The Total Economic Impact™ Of Microsoft Azure Databricks

Improved Productivity For Data Teams

Evidence and data. Interviewees consistently reported significant improvements in data team productivity after modernizing their analytics environments with Azure Databricks. These improvements were attributed to increased parallelism, reduced operational overhead, faster time to insight, and expanded access to data through simplified and more intuitive interfaces. Interviewees from financial services and healthcare organizations described a shift away from platform-imposed constraints toward environments where productivity was primarily limited by human capacity rather than infrastructure.

Prior to Databricks, many data teams relied on legacy platforms that enforced sequential processing and fixed capacity, requiring teams to wait for jobs to complete or resources to free up. This created artificial bottlenecks and limited how many users or workloads could operate concurrently. As the Global leader of data and analytics at one financial services firm explained: “With some of the historical technology that we used, things had to be done sequentially. Now that people can do multiple things at the exact same time in parallel, the bottleneck is only how fast and how many people can spin up.” Interviewees reported that this architectural shift allowed many more analysts, engineers, and data scientists to work in parallel, increasing overall throughput without adding friction as usage scaled.

These architectural benefits translated into measurable productivity gains for the interviewees’ organizations’ data teams. The senior leader of data and analytics in the financial services vertical noted, “Our productivity numbers have increased anywhere from 13% to 29%, and a lot of it has to do with either how much faster they can do stuff or how much more output they’re able to produce versus before.” Interviewees attributed these gains to shorter development cycles, faster data preparation, reduced rework, and the ability to iterate more quickly. Based on these findings, a conservative productivity improvement of 15% to 25% for Databricks users was a reasonable assumption for modeling purposes.

Several interviewees emphasized that Azure Databricks enabled their organizations to absorb growing demand without increasing headcount. The VP of data services at a healthcare company observed, “We haven’t, for the last three years, grown the team … so we’re doing more work with the same size of the team.” As data volumes, workloads, and business demand increased, their teams were able to deliver additional use cases and insights without proportional staffing increases. This represented avoided or deferred hiring costs while still achieving higher output.

Interviewees also highlighted dramatic reductions in time to deliver analytics solutions and data integrations. The same VP of data services in healthcare stated: “It would have taken seven months to pull this all together. We did it in three days.” While not all initiatives saw reductions of this magnitude, interviewees consistently cited order‑of‑magnitude improvements in how quickly new datasets, pipelines, and analytics products could be stood up. Faster delivery enabled more timely decisions and improved organizational responsiveness.

Another significant source of productivity improvement for interviewees’ organizations came from reduced time spent on platform operations and triage. In the legacy environments, data engineers often dedicated a substantial portion of their day to managing alerts, failures, and infrastructure issues. The manager of enterprise data platform at a technology company reported, “With Azure Databricks, we’re not seeing any of that.” The managed nature of Azure Databricks allowed their senior technical staff to redirect effort away from maintenance toward higher‑value analytical and engineering work.

Finally, interviewees noted that newer Databricks capabilities further expanded productivity by lowering the skill barrier for analytics. The Global leader of data and analytics at a financial services firm highlighted the value of AI‑assisted features, stating: “Having the ability to go to a Genie to just ask a natural language question and get a SQL back was very awesome. That’s a good feature.” These capabilities enabled more self‑service analytics, reduced dependency on specialized expertise, and allowed a broader set of users to derive value from data. Overall, interviewees reported that Azure Databricks materially improved productivity by increasing parallelism, reducing operational drag, accelerating delivery timelines, and enabling teams to do more with the same resources.

Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:

• It employs 150 direct technical users of the platform, such as data scientists and data engineers. The average fully burdened annual salary for a direct technical user is $170,000.

• After the deployment of Azure Databricks, these direct technical users are 15% more productive in Year 1, 23% more productive in Year 2, and 25% more productive in Year 3.

  • Azure Databricks is deployed at a rate of 50% in Year 1, 90% in Year 2, and 100% in Year 3, so overall team productivity is scaled accordingly.

• The organization also employs 1,500 indirect platform users, such as business intelligence and data analysts. The average fully burdened annual salary for an indirect platform user is $120,000.

• After the deployment of Azure Databricks, these employees are 20% more productive by Year 3 (assuming the same rollout impact as the direct users).

• Forrester assumes that the composite organization recaptures 50% of the total savings from productivity improvements.

Risks. The risk that any individual organization will experience a different financial impact from this benefit is driven by:

• The number and mix of direct technical and indirect users of the platform.

• The average fully burdened annual salary for those users.

• The rate of deployment for Azure Databricks.

• The degree of data integration and automation that exists in the legacy environment, which would impact the productivity improvement experienced.

Results. To account for these risks, Forrester adjusted this benefit downward by 10%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $39.0 million.

Reduced Infrastructure Costs

Evidence and data. Prior to adopting Azure Databricks, interviewees’ organizations relied heavily on self‑managed, on‑premises data infrastructure. These environments required upfront capital investments, ongoing hardware refresh cycles, and continuous spending on power, cooling, physical space, and maintenance labor. To ensure adequate performance and capacity, teams frequently overprovisioned hardware, resulting in substantial idle capacity and inefficient capital utilization.

As the senior leader of data and analytics at a financial services company explained, maintaining on‑premises data centers required continuous investment and often led to wasted spend due to capacity planning constraints: “When we owned our infrastructure, we had big capital expenditures, and we had to deal with racking, stacking, power, and cooling ourselves. With Azure Databricks, we don’t spend money on servers. We don’t spend money on cooling. We don’t spend money on networking. We don’t spend money on real estate.”

Interviewees pointed out that Azure Databricks eliminated hard capacity ceilings inherent in their previous environments, allowing their organizations to scale compute and users on demand rather than sizing for peak load. The Global leader of data and analytics at another financial services company echoed this statement: “We were provisioning for peak and then running at maybe 10% or 20% utilization for most of the year. The savings that we get from reducing the infrastructure costs, from being able to control and manage our spend better, those are real.”

Interviewees emphasized that Azure Databricks’ elastic job and serverless compute options were central to these savings, because they allowed their organizations to align spend with actual workload demand rather than running large clusters or appliances at idle. The shift to an elastic, consumption‑based cloud model removed the need to overprovision for peak demand, improving utilization and spend control while avoiding future hardware refreshes, while Azure‑native storage services and Delta Lake optimizations help interviewees’ organizations manage petabyte‑scale data cost‑effectively.

Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:

• Prior to deploying Azure Databricks, the composite organization maintained two data centers devoted to storage and compute for the data analytics workloads, including one in the US and one in Europe. Both are decommissioned once Azure Databricks is fully deployed.

• The organization eliminates operating expenses for these data centers, totaling $10 million per year, which includes costs for leasing/real estate, power, cooling, and maintenance personnel.

• In addition, the composite organization no longer needs to spend $2 million each year to purchase new servers and associated hardware to refresh the data centers. (This assumes $14-million worth of hardware on a seven-year refresh cycle to handle its management of 10 petabytes of data.)

• All these costs are eliminated in accordance with the composite organization’s 50%-90%-100% rollout schedule.

Risks. The risk that any individual organization will experience a different financial impact from this benefit is driven by:

• The size of its data footprint.

• The proportion of that data which is managed in an on-premises environment.

• The speed with which the organization deploys Azure Databricks.

Results. To account for these risks, Forrester adjusted this benefit downward by 15%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $19.9 million.

Increased Data Platform Resiliency

Evidence and data. Interviewees consistently cited improvements in data platform resiliency, security, and system integration after moving to Azure Databricks, emphasizing reduced operational risk, improved uptime, and stronger built‑in redundancy compared to legacy, self‑managed environments. These improvements were especially important for interviewees’ organizations operating at scale or in regulated industries where platform reliability and security were foundational requirements rather than differentiators.

Several interviewees highlighted the advantage of leveraging Microsoft’s global cloud infrastructure to achieve levels of resiliency that were difficult or cost‑prohibitive to replicate in private data centers. A manager of enterprise data platform in the technology sector explained: “Based on scale, [Microsoft] can do it on a larger scale, at a lower price point and have a lot more resiliency and redundancy and geolocation configurations. They definitely have a better uptime than the majority of our private data centers have.” Interviews viewed this shared‑responsibility model as materially reducing exposure to outages while improving service availability for analytics users worldwide.

Geographic redundancy and automated failover were frequently mentioned as key drivers of increased platform resilience. A VP of data services in healthcare noted: “We’re getting better redundancy out of the environment. So there wasn’t redundancy between or for some of these regional data warehouses. Now that’s Microsoft’s and Databricks’ problem. It fails over geographically if one region is having a bad day.” This capability reduced the need for custom disaster recovery architectures and lowered the operational burden on internal teams while improving business continuity.

Interviewees also emphasized the stability of the managed service model, particularly around maintenance and patching. In contrast to legacy platforms that required planned downtime and hands‑on administration, Azure Databricks updates occurred transparently without disrupting users or workloads. As the same healthcare VP described: “I’ve never patched it. I got a notification two Fridays ago that there was going to be a maintenance event. You know what we saw during the maintenance event? Nothing. The thing stayed up.” Interviewees consistently saw this “always on” experience as a meaningful improvement in both resiliency and security posture.

Multiple interviewees compared Azure Databricks to their organizations’ former environments, where single points of failure and fragile components created frequent and sometimes extended outages. A manager of enterprise data platform who was responsible for data platform as a service in the technology sector explained: “With our old system, when somebody destroyed the name node, that outage could last a while, and it could happen weekly or daily. With Azure Databricks, we’re not seeing that. I mean data is always there.” By removing reliance on customer‑managed infrastructure and critical components, Azure Databricks significantly reduced unplanned downtime and associated operational risk.

Taken together, interview feedback indicated that Azure Databricks materially increased platform resiliency through built‑in redundancy, geographic failover, and managed operations, while simultaneously improving security and simplifying integration with the broader Azure ecosystem. These capabilities reduced exposure to outages, lowered administrative risk, and enabled data teams to operate with greater confidence that critical analytics workloads would remain available, secure, and well-integrated with enterprise systems.

Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:

• Before deploying Azure Databricks, the composite organization experiences approximately 50 hours of unplanned downtime each year.

• Each hour of unplanned downtime costs, conservatively, $125,000. This includes:

  • $60,000 worth of time for data analytics team members who are unable to work at full capacity.
  • $15,000 worth of IT technicians’ time to identify and fix the problem.
  • $50,000 in costs for one impacted business decision (a conservative estimate).

• Unplanned outages are virtually eliminated once Azure Databricks is fully deployed.

Risks. The risk that any individual organization will experience a different financial impact from this benefit is driven by:

• The number and duration of unplanned data analytics-related outages in the legacy environment.

• The pay rate for impacted employees in data analytics and IT departments.

• The level of disruption caused to operations outside the data analytics function.

Results. To account for these risks, Forrester adjusted this benefit downward by 10%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $11.4 million.

Legacy Software Licenses

Evidence and data. Azure Databricks enabled interviewees’ organizations to retire legacy data platforms and third‑party software licenses while materially reducing the operational burden associated with database administration. Interviewees noted that these outcomes were driven by consolidation onto a cloud‑native data platform with managed infrastructure and integrated data engineering capabilities.

Prior to adopting Azure Databricks, the interviewees’ organizations relied on a patchwork of licensed database technologies and ETL tools that required significant annual spend. A senior leader of data and analytics at a financial services firm described the legacy environment as “deployed on a variety of distributed systems based on [solutions from at least five different vendors] in a much more federated fashion.” They contrasted this with the Azure Databricks model, stating that moving to the cloud eliminated not just infrastructure but the administrative cost tied to those platforms: “You don’t pay for administrators on Azure; it’s administered by them. So you can redeploy those human resources and salaries.”

The transition also enabled the removal or reduction of expensive third‑party software licenses. The VP of data services at a healthcare concern explained that consolidating pipelines and analytics workloads into Azure Databricks allowed their organization to plan the retirement of multiple licensed tools: “I’m saving money in other places because I’m getting rid of all the third‑party licenses. I pay more than a million dollars for [vendor] licenses annually. When our contract comes up for renewal, I’m not going to buy as many licenses. I might not buy any licenses.”

In addition to ETL tooling, Azure Databricks enabled the deprecation of licensed transactional and analytical databases. The same VP of data services described replacing database platforms that previously required both license fees and dedicated infrastructure investment: “One, in particular, I want to say is close to $800,000 annually from a hardware perspective and more than a million bucks with the licensing. We opted not to do that. We’ve been going source system and connecting that directly into Azure Databricks.”

Beyond license elimination, interviewees reported a dramatic reduction in the need for traditional database administrators. Managed infrastructure, automated patching, and built‑in resiliency shifted responsibilities away from platform maintenance and toward value‑added data work.

Taken together, interview evidence indicated that Azure Databricks replaced licensed legacy platforms and ETL tools at these organizations with a consumption‑based cloud service while simultaneously reducing DBA effort through managed operations. This combination allowed interviewees’ organizations to reallocate spending away from licenses and overhead toward scalable analytics, data engineering, and AI workloads.

Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:

• It maintains licensing agreements with multiple data analytics-related vendors totaling $2.5 million annually before moving to Azure Databricks.

• It eliminates these licenses as it deploys Azure Databricks, reducing payments by 50% in Year 1, 90% in Year 2, and 100% in Year 3.

• It employs five database administrators to manage its legacy data analytics technology stack. The average fully burdened annual salary for a DBA is $135,000.

• It reassigns three of those DBAs in Year 1 to higher-value projects, and the remaining two DBAs in Year 2.

Risks. The risk that any individual organization will experience a different financial impact from this benefit is driven by:

• The number of licensing agreements in place to support legacy data analytics technology stack.

• The cost of those licensing agreements.

• The speed at which the organization migrates to Azure Databricks.

Results. To account for these risks, Forrester adjusted this benefit downward by 15%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $5.4 million.

Unquantified Benefits

Interviewees mentioned the following additional benefits that their organizations experienced but were not able to quantify:

• Native, first-party integration with Azure and its other solutions and services. Interviewees reported that native connectivity with Azure OpenAI, Azure Machine Learning, Azure AI Foundry, and Microsoft Fabric eliminated custom API work and reduced integration complexity. Azure security frameworks and compliance certifications apply automatically, simplifying governance for regulated industries, which those interviewees reported was a critical benefit of consolidating their data estate in the first place. Co-engineering with Microsoft delivered performance optimizations to their organizations that was unavailable on other clouds. Azure-specific features like Azure Data Lake Storage (ADLS) integration and serverless compute reduced latency and egress fees that may plague multicloud deployments. While not a critical factor, interviewees reported that having a single-vendor relationship streamlined licensing, billing, and technical support. 

• Increased speed to insight. Azure Databricks enabled interviewees’ organizations to dramatically shorten the path between raw data and actionable insight by simplifying data pipelines, reducing handoffs, and providing direct access to analytics-ready data. Interviewees consistently emphasized that insight latency was driven less by analytical capability and more by how long it took data to move through legacy supply chains. By consolidating ingestion, processing, and analytics into a single platform, Azure Databricks allowed users at the interviewees’ organizations to focus more time on analysis and less on data preparation and coordination.
  A Global leader of data and analytics in financial services highlighted that streamlining data supply chains was the most important driver of faster insight, explaining, “Because people don’t have access to [the data] until it goes through our supply chains, making those as short as possible means that people can spend more time analyzing and have access to the information faster.”
  Several interviewees described that prior environments required data to move across multiple teams, tools, and systems before it could be analyzed. Azure Databricks centralized data engineering and analytics on a common platform and reduced these friction points. This eliminated delays caused by competing priorities, manual handoffs, and downstream rework. The VP of data services at a healthcare company explained how platform consolidation enabled their teams to get insights faster: “Instead of having to wait for someone to build something or provision something for you, you can just do it yourself. You don’t have to wait weeks or months to get access to data.” This direct access translated into faster iteration cycles and quicker validation of hypotheses. Analysts and data scientists could explore data as soon as it landed, rather than waiting for it to be modeled, extracted, or replicated into downstream systems.
  The availability of unified, scalable data also accelerated insight by enabling teams to work with complete datasets rather than samples. A senior leader of data and analytics in financial services described how Azure Databricks supported near-real-time analytics on large datasets that were previously impractical to analyze quickly. This interviewee noted, “Now we can actually analyze all of the data, not just a subset of it, and we can do it in time for it to matter.”
  Across interviews, faster time to insight was not described as a single feature benefit, but as the cumulative result of shorter data pipelines, fewer dependencies, and immediate access to governed data. Azure Databricks helped shift organizational effort away from waiting for data and toward using it — allowing insights to emerge earlier, decisions to be made sooner, and analytical work to keep pace with business needs.

• Access to new insights. Azure Databricks made data easier to access, explore, and manipulate directly by a wider range of users across the interviewees’ organizations, enabling more innovation. Instead of relying on centralized teams to prepare datasets or answer predefined questions, end users were able to interact with data themselves, experiment, and pursue new lines of inquiry. The VP of data services in healthcare described how providing direct access to Azure Databricks empowered nontechnical stakeholders to analyze data independently and generate their own insights. The interviewee said, “We gave him the tool and said ‘Brian, go ahead and interrogate, ask questions,’ and it’s firing back the correct responses.”
  This hands-on access lowered barriers to participation and encouraged curiosity-driven analysis at the interviewees’ organizations. By removing the need to wait for specialized resources or rigid reporting pipelines, Azure Databricks allowed more people to engage with data in meaningful ways. As more users worked directly with data, testing ideas, iterating quickly, and asking new questions, interviewees’ organizations saw greater experimentation and innovation. Azure Databricks shifted data analysis from a gated activity to a shared capability, expanding both who could participate and what kinds of insights could emerge.

• Enhanced governance, data separation, and access control. Interviewees explained that, prior to consolidation, data estates were distributed across regions, cloud providers, and platforms, making it difficult to enforce consistent access controls or governance standards. As the Global leader of data and analytics at a financial services organization noted, “Portions of it were on-prem, in different cloud providers, and on different platforms, but everything is on Azure Databricks now, and that is providing a consistency we didn’t have before.” Centralizing the data foundation allowed governance to be embedded earlier in the data lifecycle (“shift left”), rather than applied late or inconsistently at the point of consumption.
  Interviewees noted this architectural consolidation materially strengthened data separation and access control. In highly regulated environments, the ability to strictly segregate client data and enforce precise permissions was nonnegotiable. By standardizing on a single platform with a shared governance layer, interviewees’ organizations moved away from bespoke, use-case-specific controls toward a common model where policies were defined once and applied universally. The manager of enterprise data platform at a technology company explained: “One of the major pain points before was that we could not offer granular access for people. Databricks introduced something called Unity Catalog. … It tracks where data is, who has access to it, and the lineage as well. Table level, column level, row level masking — those are some of the things that only Unity Catalog offers.” These capabilities reduced reliance on manual controls, localized workarounds, and duplicative implementations, while improving auditability and confidence in data use.
  While interviewees did not quantify this benefit in financial terms, its strategic value was clear. Enhanced governance and access control enabled organizational coherence across geographies and service lines for the interviewees’ organizations, reduced long-term technical debt, and created a stable foundation for future capabilities such as AI, cross-industry benchmarking, and scalable analytics.

Flexibility

The value of flexibility is unique to each customer. There are multiple scenarios in which a customer might implement Azure Databricks and later realize additional uses and business opportunities, including:

Organizational alignment on a single, global data foundation (elimination of structural fragmentation). Across interviewees’ organizations, there was a strategic shift from fragmented, regional, and hybrid data environments toward a single, consistent global data foundation. This “shift left” moved standardization earlier in the data supply chain by replacing bespoke regional warehouses and use‑case‑specific pipelines with a shared platform, common taxonomies, and unified data planes. As a result, their teams reduced duplicated effort and stopped rebuilding similar data assets independently across geographies and business units.

While interviewees did not quantify this benefit financially, they consistently emphasized its long‑term strategic value. Aligning on one data foundation improved organizational coherence by enabling their teams to work from the same definitions and datasets, slowed the accumulation of technical debt, and created a durable base for future capabilities. This unified approach enabled advanced use cases — such as cross‑region analytics, AI, and benchmarking — that were impractical or impossible in the interviewees’ organizations’ fragmented legacy environments.

Interviewees noted the benefit accrued over time and expressed itself as strategic optionality rather than immediate cost savings. Participants articulated a clear “build once, consume everywhere” principle, noting that a unified foundation allows new analytics and services to be added without repeatedly reengineering core infrastructure. This optionality strengthens long‑term agility and resilience, even when short‑term migration costs are material.

Flexibility would also be quantified when evaluated as part of a specific project (described in more detail in Total Economic Impact Approach).