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Total Economic Impact
Cost Savings And Business Benefits Enabled By BigQuery And BigLake
A FORRESTER TOTAL ECONOMIC IMPACT STUDY COMMISSIONED BY Google, December 2025
Total Economic Impact
A FORRESTER TOTAL ECONOMIC IMPACT STUDY COMMISSIONED BY Google, December 2025
Specialized AI models, apps, and agents are reshaping how today’s businesses operate and serve customers. Firms that leverage their proprietary data, knowledge, and expertise for AI-enabled use cases stand to unlock productivity gains, better customer experiences, and the ability to reinvent business models and new revenue streams.1 This shift is forcing enterprises to rethink their data and analytics strategies. Traditional data warehouses and data lakes are unable to meet the growing demands of businesses due to siloed data architectures and limitations in agility, scalability, integration, automation, and governance. These challenges intensify as AI agents and enterprise applications generate ever-growing volumes of data. Traditional data warehouses offer advanced analytics capabilities, transaction support, and data governance in proprietary data formats, limiting the ability to use multiple compute engines on the same copy of data. Data lakes provide flexibility to use multiple compute engines but lack traditional data warehouse capabilities.
Data lakehouses combine the best of both worlds, enabling multi-engine interoperability with open table formats like Apache Iceberg, helping organizations optimize their storage costs and reduce extract, transform, load (ETL) complexity without losing the analytics capabilities, transaction support, and governance of traditional data warehouses. They are designed to support structured and unstructured data and work with multiple engines, such as Spark and SQL, to support new AI use cases, advanced automation, expanded access to enterprise data, and data science acceleration. Forrester’s research sees broad growth in data lakehouse initiatives across industries, with enterprises leveraging this data architecture to accelerate new and emerging business cases, such as business intelligence, data science, agentic AI experiences, IoT insights, business 360, and real-time insights, while also reducing costs and improving data governance.2
BigLake is an intelligent data management layer that helps organizations build modern, open data lakehouses. It acts as a unifying fabric for open-format data like Apache Iceberg, dismantling the historic trade-off between the flexibility of a data lake and the performance and governance of a proprietary data warehouse. BigLake’s primary economic impact stems from its autonomous performance management, which can automatically handle burdensome data optimization tasks. This can directly lower the total cost of ownership (TCO) by eliminating the engineering toil. Its serverless metastore can enable seamless multi-engine interoperability, while its deep integration with Dataplex Universal Catalog can deliver unified governance.
Google commissioned Forrester Consulting to conduct a Total Economic Impact™ (TEI) study and examine the potential return on investment (ROI) enterprises may realize by building a data lakehouse on Google Cloud with BigQuery and BigLake.3 The purpose of this study is to provide readers with a framework to evaluate the potential financial impact of BigQuery and BigLake on their organizations.
To better understand the benefits, costs, and risks associated with this investment, Forrester interviewed four decision-makers and surveyed 217 respondents with experience using BigQuery and BigLake for their organization’s data lakehouses. For the purposes of this study, Forrester aggregated the experiences of the interviewees and survey respondents and combined the results into a single composite organization, which is a global company with $10 billion in annual revenue and 7,500 employees.
Interviewees and survey respondents reported that prior to building their data lakehouse with Google Cloud, their organizations utilized various data lakes and data warehouses for storing and managing data, including self-managed Apache Iceberg tables, to support analytics, data science, and AI/ML. However, these systems failed to meet their growing business demands due to limited agility, performance, scale, and governance. These limitations ultimately impacted productivity for data teams and analysts, reduced time to value for analytics initiatives, such as customer churn analysis, and increased infrastructure and management costs.
After the investment in BigQuery and BigLake, the interviewees said their organizations gained a unified and flexible data platform to support modern analytics capabilities, including AI/ML, business intelligence, data science, and ad hoc reporting. Key results from the investment include infrastructure cost savings, productivity gains for data teams and analysts, and incremental revenue from increased customer acquisition.
Base: 217 decision-makers in IT, data analytics, data platform, or engineering roles at organizations using BigQuery and BigLake
Source: A commissioned study conducted by Forrester Consulting on behalf of Google, July 2025
Quantified benefits. Three-year, risk-adjusted present value (PV) quantified benefits for the composite organization include:
Avoided legacy infrastructure costs of up to $14.9 million annually. The composite organization depreciates legacy data warehouses and data lake solutions, reducing infrastructure spend and avoiding additional costs associated with data duplication. Over three years, the infrastructure savings are worth $30.2 million for the composite organization.
A 38% productivity gain for data engineers. With BigQuery and BigLake, the composite organization builds a unified data platform, enabling data engineers to more easily build and maintain data pipelines, ensure data quality and consistency across workflows, and optimize performance. Over three years, the productivity improvements are worth $3.1 million for the composite organization.
A 35% productivity gain for data analysts. With a single access layer and catalog for data across various formats, data analysts at the composite see a reduction in the time required to access and analyze data from multiple sources, accelerating time to insight. Over three years, the productivity improvements are worth $5.4 million for the composite organization.
A 33% productivity improvement for developers. Developers at the composite organization experience productivity improvements in integrating ML and AI tools, debugging and troubleshooting data issues, accessing various data types, and collaborating with analysts and data scientists. Over three years, the productivity improvements are worth $1.3 million for the composite organization.
A 33% productivity improvement for data architects. Data architects experience productivity improvements related to integrating diverse data sources into a single platform, ensuring performance and cost optimization across workloads, and implementing data governance and access control policies. Over three years, the productivity improvements are worth $2.7 million for the composite organization.
A 33% productivity improvement for data scientists. The composite’s data scientists gain a unified platform that helps them more easily integrate machine learning models and tools, query datasets, and prepare and transform data for modeling. Over three years, the productivity improvements are worth $1.8 million for the composite organization.
Up to $100 million in annual incremental revenue from increased customer acquisition. With BigQuery and BigLake powering the data lakehouse, the composite organization can support new use cases for AI/ML, business intelligence, and data science that were not possible in its legacy environment. These new uses enable the composite organization to unlock deeper insights and improve customer acquisition and retention. Over three years, the cumulative revenue is worth $225 million for the composite organization, and the composite experiences a profit of $17.5 million.
Unquantified benefits. Benefits that provide value for the composite organization but are not quantified for this study include:
Data and AI on a single platform. The composite organization builds a unified data platform upon which it can easily leverage Vertex AI models, advanced features such as data engineering agents and Gemini-assisted query capabilities, and open-source engines like Apache Spark. This accelerates innovation for advanced use cases, including generative AI (genAI) and agentic AI.
Security and governance. With a unified data architecture, the composite organization can more easily implement governance and security controls and leverage Google Cloud’s enterprise-grade security features. This helps it securely manage and scale analytics and AI initiatives.
Integration of operational data. The composite organization integrates operational data, including PostgreSQL, into its analytical systems in BigQuery to support modern AI-driven applications such as conversational AI, chatbots for customer service, and real-time personalization.
Interoperability. With BigLake’s support for Apache Iceberg — including the REST catalog — and Hive API, the composite builds an open data lakehouse, enabling interoperability across multiple engines and cloud platforms over a single copy of its data.
Costs. Three-year, risk-adjusted PV costs for the composite organization include:
Storage and compute costs. The composite organization incurs $24.9 million in storage and compute costs for the data lakehouse over three years.
Migration and support costs. The organization migrates to the data lakehouse over an 18-month period and has two FTEs dedicated to ongoing management, driving labor costs of $3.7 million.
The financial analysis that is based on the interviews and survey found that a composite organization experiences benefits of $62.2 million over three years versus costs of $28.6 million, adding up to a net present value (NPV) of $33.6 million and an ROI of 117%.
Return on investment (ROI)
Benefits PV
Net present value (NPV)
Payback
An enterprise’s ability to scale its data platform to handle growing data volumes, diverse and complex data, and a growing number of diverse users is crucial to the success of data and AI innovation. This includes breaking down the silos between operational and analytical workloads and allowing operational applications, including PostgreSQL databases, to directly tap into the vast and rich data within the lakehouse — all without the need for cumbersome movement of data. This empowers developers to build applications that leverage both real-time transactional data and historical analytical data. To that end, a data lakehouse combines both data warehouses and data lakes to deliver a flexible, unified platform that supports modern analytics use cases, including data science, business intelligence (BI), and AI/ML, to ultimately drive faster insights, improve decision-making, and embed AI into workflows and customer experiences. These modern data platforms are fundamentally changing how organizations collect, manage, and utilize data by embedding intelligent automation, seamless data integration, and personalized user experiences. A data lakehouse not only optimizes platform costs by eliminating data redundancies but also enhances security and governance, provides real-time data activation, and enables data consistency across workloads.
Forrester’s research finds that support for open table formats is an essential capability that decision-makers must evaluate as part of their organization’s data lakehouse implementations, with Apache Iceberg emerging as one of the most prominent open table formats for this purpose, driven by widespread adoption and industry support. Apache Iceberg has become a foundational technology for open data lakehouse architectures due to its robust support for schema evolution, time travel, and ACID compliance.4 Google Cloud continues to advance its offerings for customers to bring Google’s infrastructure advantage to Iceberg, introducing BigLake tables for Apache Iceberg in BigQuery, which provides the foundation for building open format lakehouses on Google Cloud. BigLake Iceberg tables in BigQuery offer the same fully managed experience as standard BigQuery tables but store data in customer-owned storage buckets, such as Google Cloud Storage, while also supporting the Iceberg table format for better interoperability with open-source and third-party compute engines on a single copy of data.
To better understand the adoption drivers and benefits associated with Apache Iceberg, Forrester conducted a survey of 217 respondents in IT, data analytics, data platform, and engineering roles at cross-industry organizations that used BigQuery, BigLake, and Apache Iceberg. When asked about their organization’s plans for Iceberg, 24% reported they had already implemented Iceberg, 42% were in the process of expanding their deployment, and 29% planned to adopt it within the next 12 months. Interoperability was cited as a key factor for respondents in their data lakehouse strategy, with 86% of respondents rating interoperability across engines and clouds as “Important” or “Critical.” As such, respondents indicated that they chose Iceberg to avoid vendor lock-in; have flexibility to build multi-engine, multicloud architectures; and benefit from broad ecosystem support.
While organizations can choose between fully managed and self-managed Iceberg experiences through Google’s offerings, our survey found that preferences leaned towards the former, with 73% of respondents reporting using either a fully managed service or platform for Iceberg operations or a partially managed approach. These respondents indicated that they increasingly value operational simplicity and enterprise-grade capabilities when adopting open table formats for their data lakehouses, citing enhanced data governance and security, automatic table management, scalability, and simplified metadata management as top benefits of a fully managed or partially managed experience. They found that BigQuery extends these advantages by offering features such as advanced runtimes, high throughput streaming, and fine-grained ETL operations.
Base: 217 decision-makers in IT, data analytics, data platform, or engineering roles at organizations using BigQuery and BigLake
Source: A commissioned study conducted by Forrester Consulting on behalf of Google, July 2025
Interviewees shared that BigLake and its support for Apache Iceberg enabled their organizations to address the limitations of their prior data lake and data warehouse environments and unify data access, management, and governance into a single platform, while also supporting interoperability with data processing engines, such as Spark. They shared the following experiences:
The staff data engineer at a retail company shared that their organization previously evaluated an open-source, self-managed approach for Apache Iceberg and later pivoted to building the lakehouse with BigQuery and BigLake due to its support for Iceberg. This enabled their organization to take advantage of Google Cloud’s enterprise-grade features for security and metadata management while retaining the benefits of an open architecture. The interviewee said: “Using open table formats over cloud objects was the way that the tech landscape was moving. Iceberg comes with extra capabilities that are very interesting to us, such as storage partition joins that you can do in Spark, the ability to time travel, and ability to branch and tag off tables.”
The VP of data architecture at a telecommunications company shared that BigLake’s support for Iceberg was an essential capability in building a unified data ecosystem on Google Cloud while enabling interoperability for tools like Spark to drive big data analysis. They explained: “We were trying to build a data ecosystem where we can unify the SQL ways of doing things and the Spark ways of doing things. When you work with SQL you are looking at data warehouses whereas with Spark, we are looking at big data or a data lake. We wanted to combine these two approaches. The primary requirement for this was to have a common format to store data and this is where Apache Iceberg comes into the picture, which BigQuery had native support for.”
The product manager at a media company shared that their data lake on Google Cloud Storage didn’t follow standard file structure conventions, making it difficult to easily leverage certain processing engines. With the data lakehouse architecture at their organization, Apache Iceberg operated as a metadata abstraction layer while BigLake acted as a bridge enabling BigQuery to query the Iceberg tables. The approach enabled the interviewee’s organization to leverage BigQuery on Google Cloud Storage stored data without duplication. They shared, “BigLake and Iceberg together were the solution we needed to enable direct querying of our data in Google Cloud Storage.”
| Role | Industry | Region | Employees | Annual Revenue |
|---|---|---|---|---|
| Staff data engineer | Retail | US headquarters, global operations | 2,400 | $2.8 billion |
| Senior engineering manager | Technology | US headquarters, global operations | 4,900 | $5.3 billion |
| Product manager | Media | Europe headquarters, global operations | 7,000 | €15.67 billion |
| VP of data architecture | Telecommunications | Europe headquarters, Europe operations | 200,000 | €115 billion |
Prior to building data lakehouses on Google Cloud, the survey respondents’ and interviewees’ organizations had a mix of data architectures, including data lakes, cloud data warehouses, relational databases, and on-premises data warehouses. The interviewees’ organizations were typically leveraging various self-managed data lakes on Google Cloud Storage. One interviewee reported that their organization managed a sprawling legacy environment composed of 40 to 50 on-premises data warehouses and data lakes.
Interviewees and survey respondents noted how their organizations struggled with common challenges, including:
Cost optimization. Interviewees and survey respondents alike reported that their organizations struggled to optimize costs for storing and managing data to support their organizations’ analytics, data sciences, and AI/ML requirements. In the survey, 46% of respondents reported rising data costs related to managing structured and unstructured data as a challenge they faced before implementing their data lakehouse architectures. Interviewees reported that data was often duplicated from data lakes to enable processing and querying, leading to massive costs and resource inefficiencies. For example, the product manager at a media company shared that duplication efforts led to over $1 million per month in excess storage costs and incurred additional consumption of compute resources.
Operational overhead. Platforms teams at the interviewees’ and survey respondents’ organizations were required to manage multiple sources of data, complex pipelines, and redundant data due to data duplication. In fact, 54% of survey respondents reported complex ETL pipelines as a top challenge before the lakehouse architecture. The product manager at a media company said, “Producers had to configure and maintain the workflows to load the data across into BigQuery. And then, we as a platform team had to look after all of the data and workflows, copying it across two different places.” The VP of data architecture at a telecommunications company reported significant resource costs associated with managing its legacy on-premises environment, sharing that their prior platform operations team was composed of over 100 FTEs.
Poor data governance. Several interviewees and survey respondents reported that ensuring data protection and privacy was a challenge in their data lake environments. Additionally, tracking lineage, assuring authoritative sources, and maintaining data integrity across diverse data types became an overwhelming task that impacted discoverability across teams. In fact, the majority of survey respondents cited data governance as a challenge before the lakehouse architecture. The staff data engineer at a retail company said, “Specifically, with our data lake, we have no standards around what should be written, where, and naming conventions. It’s hard to understand what other data teams have already created and manage data governance. The only way to know what was in a file was to read it, since there was no schema information that you could read.”
Fragmented user experiences and slow time to innovation. Inconsistent workflows and tooling across data lakes and warehouses created friction for both data producers and consumers, which ultimately slowed innovation for analytics, data science, and AI/ML projects at the survey respondents’ and interviewees’ organizations. Their data lakes often lacked strong governance, broad metadata, and flexible structures, becoming data swamps that were difficult to navigate, trust, find relevant data in, and extract value from. Without predefined structures and standard data rules, access and use required specialized skills and knowledge that slowed down insights. Furthermore, interviewees shared that data consumers often had to use different tools to work with their data depending on where it was stored. The VP of data architecture at a telecommunications company shared that it could often take several days to discover and surface relevant data across systems for analysis. In some cases, the time to move from an idea to a production-ready insight could take several months for more complex initiatives. The senior engineering manager at a technology company shared that machine learning engineers and data scientists had to either build and run complex Spark jobs or duplicate data to support their work. They shared that these processes often took several days to complete, reducing iteration cycles for their projects.
Base: 217 decision-makers in IT, data analytics, data platform, or engineering roles at organizations using BigQuery and BigLake
Source: A commissioned study conducted by Forrester Consulting on behalf of Google, July 2025
Base: 217 decision-makers in IT, data analytics, data platform, or engineering roles at organizations using BigQuery and BigLake
Source: A commissioned study conducted by Forrester Consulting on behalf of Google, July 2025
Based on the interviews and survey, Forrester constructed a TEI framework, a composite company, and an ROI analysis that illustrates the areas financially affected. The composite organization is representative of the interviewees’ organizations, and it is used to present the aggregate financial analysis in the next section. The composite organization has the following characteristics:
Description of composite. The composite organization is a global company with 7,500 employees that generates $10 billion in annual revenue. Analytics, data science, AI, and machine learning capabilities are essential for the company’s ability to acquire, serve and retain customers, helping the organization automate complex processes and inform strategic decision-making. The company has a data team composed of 375 FTEs, including 31 data engineers, 74 developers, 25 data architects, and 21 data scientists. It also has 375 data analysts that work closely with the data team to deliver insights to stakeholders across the business.5 Before implementing the data lakehouse architecture on Google Cloud, the composite organization had multiple on-premises data warehouses and data lakes, requiring staff to work across these sources to support data science, business intelligence, AI/ML, and ad hoc reporting.
Deployment characteristics. The composite organization migrates to the data lakehouse on Google Cloud over a period of 18 months, completing the migration midway through Year 1. It continues to expand the lakehouse over time, with data volumes reaching 50 petabytes (PBs) of data in Year 1, 100 PBs in Year 2, and 150 PBs in Year 3.
$10 billion in annual revenue
7,500 employees
375 data staff
150 PBs in the data lakehouse by Year 3
| Ref. | Benefit | Year 1 | Year 2 | Year 3 | Total | Present Value |
|---|---|---|---|---|---|---|
| Atr | Avoided legacy infrastructure costs | $7,437,500 | $14,875,000 | $14,875,000 | $37,187,500 | $30,230,560 |
| Btr | Data engineer productivity savings | $751,417 | $1,502,834 | $1,502,834 | $3,757,084 | $3,054,219 |
| Ctr | Data analyst productivity savings | $1,366,242 | $2,656,582 | $2,656,582 | $6,679,407 | $5,433,491 |
| Dtr | Developer productivity savings | $337,537 | $655,219 | $655,219 | $1,647,975 | $1,340,631 |
| Etr | Data architect productivity savings | $688,500 | $1,336,500 | $1,336,500 | $3,361,500 | $2,734,587 |
| Ftr | Data scientist productivity savings | $462,672 | $898,128 | $898,128 | $2,258,928 | $1,837,642 |
| Gtr | Incremental profit from increased customer acquisition, time to market, and innovation | $4,800,000 | $7,200,000 | $9,600,000 | $21,600,000 | $17,526,672 |
| Total benefits (risk-adjusted) | $15,843,869 | $29,124,263 | $31,524,263 | $76,492,394 | $62,157,802 |
Evidence and data. Survey respondents reported that their organizations reduced costs associated with depreciated legacy data warehouse and data lake solutions, ETL, and compute by building data lakehouses on Google Cloud.6 Similarly, the VP of data architecture at a telecommunications company shared that their organization was on track to significantly reduce its infrastructure spend with the migration to the data lakehouse, citing that annual infrastructure costs would be reduced from $15 million to $20 million on legacy systems down to an estimated $3 million to $5 million on Google Cloud once the migration was complete.
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
Prior to the data lakehouse architecture, the composite’s legacy infrastructure costs are $17.5 million annually composed of storage and compute costs.
Legacy systems run in parallel during the transition to the data lakehouse and are phased out as workloads are migrated. The composite organization realizes a 50% reduction in costs in Year 1 in line with the migration rate and 100% of the cost reduction by Year 2. This leads to $8,750,000 in cost savings in Year 1 and $17.5 million in Years 2 and 3.
Risks. Forrester recognizes that these results may not be representative of all experiences. The impact of this benefit will vary depending on the following:
An organization’s legacy infrastructure and associated costs before migrating to a data lakehouse architecture.
The ability and speed with which an organization can decommission legacy data architectures.
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 $30.2 million.
Avoided legacy infrastructure costs
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|
| A1 | Legacy infrastructure costs prior to the data lakehouse | Interviews | $17,500,000 | $17,500,000 | $17,500,000 | |
| A2 | Legacy infrastructure cost reduction realization | Interviews | 50% | 100% | 100% | |
| At | Avoided legacy infrastructure costs | A1*A2 | $8,750,000 | $17,500,000 | $17,500,000 | |
| Risk adjustment | ↓15% | |||||
| Atr | Avoided legacy infrastructure costs (risk-adjusted) | $7,437,500 | $14,875,000 | $14,875,000 | ||
| Three-year total: $37,187,500 | Three-year present value: $30,230,560 | |||||
Evidence and data. Survey respondents and interviewees reported that data engineers were one of the top roles that experienced productivity improvements following their organizations’ data lakehouse implementations. Respondents’ and interviewees’ organizations gained a unified platform for managing data from various sources, enabling data engineers to build data pipelines from a single data repository. As a result, respondents reported a 38% productivity improvement for these data engineers, citing efficiency improvements in building and maintaining data pipelines, ensuring data quality and consistency across workflows, and optimizing performance. The product manager at a media company shared: “We have data engineers working on complex data processing with tools like Apache Beam and [Google Cloud] Dataflow to produce the cloud storage datasets. When they’re creating these new datasets, they can make that data almost instantly queryable rather than having to go through an extra step of duplicating the data.”
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
The composite organization has 31 data engineers.
Data engineers experience a 38% productivity improvement once the data lakehouse migration is complete. In Year 1, they realize half of this gain as 50% of workloads have been migrated. In Years 2 and 3, data engineers realize the entire 38% productivity gain as the migration is complete.
The average fully burdened annual salary for a data engineer is $189,000.
Each data engineer recaptures 75% of the time savings productively.
Risks. Forrester recognizes that these results may not be representative of all experiences. The impact of this benefit will vary depending on the following:
An organization’s legacy data architecture.
The number of data engineers at an organization.
The rate at which productivity savings are realized.
The likelihood that time savings are recaptured productively.
Variation in data engineer salaries.
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 $3.1 million.
Productivity improvement for data engineers
Base: 75 decision-makers in IT, data analytics, data platform, or engineering roles at organizations using BigQuery and BigLake who noted data engineers have become more productive due to these solutions
Source: A commissioned study conducted by Forrester Consulting on behalf of Google, July 2025
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|
| B1 | Data engineers | Composite | 31 | 31 | 31 | |
| B2 | Productivity improvements due to BigQuery and BigLake | Y1: 38%*50 Y2 and Y3: 38% |
19% | 38% | 38% | |
| B3 | Fully burdened annual salary for a data engineer | Composite | $189,000 | $189,000 | $189,000 | |
| B4 | Productivity recapture | Composite | 75% | 75% | 75% | |
| Bt | Data engineer productivity savings | B1*B2*B3*B4 | $834,908 | $1,669,815 | $1,669,815 | |
| Risk adjustment | ↓10% | |||||
| Btr | Data engineer productivity savings (risk-adjusted) | $751,417 | $1,502,834 | $1,502,834 | ||
| Three-year total: $3,757,084 | Three-year present value: $3,054,219 | |||||
Evidence and data. Survey respondents also reported that data analysts also experienced productivity improvements following their organizations’ data lakehouse implementation. With a unified access layer and catalog for data across various formats, analysts at their organizations saw improved efficiency for query and analysis work. These analysts were able to directly query Apache Iceberg tables using BigLake with BigQuery’s analytical capabilities and SQL interface, accelerating time to insight. As a result, survey respondents reported a 35% productivity improvement for data analysts, citing efficiency improvements in accessing and analyzing data from multiple sources, creating dashboards and reports, and collaborating with data engineers and data scientists. In fact, the senior engineering manager at a technology company explained that the time to run queries was reduced from several days in the data lake to just “minutes” through the data lakehouse architecture.
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
There are 375 data analysts at the composite organization.
Analysts spend 25% of their time working across legacy data warehouses and lakes for analysis work.
Data analysts experience a 35% productivity improvement once the data lakehouse migration is complete. In Year 1, they realize half of this gain as 50% of workloads have been migrated. In Years 2 and 3, data analysts realize the entire 35% productivity gain with the migration is complete.
The average fully burdened annual salary for a data analyst is $127,000.
Each data engineer recaptures 75% of the time savings.
Risks. Forrester recognizes that these results may not be representative of all experiences. The impact of this benefit will vary depending on the following:
An organization’s prior data architecture.
The number of data analysts at an organization.
The amount of time data analysts spend analyzing data from legacy architectures before BigQuery and BigLake.
Variation in data analyst salaries.
The likelihood that time savings are recaptured productively.
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.
Productivity improvement for data analysts
Base: 75 decision-makers in IT, data analytics, data platform, or engineering roles at organizations using BigQuery and BigLake who noted data analysts have become more productive due to these solutions
Source: A commissioned study conducted by Forrester Consulting on behalf of Google, July 2025
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|
| C1 | Data analysts | Composite | 375 | 375 | 375 | |
| C2 | Percentage of time analyzing data across legacy architectures | Composite | 25% | 25% | 25% | |
| C3 | Productivity improvements due to BigQuery and BigLake | Y1: 35%*50 Y2 & Y3: 35% |
18% | 35% | 35% | |
| C4 | Fully burdened annual salary for a data analyst | Composite | $127,000 | $127,000 | $127,000 | |
| C5 | Productivity recapture | Composite | 75% | 75% | 75% | |
| Ct | Data analyst productivity savings | C1*C2*C3*C4*C5 | $1,607,344 | $3,125,391 | $3,125,391 | |
| Risk adjustment | ↓15% | |||||
| Ctr | Data analyst productivity savings (risk-adjusted) | $1,366,242 | $2,656,582 | $2,656,582 | ||
| Three-year total: $6,679,407 | Three-year present value: $5,433,491 | |||||
Evidence and data. With the data lakehouse architecture, developers at the survey respondents’ organizations experienced productivity gains related to integrating ML and AI tools, debugging and troubleshooting data issues, accessing various data types, and collaborating with analysts and data scientists. As a result, survey respondents reported a 33% productivity improvement.
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
The composite organization has 74 developers on its data team who spend 25% of their time accessing and integrating data across legacy data warehouses and lakes prior to BigQuery and BigLake.
Developers experience a 33% productivity improvement once the lakehouse migration is complete. In Year 1, they realize half of this gain as 50% of workloads have been migrated. In Years 2 and 3, developers realize the entire 33% productivity gain as the migration is complete.
The average fully burdened annual salary for a developer is $159,000.
Each developer recaptures 75% of their time savings productively.
Risks. Forrester recognizes that these results may not be representative of all experiences. The impact of this benefit will vary depending on the following:
The number of developers at an organization.
The amount of time developers spend accessing and utilizing data from warehouses and lakes before the lakehouse.
Variation in developer salaries.
The likelihood that time savings are recaptured productively.
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 $1.3 million.
Productivity improvement for developers
Base: 62 decision-makers in IT, data analytics, data platform, or engineering roles at organizations using BigQuery and BigLake who noted developers have become more productive due to these solutions
Source: A commissioned study conducted by Forrester Consulting on behalf of Google, July 2025
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|
| D1 | Developers on the data team | Composite | 74 | 74 | 74 | |
| D2 | Percentage of time spent working with legacy data warehouses and lakes | Composite | 25% | 25% | 25% | |
| D3 | Productivity improvement due to BigQuery and BigLake | Y1: 33%*50 Y2 and Y3: 33% |
17% | 33% | 33% | |
| D4 | Fully burdened annual salary for a developer | Composite | $159,000 | $159,000 | $159,000 | |
| D5 | Productivity recapture | Composite | 75% | 75% | 75% | |
| Dt | Developer productivity savings | D1*D2*D3*D4*D5 | $375,041 | $728,021 | $728,021 | |
| Risk adjustment | ↓10% | |||||
| Dtr | Developer productivity savings (risk-adjusted) | $337,537 | $655,219 | $655,219 | ||
| Three-year total: $1,647,975 | Three-year present value: $1,340,631 | |||||
Evidence and data. With BigQuery and BigLake, the survey respondents’ and interviewees’ organizations consolidated legacy warehouses and data lakes into a single, unified architecture, simplified governance through centralized metadata management and platform tooling, and reduced overall overhead for data architects. Survey respondents reported a 33% productivity improvement for these roles, citing efficiency improvements related to data integration, performance and cost optimization, and managing governance and access policies.
Base: 57 decision-makers in IT, data analytics, data platform, or engineering roles at organizations using BigQuery and BigLake who noted data architects have become more productive due to these solutions
Source: A commissioned study conducted by Forrester Consulting on behalf of Google, July 2025
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
There are 25 data architects at the composite organization.
Data architects experience a 33% productivity improvement once the data lakehouse migration is complete. In Year 1, they realize half of this gain as 50% of workloads have been migrated. In Years 2 and 3, data architects realize the entire 33% productivity gain as the migration is complete.
The average fully burdened annual salary for a data architect is $240,000.
Each data architect recaptures 75% of the time savings productively.
Risks. Forrester recognizes that these results may not be representative of all experiences. The impact of this benefit will vary depending on the following:
The number of data architects at an organization.
The speed of migration to the data lakehouse architecture and realization rate of productivity gains.
Variation in data architect salaries.
The likelihood that time savings are recaptured productively.
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 $2.7 million.
Productivity improvement for data architects
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|
| E1 | Data architects | Composite | 25 | 25 | 25 | |
| E2 | Productivity improvement due to BigQuery and BigLake | Y1: 33%*50 Y2 and Y3: 33% |
17% | 33% | 33% | |
| E3 | Fully burdened annual salary for a data architect | Composite | $240,000 | $240,000 | $240,000 | |
| E4 | Productivity recapture | Composite | 75% | 75% | 75% | |
| Et | Data architect productivity savings | E1*E2*E3*E4 | $765,000 | $1,485,000 | $1,485,000 | |
| Risk adjustment | ↓10% | |||||
| Etr | Data architect productivity savings (risk-adjusted) | $688,500 | $1,336,500 | $1,336,500 | ||
| Three-year total: $3,361,500 | Three-year present value: $2,734,587 | |||||
Evidence and data. With the data lakehouse architecture, interviewees’ and survey respondents’ organizations gained a unified platform with support for data aggregation and transformation, building machine learning models, and feature engineering in an integrated data platform. Data scientists at these organizations could more easily prepare, explore, process, and analyze large amounts of data quickly, helping improve time to value for business insights and models. The VP of data architecture at a telecommunications company shared that data science workflows, which previously took up to a week to complete, could now be accomplished in less than a day. The senior engineering manager at a technology company shared that accessing, preparing, and analyzing data was often a slow process before the data lakehouse implementation, requiring days of effort to build Spark jobs or duplicate data. Interviewees noted that by moving to the lakehouse, data scientists could simply write SQL queries, reducing the process from days to minutes. Similarly, survey respondents reported a 33% productivity improvement for data scientists, citing efficiency improvements in integrating machine learning models and tools, querying datasets, and preparing and transforming data for modeling.
Base: 50 decision-makers in IT, data analytics, data platform, or engineering roles at organizations using BigQuery and BigLake who noted data scientists have become more productive due to these solutions
Source: A commissioned study conducted by Forrester Consulting on behalf of Google, July 2025
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
There are 21 data scientists at the composite organization.
Data scientists experience a 33% productivity improvement once the data lakehouse migration is complete. In Year 1, they realize half of this gain as 50% of workloads have been migrated. In Years 2 and 3, data scientists realize the entire 33% productivity gain as the migration is complete.
The average fully burdened annual salary for a data scientist is $192,000.
Each data scientist recaptures 75% of the time savings productively.
Risks. Forrester recognizes that these results may not be representative of all experiences. The impact of this benefit will vary depending on the following:
The number of data scientists at an organization.
Prior data architectures.
Variation in salaries for data scientists.
The likelihood that time savings are recaptured productively.
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 $1.8 million.
Productivity improvement for data scientists
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|
| F1 | Data scientists | Composite | 21 | 21 | 21 | |
| F2 | Productivity improvement due to BigQuery and BigLake | Y1: 33%*50 Y2 and Y3: 33% |
17% | 33% | 33% | |
| F3 | Fully burdened annual salary for a data scientist | Composite | $192,000 | $192,000 | $192,000 | |
| F4 | Productivity recapture | Composite | 75% | 75% | 75% | |
| Ft | Data scientist productivity savings | F1*F2*F3*F4 | $514,080 | $997,920 | $997,920 | |
| Risk adjustment | ↓10% | |||||
| Ftr | Data scientist productivity savings (risk-adjusted) | $462,672 | $898,128 | $898,128 | ||
| Three-year total: $2,258,928 | Three-year present value: $1,837,642 | |||||
Evidence and data. With BigQuery and BigLake, survey respondents’ and interviewees’ organizations overcame limitations of their prior architectures, gaining a unified data platform to support modern analytics, including AI/ML, business intelligence, data science, and ad hoc reporting. This helped several of their organizations unlock deeper insights to support business outcomes including increased customer acquisition. Survey respondents and interviewees shared the following experiences:
Over 40% of survey respondents reported improved business growth as a result of their organization building a data lakehouse on Google Cloud. These respondents noted that their organizations saw increased customer acquisition and retention, faster time to market for new products and services, and greater innovation velocity by adopting the data lakehouse architecture.7 Survey respondents also stated that building their data lakehouses with BigQuery and BigLake enabled their organizations to pursue new use cases that were not possible in prior environments, including real-time analytics, AI/ML, business intelligence, and ad hoc querying.
The VP of architecture at a telecommunications company shared that critical analytical processes, such as customer churn analysis, experienced a 22x performance improvement by modernizing their organization’s data architecture. By migrating away from a fragmented data warehouse and data lake environment to a unified data platform, this interviewee’s organization eliminated bottlenecks caused by limited data discoverability, nightly batch processing, and manual data movement, enabling critical analytics processes to run significantly faster. They said: “Before, it could take analysts three weeks to find where the data they needed was, where they should run a query, and so forth. In this new ecosystem, they’re able to do it in two days. They have a unified catalog similar to an index book, so they know exactly where to go and what to write.” Additionally, the interviewee highlighted that time to market for initiatives, including agentic use cases, was significantly accelerated by the ability to easily leverage LLMs, such as Gemini, within their data platform.
The senior engineering manager at a technology company explained how the lakehouse architecture helped their organization train models on larger datasets, improving accuracy and depth of insight. With the data lakehouse, the interviewee’s company could cost-effectively scale model training to 400 days of historical data, enabling models to better capture seasonality and user behavior patterns. These enhancements supported deeper insights and decision-making in areas, such as advertising strategies, for the interviewee’s organization. The interviewee also shared that improved productivity for data science teams helped increase iteration cycles for their projects.
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
The composite organization generates $10 billion in annual revenue before the data lakehouse.
By leveraging BigQuery and BigLake, the composite organization can pursue analytics, data science, and AI/ML use cases that were not possible with previous data architectures; speed up iteration cycles for existing initiatives; and train models using larger datasets to improve their accuracy and performance. These capabilities enable the composite organization to unlock deeper insights, and improve customer acquisition and retention, leading to a 0.5% increase in revenue in Year 1, a 0.75% increase in Year 2, and a 1% increase in Year 3.
The composite organization has a profit margin of 12%.
Risks. Forrester recognizes that these results may not be representative of all experiences. The impact of this benefit will vary depending on the following:
An organization’s annual revenue.
The degree to which analytics, data science, and AI/ML impact an organization’s ability to generate revenue.
An organization’s profit margin.
External factors, such as economic conditions and market dynamics, may impact customer demand.
Results. To account for these risks, Forrester adjusted this benefit downward by 20%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $17.5 million.
Increase in annual incremental revenue by Year 3
Base: 217 decision-makers in IT, data analytics, data platform, or engineering roles at organizations using BigQuery and BigLake
Source: A commissioned study conducted by Forrester Consulting on behalf of Google, July 2025
Base: 93 decision-makers in IT, data analytics, data platform, or engineering roles at organizations using BigQuery and BigLake who said these solutions improved business growth
Source: A commissioned study conducted by Forrester Consulting on behalf of Google, July 2025
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|
| G1 | Annual revenue before the data lakehouse | Composite | $10,000,000,000 | $10,000,000,000 | $10,000,000,000 | |
| G2 | Annual revenue increase with the data lakehouse | Composite | 0.50% | 0.75% | 1.00% | |
| G3 | Incremental revenue | G1*G2 | $50,000,000 | $75,000,000 | $100,000,000 | |
| G4 | Profit margin | TEI methodology | 12% | 12% | 12% | |
| Gt | Incremental profit from increased customer acquisition and innovation | G3*G4 | $6,000,000 | $9,000,000 | $12,000,000 | |
| Risk adjustment | ↓20% | |||||
| Gtr | Incremental profit from increased customer acquisition (risk-adjusted) | $4,800,000 | $7,200,000 | $9,600,000 | ||
| Three-year total: $21,600,000 | Three-year present value: $17,526,672 | |||||
Interviewees and survey respondents mentioned the following additional benefits that their organizations experienced but were not able to quantify:
Data and AI on a single platform. By building an open data lakehouse architecture with Google Cloud and Apache Iceberg, the interviewees’ organizations avoided vendor lock-in, providing the flexibility to leverage both open-source engines and AI capabilities in BigQuery on a single copy of data. With BigQuery, the interviewees’ organizations easily utilized pretrained ML models, Vertex AI-trained models with 1st party connections to multimodal data and vectors, and features such as data engineering agents and Gemini-assisted query capabilities to accelerate innovation and improve productivity. The VP of data architecture at a telecommunications company shared: “Google excels in terms of the data and AI capabilities we were looking for. When it came to our data and analytics landscape, we were not trying to pick one or two of the best big technologies. We were looking at the entire ecosystem and Google provides a series of tools that we can leverage. … When it comes to foundational ML algorithms, custom ML algorithms, and genAI capabilities, a lot of that comes out of the box in BigQuery, which is very powerful.”
Security and governance. With the data lakehouse, survey respondents’ and interviewees’ organizations consolidated access and management across structured and unstructured data into a single repository, making it easier to implement and deliver governance and security controls. They also benefited from enterprise-grade security features in the Google Cloud ecosystem, helping them securely manage and scale their analytics and AI use cases.8 The VP of data architecture shared that moving from a sprawling data landscape to a single, data ecosystem significantly simplified governance and enabled them to leverage BigQuery’s security capabilities. They said: “We’ve inherited all of the security controls from BigQuery itself. It abstracts all of those complications for you. In terms of IAM [identity and access management] and auditing, all of that is centrally available as part of different capabilities in the Security Command Center or Cloud audit logs.”
Integration of operational data. Forrester’s research finds that the ability to seamlessly integrate both transactional and analytical workloads is critical to supporting modern AI-driven applications such as conversational AI, chatbots for customer service, and real-time personalization.9 The interviewee at the telecom company and an overwhelming majority of survey respondents shared that their organizations integrated operational data from database solutions, including CloudSQL, AlloyDB, and Spanner, into their analytical systems in BigQuery. These survey respondents shared that these capabilities improved time to market, eliminated expensive data movement, reduced latency between federated systems, and enabled productivity gains for teams.10
Interoperability. By leveraging Apache Iceberg, the survey respondents’ and interviewees’ organizations built open lakehouse architectures, enabling interoperability across multiple engines and cloud platforms, which allowed data teams to apply the best tools for their projects over a single copy of their data. For instance, the VP of data architecture at a telecommunications company shared that BigQuery’s support for Apache Iceberg was a key enabler in their effort to unify SQL and Spark-based data processing: “It’s very difficult to make the data interoperable between these different modes of execution. BigQuery and BigLake were a savior for that with the support of Apache Iceberg.”
The value of flexibility is unique to each customer. There are multiple scenarios in which a customer might implement a data lakehouse architecture on Google Cloud and later realize additional uses and business opportunities, including:
Avoided vendor lock-in and cost control. By leveraging Apache Iceberg and Google Cloud for their data lakehouses, organizations avoided vendor lock-in, ensuring they had the flexibility to use the best tools and engines for their workloads. This architecture also allowed the interviewees’ organizations to store large volumes of data on low-cost storage in Google Cloud Storage, enabling cost control as volumes grow. For example, the senior engineering manager at a technology company noted that utilizing Iceberg helped their organization save millions of dollars in storage costs by allowing data to be stored on Google Cloud Storage while maintaining accessibility for both open-source engines and BigQuery.
Increased agentic AI and genAI innovation. Data lakehouses enabled flexibility to innovate on agentic AI and genAI use cases at the interviewees’ organizations. Forrester’s research found that AI and data-driven innovation are rapidly accelerating demand for modern data platforms, including data lakehouses.11 By simplifying complex data environments and embedding AI capabilities, data lakehouses enabled the interviewees’ businesses to scale use cases and innovate more effectively. With BigLake and BigQuery, the interviewees’ organizations could build data lakehouses that unify access to structured, unstructured, and semistructured data across multiple sources and leverage robust features for metadata management, data validation, lineage, and auditing. This data architecture lays the foundation for building out agentic AI and genAI use cases that can securely leverage, reason, and act on wide sets of enterprise data.12
Flexibility would also be quantified when evaluated as part of a specific project (described in more detail in Total Economic Impact Approach).
| Ref. | Cost | Initial | Year 1 | Year 2 | Year 3 | Total | Present Value |
|---|---|---|---|---|---|---|---|
| Htr | Storage and compute costs | $0 | $5,170,000 | $10,340,000 | $15,510,000 | $31,020,000 | $24,898,347 |
| Itr | Migration and support costs | $2,079,000 | $1,039,500 | $415,800 | $415,800 | $3,950,100 | $3,680,033 |
| Total costs (risk-adjusted) | $2,079,000 | $6,209,500 | $10,755,800 | $15,925,800 | $34,970,100 | $28,578,380 |
Evidence and data. Survey respondents reported compute and cloud storage costs as the primary ongoing costs associated with their organizations’ data lakehouses.13 Interviewees reported approximate storage and compute costs of between $60,000 to $175,000 per petabyte for their organizations’ data lakehouses. The VP of data architecture at a telecommunications company shared that their organization also incurred costs for peripheral services, such as BigQuery notebooks, Dataplex, and BigQuery Dataframe. Pricing may vary. Contact Google for additional details.
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
The composite organization stores 50 petabytes of data in the data lakehouse in Year 1, 100 petabytes in Year 2, and 150 petabytes in Year 3.
Annual storage and compute costs per petabyte of data are $94,000 for the composite organization.
Risks. Forrester recognizes that these results may not be representative of all experiences. The impact of this cost will vary depending on the following:
The size of an organization’s data lakehouse.
Compute usage.
Pricing factors, including on-demand and capacity pricing options, storage configurations, and discounts.
Results. To account for these risks, Forrester adjusted this cost upward by 10%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $24.9 million.
| Ref. | Metric | Source | Initial | Year 1 | Year 2 | Year 3 |
|---|---|---|---|---|---|---|
| H1 | Petabytes of data in the data lakehouse | Composite | 50 | 100 | 150 | |
| H2 | Storage and compute costs per petabyte | Interviews | $94,000 | $94,000 | $94,000 | |
| Ht | Storage and compute costs | H1*H2 | $4,700,000 | $9,400,000 | $14,100,000 | |
| Risk adjustment | ↑10% | |||||
| Htr | Storage and compute costs (risk-adjusted) | $0 | $5,170,000 | $10,340,000 | $15,510,000 | |
| Three-year total: $31,020,000 | Three-year present value: $24,898,347 | |||||
Evidence and data. While most survey respondents reported their organizations implemented their data lakehouses within less than six months, interviewees described ongoing, multiyear migration timelines due to complex legacy data ecosystems.14 Interviewees shared that priority workloads were migrated first, including that of business-critical applications, training datasets, and feature-related workloads. Implementation efforts were led by core migration teams composed of data engineers, platform engineers, data scientists, and product managers, with team sizes varying based on the scope of the implementation. For instance, the staff data engineer at a retail company shared that a team of six dedicated half of their workload to building out its ongoing proof of concept while the VP of data architecture at a telecommunications company reported that a team of 40 FTEs was involved in building and enabling the platform.
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
The composite organization migrates to the data lakehouse architecture over a year and a half. During the initial period, 10 FTEs are involved in the migration process. In Year 1, 5 FTEs are dedicated to the migration process, which is completed halfway through the year.
In Year 2 and Year 3, two FTEs are dedicated to ongoing management of the data lakehouse.
The average fully burdened annual salary for a resource involved in migration and ongoing management is $189,000.
Risks. Forrester recognizes that these results may not be representative of all experiences. The impact of this cost will vary depending on the following:
The size of an organization’s data lakehouse.
Resources required for implementation efforts and variation in salaries and professional services costs.
The complexity of an organization’s prior environment.
Results. To account for these risks, Forrester adjusted this cost upward by 10%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $3.7 million.
| Ref. | Metric | Source | Initial | Year 1 | Year 2 | Year 3 |
|---|---|---|---|---|---|---|
| I1 | FTEs dedicated to migration and support | Interviews | 10 | 5 | 2 | 2 |
| I2 | Fully burdened annual salary for a resource dedicated to migration and support | Composite | $189,000 | $189,000 | $189,000 | $189,000 |
| It | Migration and support costs | I1*I2 | $1,890,000 | $945,000 | $378,000 | $378,000 |
| Risk adjustment | ↑10% | |||||
| Itr | Migration and support costs (risk-adjusted) | $2,079,000 | $1,039,500 | $415,800 | $415,800 | |
| Three-year total: $3,950,100 | Three-year present value: $3,680,033 | |||||
| Initial | Year 1 | Year 2 | Year 3 | Total | Present Value | |
|---|---|---|---|---|---|---|
| Total costs | ($2,079,000) | ($6,209,500) | ($10,755,800) | ($15,925,800) | ($34,970,100) | ($28,578,380) |
| Total benefits | $0 | $15,843,869 | $29,124,263 | $31,524,263 | $76,492,394 | $62,157,802 |
| Net benefits | ($2,079,000) | $9,634,369 | $18,368,463 | $15,598,463 | $41,522,294 | $33,579,422 |
| ROI | 117% | |||||
| Payback | <6 months |
The financial results calculated in the Benefits and Costs sections can be used to determine the ROI, NPV, and payback period for the composite organization’s investment. Forrester assumes a yearly discount rate of 10% for this analysis.
These risk-adjusted ROI, NPV, and payback period values are determined by applying risk-adjustment factors to the unadjusted results in each Benefit and Cost section.
The initial investment column contains costs incurred at “time 0” or at the beginning of Year 1 that are not discounted. All other cash flows are discounted using the discount rate at the end of the year. PV calculations are calculated for each total cost and benefit estimate. NPV calculations in the summary tables are the sum of the initial investment and the discounted cash flows in each year. Sums and present value calculations of the Total Benefits, Total Costs, and Cash Flow tables may not exactly add up, as some rounding may occur.
From the information provided in the interviews and survey, Forrester constructed a Total Economic Impact™ framework for those organizations considering an investment in BigQuery and BigLake.
The objective of the framework is to identify the cost, benefit, flexibility, and risk factors that affect the investment decision. Forrester took a multistep approach to evaluate the impact that BigQuery and BigLake can have on an organization.
Interviewed Google stakeholders and Forrester analysts to gather data relative to BigQuery and BigLake.
Interviewed four decision-makers and surveyed [y] respondents at organizations using BigQuery and BigLake to obtain data about costs, benefits, and risks.
Designed a composite organization based on characteristics of the interviewees’ and survey respondents’ organizations.
Constructed a financial model representative of the interviews and survey using the TEI methodology and risk-adjusted the financial model based on issues and concerns of the interviewees and survey respondents.
Employed four fundamental elements of TEI in modeling the investment impact: benefits, costs, flexibility, and risks. Given the increasing sophistication of ROI analyses related to IT investments, Forrester’s TEI methodology provides a complete picture of the total economic impact of purchase decisions. Please see Appendix A for additional information on the TEI methodology.
Benefits represent the value the solution delivers to the business. The TEI methodology places equal weight on the measure of benefits and costs, allowing for a full examination of the solution’s effect on the entire organization.
Costs comprise all expenses necessary to deliver the proposed value, or benefits, of the solution. The methodology captures implementation and ongoing costs associated with the solution.
Flexibility represents the strategic value that can be obtained for some future additional investment building on top of the initial investment already made. The ability to capture that benefit has a PV that can be estimated.
Risks measure the uncertainty of benefit and cost estimates given: 1) the likelihood that estimates will meet original projections and 2) the likelihood that estimates will be tracked over time. TEI risk factors are based on “triangular distribution.”
The present or current value of (discounted) cost and benefit estimates given at an interest rate (the discount rate). The PV of costs and benefits feed into the total NPV of cash flows.
The present or current value of (discounted) future net cash flows given an interest rate (the discount rate). A positive project NPV normally indicates that the investment should be made unless other projects have higher NPVs.
A project’s expected return in percentage terms. ROI is calculated by dividing net benefits (benefits less costs) by costs.
The interest rate used in cash flow analysis to take into account the time value of money. Organizations typically use discount rates between 8% and 16%.
The breakeven point for an investment. This is the point in time at which net benefits (benefits minus costs) equal initial investment or cost.
Total Economic Impact is a methodology developed by Forrester Research that enhances a company’s technology decision-making processes and assists solution providers in communicating their value proposition to clients. The TEI methodology helps companies demonstrate, justify, and realize the tangible value of business and technology initiatives to both senior management and other key stakeholders.
| ROLE | |
|---|---|
| Manager (manage a team of functional practitioners) | 33% |
| Director (manage a team of managers and high-level contributors) | 31% |
| Vice president (in charge of one/several large departments) | 24% |
| C-level executive (e.g., CEO, CMO) | 12% |
| INDUSTRY | |
|---|---|
| Technology and/or technology services | 17% |
| Healthcare | 15% |
| Financial services and/or insurance | 15% |
| Retail | 12% |
| Telecommunications services | 11% |
| Consumer product goods and/or manufacturing | 10% |
| Manufacturing and materials | 8% |
| Transportation and logistics | 5% |
| Travel and hospitality | 5% |
| Business or professional services | 3% |
| ANNUAL REVENUE | |
|---|---|
| $300M to $399M | 14% |
| $400M to $499M | 31% |
| $500M to $999M | 28% |
| $1B to $5B | 19% |
| >$5B | 8% |
| CURRENT DATA ARCHIECTURE(S) | |
|---|---|
| Data lakehouse architecture | 100% |
| Data lake | 100% |
| Data warehouse | 62% |
| Data fabric architecture | 36% |
| Data mesh architecture | 17% |
| Note: Percentages may not total 100 due to rounding |
1 Source: Turn Your Proprietary Knowledge Into AI Advantage, Forrester Research, Inc., December 23, 2024.
2 Source: The Forrester Wave™: Data Lakehouses, Q2 2024, Forrester Research, Inc., April 30, 2024; Data Lakehouse Is The New Data Warehouse And Data Lake, Forrester Research, Inc., August 31, 2023; The Future Of Data Platforms, Forrester Research, Inc., October 10, 2025.
3 Total Economic Impact is a methodology developed by Forrester Research that enhances a company’s technology decision-making processes and assists solution providers in communicating their value proposition to clients. The TEI methodology helps companies demonstrate, justify, and realize the tangible value of business and technology initiatives to both senior management and other key stakeholders.
4 Source: Principles Of The Modern Data Platform, Forrester Research, Inc., October 29, 2024; Data Lakehouse Platform Capabilities Checklist, Forrester Research, Inc., April 11, 2024. Open Table Formats Are Reshaping Modern Data Architectures, Forrester Research, Inc., October 28, 2025.
5 Source: The Data, Analytics, And AI Org By The Numbers, Forrester Research, Inc., October 29, 2024; The size of the data team and number of data engineers, developers, data architects, and data scientists is based on this Forrester’ report’s survey data. The research finds that data teams within medium-size enterprises (5,000 to 19,000 employees) comprise 5% of total headcount. The composite organization has 7,500 employees, indicating a data team size of 375. Based on survey data around the average number of employees in enterprise data teams across industries for a medium-size enterprise, Forrester determined that 8% are data engineers, 20% are software engineers, 11% are data architects, and 5% are data scientists.
6 This information in the paragraph is based on a survey of 217 IT decision-makers. Of these respondents, 33% selected “Reduced total cost of ownership”, when responding to the following question: “Which of the following benefits have you experienced as a result of building your data lakehouse with BigQuery and BigLake?”; these 33% were then asked the question, “In which of the following areas have you reduced costs by building your data lakehouse with BigQuery and BigLake?” to which 49% selected “Deprecated legacy data warehouse/data lake solutions,” 46% selected “ETL cost,” and 46% selected “Compute”.
7 The information in this sentence is based on a survey of 217 IT decision-makers when they were asked the question, “Which of the following benefits have you experienced as a result of building your data lakehouse with BigQuery and BigLake?” Of these respondents, 43% selected “Improved business growth”.
8 The information in this sentence is based on a survey of 217 IT decision-makers when they were asked the question, “Which of the following benefits have you experienced as a result of building your data lakehouse with BigQuery and BigLake?” Of these respondents, 42% selected “Enterprise-grade, fine-grained security.
9 Noel Yuhanna, Translytical Databases Are Fueling Modern AI Apps, Forrester Blogs.
10 The information in this paragraph is based on a survey of 217 IT decision-makers when they were asked the question, “Did you integrate your operational data from Google Cloud database solutions into your analytical systems in BigQuery?” Of these respondents, 98% selected “Yes.” When asked the question, “You indicated that you’ve integrated your operational data from Google Cloud database solutions into your analytical systems in BigQuery. Which database solutions did you use?,” “”85% selected “CloudSQL”, 55% selected “AlloyDB”, and 46% selected “Spanner.
11 Source: The Future Of Data Platforms, Forrester Research, Inc., October 10, 2025
12 Source: Agentic AI Is Rising And Will Reforge Businesses That Embrace It, Forrester Research, Inc., March 7, 2025.
13 The information in this paragraph is based on a survey of 217 IT decision-makers when they were asked the question, “To the best of your knowledge, what are the ongoing costs associated with your organization’s data lakehouse with BigQuery and BigLake?” Of these respondents, 64% selected “BigQuery compute costs” and 62% selected “Cloud storage costs.
14 The information in this paragraph is based on a survey of 217 IT decision-makers. Of these respondents, 67% reported that it took six months or less to implement their organization’s data lakehouses.
Readers should be aware of the following:
This study is commissioned by Google and delivered by Forrester Consulting. It is not meant to be used as a competitive analysis.
Forrester makes no assumptions as to the potential ROI that other organizations will receive. Forrester strongly advises that readers use their own estimates within the framework provided in the study to determine the appropriateness of an investment in BigQuery and BigLake.
Google reviewed and provided feedback to Forrester, but Forrester maintains editorial control over the study and its findings and does not accept changes to the study that contradict Forrester’s findings or obscure the meaning of the study.
Google provided the customer names for the interviews but did not participate in the interviews.
Forrester fielded the double-blind survey using a third-party survey partner.
Kara Luk
December 2025
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