Executive Summary
Despite significant investments in data lakes, warehouses, and catalogs, many organizations still struggle to realize meaningful, day‑to‑day business value because data remains difficult to find, access, and reuse across business teams and AI agents. Solutions that simplify and scale data consumption enable organizations to better leverage their data assets, resulting in faster decisions, operational efficiency and productivity, and better use of existing data resources.
Huwise is a data product marketplace built on top of existing data solutions to organize and distribute data products to human and AI systems. By enabling self‑service discovery, use, access, and governance of trusted data products, Huwise can reduce manual data delivery and decrease duplicate analytics effort across teams. In doing so, the solution helps organizations more fully realize the value of prior data investments by increasing organizationwide data reuse, adoption, operational efficiency, and productivity.
Huwise commissioned Forrester Consulting to conduct a Total Economic Impact™ (TEI) study and examine the potential return on investment (ROI) enterprises may realize by deploying its data product marketplace solution. The purpose of this study is to provide readers with a framework to evaluate the potential financial impact of Huwise on their organizations.
To better understand the benefits, costs, and risks associated with this investment, Forrester interviewed four decision-makers with experience using Huwise. For the purposes of this study, Forrester aggregated the experiences of the interviewees and combined the results into a single composite organization with 10,000 employees globally.
Interviewees reported that prior to using Huwise, their organizations operated in a fragmented data environment with multiple disconnected business intelligence (BI) tools, dashboards, and data platforms. Business users struggled to find, understand, use, and trust data products due to overly technical interfaces, inconsistent definitions, duplicated metrics, and limited documentation, while data assets had low visibility and reuse by business teams and in AI use cases. Access to data was largely IT-driven, slow, and difficult to scale, leading analysts and data teams to spend excessive time preparing data rather than delivering insights.
With Huwise, interviewees saw a shift to a centralized, internal data product marketplace that unified access to governed data products. Their organizations were able to make trusted data products easy to discover, understand, and reuse through self-service, reducing dependency on IT and duplicate analytics work.
Key results from the investment include increased data reuse, faster access to data for business users and AI agents, and improved productivity across analytics and data engineering teams.
Key Findings
Quantified benefits. Three-year, risk-adjusted present value (PV) quantified benefits for the composite organization include:
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Accelerated time to insight and improved productivity for business users. Huwise enables faster time to insight by giving business users (typically in sales, marketing, and other operational roles) self‑service access to trusted, ready‑to‑use data products in a single source. By centralizing discovery, documentation, and access within a single, intuitive marketplace, users spend less time searching for data or validating definitions and more time analyzing information. As a result, insights that previously required coordination and validation can be delivered more quickly and consistently across teams and AI agents. Over three years, these time savings for business users are worth $4.5 million to the composite organization.
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Improved productivity in data search and preparation for data teams. Huwise helps analysts reduce time spent searching for and preparing data by centralizing discovery and documentation in a single platform. Instead of navigating multiple systems, analysts can quickly identify certified data products with clear definitions, ownership, and usage context. This reduces time spent cross-checking and reconciling data before work can begin and feed into AI systems. Over three years, time savings for data analysts are worth $3.0 million to the composite organization.
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Reduced analytics effort and duplication for data teams. By promoting the reuse of shared, governed data products, Huwise minimizes the need for analysts to repeatedly recreate the same data assets for various teams and AI agents. Certified data products serve as common building blocks for reports, dashboards, and analyses, reducing parallel work and inconsistencies. As a result, analysts can focus on value-added analysis and deeper insights. Eliminating unnecessary duplication efforts saves the composite organization $223,000 over three years.
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Avoided IT costs. By providing business and data users with self-service discovery and access to trusted data products, the composite can significantly decrease the number of support tickets, ad hoc extracts, and custom data requests that require IT involvement. In addition, improved reuse and visibility of existing data assets reduces the need to purchase duplicate or overlapping data licenses and tools, allowing the composite to make more efficient use of its current technology investments while containing incremental IT spend. Avoided IT costs are worth $111,000 to the composite over three years.
Unquantified benefits. Benefits that provide value for the composite organization but are not quantified for this study include:
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Enhanced future readiness for agentic AI. Huwise provides a single, trusted entry point to curated data by standardizing how data assets are described, discovered, and reused, which enables scalable data access, supports AI-driven and self-service workflows, and reduces time spent on data search and preparation.
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Increased value realization from prior data investments. Although the composite had invested heavily in data lakes, data catalogs and warehouses, these platforms were often inaccessible or underutilized by business users, limiting their real-world impact. Huwise bridges this gap by connecting, harmonizing, and contextualizing existing data assets, making them more easily accessible and usable, thereby unlocking data value across the organization.
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Improved data literacy and data‑driven culture. Over time, the self-service use of data supports stronger data literacy, higher user engagement, and greater confidence in using data for decision-making across the composite organization.
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Simplified deployment and reduced long‑term operational costs. Compared to building and maintaining a custom data marketplace in-house, interviewees noted that Huwise was faster to deploy and easier to maintain, reducing reliance on IT and engineering resources.
Costs. Three-year, risk-adjusted PV costs for the composite organization include:
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Huwise license fees. These fees include access to a SaaS data product marketplace platform designed to help organizations catalog, visualize, and share data. The license covers core marketplace functionality, feature‑based platform capabilities, defined data and API consumption quotas, standard platform support and maintenance, and access to customer success services. These fees add up to $970,000 over three years.
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Implementation and deployment costs. These costs encompass internal resources dedicated to setting up the platform as well as professional services fees for data porting and data source integration. These costs amount to $395,000 over three years.
The financial analysis that is based on the interviews found that a composite organization experiences benefits of $7.8 million over three years versus costs of $1.4 million, adding up to a net present value (NPV) of $6.5 million and an ROI of 474%.
Key Statistics
474%
Return on investment (ROI)
$7.8M
Benefits PV
$6.5M
Net present value (NPV)
<6 months
Payback
Benefits (Three-Year)
The Huwise Customer Journey
Drivers leading to the Huwise investment
Interviews
| Role | Industry | Region | Employees |
|---|---|---|---|
| Chief data officer (CDO) | Financial services | France | 350,000 |
| VP, enterprise data and analytics | Enterprise software | North America, EMEA, and Asia‑Pacific operations; customers worldwide | ~20,000 |
| Group CDO and group head of data management | Insurance and risk management | France, with global presence, operating in 100+ countries across Europe, Americas, Asia‑Pacific, Middle East & Africa | ~5,000 |
| IoT, smart sensors, and data solutions lead | Environmental services | France, With global operations across Europe, North America, Asia‑Pacific, Middle East, Africa, and Latin America | ~200 |
Key Challenges
Interviewees noted how their organizations struggled with common challenges, including:
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Fragmented data landscapes across disconnected BI tools and platforms. Interviewees said their organizations operated multiple BI tools, dashboards, and data platforms that were poorly integrated. This fragmentation increased complexity, duplicated effort, and made it difficult to establish a single source of truth for reporting and analytics.
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Low trust in data. Business users struggled to find, understand, and trust data because of inconsistent metric definitions, duplicated KPIs, and immature documentation.
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Limited visibility and data asset reuse. Reports and dashboards were underutilized, often serving fewer than two users per asset. Analysts spent a disproportionate amount of time preparing and reconciling data rather than delivering insights, reducing overall analytics productivity.
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Slow, IT-driven data access that did not scale. Access to data was largely controlled by IT, creating long request queues and delays. This centralized model limited business agility and made it difficult to scale self-service analytics organizationwide.
Composite Organization
Based on the interviews, 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:
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Description of composite. The composite organization is a large European-headquartered enterprise with global operations across approximately 50 countries. It generates around $5 billion in annual revenue and employs about 10,000 people worldwide. The organization operates a highly complex data environment that includes cloud data platforms, on‑premises systems, BI tools, APIs, and external data sources. Prior to adopting Huwise, data access and analytics were fragmented across multiple tools, leading to inconsistent definitions, duplicated dashboards, low data reuse, and heavy reliance on IT and data teams for routine data requests.
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Deployment characteristics. The composite organization deploys Huwise incrementally, beginning with a limited rollout to data teams and priority use cases following a short implementation period. It layers the solution on top of existing data platforms, BI tools, and metadata systems, enabling fast technical setup without major infrastructure changes. As part of the proof of concept, Huwise is introduced to employees in data-centered roles (e.g., analysts, engineers, BI specialists), comprising approximately 2% of the organization. Adoption then gradually expands to employees across more functions, business units, and regions as use cases mature. Due to the platform’s user-friendly interface, users require minimal training and reach productive usage within less than one year, supporting enterprisewide scale over time.
KEY ASSUMPTIONS
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$5 billion revenue
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10,000 employees
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~200 employees in data, analytics, and/or BI roles
Analysis Of Benefits
Quantified benefit data as applied to the composite
Total Benefits
| Ref. | Benefit | Year 1 | Year 2 | Year 3 | Total | Present Value |
|---|---|---|---|---|---|---|
| Atr | Accelerated time to insight and improved productivity for business users. | $1,015,750 | $2,031,500 | $2,539,375 | $5,586,625 | $4,510,205 |
| Btr | Improved productivity in data search and preparation for data teams. | $1,200,000 | $1,200,000 | $1,200,000 | $3,600,000 | $2,984,222 |
| Ctr | Reduced analytics effort and duplication for data teams | $89,600 | $89,600 | $89,600 | $268,800 | $222,822 |
| Dtr | Avoided IT costs | $44,800 | $44,800 | $44,800 | $134,400 | $111,411 |
| Total benefits (risk-adjusted) | $2,350,150 | $3,365,900 | $3,873,775 | $9,589,825 | $7,828,660 |
Accelerated Time To Insight And Improved Productivity For Business Users
Evidence and data. Although they already had data catalogs in place, interviewees described similar challenges with training business users to access and use the data available to them. This resulted in poor user adoption and engagement, with one interviewee describing that they saw “less than two users per report” created on average.
Interviewees reported several improvements in how end users interacted with data:
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The simplicity of the Huwise user interface enabled self-service data consumption for users without data analytics expertise. As a result, the CDO in financial services said they’d seen a significant reduction in the number of data requests or tickets from employees.
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The shift to a centralized data product marketplace also improved employees’ trust in data. At an enterprise software company, Huwise helped to break down data silos and eliminate duplicate reports and dashboards. The VP of enterprise data and analytics shared that having a common, certified view of sales and marketing metrics such as annual recurring revenue, churn, pipeline, and usage reduced discussions on data validity and improved collaboration between sales and marketing teams.
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
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Of 10,000 employees, approximately 15% to 20% are active data users who regularly use dashboards, reports, or analytics tools as part of their daily decision-making. This includes roles in sales, marketing, product, operations, finance, and HR.
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The composite deploys Huwise to 5% of employees in Year 1, gradually scaling to 10% and 12.5% over Years 2 and 3.
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Before Huwise, business users typically spent an average of 0.5 hours (30 minutes) per day searching for data and validating it with colleagues or across different sources. With easier access to trusted data through Huwise, they reduce this time by 50%.
Risks. Some factors that can impact how much time savings end users experience include:
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The industry and nature of their work.
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General data maturity of an organization, including employees’ data literacy skills and data product quality.
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Use cases for implementation of Huwise and adoption rates.
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 $4.5 million.
50%
Reduced business user time spent on data searching with Huwise
Accelerated Time To Insight And Improved Productivity For Business Users.
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|
| A1 | Employees | Composite | 10,000 | 10,000 | 10,000 | |
| A2 | Percentage of employees with data-centered functions | Interviews | 5.0% | 10.0% | 12.5% | |
| A3 | Employees with data-centered functions | A1*A2 | 500 | 1,000 | 1,250 | |
| A4 | Daily time spent searching for and validating data (hours) | Interviews | 0.50 | 0.50 | 0.50 | |
| A5 | Time savings on data search and validation | Interviews | 50% | 50% | 50% | |
| A6 | Total time savings on data search and validation for business users (hours) | A3*A4*A5*250 | 31,250 | 62,500 | 78,125 | |
| A7 | Fully burdened hourly rate for an employee | TEI methodology | $40.63 | $40.63 | $40.63 | |
| At | Accelerated time to insight and improved productivity for business users. | A6*A7 | $1,269,688 | $2,539,375 | $3,174,219 | |
| Risk adjustment | ↓20% | |||||
| Atr | Accelerated time to insight and improved productivity for business users. (risk-adjusted) | $1,015,750 | $2,031,500 | $2,539,375 | ||
| Three-year total: $5,586,625 | Three-year present value: $4,510,205 | |||||
Improved productivity in data search and preparation for data teams.
Evidence and data. Business end users were not the only ones who benefited from using a data product marketplace. Data analysts well trained in working with data also benefited from having a unified platform as the ease of discovery sped up their data search.
One interviewee described that data product reuse also significantly increased among power users like data scientists and analysts. While they previously spent most of their time preparing raw data, analysts could now easily find and pull data from existing reports.
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
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About 2% of employees are in data, analytics, or BI roles. Their work heavily involves working with data and preparing reports for business users.
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These employees spend about 1.2 hours every day preparing raw data. With Huwise, they reduce the time spent on this data search by 50%.
Risks. Some factors that can impact how much time savings data analysts experience include:
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Use cases for implementation of Huwise.
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General data maturity of an organization, including employees’ data literacy skills and data product quality.
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 $3.0 million.
50%
Analyst time savings on data search and preparation
Improved Productivity In Data Search And Preparation For Data Teams.
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|
| B1 | Employees | Composite | 10,000 | 10,000 | 10,000 | |
| B2 | Percentage of employees in data/analytics/BI functions | Interviews | 2.0% | 2.0% | 2.0% | |
| B3 | Daily time spent searching for and preparing data (hours) | Interviews | 1.2 | 1.2 | 1.2 | |
| B4 | Time savings on data search and preparation | Interviews | 50% | 50% | 50% | |
| B5 | Total time savings on data search and validation for data team (hours) | B1*B2*B3*B4 *250 |
30,000 | 30,000 | 30,000 | |
| B6 | Fully burdened hourly rate for a data analyst | TEI methodology | $50.00 | $50.00 | $50.00 | |
| Bt | Improved productivity in data search and preparation for data teams. | B5*B6 | $1,500,000 | $1,500,000 | $1,500,000 | |
| Risk adjustment | ↓20% | |||||
| Btr | Improved productivity in data search and preparation for data teams. (risk-adjusted) | $1,200,000 | $1,200,000 | $1,200,000 | ||
| Three-year total: $3,600,000 | Three-year present value: $2,984,222 | |||||
Reduced Analytics Effort And Duplication For Data Teams
Evidence and data. Interviewees highlighted that prior to adopting Huwise, data analytics teams received frequent, repetitive data requests from business users, many of which required recreating similar datasets or reports multiple times. This led to duplicated effort across teams, complexity in establishing a single source of truth, and inefficient use of analyst resources. With Huwise, business users were able to self-serve from a centralized repository of curated data products, significantly reducing the volume of incoming requests. Analysts also benefited from increased reuse of existing data assets, minimizing the need to rebuild similar reports or datasets from scratch.
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
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Data and analytics teams receive 160 data requests annually, a significant portion of which are duplicative. With Huwise enabling self-service and reuse of certified data products, the total number of data requests received by analytics teams decreases by approximately 75%.
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It used to take an average of 16 hours for data teams to create a new data product. With Huwise, they reduce this time and effort by 50%.
Risks. Some factors that can impact the reduction of duplicated work include:
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Change management challenges, or resistance from employees to shift toward more self-service.
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General data maturity of an organization, including employees’ data literacy skills and data product quality.
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 $223,000.
75%
Reduction in number of new data products created annually, resulting in fewer, yet trusted, high-value data products
Reduced Analytics Effort And Duplication For Data Teams
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|
| C1 | New data products created before Huwise (annually) | Interviews | 160 | 160 | 160 | |
| C2 | New data products created with Huwise (annually) | 25%*C1 | 40 | 40 | 40 | |
| C3 | Analyst time to create a new data product (hours) | Interviews | 16 | 16 | 16 | |
| C4 | Reduction in time to create a new data product (hours) | 50%*C3 | 8 | 8 | 8 | |
| C5 | Total time savings on data product creation (hours) | (C1*C3)-(C2*C4) | 2,240 | 2,240 | 2,240 | |
| C6 | Fully burdened hourly rate for a data analyst | TEI methodology | $50.00 | $50.00 | $50.00 | |
| Ct | Reduced analytics effort and duplication for data teams | C5*C6 | $112,000 | $112,000 | $112,000 | |
| Risk adjustment | ↓20% | |||||
| Ctr | Reduced analytics effort and duplication for data teams (risk-adjusted) | $89,600 | $89,600 | $89,600 | ||
| Three-year total: $268,800 | Three-year present value: $222,822 | |||||
Avoided IT Costs
Evidence and data. Interviewees reported that prior to Huwise, fragmented data environments drove significant reliance on IT support for data access, troubleshooting, and tool-related issues. For instance, with each new data report or dashboard that data analysts had to build, IT teams were involved in activities such as building custom data connections and ensuring the right data access.
Additionally, there were often multiple overlapping tools and data licenses across departments due to lack of standardization. With Huwise providing a unified platform for data access and consumption, organizations experienced a reduction in IT support tickets related to data issues as well as improved visibility into tool usage. This also enabled consolidation of redundant tools and licenses, lowering overall IT spend.
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
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IT support tickets and data requests decrease at the same rate, as described in the previous section.
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There is a license consolidation for 50% of data analysts (or power users) on data visualization tools as they transition to using Huwise, equivalent to $200 per user annually.
Risks. Some factors that can impact IT cost savings include:
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Integration complexity.
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Ease and timing of consolidating legacy tools and licenses.
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 $111,000.
Avoided IT Costs
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|
| D1 | IT support tickets avoided with Huwise | C1-C2 | 120 | 120 | 120 | |
| D2 | IT time spent per support ticket (hours) | Interviews | 6 | 6 | 6 | |
| D3 | Total IT time avoided with Huwise (hours) | D1*D2 | 720 | 720 | 720 | |
| D4 | Fully burdened hourly rate for an IT employee | TEI methodology | $50.00 | $50.00 | $50.00 | |
| D5 | IT support costs avoided | D3*D4 | $36,000 | $36,000 | $36,000 | |
| D6 | Data licenses avoided | 50%*B1*B2 | 100 | 100 | 100 | |
| D7 | Average annual license fees | Composite | $200 | $200 | $200 | |
| D8 | Total license fees avoided | D6*D7 | $20,000 | $20,000 | $20,000 | |
| Dt | Avoided IT costs | D5+D8 | $56,000 | $56,000 | $56,000 | |
| Risk adjustment | ↓20% | |||||
| Dtr | Avoided IT costs (risk-adjusted) | $44,800 | $44,800 | $44,800 | ||
| Three-year total: $134,400 | Three-year present value: $111,411 | |||||
Unquantified Benefits
Interviewees mentioned the following additional benefits that their organizations experienced but were not able to quantify:
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Increased value realization from prior data investments. Although organizations have invested heavily in data lakes, data catalogs, and warehouses, these platforms often remain inaccessible or underutilized by business users, limiting their real-world impact. Huwise bridges this gap by connecting, harmonizing, and contextualizing existing data assets, making them directly usable for decision-making, analytics, and AI — thereby unlocking tangible value from these prior investments.
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Improved data literacy and data‑driven culture. Through easier and more intuitive data discovery, embedded analytics, and AI-boosted data consumption features, Huwise makes data more accessible and understandable for all users. Over time, this supports stronger data literacy, higher user engagement, and greater confidence using data in decision-making.
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Increased organizational agility and future readiness for agentic AI enablement. Huwise provides a single, trusted entry point to curated data by standardizing how organizations describe, discover, and reuse data assets. This feature enables scalable data access, which supports AI-driven and self-service workflows and reduces time spent on data search and preparation.
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Simplified deployment and reduced long‑term operational costs. Compared to building and maintaining a custom data marketplace in-house, interviewees noted that Huwise was faster to deploy and easier to maintain. This simplicity reduced reliance on IT and engineering resources and minimized ongoing platform management effort.
Flexibility
The value of flexibility is unique to each customer. There are multiple scenarios in which a customer might implement Huwise and later realize additional uses and business opportunities, including:
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Expansion of data product use cases. Organizations can extend Huwise beyond initial reporting use cases to support advanced analytics, AI models, and operational data products without significant rework.
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Onboarding of new data domains and sources. As business needs evolve, teams can easily integrate new data assets (e.g., external data, real-time streams) into the marketplace, expanding their value over time.
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Enablement of new AI and automation initiatives. With Huwise’s trusted and well-governed data foundation, organizations can accelerate their adoption of emerging AI use cases such as agentic workflows and intelligent automation.
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New monetization or data-sharing opportunities. Organizations may leverage Huwise to package and share data products across internal business units or with external partners.
Flexibility would also be quantified when evaluated as part of a specific project (described in more detail in Total Economic Impact Approach).
Analysis Of Costs
Quantified cost data as applied to the composite
Total Costs
| Ref. | Cost | Initial | Year 1 | Year 2 | Year 3 | Total | Present Value |
|---|---|---|---|---|---|---|---|
| Etr | Huwise license fees | $0 | $308,000 | $385,000 | $495,000 | $1,188,000 | $970,083 |
| Ftr | Implementation and deployment costs | $162,150 | $93,449 | $93,449 | $93,449 | $442,497 | $394,544 |
| Total costs (risk-adjusted) | $162,150 | $401,449 | $478,449 | $588,449 | $1,630,497 | $1,364,627 |
Huwise License Fees
Evidence and data. Interviewees reported that Huwise license fees were structured as an annual subscription and scaled over time as usage expanded across teams, use cases, and data volumes. License costs typically increased as organizations onboarded more users, integrated additional data sources, and extended Huwise to support broader AI‑driven use cases.
Modeling and assumptions. Based on the interviews, Forrester assumes the composite organization licenses Huwise on a subscription basis with costs that increase over time as adoption scales. The composite organization begins with a lower license fee in Year 1, reflecting an initial rollout to core data teams and priority business users and expands usage in Years 2 and 3 as adoption grows across business functions, regions, and AI use cases.
Risks. Actual license fees may vary based on the following factors:
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Organization size, data volume, number of users, and selected functionality.
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Faster or broader adoption than modeled could increase license costs beyond initial assumptions.
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Changes in organizational priorities or scope of AI initiatives could alter licensing needs over time.
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 $970,000.
Huwise License Fees
| Ref. | Metric | Source | Initial | Year 1 | Year 2 | Year 3 |
|---|---|---|---|---|---|---|
| E1 | Huwise license fees | Composite | $280,000 | $350,000 | $450,000 | |
| Et | Huwise license fees | E1 | $0 | $280,000 | $350,000 | $450,000 |
| Risk adjustment | ↑10% | |||||
| Etr | Huwise license fees (risk-adjusted) | $0 | $308,000 | $385,000 | $495,000 | |
| Three-year total: $1,188,000 | Three-year present value: $970,083 | |||||
Implementation And Deployment Costs
Evidence and data. Interviewees described the technical setup as relatively fast, with most effort focused on documentation, governance alignment, and change management. They noted that as a SaaS platform with an intuitive design, Huwise was easy to use and administer. Interviewees also highlighted strong vendor support during early dashboard and use‑case development, which helped accelerate initial adoption.
They reported a fast learning curve and noted that the consumer‑grade user experience enabled analysts and business users, including low‑data‑literacy users, to become productive with minimal or no formal training.
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
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The composite organization incurs one‑time implementation and deployment costs during the initial rollout of Huwise.
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These costs include internal IT and data team effort for platform setup, data source integration, and initial onboarding of analysts and business users.
Risks. Actual implementation effort may vary depending on the complexity of existing data infrastructure and number of integrations required.
Results. To account for these risks, Forrester adjusted this cost upward by 15%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $395,000.
Implementation And Deployment Costs
| Ref. | Metric | Source | Initial | Year 1 | Year 2 | Year 3 |
|---|---|---|---|---|---|---|
| F1 | Platform setup and configuration time (hours) | Interviews | 80 | |||
| F2 | Data source integration time (hours) | Interviews | 60 | |||
| F3 | FTEs involved in implementation | Interviews | 3 | |||
| F4 | Total IT time spent on implementation (hours) | (F1+F2)*F3 | 420 | |||
| F5 | Fully burdened hourly rate for an IT employee | D4 | $50.00 | |||
| F6 | IT implementation costs | F4*F5 | $21,000 | |||
| F7 | Data analyst onboarding time (hours per FTE) | Interviews | 12 | |||
| F8 | Total time spent on onboarding for data analysts (hours) | F7*B1*B2 | 2,400 | |||
| F9 | Business user onboarding time (hours) | Interviews | 4 | 4 | 4 | |
| F10 | Business users onboarded | 5%*A1 | 500 | 500 | 500 | |
| F11 | Total time spent on onboarding for business users (hours) | F9*F10 | 2,000 | 2,000 | 2,000 | |
| F12 | Fully burdened hourly rate for a data analyst/employee | TEI methodology | $50.00 | $40.63 | $40.63 | $40.63 |
| F13 | Total onboarding effort | (F8+F11)*F12 | $120,000 | $81,260 | $81,260 | $81,260 |
| Ft | Implementation and deployment costs | F6+F13 | $141,000 | $81,260 | $81,260 | $81,260 |
| Risk adjustment | ↑15% | |||||
| Ftr | Implementation and deployment costs (risk-adjusted) | $162,150 | $93,449 | $93,449 | $93,449 | |
| Three-year total: $442,497 | Three-year present value: $394,544 | |||||
Financial Summary
Consolidated Three-Year, Risk-Adjusted Metrics
Cash Flow Chart (Risk-Adjusted)
Cash Flow Analysis (Risk-Adjusted)
| Initial | Year 1 | Year 2 | Year 3 | Total | Present Value | |
|---|---|---|---|---|---|---|
| Total costs | ($162,150) | ($401,449) | ($478,449) | ($588,449) | ($1,630,497) | ($1,364,627) |
| Total benefits | $0 | $2,350,150 | $3,365,900 | $3,873,775 | $9,589,825 | $7,828,660 |
| Net benefits | ($162,150) | $1,948,701 | $2,887,451 | $3,285,326 | $7,959,328 | $6,464,033 |
| ROI | 474% | |||||
| Payback | <6 months |
Please Note
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, Forrester constructed a Total Economic Impact™ framework for those organizations considering an investment in Huwise.
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 Huwise can have on an organization.
Due Diligence
Interviewed Huwise stakeholders and Forrester analysts to gather data relative to its data product marketplace.
Interviews
Interviewed four decision-makers at organizations using Huwise to obtain data about costs, benefits, and risks.
Composite Organization
Designed a composite organization based on characteristics of the interviewees’ organizations.
Financial Model Framework
Constructed a financial model representative of the interviews using the TEI methodology and risk-adjusted the financial model based on issues and concerns of the interviewees.
Case Study
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.
Total Economic Impact Approach
Benefits
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
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
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
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.”
Financial Terminology
Present value (PV)
The present or current value of (discounted) cost and benefit estimates given at an interest rate (the discount rate). The PVs of costs and benefits feed into the total NPV of cash flows.
Net present value (NPV)
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.
Return on investment (ROI)
A project’s expected return in percentage terms. ROI is calculated by dividing net benefits (benefits less costs) by costs.
Discount rate
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%.
Payback
The breakeven point for an investment. This is the point in time at which net benefits (benefits minus costs) equal initial investment or cost.
Appendix A
Total Economic Impact
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.
Disclosures
Readers should be aware of the following:
This study is commissioned by Huwise 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 the Huwise data marketplace solution for any interactive functionality, the intent is for the questions to solicit inputs specific to a prospect’s business. Forrester believes that this analysis is representative of what companies may achieve with Huwise based on the inputs provided and any assumptions made. Forrester does not endorse Huwise or its offerings. Although great care has been taken to ensure the accuracy and completeness of this model, Huwise and Forrester Research are unable to accept any legal responsibility for any actions taken on the basis of the information contained herein. The interactive tool is provided ‘AS IS,’ and Forrester and Huwise make no warranties of any kind.
Huwise 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.
Huwise provided the customer names for the interviews but did not participate in the interviews.
Consulting Team:
Josephine Phua
Published
September 2026
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