The Total Economic Impact™ Of Dataiku
Cost Savings And Business Benefits Enabled By Dataiku
A Forrester Total Economic Impact™ Study Commissioned By Dataiku, February 2024
In 2024, Forrester predicts that enterprises will be proactive and motivated to develop a meaningful AI strategy, while still considering governance and risk concerns.1 Dataiku offers organizations an AI platform that empowers them in their data analytics efforts and caters to both data and business users. This analysis found that by using Dataiku, organizations experience significant efficiency savings for both data and business users, as well as improved decision-making and considerable cost reductions.
Dataiku is a software company that offers an AI platform to help organizations get the ultimate value from data. Their platform enables organizations to build, deploy, and monitor machine learning models (including both traditional analytics projects and generative AI); perform data preparation and exploration; and design machine learning workflows. Dataiku aims to empower all individuals in an organization to extract valuable insights from their data and make data-driven decisions.
Dataiku commissioned Forrester Consulting to conduct a Total Economic Impact™ (TEI) study and examine the potential return on investment (ROI) enterprises may realize by deploying Dataiku.2 The purpose of this study is to provide readers with a framework to evaluate the potential financial impact of Dataiku on their organizations.
Return on investment (ROI)
413%
Net present value (NPV)
$23.50M
To better understand the benefits, costs, and risks associated with this investment, Forrester interviewed four representatives with experience using Dataiku. For the purposes of this study, Forrester aggregated the interviewees’ experiences and combined the results into a single composite organization that has $10 billion in revenue and is looking to advance its data analytics capabilities.
Interviewed data leaders said that prior to using Dataiku, their organizations relied on in-house tools, which did not meet their analytics needs — especially for AI projects. These in-house tools also did not offer multicloud deployment options. The interviewees’ organizations additionally relied on external third-party vendor models, which lacked transparency and incurred significant consulting expenses. The interviewed decision-makers had concerns about the lack of collaboration and scalability. This was due to data not being appropriately shared across teams and departments, leading to inaccurate data analysis from siloed data. Forrester research suggests that AI/ML platforms can enable AI teams to effectively collaborate, ideate, develop, test, deploy, and monitor AI applications.3
After the investment in Dataiku, the interviewees noted how their organizations democratized access to and the usage of data to multiple roles across their organizations. Interviewees witnessed a substantial positive impact, especially from a standpoint of user productivity, cost efficiency, and decision-making improvements.
Key Findings
Quantified benefits. Three-year, risk-adjusted present value (PV) quantified benefits for the composite organization include:
- Increased user productivity. The Dataiku platform offers the composite’s data science, data engineering, business user, and business analysts teams a place to work with data. Users are empowered to use the platform and leverage the data they have available. As a result, by using the Dataiku platform, users see a significant improvement in their productivity on a day-to-day basis and across different phases of their work, from data extraction and analysis to model deployment and monitoring.
- Reduced costs on data analytics tools and consultancies/third-party providers. Before Dataiku, the composite organization relied on third-party tools and organizations to carry out data analytics work. By using Dataiku and empowering its own workforce to work with data, the composite realizes substantial cost savings because it no longer needs to leverage as many external tools and/or third-party organizations.
- Business users’ efficiency savings. By leveraging the models developed with Dataiku’s help, business users and data analytics users at the composite realize considerable efficiency savings. This is especially true when it comes to manual processes that previously required a substantial manual effort and are now automated thanks to a data science solution developed through Dataiku.
- Improved decision-making. Lastly, Dataiku supports the composite organization from a top-line standpoint, impacting the composite’s revenue directly. In fact, the composite uses Dataiku in various activities that impact revenue, including but not limited to risk estimation efforts and loss recovery activities. The composite’s users are now better suited to carry out these activities thanks to the democratization of data access and usage.
Unquantified benefits. Benefits that provide value for the interviewees’ organizations but are not quantified for this study include:
- Faster time to value. By using the Dataiku solution, the interviewees’ organizations’ users brought solutions to market faster than in their previous environments.
- Democratized access to data. The interviewees’ organizations’ users, whether proficient data scientists or business users, used and gained value from the Dataiku platform. The democratization of data access and usage made the platform desirable for both data and business users.
- Enhanced collaboration and knowledge sharing. By using the Dataiku platform, interviewees noted that their users were encouraged to share knowledge, resulting in increased levels of collaboration across teams and departments.
- Improved user experience. The interviewees noted their organizations’ users were extremely satisfied by the ease of use of the platform and positively called out the Dataiku UX.
- Increased data security and compliance. When working with Dataiku, the interviewees noted their users leveraged their in-house data without relying on third parties.
- Improved governance. When using Dataiku, interviewees noted that they could apply different governance models to different use cases, and users were empowered to use Dataiku by themselves in a safe environment. Interviewees’ organizations benefited from centralized control and visibility around what users were doing, therefore improving governance. This resulted in increased levels of data security and compliance within the interviewees’ organizations.
Costs. Three-year, risk-adjusted PV costs for the composite organization include:
- Platform license fees. Dataiku charges the composite based on the number of users who are active on the platform.
- Implementation and ongoing management costs. These costs are associated with the initial implementation of the Dataiku platform and the ongoing management of it. The ongoing management of the platform can be divided into two categories: user support activities and platform admin and training activities.
- User training costs. The composite dedicates time and resources to train its users on the Dataiku platform.
The representative interviews and financial analysis found that a composite organization experiences benefits of $29.19 million over three years versus costs of $5.69 million, adding up to a net present value (NPV) of $23.50 million and an ROI of 413%.
Key Statistics
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Return on investment (ROI)
413%
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Benefits PV
$29.19M
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Net present value (NPV)
$23.50M
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Payback
<6 months
Benefits (Three-Year)
Increased user productivity Reduced costs on data analytics tools and consultancies/thired party providers Business users' efficiency savings Improved decision makingTEI Framework And Methodology
From the information provided in the interviews, Forrester constructed a Total Economic Impact™ framework for those organizations considering an investment Dataiku.
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 Dataiku can have on an organization.
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Due Diligence
Interviewed Dataiku stakeholders and Forrester analysts to gather data relative to Dataiku.
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Interviews
Interviewed four representatives at organizations using Dataiku to obtain data about costs, benefits, and risks.
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Composite Organization
Designed a composite organization based on characteristics of the interviewees’ organizations.
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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.
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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.
Disclosures
Readers should be aware of the following:
This study is commissioned by Dataiku 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 Dataiku.
Dataiku 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.
Dataiku provided the customer names for the interviews but did not participate in the interviews.
Consulting Team:
Elia Gollini
Bharath Sivan
Sanny Mok
Drivers leading to the Dataiku investment
Interviews
| Role | Industry | Number Of | Revenue |
|---|---|---|---|
| Chief data scientist | Financial services | 500 | $9 billion |
| Head of data and analytics | Fashion | 350 | $6 billion |
| Analytics and data science product owner | Pharmaceuticals | 900 | $50 billion |
| Data science lead | Energy | 1,100 | $8 billion |
Key Challenges
Before investing in Dataiku, interviewees noted they had siloed data and individual data analysis across their organizations. They needed a shared data platform to empower business and data users. The interviewees noted how their organizations struggled with common challenges, including:
- Empowering both data and business users to work with data and make data-driven decisions. Interviewees highlighted how they were struggling to democratize access to data across their organizations, especially in making business users work with and make decisions based on data. The head of data and analytics at a fashion business noted, “We wanted a common platform that could support all types of users, from those with high levels of sophistication to those with business analyst capabilities.”
- Enabling scalability and the ability to handle complex data and analytics on desktops. Prior to leveraging Dataiku, the interviewed decision-makers struggled to scale their data and analytics initiatives and were challenged when dealing with multiple data sources scattered across their organizations. However, Dataiku provided a solution for such a challenge. The chief data scientist at a financial services organization stated: “Dataiku has allowed us to scale beyond the limitations of desktop- or server-based solutions. With its compatibility with our cloud infrastructure and its ability to handle large volumes of data, we have been able to consolidate our data and empower our users to work with it more efficiently.”
- Removing data silos and inefficiencies caused by a lack of shared data. Before using Dataiku, interviewees shared how it was difficult for them to guarantee up-to-date information and access to data to its users, leading to an overall lack of shared data across teams. The chief data scientist at a financial services firm noted: “Dataiku has played a crucial role in breaking down data silos within our organization. With its shared data environment and data catalog, we have been able to promote data sharing and collaboration across teams.”
- Integrating and managing external data sources. Interviewees shared common challenges around the difficulties they faced due to the variety of external sources of data they leverage. The interviewees’ organizations lacked the tools and capabilities to efficiently connect and manage external sources of data.
Solution Requirements
The interviewees’ organizations searched for a solution that could:
- Be scalable and compatible with different cloud providers.
- Cater to both business users and coders.
- Handle a wide range of data sources.
- Have an open-source nature and user-friendly interface.
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 four interviewees, and it is used to present the aggregate financial analysis in the next section. The composite organization has the following characteristics:
Description of composite. A global organization with $10 billion in revenue and 40,000 employees that is looking to advance its data analytics capabilities. The composite has siloed data and individual data analysis. It needed a shared data platform to empower business and data users. In its previous state, the composite faced key challenges like democratizing data access and usage, improving efficiency, reducing time to value, automating manual processes, and improving compliance. Dataiku helps the composite overcome these challenges.
Deployment characteristics. Though Dataiku does offer a cloud-based solution, the composite has an on-premises Dataiku deployment and starts using Dataiku with 50 licenses in Year 1. In Years 2 and 3, the number of licenses and users of Dataiku increases to 300 and 700, respectively. Half of the users can be categorized as data science/data engineering users, while the other half represents business users and data analysts.
Quantified benefit data as applied to the composite
Total Benefits
| Ref. | Benefit | Year 1 | Year 2 | Year 3 | Total | Present Value |
|---|---|---|---|---|---|---|
| Atr | Increased user productivity | $541,048 | $3,232,921 | $7,521,205 | $11,295,173 | $8,814,489 |
| Btr | Reduced costs on data analytics tools and consultancies/third-party providers | $860,000 | $1,400,000 | $2,240,000 | $4,500,000 | $3,621,788 |
| Ctr | Business users’ efficiency savings | $67,680 | $487,296 | $1,516,032 | $2,071,008 | $1,603,269 |
| Dtr | Improved decision-making | $2,760,000 | $5,040,000 | $11,280,000 | $19,080,000 | $15,149,211 |
| Total benefits (risk-adjusted) | $4,228,728 | $10,160,217 | $22,557,237 | $36,946,181 | $29,188,757 | |
Increased User Productivity
Evidence and data. Interviewees highlighted how using the Dataiku platform helped their data science and data engineer users benefit from substantial efficiency savings. Users reused data products and models, improving their efficiency.
- Interviewees noted that when working on data initiatives, their data scientists and engineers needed to go through various phases to develop a data product or project. The key phases were data analysis, extraction and preparation, and the model lifecycle phases (including model training, evaluation and validation, model deployment, and model monitoring).
- Interviewees experienced significant efficiency savings across all phases of their data work. The data science lead at an energy company highlighted how the time needed per project for data analysis and extraction decreased from around 200 hours per project to around 30 hours per project.
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
- Half of the composite users are data scientists and data engineers and work on the activities of data analysis, extraction, and preparation.
- Out of the data scientists and engineers, there is a subset of users who also work on the model lifecycle activities. Seven users do so in Year 1, 40 in Year 2 and 90 in Year 3 as the composite users working on Dataiku grow.
- The composite experiences a time reduction of 71% on average for activities of data analysis, extraction, and preparation.
- The composite experiences a time reduction of 42% on average for model lifecycle activities, including model training, deployment, and monitoring.
- The average developer fully burdened salary is $78 per hour. The standard TEI burden rate is 35% and accounts for additional costs of employment, such as benefits (e.g., healthcare, insurance, bonuses, etc.), technology, office space, and employer taxes.
- A productivity capture rate of 50% is applied as a TEI standard as it can be assumed that only a certain part of the time saved is reallocated by users to productive work
Risks. This benefit may vary for different organizations based on:
- The number of data scientists and data engineers working on data projects.
- The time dedicated to the data analysis, extraction, and preparation activities as well as the model lifecycle activities.
- The average developer salary.
- The organization’s previous ability to work on model lifecycle activities. It is important to note that some organizations may not have been able to work on model lifecycle activities before using Dataiku. One interviewee noted that before using Dataiku, their organization did not have the capacity and ability to work on model lifecycle activities, and therefore cannot quantify the efficiency savings as in the previous environment model lifecycle activities were simply not possible. In this specific case, the benefit would be the enablement of the model lifecycle activities itself rather than an efficiency saving.
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 $8.8 million.
Increased User Productivity
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 |
|---|---|---|---|---|---|
| A1 | Total number of data science/analytics projects | Composite | 4 | 20 | 50 |
| A2 | Total number of users extracting and analyzing data (data scientists/data engineers) | Interviews | 25 | 150 | 350 |
| A3 | Time needed before Dataiku for data extraction and analysis per user (days) | Interviews | 105 | 105 | 105 |
| A4 | Percentage reduction in time needed to extract and analyze data due to Dataiku | Interviews | 71% | 71% | 71% |
| A5 | Average developer salary (daily) | TEI standard | $624 | $624 | $624 |
| A6 | Productivity capture rate | TEI standard | 50% | 50% | 50% |
| A7 | Subtotal: Faster data analysis and extraction | A2*A3*A4*A5*A6 | $581,490 | $3,488,940 | $8,140,860 |
| A8 | Total number of users working on model training, deployment, and monitoring (data scientists/data engineers) | Interviews | 7 | 40 | 90 |
| A9 | Time needed for model lifecycle before Dataiku (days) | Interviews | 60 | 60 | 60 |
| A10 | Percentage reduction in model lifecycle time due to Dataiku | Interviews | 42% | 42% | 42% |
| A11 | Subtotal: Model lifecycle efficiency savings | A8*A9*A10*A5*A6 | $55,037 | $314,496 | $707,616 |
| At | Increased user productivity | A7+A11 | $636,527 | $3,803,436 | $8,848,476 |
| Risk adjustment | ↓15% | ||||
| Atr | Increased user productivity (risk-adjusted) | $541,048 | $3,232,921 | $7,521,205 | |
| Three-year total: $11,295,173 | Three-year present value: $8,814,489 | ||||
Reduced Costs On Data Analytics Tools And Consultancies/Third-Party Providers
Evidence and data. Before using the Dataiku platform, interviewees underlined how their organizations relied on many additional tools to enable data analysis within their organizations and leveraged external third parties, such as consultancies, to carry out data analysis work. By using Dataiku and enabling their own users to work with data, the interviewees’ organizations realized cost savings from data analytics tools and consultancies/third-party providers. The analytics and data science product owner at a pharmaceutical company has highlighted: “We are moving around 250 users from a statistical tool to Dataiku. Our annual contract was around $2.2 million or $2.1 million. Now, we have decommissioned that.”
The same interviewee also said: “Earlier, we used to send data to a consulting company, and they used to run it for us. They were charging us nearly a million dollars a year to run it. We stopped this and brought it in-house.”
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
- The composite reduces the number of licenses for other analytical tools gradually. In Year 1, 50 licenses are eliminated, while 100 are eliminated in Year 2 and 200 in Year 3 as it expands Dataiku adoption and trains more employees to use the platform.
- The average analytical tool license fee is $6,500 per user per year, including management time for legacy tools.
- The cost avoidance for leveraging consultancies goes up from $750,000 in Year 1 to $1.1 million in Year 2 and $1.5 million in Year 3 as more composite users become proficient with Dataiku and data analysis work is gradually moved in-house.
Risks. This benefit may vary for different organizations based on:
- An organization’s level of reliance on external analytical tools and consultancies/third-party providers.
- The speed at which an organization moves away from external tools and consultancies/third-party providers.
- The average analytical tool license fee and consultancies fees.
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 $3.6 million.
Reduced Costs On Data Analytics Tools And Consultancies/Third-Party Providers
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|
| B1 | Number of licenses reduced from other analytical tools | Interviews | 50 | 100 | 200 | |
| B2 | Average analytical tool license fee per user | Interviews | $6,500 | $6,500 | $6,500 | |
| B3 | Reduced cost on data analytics tools | B1*B2 | $325,000 | $650,000 | $1,300,000 | |
| B4 | Cost avoidance from leveraging consultancies/third-party providers | Interviews | $750,000 | $1,100,000 | $1,500,000 | |
| Bt | Reduced costs on data analytics tools and consultancies/third-party providers | B3+B4 | $1,075,000 | $1,750,000 | $2,800,000 | |
| Risk adjustment | ↓20% | |||||
| Btr | Reduced costs on data analytics tools and consultancies/third-party providers (risk-adjusted) | $860,000 | $1,400,000 | $2,240,000 | ||
| Three-year total: $4,500,000 | Three-year present value: $3,621,788 | |||||
Business Users’ Efficiency Savings
Evidence and data. The interviewed data leader have highlighted how the outcomes of the work done on Dataiku benefited their entire organizations. Their organizations realized efficiencies thanks to Dataiku, mainly due to the automation of a variety of manual processes. Interviewees provided various examples of processes that previously required substantial manual effort and that were automated:
- The chief data scientist and a financial services organization mentioned: “We had a regulatory and compliance piece that took on average six months and required development resources. Now, it takes a week and does not require development resources but business resources.”
- The head of data and analytics at a fashion company underlined: “We had a lot of cases where people would be trying to merge information that they had on their desktop, whether it’s something from spreadsheets or something they got from a third party. These processes are now automated thanks to Dataiku.”
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
- Half of the composite’s users are business users and data analysts. Each spend 60 days on manual tasks prior to adopting Dataiku.
- Automation reduces manual work by 50% in Year 1, 60% in Year 2, and 80% in Year 3 as users become more proficient with the Dataiku platform.
- The average fully-burdened business user salary is $47 per hour.
- A productivity capture rate of 30% is applied as there is little visibility onto what is done by users with the time saved and considering that we have a broad group of employees with highly variable ways in which the time savings may impact them and how it might benefit the business.
Risks. This benefit may vary for different organizations based on:
- The number of business users and data analysts leveraging data in an organization.
- The amount of manual processes involved in the users’ day-to-day jobs.
- The business users/data analysts’ salaries.
- The way in which the time saved is reinvested into valuable activities for the organization.
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 $1.6 million.
Business Users’ Efficiency Savings
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|
| C1 | Number of business users | Composite | 25 | 150 | 350 | |
| C2 | Days spent on manual processes before Dataiku | Interviews | 60 | 60 | 60 | |
| C3 | Percentage of time saved due to Dataiku | Interviews | 50% | 60% | 80% | |
| C4 | Average business user salary (hourly) | TEI standard | $47 | $47 | $47 | |
| C5 | Productivity capture rate | TEI standard | 30% | 30% | 30% | |
| Ct | Business users’ efficiency savings | C1*C2*C3*(C4*8)*C5 | $84,600 | $609,120 | $1,895,040 | |
| Risk adjustment | ↓20% | |||||
| Ctr | Business users’ efficiency savings (risk-adjusted) | $67,680 | $487,296 | $1,516,032 | ||
| Three-year total: $2,071,008 | Three-year present value: $1,603,269 | |||||
Improved Decision-Making
Evidence and data. Interviewees described the impact Dataiku has on their companies’ revenues. The interviewees organizations used Dataiku for analysis and prediction purposes. As Dataiku adoption increased, the percentage of revenue that was analyzed and predicted increased throughout the years and interviewees attributed a part of the impact directly to Dataiku. On top of this, interviewees noted their organizations also used Dataiku for risk estimation use cases and recovered losses they would have previously faced thanks to the democratized usage and access of data across their organizations. The data science lead at an energy company emphasized how now their organization tackled issues precisely because of the data democratization enabled by Dataiku, stating, “The key value is that the platform is easy enough to be used by, for example, a maintenance technician and not only by someone with a data science degree.”
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
- The percentage of revenue impacted by Dataiku’s usage goes gradually up from 10% in Year 1, 15% in Year 2, and 30% in Year 3.
- One percent of each year’s revenue impact is attributed to Dataiku. This 1% of revenue would not have been captured by the composite without using the Dataiku platform.
- The average amount of yearly losses the composite previously faced that could not be tackled is estimated to be around $37.5 million yearly.
- Before Dataiku, most issues could not be identified and tackled as users were not able to use data effectively to identify and tackle the issues. In Year 1, 15% of issues can be identified due to Dataiku’s data access and usage democratization. In Year 2, the percentage goes up to 30% and it rises to 70% in Year 3.
- An operating profit margin of 12% is applied as per TEI standards.
- A 40% Dataiku attribution is applied to the loss recovery for the issues that can be identified due to Dataiku’s data access and usage democratization. This is because there may be other factors, such as process changes or trainings, leading to the loss recovery not necessarily dependent on Dataiku. Issues may also still occur even though they can be identified with Dataiku.
Risks. This benefit may vary for organizations based on:
- An organization’s revenue.
- The percentage of revenue impacted by Dataiku.
- An organization’s operating profit margins.
- The average amount of yearly losses incurred by an organization.
- Other use cases that organizations may have other than the ones mentioned in which Dataiku can enhance decision-making, ultimately leading to a revenue improvement.
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 $15.1 million.
Improved Decision-Making
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|
| D1 | Composite revenue | Composite | $10,000,000,000 | $10,000,000,000 | $10,000,000,000 | |
| D2 | Percentage of revenue impacted by Dataiku’s usage | Interviews | 10% | 15% | 30% | |
| D3 | Percentage of revenue realized due to Dataiku | Interviews | 1% | 1% | 1% | |
| D4 | Operating profit margin | TEI standard | 12% | 12% | 12% | |
| D5 | Subtotal: Commercial impact on revenue due to Dataiku | D1*D2*D3*D4 | $1,200,000 | $1,800,000 | $3,600,000 | |
| D6 | Average amount of yearly losses faced that could not be tackled before Dataiku | Interviews | $37,500,000 | $37,500,000 | $37,500,000 | |
| D7 | Percentage of issues that can be identified due to Dataiku’s data access and usage democratization | Interviews | 15% | 30% | 70% | |
| D8 | Dataiku attribution | Composite | 40% | 40% | 40% | |
| D9 | Subtotal: Ability to recover losses due to Dataiku’s data democratization | D6*D7*D8 | $2,250,000 | $4,500,000 | $10,500,000 | |
| Dt | Improved decision-making | D5+D9 | $3,450,000 | $6,300,000 | $14,100,000 | |
| Risk adjustment | ↓20% | |||||
| Dtr | Improved decision-making (risk-adjusted) | $2,760,000 | $5,040,000 | $11,280,000 | ||
| Three-year total: $19,080,000 | Three-year present value: $15,149,211 | |||||
Unquantified Benefits
Interviewees mentioned the following additional benefits that their organizations experienced but were not able to quantify:
- Faster time to value. Interviewees noted that by using Dataiku, their users not only realized efficiency savings, but also significantly reduced the time needed to bring new solutions to market. The data science lead at an energy company mentioned: “With Dataiku, we have been able to significantly reduce the time to market for solutions. What used to take us 12 to 18 months can now be achieved in three to six months, resulting in a year’s worth of business impact. It has allowed us to be more agile and responsive to the challenges we face.”
- Democratized access to data. Dataiku made data analysis and data-driven decision-making accessible to a wider range of employees. The platform supported both data proficient users as well as business users, leading to high levels of engagement from both sides.
- Enhanced collaboration and knowledge sharing. The adoption of Dataiku created a community-based help system within the interviewees’ organizations, where users could seek assistance and share knowledge. In this way, silos existing between teams and/or departments at the interviewees’ organizations were brought down.
- Improved user experience. Interviewees noted that Dataiku provided a seamless and user-friendly interface, making it easier for users at their organizations to access and use the platform. This resulted in increased user satisfaction and productivity.
- Increased data security and compliance. By bringing data processing in-house and utilizing the governance and compliance features of Dataiku, interviewees noted that their organizations improved data security and ensured compliance with industry regulations. The analytics and data science product owner at a pharmaceutical company has emphasized, “We don’t need to send any data outside, so our data privacy and security are intact.”
- Improved governance. Interviewees noted that the nature of the Dataiku platform gave their organizations a way to use different governance models for different use cases, allowing their users to use Dataiku by themselves in a safe environment. The head of data and analytics at a fashion company stated: “With Dataiku, we have everybody in the same place, and this gives us a level of central control because we have visibility to everything that’s going on. We can see all of the different use cases.”
Flexibility
The value of flexibility is unique to each customer. There are multiple scenarios in which a customer might implement Dataiku and later realize additional uses and business opportunities, including:
- Innovating with generative AI and LLM Mesh while keeping governance in mind. Thanks to the newly released LLM Mesh functionality, the interviewees could experiment and innovate in various ways and be reassured from a risk mitigation and compliance standpoint. The data science lead at an energy company pointed out, “Dataiku’s LLM Mesh provides a way to utilize LLMs [large language models] in a controlled and auditable manner, which allows us to ensure that the models used by the data scientists are validated from a legal perspective.” Additionally, the analytics and data science product owner from a pharmaceutical company has underlined: “I’m looking forward to the LLMs. That’s where the innovation comes into picture. The LLM Mesh will allow us to innovate in an agnostic way, which is of essence when it comes to data security and compliance.” Forrester research suggests that generative AI-powered applications are not built on a single LLM, but rather require different models to fulfill different tasks.4
- Developing generative AI (genAI) use cases. According to Forrester’s data, more than 90% of global enterprise AI decision-makers have concrete plans to implement generative AI both for internal and external customer-facing use cases.5 Dataiku enhances traditional AI use cases with genAI capabilities. Interviewees noted their organizations look forward to leveraging more and more of the Dataiku platform and its LLM Mesh functionality to drive efficiencies and help innovate. The head of data and analytics at a fashion company said: “We’ve got a lot of use cases going in the genAI space. We’ve got hundreds of people who are thinking about how to leverage genAI, not just querying on ChatGPT and writing a nice prompt but thinking about how to build it into a workflow, and we’ve got a couple of those use cases that went live already.”
- Improving efficiencies from a machine-learning operations (MLOps) standpoint. Interviewees highlighted how Dataiku continued to support their organizations in their MLOps endeavors. The analytics and data science product owner at a pharmaceutical company stated, “Dataiku will bring me more efficiency on what my processes are, and my operational efficiency will come in and I will save some operational cost.”
Flexibility would also be quantified when evaluated as part of a specific project (described in more detail in Appendix A).
Quantified cost data as applied to the composite
Total Costs
| Ref. | Cost | Initial | Year 1 | Year 2 | Year 3 | Total | Present Value |
|---|---|---|---|---|---|---|---|
| Etr | Platform license fees | $0 | $302,500 | $1,650,000 | $3,465,000 | $5,417,500 | $4,241,942 |
| Ftr | Implementation and ongoing maintenance costs | $124,080 | $215,072 | $322,608 | $430,144 | $1,091,904 | $909,392 |
| Gtr | User training costs | $0 | $48,720 | $243,600 | $389,760 | $682,080 | $538,446 |
| Total costs (risk-adjusted) | $124,080 | $566,292 | $2,216,208 | $4,284,904 | $7,191,484 | $5,689,780 | |
Platform License Fees
Evidence and data. Interviewees noted that Dataiku is charged on a per user basis. The platform license fee increased as the number of users in the interviewees’ organizations grows.
Modeling and assumptions. To quantify this cost, Forrester assumes the following:
- The composite has 50 licenses in Year 1, 300 in Year 2, and 700 in Year 3.
- The license fee charges per user decreases if the number of licenses significantly increases
Risks. This cost may vary for different organizations based on:
- The total number of licenses/active users.
- The rate at which an organization increases the number of licenses in the Dataiku platform.
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 $4.2 million.
Platform License Fees
| Ref. | Metric | Source | Initial | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|---|
| E1 | Total platform license fees | Interviews | $275,000 | $1,500,000 | $3,150,000 | ||
| Et | Platform license fees | E1 | $275,000 | $1,500,000 | $3,150,000 | ||
| Risk adjustment | ↑10% | ||||||
| Etr | Platform license fees (risk-adjusted) | $0 | $302,500 | $1,650,000 | $3,465,000 | ||
| Three-year total: $5,417,500 | Three-year present value: $4,241,942 | ||||||
Implementation And Ongoing Maintenance Costs
Evidence and data. Interviewees said their organizations allocated resources for the implementation of the Dataiku platform. Once the platform was implemented, the interviewees also dedicated resources to the ongoing management of the platform. These resources mainly focused on platform administration and training and user support.
Modeling and assumptions. To quantify this cost, Forrester assumes the following:
- The composite allocates four FTEs for 15 weeks for implementation.
- In Year 1, there are two resources dedicated to platform admin and training and user support. In Year 2, one extra resource is dedicated to training and user support and another extra resource is again dedicated to training and user support in Year 3.
- The average business user annual salary is $97,760.
- Although this is not the case for the composite organization, a Dataiku customer opting to use Dataiku Cloud could potentially experience lower ongoing management costs.
Risks. This cost may vary for different organizations based on:
- The length of implementation and the number of resources dedicated to it.
- The amount of resources dedicated to platform admin and training and user support.
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 $909,000.
Implementation And Ongoing Maintenance Costs
| Ref. | Metric | Source | Initial | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|---|
| F1 | FTEs dedicated to implementation | Interviews | 4 | ||||
| F2 | Length of implementation (days) | Interviews | 75 | ||||
| F3 | Platform admin FTEs | Composite | 1 | 1 | 1 | ||
| F4 | Training and user support FTEs | Composite | 1 | 2 | 3 | ||
| F5 | Average business user salary (yearly) | TEI standard | $97,760 | $97,760 | $97,760 | $97,760 | |
| Ft | Implementation and ongoing maintenance costs | F1*F2*(F6/260)+ ((F3+F4)*F5) | $112,800 | $195,520 | $293,280 | $391,040 | |
| Risk adjustment | ↑10% | ||||||
| Ftr | Implementation and ongoing maintenance costs (risk-adjusted) | $124,080 | $215,072 | $322,608 | $430,144 | ||
| Three-year total: $1,091,904 | Three-year present value: $909,392 | ||||||
User Training Costs
Evidence and data. Interviewees noted that user training on Dataiku differed depending on the type of user. Users needed to dedicate some of their own time to understand the platform and get used to working with it. The time needed for users to train on the platform varied depending on whether they were data scientists and engineers, or business users and data analysts.
Modeling and assumptions. To quantify this cost, Forrester assumes the following:
- Half the composite users are data scientists/data engineers, and the other half are business users/data analysts.
- Data scientists and data engineers dedicate 16 hours of their time to train on the Dataiku platform.
- Business users and data analysts dedicate 8 hours of their time to train on the Dataiku platform.
- The average developer salary is $78 per hour.
- The average business user salary is $47 per hour.
Risks. This cost may vary for different organizations based on:
- The split between data scientists/data engineers and business users/data analysts.
- The salaries of data scientists/data engineers and business users/data analysts.
- Users’ reluctance to move away from their existing analytics tools.
Results. To account for these risks, Forrester adjusted this cost upward by 20%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $538,000.
User Training Costs
| Ref. | Metric | Source | Initial | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|---|
| G1 | Number of data scientists and data engineers | Composite | 25 | 150 | 350 | ||
| G2 | Net-new data scientists and data engineers | Composite | 25 | 125 | 200 | ||
| G3 | Training hours per data scientist and data engineer | Assumption | 16 | 16 | 16 | ||
| G4 | Average data scientist and data engineer salary (hourly) | TEI standard | $78 | $78 | $78 | ||
| G5 | Number of data analysts and business users | Composite | 25 | 150 | 350 | ||
| G6 | Net new data analysts and business users | Composite | 25 | 125 | 200 | ||
| G7 | Training hours per data analysts and business users | Composite | 8 | 8 | 8 | ||
| G8 | Average data analyst and business users salary (hourly) | TEI standard | $47 | $47 | $47 | ||
| Gt | User training costs | (G2*G3*G4)+ | $0 | $40,600 | $203,000 | $324,800 | |
| Risk adjustment | ↑20% | ||||||
| Gtr | User training costs (risk-adjusted) | $0 | $48,720 | $243,600 | $389,760 | ||
| Three-year total: $682,080 | Three-year present value: $538,446 | ||||||
Consolidated Three-Year, Risk-Adjusted Metrics
Cash Flow Chart (Risk-Adjusted)
Total costs Total benefits Cumulative net benefits-
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.
Cash Flow Analysis (Risk-Adjusted Estimates)
| Initial | Year 1 | Year 2 | Year 3 | Total | Present Value | |
|---|---|---|---|---|---|---|
| Total costs | ($124,080) | ($566,292) | ($2,216,208) | ($4,284,904) | ($7,191,484) | ($5,689,780) |
| Total benefits | $0 | $4,228,728 | $10,160,217 | $22,557,237 | $36,946,181 | $29,188,757 |
| Net benefits | ($124,080) | $3,662,436 | $7,944,009 | $18,272,333 | $29,754,697 | $23,498,977 |
| ROI | 413% | |||||
| Payback | <6 months | |||||
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 vendors in communicating the value proposition of their products and services to clients. The TEI methodology helps companies demonstrate, justify, and realize the tangible value of IT initiatives to both senior management and other key business stakeholders.
Total Economic Impact Approach
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Benefits represent the value delivered to the business by the product. The TEI methodology places equal weight on the measure of benefits and the measure of costs, allowing for a full examination of the effect of the technology on the entire organization.
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Costs consider all expenses necessary to deliver the proposed value, or benefits, of the product. The cost category within TEI captures incremental costs over the existing environment for ongoing costs associated with the solution.
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Flexibility represents the strategic value that can be obtained for some future additional investment building on top of the initial investment already made. Having the ability to capture that benefit has a PV that can be estimated.
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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 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.
Appendix B: ENDNOTES
1 Source: “Predictions 2024: Artificial Intelligence,” Forrester Research, Inc., October 26, 2023.
2 Total Economic Impact is a methodology developed by Forrester Research that enhances a company’s technology decision-making processes and assists vendors in communicating the value proposition of their products and services to clients. The TEI methodology helps companies demonstrate, justify, and realize the tangible value of IT initiatives to both senior management and other key business stakeholders.
3 Source: “Enterprises Must Invest In AI Platforms To Empower Multirole AI Teams,” Forrester Research, Inc., August 26, 2022.
4 Source: “The Architect’s Guide To Generative AI,” Forrester Research, Inc., January 12, 2024.
5 Source: “The State Of Generative AI, 2024,” Forrester Research, Inc., January 26, 2024.
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