data sources and workloads can move onto the platform over time when the business case supports it.
So, where does Analytics8 fit in?
Databricks offers a wide range of capabilities across the data lifecycle. With so much that organizations could do, the harder question is usually what they should do first and why.
“Understanding the technology is only the first step,” Tom said.
“Where we really add value is knowing how organizations in that industry can benefit from Databricks and bringing best practices and proven approaches directly into the implementation.”
That only happens when the work is tied to a clear business objective. The priority might be faster decisions, lower operational costs, a trusted source of data, or a new revenue stream. Sometimes it is an advanced AI use case. Sometimes it is fixing a foundational problem that has limited the business for years. Our role is to help clients identify what will create the most value and focus the implementation there.
The case for Databricks in 30 seconds
When I asked Tom how he would answer that question if he had only 30 seconds with another CEO, he simplified it to two ideas: the rate of innovation and the ability to control costs while making the investment.
CEOs want to know that a major technology investment can create greater value over time. They also don’t want to pay up front for capacity and functionality the business is not yet using.
Databricks offers a way to balance those concerns: start with the use cases that matter now, manage the investment as adoption grows, and continue benefiting as the platform evolves.
Databricks will not be the answer to every technology question, and choosing it does not replace the need for a sound data strategy. But for organizations that want to modernize their data foundation, improve engineering speed, and build toward analytics and AI on a platform designed to last, it’s easy to understand why the case keeps getting stronger.
