The Total Economic Impact™ Of Dagster+ Pro
Cost Savings And Business Benefits Enabled By Dagster+ Pro
A Forrester Total Economic Impact™ Study Commissioned By Dagster, October 2024
By deploying Dagster+ Pro, organizations may save time for their data engineering teams and be able to redeploy that time to higher-value and more strategic objectives instead of spending it on manual tasks and data pipeline error resolution. They can also complete their corporate initiatives more quickly, helping to reduce operating costs or increase revenues.
Dagster commissioned Forrester Consulting to conduct a Total Economic Impact™ (TEI) study and examine the potential return on investment (ROI) enterprises may realize by deploying Dagster+ Pro.1 The purpose of this study is to provide readers with a framework to evaluate the potential financial impact of Dagster+ Pro on their organizations.
Return on investment (ROI)
432%
Net present value (NPV)
$3.95M
To better understand the benefits, costs, and risks associated with this investment, Forrester interviewed four decision-makers from companies using Dagster+ Pro. For the purposes of this study, Forrester aggregated the interviewees’ experiences and combined the results into a single composite organization that is a global, multibillion-dollar organization based in North America.
Interviewees said that prior to using Dagster+ Pro, their organizations used a combination of several software tools to support their data pipelines. Their organizations struggled with common challenges that included excessive manual effort to build and manage data pipelines, frequent pipeline failures, and a lack of visibility that made it difficult to anticipate, diagnose, or remedy those failures. These challenges limited the capacity of their organizations’ data engineering teams for higher-level, more strategic efforts and timely support of corporate initiatives.
Interviewees said that after the investment in Dagster+ Pro, their organizations reduced pipeline errors by gaining visibility across their data orchestration efforts and that error reduction and automation of many previously manual tasks saved considerable time for data engineers. Their organizations’ corporate initiatives to reduce costs or increase revenues delivered value faster because the associated data management development work was completed sooner.
Key Findings
Quantified benefits. Three-year, risk-adjusted present value (PV) quantified benefits for the composite organization include:
- Faster time to value valued at $1.7 million. Because the composite organization completes the data management development work around corporate initiatives faster after deploying Dagster+ Pro, each initiative starts to deliver value (cost savings or incremental revenue) sooner. Over three years and a cumulative total of 72 corporate initiatives, the shorter development cycle is worth nearly $1.7 million for the composite organization.
- Time savings for data platform engineers valued at $740,000. Dagster+ Pro reduces the time the composite organization’s data platform engineers spend addressing escalated pipeline stability issues and platform-level considerations (e.g., setting up development and production environments, managing and enhancing the performance of data operations), and it saves them additional time by addressing other inefficiencies. In total, the data platform engineers can reallocate 45% of their time to other priorities, which is worth nearly $740,000 to the composite organization over three years.
- Time savings for data pipeline builders valued at $1.4 million. As with the data platform engineers, Dagster+ Pro also saves time for the composite organization’s data pipeline builders because of more stable pipelines and numerous efficiencies including automation of previously manual tasks. However, the impact for them is primarily at the level of building and managing individual pipelines. The data pipeline builders save 30% of their time, which is worth more than $1.4 million to the composite organization.
- Time savings for data consumers valued at $492,000. Because Dagster+ Pro enables the composite organization to reduce the frequency and severity of errors in its continuous integration/continuous delivery (CI/CD) workflows, its data asset consumers spend less time trying to work around those errors and engaging with engineers to resolve them. Heavy consumers of data (e.g., data analysts, data scientists) save a greater percentage (7.5%) of their total work time than light users (2%), with a combined value to the composite organization of nearly $492,000.
- Cost savings from retiring a legacy solution valued at $361,000. With Dagster+ Pro, the composite organization retires its legacy solution, which was a combination of several software tools that supported its data pipelines. Its savings on software licenses, infrastructure, and people time to manage that solution total nearly $361,000.
- Reduced data platform operating costs valued at $185,000. Because of efficiencies enabled by Dagster+ Pro, the composite organization reduces its compute costs related to its data platform by 50%, saving it nearly $185,000.
Unquantified benefits. Benefits that provide value for the composite organization but are not quantified for this study include:
- Greater availability for data engineers to pursue higher-value initiatives. By reducing or eliminating many time-consuming tasks for the organization’s data engineering team, Dagster+ Pro frees up their time for more strategic and valuable efforts such as improving the overall data platform or supporting new corporate initiatives to reduce costs or increase revenue.
- Reduction in data asset downtime. Because the composite organization has fewer pipeline errors and less data asset downtime (e.g., due to data assets being out of date or inaccessible due to technical issues) after deploying Dagster+ Pro, it incurs less economic harm from disruption of its critical processes and reporting.
- Enablement of high-value new use cases for data assets. With Dagster+ Pro, the composite organization can implement strategic and valuable new data assets and ways of using those data assets that previously would have been very difficult or impossible to execute.
- Potential to address some needs via Dagster+ Pro instead of buying a new application. Dagster+ Pro enables the composite organization to more easily build certain applications instead of buying them or to supplement vendor applications that prove incomplete. Even with the required investment of staff time, this holds potential for net savings.
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Costs. Three-year, risk-adjusted PV costs for the composite organization include:
- Dagster subscription costs of $351,000. The composite organization pays Dagster subscription costs based on the number of seats purchased, the volume of consumption fees (Dagster credits) incurred, and a platform fee based on revenue, number of employees, number of seats, and number of Dagster credits. The composite organization pays for 35 seats. It buys 5 million credits in Year 1, 10 million credits in Year 2, and 15 million credits in Year 3.
- Third-party services costs of $107,000. The composite organization implements Dagster+ Pro in hybrid mode and pays for external services to manage cluster configurations and run Dagster+ Pro workloads.
- Internal labor of $457,000 for implementation and ongoing management and support. The composite organization implements Dagster+ Pro and manages and optimizes it on an ongoing basis using an internal team. Implementation includes technical deployment, migration of existing data assets, and training of data platform engineers and data pipeline builders.
The representative interviews and financial analysis found that a composite organization experiences benefits of $4.87 million over three years versus costs of $915,000, adding up to a net present value (NPV) of $3.95 million and an ROI of 432%.
Key Statistics
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Return on investment (ROI)
432%
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Benefits PV
$4.87M
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Net present value (NPV)
$3.95M
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Payback
<6 months
Benefits (Three-Year)
Faster time-to-value Time savings for data platform engineers Time savings for data pipeline builders Time savings for data consumers Cost savings from retiring legacy solution Reduction in data platform operating costs
TEI Framework And Methodology
From the information provided in the interviews, Forrester constructed a Total Economic Impact™ framework for those organizations considering an investment in Dagster+ Pro.
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 Dagster+ Pro can have on an organization.
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Due Diligence
Interviewed Dagster stakeholders and Forrester analysts to gather data relative to Dagster+ Pro.
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Interviews
Interviewed four representatives at organizations using Dagster+ Pro 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 Dagster 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 Dagster+ Pro.
Dagster 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.
Dagster provided the customer names for the interviews but did not participate in the interviews.
Consulting Lead:
Mary Anne North
Drivers leading to the Dagster+ Pro investment
Interviews
| Role | Industry | Region | Revenue |
|---|---|---|---|
| VP, data & analytics platform | Retail | Headquartered in North America, global operations | $2.5 billion |
| Head of data engineering | Food | Headquartered in North America, global operations | $27 billion |
| Assistant vice president (AVP) of data analytics | Consumer financial services | Headquartered in North America, US operations | $2.5 billion |
| Senior engineer manager, data | Media | Headquartered in North America, global operations | $750 million |
Key Challenges
Prior to investing in Dagster+ Pro, interviewees’ organizations used combinations of several software tools to orchestrate their data pipelines that produced data assets, drove reports, and fed data into operational systems.
The interviewees noted how their organizations struggled with common challenges, including:
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Excessive labor required to build pipelines
and address the resulting data quality and stability issues. The organizations’ legacy solutions created time-consuming problems
across several groups of employees. Numerous inefficiencies around building
data pipelines and the frequent need to diagnose and attempt to fix pipeline
failures (often unsuccessfully) consumed the time of data pipeline builders.
Some interviewees estimated their organization’s data pipeline builders
spent half or more of their time on rework and trying to troubleshoot and
fix pipeline failures.
When related issues got escalated to the data platform team, those issues demanded data platform team effort as well. Downstream, data consumers (e.g., staff who work with data assets) spent time grappling with data errors and working with engineers to resolve them.
The head of data engineering in the food industry said: “We were having a lot of issues with the stability of what the data engineers were building out. There was a major escalation probably every week — close to maybe every day — before we switched over.” They said the lack of automated capabilities and other efficiencies for building and troubleshooting typically led their organization to simply “throw more engineers at the problem.” -
Lack of visibility to data management
dependencies and lack of ability to test the impact of proposed pipeline
changes. Interviewees said their organizations’ data
platform teams and data platform builders lacked a single pane of glass to
comprehensively understand, monitor, and manage orchestration processes.
They referenced operating blindly due to an inability to identify
interconnections and dependencies across data pipelines and assets. They
also said they were unable to avoid errors by testing the impact of a new
pipeline or changes to an existing pipeline before putting those into
production.
The senior engineering manager, data, at a media organization noted, “We didn’t have staging environments to test stuff before deploying it, so we’d often break things in production.” The VP, data & analytics platform, at a retail organization said: “Our resources spent an inordinate amount of time trying to coordinate, but [they] still were breaking each other’s stuff because they didn’t understand the dependencies that existed. They couldn’t see what was consuming what from other models.” The head of data engineering at a food organization said, “Even something as fundamental as being able to test code didn’t exist on our prior platform.” - Limited ability to diagnose and remedy workflow errors in production. Lack of visibility and automation impeded diagnostics when workflows failed. The VP, data & analytics platform, at a retail organization remarked, “If you can’t identify a bottleneck, how can you remove it?”
- Fragmented pipeline scheduling. The VP, data & analytics platform, at a retail organization explained: “We relied on a ‘fingers crossed’ approach to job scheduling, manually adding buffers between tasks and constantly firefighting when things inevitably overlapped. This led to frequent cancellations and rework, severely hindering our productivity.”
- Inability to compare metrics across a pipeline’s runs. The VP, data and analytics platform, at a retail organization noted: “We run the same jobs over and over with new data, but we didn’t have an easy way to compare metrics from one run to the next. If we made a code change, there was no quick way to tell if it improved things or not. It was all very manual and hard to scale.”
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Financial impact of downtime resulting from
workflow errors. The frequency and severity of errors
in CI/CD workflows for crucial data-dependent systems left the interviewees’
organizations vulnerable to resulting downtime because data assets were out
of date or inaccessible due to technical issues, and the potential business
impact of that downtime often was significant. The VP, data and platform, at
a retail organization observed: “What opportunities do you lose when
downtime prevents you from keeping a finger on the pulse of your business in
near real time? It’s hard to quantify, but you know they’re there. We might
have increased or decreased markdowns on particular items based on how well
they were or were not selling during sales events, but we couldn’t see that
[because of downtime]. So instead, you’re left making decisions based on gut
feel and past xperience rather than real data.”
The AVP of data analytics at a consumer financial services company said: “We have several critical reporting processes that people are running our business on that previously could be down for a day at a time several times in a month. Incorrect data can impact decisions or cause financial errors.”
The senior engineer manager, data, at a media company said that for their company, downtime could lead to an inability to decide where to spend on paid campaigns, determine what is making money for affiliate partners, or select articles to prioritize for refresh based on traffic — all of which can affect the company’s performance. - Increasingly sophisticated needs. Interviewees noted that as their organizations’ data management needs (including data orchestration) grew more complex and challenging, their legacy solutions weren’t up to the job. The AVP of data analytics in consumer financial services noted that a major initiative to automate certain actuarial financial reporting processes required their organization to improve its ability to build and run financial models, including execution of the associated data-processing steps.
- Lack of bandwidth for data engineering teams to effectively support corporate initiatives and data transformation. Interviewees said that because data pipeline builders spent so much of their time fixing problems with existing pipelines, they were slow to start and finish developing the data management underpinnings of corporate initiatives intended to reduce operating costs or generate incremental revenue. Similarly, data platform engineers’ substantial remedial efforts left them with limited capacity to proactively move the entire company forward by strengthening its overall data platform and advancing its strategic corporate initiatives around data transformation. The organizations viewed their data platforms and data engineering teams as speedbumps instead of accelerators. The head of data engineering in the food industry explained that since it took so much time to do almost anything, their organization’s data engineers never got to higher-level and more strategic tasks.
Investment Objectives
The interviewees’ organizations searched for a solution that would:
- Improve their orchestration capabilities and support their broader data transformation objectives.
- Support varied behavior by environment (e.g., development versus production).
- Simplify diagnosis and resolution of pipeline errors.
- Minimize effort required to set up and manage the solution’s hosting/infrastructure.
- Scale easily.
- Deliver on a vision of software-defined assets, including visibility to connections and dependencies across pipelines and assets.
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. The composite organization is a global multibillion dollar company. It is headquartered in North America and serves a worldwide customer base. The company’s ability to effectively and efficiently develop and manage data pipelines so that many functional areas can work with high-quality timely data is critical to its operations, growth, and profitability. Prior to using Dagster+ Pro, the composite organization relied upon a combination of several software tools to support its data pipelines. Its minimal ability to test pipelines before production with those tools caused frequent errors when those pipelines were run, and the lack of a comprehensive view of those pipelines’ lineage and statuses made troubleshooting time-consuming and often inconclusive. The composite’s data platform team and data pipeline builders had minimal time for higher-impact and more strategic development work.
Deployment characteristics. To simplify and strengthen its data platform and data orchestration, the composite organization evaluates several vendors and selects Dagster+ Pro. Implementation includes technical deployment (hybrid mode), migration of existing data assets (in a volume representative of the interviewees’ companies) to Dagster+ Pro, and training and change management for internal staff who will use Dagster+ Pro. A total of eight data platform engineers, 25 data pipeline builders, and 150 data consumers (including 25 heavy users) use Dagster+ Pro on a broad range of use cases. The number of data assets increases over several years as the composite organization identifies additional use cases with which to use Dagster+ Pro. The composite organization continues to make minor enhancements to its use of Dagster+ Pro, including new features as Dagster launches them.
Quantified benefit data as applied to the composite
Total Benefits
| Ref. | Benefit | Year 1 | Year 2 | Year 3 | Total | Present Value |
|---|---|---|---|---|---|---|
| Atr | Faster time to value | $599,040 | $718,848 | $718,848 | $2,036,736 | $1,678,752 |
| Btr | Time savings for data platform engineers | $275,558 | $310,003 | $310,003 | $895,565 | $739,619 |
| Ctr | Time savings for data pipeline builders | $503,100 | $603,720 | $603,720 | $1,710,540 | $1,409,890 |
| Dtr | Time savings for data consumers | $197,640 | $197,640 | $197,640 | $592,920 | $491,501 |
| Etr | Cost savings from retiring legacy solution | $145,086 | $145,078 | $145,078 | $435,242 | $360,794 |
| Ftr | Reduction in data platform operating costs | $74,375 | $74,375 | $74,375 | $223,125 | $184,960 |
| Total benefits (risk-adjusted) | $1,794,799 | $2,049,664 | $2,049,664 | $5,894,128 | $4,865,516 | |
Faster Time To Value
Evidence and data. Interviewees said their organizations completed data management development work around corporate initiatives faster after deploying Dagster+ Pro and that each initiative started to deliver value (cost savings or incremental revenue) sooner.
Interviewees cited multiple accelerators:
- Time freed up for data engineers. Dagster+ Pro saved substantial time for data platform engineers and data platform builders via multiple efficiencies. This increased their available time to work on new initiatives and decreased backlogs that had chronically delayed their availability to be assigned to new initiatives.
- Fewer distractions. Interviewees said distractions like an unpredictable and time-consuming daily burden of resolving errors interfered with the data engineering team’s ability to start and continue their work on new initiatives.
- A growing inventory of models and assets that can be reused. Interviewees said data engineering staff gained time to build new data analytics models after deploying Dagster+ Pro and that they could build those models faster, so their organizations gained a growing inventory of analytics models and data assets that could be reused. The VP, data & analytics platform, at a retail organization explained: “Those models are multipurpose, so we’re building the foundations of data that we can then leverage for lots of different use cases. It’s like we had added a whole bunch more people, and [we] can drive initiatives forward that much faster as a result.”
- Flexibility and additional capacity provided by an expanded pool of data pipeline builders. The AVP of data analytics in the consumer financial services industry described a recent initiative at their organization where, with guidance from data engineers, a data scientist leveraged Dagster+ Pro to do things that previously had been outside their area of expertise. As a result, that data scientist didn’t have to submit requirements to the data engineering team and wait until that team had worked through its backlog and had time to address them. Instead, the data scientist could immediately start to do things on their own. The AVP noted that such flexibility could trim months from some initiatives.
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
- Without Dagster+ Pro, the composite’s average elapsed time for IT development work is 16 weeks per corporate initiative.
- The composite attributes Dagster+ Pro with reducing that elapsed time by 25% in Year 1 and by 30% in years 2 and Year 3.
- Each year, the composite executes 16 initiatives to reduce costs. Each initiative saves $10,000 per week when taken live.
- Each year, the composite executes eight initiatives to increase revenue. Each initiative generates $40,000 per week when taken live.
- Forrester applied a net margin of 8.5% to the top-line revenue growth to focus the study on bottom-line financial improvement.2
Risks. These results may not be representative of all experiences because time savings for data platform engineers can vary based on:
- The prior state and overall maturity of the organization’s data management and project management.
- The extent to which the organization leverages Dagster+ Pro’s capabilities.
- The number of investment initiatives intended to directly reduce costs or generate revenue.
- The nature of those initiatives and the resulting average weekly expense savings or incremental revenue from each of those.
- The organization’s typical operating pace, which is influenced by the organization’s age and culture.
- The organization’s operating margin, which is influenced by geography, industry, and competitive landscape.
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 $1.7 million.
Faster Time To Value
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 |
|---|---|---|---|---|---|
| A1 | Average elapsed time for IT development work per initiative without Dagster+ Pro (weeks) | Interviews | 16 | 16 | 16 |
| A2 | Reduction in elapsed time attributed to Dagster+ Pro | Interviews | 25% | 30% | 30% |
| A3 | Subtotal: Reduction in elapsed time with Dagster+ Pro (weeks) | A1*A2 | 4.0 | 4.8 | 4.8 |
| A4 | Initiatives to reduce business costs | Composite | 16 | 16 | 16 |
| A5 | Average business cost savings for each initiative produced each week when taken live | Composite | $10,000 | $10,000 | $10,000 |
| A6 | Subtotal: Incremental cost savings from faster completion | A3*A4*A5 | $640,000 | $768,000 | $768,000 |
| A7 | Initiatives to increase revenue | Composite | 8 | 8 | 8 |
| A8 | Average incremental revenue generated for each initiative each week when taken live | Composite | $40,000 | $40,000 | $40,000 |
| A9 | Operating margin | Research data | 8.5% | 8.5% | 8.5% |
| A10 | Subtotal: Incremental operating income from faster completion | A3*A7*A8*A9 | $108,800 | $130,560 | $130,560 |
| At | Faster time to value | A6+A10 | $748,800 | $898,560 | $898,560 |
| Risk adjustment | ↓20% | ||||
| Atr | Faster time to value (risk-adjusted) | $599,040 | $718,848 | $718,848 | |
| Three-year total: $2,036,736 | Three-year present value: $1,678,752 | ||||
Time Savings For Data Platform Engineers
Evidence and data. Interviewees said Dagster+ Pro reduced the time data platform engineers spent addressing escalated pipeline stability issues and platform-level considerations (e.g., setting up development and production environments and managing and enhancing the performance of data operations) and that it saved additional time by addressing other inefficiencies.
Examples include:
- Reduction in escalated pipeline failures and the time needed to resolve them. Dagster+ Pro increased the ability of data engineering teams to understand interconnectedness and dependencies — what happens when for a model and what is consumed from other models — all without manual effort. This saved time for data platform engineers because, although initial attempts to determine those connections typically were handled by data pipeline builders, subsequent pipeline failures in production were common before using Dagster+ Pro and some of the resulting issues had been escalated to the data platform engineers. After Dagster+ Pro was deployed, data platform engineers had fewer escalations to address, and they could diagnose and resolve them faster because of better visibility and automated diagnostics.
- Centralized resolution of prevalent issues and launch of new orchestration functionality. The head of data engineering at a food organization said: “Now, when we run into a widespread issue, we can solve it centrally. So, everyone benefits across the board. And when we come up with something new, it’s delivered centrally instead of having to really plan it out and integrate it separately for each use case.”
- Automation of previously manual oversight. The same interviewee noted that before using Dagster+ Pro, three people at their organization were dedicated to managing pull requests, releases, and deployments of pipelines and code because of the nature of legacy CI/CD processes. Now, those individuals can be redeployed to higher-level efforts.
- Decoupling of pipeline failures. Interviewees said Dagster+ Pro reduced time spent on cascading pipeline failures. The head of data engineering at a food organization explained: “Things were more coupled in our prior platform. One pipeline failure could end up bringing down a lot of others, as well. Now, we have a different approach where each artifact in our environment is tied to a single Dagster job. And because they’re more decoupled, it’s a lot easier to maintain and support behind the scenes.”
- Ability to compare metrics across one run to the next. Interviewees said Dagster+ Pro allows data platform engineers and data pipeline builders to gauge the impact of code changes at scale and without manual effort.
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
- The composite organization has eight data platform engineers.
- The composite attributes Dagster+ Pro with saving data platform engineers 40% of their time in Year 1 and 45% in years 2 and 3.
- The fully burdened hourly compensation for a data platform engineer is $92.
- Data platform engineers reinvest half of the time they save into tasks that provide the organization with greater value.
Risks. These results may not be representative of all experiences because time savings for data platform engineers can vary based on:
- The prior state and overall maturity of the organization’s data management.
- The extent to which the organization leverages Dagster+ Pro’s capabilities.
- The number of data platform engineers.
- Prevailing local compensation rates.
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 $740,000.
Time Savings For Data Platform Engineers
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 |
|---|---|---|---|---|---|
| B1 | Data platform engineers | Composite | 8 | 8 | 8 |
| B2 | Percent of time saved | Interviews | 40% | 45% | 45% |
| B3 | Time saved per data platform engineer (hours) | B2*2,080 | 832 | 936 | 936 |
| B4 | Fully burdened hourly compensation for a data platform engineer | TEI methodology | $92 | $92 | $92 |
| B5 | Productivity realization factor | TEI methodology | 50% | 50% | 50% |
| Bt | Time savings for data platform engineers | B1*B3*B4*B5 | $306,176 | $344,448 | $344,448 |
| Risk adjustment | ↓10% | ||||
| Btr | Time savings for data platform engineers (risk-adjusted) | $275,558 | $310,003 | $310,003 | |
| Three-year total: $895,565 | Three-year present value: $739,619 | ||||
Time Savings For Data Pipeline Builders
Evidence and data. As with data platform engineers, interviewees said Dagster+ Pro also saved time for data pipeline builders because of better pipeline stability and numerous efficiencies. This was primarily at the level of building and managing individual pipelines versus addressing escalated issues or platform-level considerations as data platform engineers did.
Interviewees said:
- Data pipeline builders spent less time addressing data pipeline failures. The reduction in pipeline failures significantly cut the time data pipeline builders had to spend trying to diagnose and fix them. The head of data engineering in the food industry estimated that pipeline builders at their organization previously spent half or more of their time on rework and trying to troubleshoot and fix pipeline failures. But with Dagster+ Pro, the failure rate dropped from 12% to 2%, with the remaining 2% typically related to source systems — not the platform. The AVP of data analytics in consumer financial services noted that debugging errors became much easier with Dagster+ Pro.
- Dagster+ Pro improved visibility to the connections and dependencies among different jobs and the impact of potential changes without manual effort. Interviewees said having this visibility reduced the substantial time data pipeline builders spent attempting to coordinate with each other to avoid breaking each other’s models and fixing problems they nonetheless created because they couldn’t see the dependencies that existed. The VP, data & analytics platform, in retail said: “Now, our engineers don’t spend as much time trying to figure out if they’ll do something that will break someone’s model. Instead, they get alerted to that as part of their pull requests.” The head of data engineering in the food industry said, “[Dagster+ Pro] allowed us to shift from a reactive mode to a shift left approach, like allowing us to do validations against the sources that we’re trying to pull from.”
- Dagster+ Pro improved the ability to identify bottlenecks. The head of data engineering at a food organization said: “Our prior platform hadn’t lent itself to investigating and figuring out the root cause of issues. For something as simple as logging, I would have to go through 10 different layers before I got the information I needed. But in [Dagster+ Pro], you just click on something, and you see everything that that’s going on.”
Interviewees also said Dagster+ Pro provided numerous other efficiencies, such as less complicated scheduling, less time spent on data ingestion, getting feedback from the user interface while still working in a development environment, having access to additional lineage between datasets, and the ability to compare metrics across one run to the next and determine impact of code changes at scale and without manual effort.
The VP, data & analytics platform, at a retail organization noted that time savings attributable to Dagster+ Pro may grow over time due to increasing complexity of the models and the relationships between them.
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
- The composite organization has 25 data pipeline builders.
- The composite attributes 25% of the time savings for data pipeline builders in Year 1 and 30% of the savings in years 2 and 3 to Dagster+ Pro.
- The fully burdened hourly compensation for a data pipeline builder is $86.
- Data pipeline builders reinvest half of the time they save into tasks that provide the organization with greater value.
Risks. These results may not be representative of all experiences because time savings for data pipeline builders can vary based on:
- The prior state and overall maturity of the organization’s data management.
- The extent to which the organization leverages Dagster+ Pro’s capabilities.
- The number of data pipeline builders.
- Prevailing local compensation rates.
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 $1.4 million.
Time Savings For Data Pipeline Builders
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 |
|---|---|---|---|---|---|
| C1 | Data pipeline builders | Composite | 25 | 25 | 25 |
| C2 | Percent of time saved | Interviews | 25% | 30% | 30% |
| C3 | Time saved per data pipeline builder | C2*2,080 | 520 | 624 | 624 |
| C4 | Fully burdened hourly compensation for a data pipeline builder | TEI methodology | $86 | $86 | $86 |
| C5 | Productivity realization factor | TEI methodology | 50% | 50% | 50% |
| Ct | Time savings for data pipeline builders | C1*C3*C4*C5 | $559,000 | $670,800 | $670,800 |
| Risk adjustment | ↓10% | ||||
| Ctr | Time savings for data pipeline builders (risk-adjusted) | $503,100 | $603,720 | $603,720 | |
| Three-year total: $1,710,540 | Three-year present value: $1,409,890 | ||||
Time Savings For Data Consumers
Evidence and data. Interviewees said that Dagster+ Pro enabled their organizations to reduce the frequency and severity of errors in CI/CD workflows, so data asset consumers spent less time trying to work around those errors and engaging with engineers to resolve them.
Heavy consumers of data (e.g., data analysts, data scientists) spent most of their days working with data assets while light consumers spent less time on this. As a result, heavy consumers saved a greater percentage of their total work time.
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
- The composite organization has 25 heavy consumers of data assets and 125 light consumers of data assets.
- The composite attributes Dagster+ Pro with time savings of 7.5% for heavy consumers and 2% for light consumers.
- The blended fully burdened hourly compensation for a role representative of data asset consumers is $48.
- Data asset consumers reinvest half of the saved time into tasks that provide the organization with greater value.
Risks. These results may not be representative of all experiences because time savings for data consumers can vary based on:
- The prior state and overall maturity of the organization’s data management.
- The extent to which the organization leverages Dagster+ Pro’s capabilities.
- The number of data consumers.
- The amount of time in a typical workday each consumer spends working with data assets.
- Prevailing local compensation rates.
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 $492,000.
Time Savings For Data Consumers
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 |
|---|---|---|---|---|---|
| D1 | Heavy consumers of data assets | Composite | 25 | 25 | 25 |
| D2 | Percent of total work time saved | Interviews | 7.5% | 7.5% | 7.5% |
| D3 | Time saved per heavy consumer (hours) | D2*2,080 | 156 | 156 | 156 |
| D4 | Light consumers of data assets | Composite | 125 | 125 | 125 |
| D5 | Percent of total work time saved | Interviews | 2% | 2% | 2% |
| D6 | Time saved per light consumer (hours) | D5*2,080 | 42 | 42 | 42 |
| D7 | Blended fully burdened hourly compensation for roles representative of data asset consumers | TEI methodology | $48 | $48 | $48 |
| D8 | Productivity realization factor | TEI methodology | 50% | 50% | 50% |
| Dt | Time savings for data consumers |
((D1*D3)+(D4* D6))*D7*D8 |
$219,600 | $219,600 | $219,600 |
| Risk adjustment | ↓10% | ||||
| Dtr | Time savings for data consumers (risk-adjusted) | $197,640 | $197,640 | $197,640 | |
| Three-year total: $592,920 | Three-year present value: $491,501 | ||||
Cost Savings From Retiring Legacy Solution
Evidence and data. Interviewees reported that after their organizations replaced their prior solutions with Dagster+ Pro, they saved on software licenses, infrastructure (virtual machines) on which the software ran, and people time to manage their prior solutions. These are net savings since their organizations now incur ongoing costs associated with Dagster+ Pro.
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
- After deploying Dagster+ Pro, the composite organization retires legacy solutions with prior annual costs of $75,000 for software and infrastructure.
- One data platform engineer previously spent half of their time managing and maintaining those legacy solutions.
Risks. These results may not be representative of all experiences because cost savings from retiring a legacy solution can vary based on:
- The number and nature of nature of the organization’s legacy software programs to support data pipelines.
- The number of end users of the legacy solution.
- The infrastructure needed for the legacy solution.
- The internal labor needed to manage the legacy solution.
- Prevailing local compensation rates.
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 $361,000.
Cost Savings From Retiring Legacy Solution
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 |
|---|---|---|---|---|---|
| E1 | Software and infrastructure cost | Interviews | $75,000 | $75,000 | $75,000 |
| E2 | Internal labor cost | TEI methodology | $95,689 | $95,680 | $95,680 |
| Et | Cost savings from retiring legacy solution | E1+E2 | $170,689 | $170,680 | $170,680 |
| Risk adjustment | ↓15% | ||||
| Etr | Cost savings from retiring legacy solution (risk-adjusted) | $145,086 | $145,078 | $145,078 | |
| Three-year total: $435,242 | Three-year present value: $360,794 | ||||
Reduction In Data Platform Operating Costs
Evidence and data. Interviewees said their organizations saved on their data platform operating costs because of efficiencies enabled by Dagster+ Pro, primarily around compute costs. The head of data engineering at a food organization explained: “It took a lot more machines to be able to run the old platform. Now, we’re able to fine-tune our compute costs. It’s more aligned to our actual usage, and it scales up and down as that increases or decreases. Before, it was more fixed, so it was operating inefficiently. It wasn’t as responsive to the actual compute that we were doing. Since we migrated, we’ve probably doubled our use cases. But, because of all the efficiencies, we’re still paying less than 50% of the original cost.”
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
- Before deploying Dagster+ Pro, the composite organization spent $175,000 each year on compute costs related to the data platform.
- After deploying Dagster+ Pro, the composite attributes to it a 50% reduction in those costs.
Risks. These results may not be representative of all experiences because the reduction in data platform operating costs can vary based on:
- The extent to which the organization leverages Dagster+ Pro’s capabilities.
- Data platform operating costs prior to deployment of Dagster+ Pro.
- The nature of the organization’s data platform operations after Dagster+ Pro is deployed and the resulting operating costs.
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 $185,000.
Reduction In Data Platform Operating Costs
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|
| F1 | Prior data platform-related compute costs | Interviews | $175,000 | $175,000 | $175,000 | |
| F2 | Percent reduction with Dagster+ Pro | Interviews | 50% | 50% | 50% | |
| Ft | Reduction in data platform operating costs | F1*F2 | $87,500 | $87,500 | $87,500 | |
| Risk adjustment | ↓15% | |||||
| Ftr | Reduction in data platform operating costs (risk-adjusted) | $74,375 | $74,375 | $74,375 | ||
| Three-year total: $223,125 | Three-year present value: $184,960 | |||||
Unquantified Benefits
Interviewees mentioned the following additional benefits that their organizations experienced but were not able to quantify:
-
Greater availability for data engineers to
pursue higher-value initiatives. Dagster+ Pro enabled
interviewees’ organizations to reduce or eliminate many time-consuming tasks
for their data engineering teams. As a result, data platform engineers could
spend more time improving their company’s data platform, advancing its
strategic corporate initiatives around data transformation, and supporting
data platform builders and data consumers. Data pipeline builders could
spend more time developing the data management requirements for corporate
initiatives to cut operating costs or boost revenue.
The VP, data & analytics platform, at a retail organization said: “We’ve used our data platform team to reduce the cognitive load of people creating data assets. They don’t need to understand how everything works behind the scenes; they can focus their time on making data actionable.”
The AVP of data analytics at a consumer financial services organization explained that by freeing up data engineers to support new initiatives, Dagster+ Pro played a role in growing the business. - Reduction in data asset downtime. Interviewees said that with Dagster+ Pro, their organizations reduced data pipeline errors and resultant data asset downtime and business impact. In addition to saving time for data engineering teams, this decreased the time that some business staff previously spent trying to work around the errors or being unable to work. More strategically, interviewees’ organizations reduced the economic losses created when downtime disrupted critical processes and reporting.
-
Enablement of new, high-value use cases for
data assets. In addition to noting faster delivery on
initiatives that could have been executed before Dagster+ Pro, interviewees
described strategic and valuable new use cases their organizations launched
using data analytics orchestrated by Dagster+ Pro that previously would have
been very difficult or impossible to execute.
One interviewee said: “All the data that we’re building and the related assets and models are orchestrated by Dagster. So, from the time the data lands in the source through the subsequent controlling of the transformation processes to model it and structure it out and then triggering the downstream integrations that push that data to the next piece once it’s been updated: That is all controlled by Dagster.”
The interviewee gave several examples:- Dagster+ Pro provided the ability to analyze how well various products were selling much faster and better than before. The interviewee said this allowed their organization to make more timely decisions that increased profit margins and that the elapsed time to produce certain critical reports dropped from days to hours: “We’ve automated the vast majority of it, and now they’re making quick decisions.”
- Dagster+ Pro improved personalization efforts. The interviewee credited Dagster+ Pro with getting data in the right place at the right time to make valuable personalization efforts possible.
- Potential to address some needs via Dagster+ Pro instead of buying a new application. The AVP of data analytics in consumer financial services noted that many of their organization’s corporate initiatives had historically involved purchasing a new application. But they noted that Dagster+ Pro provided a platform and scheduling capabilities with which the organization could build and operate its own solutions for varied use cases instead of seeking external solutions. While acknowledging the required investment of staff time, the interviewee said they believe their organization has realized net savings from this approach, and they also mentioned using Dagster+ Pro to supplement vendor applications the organization had purchased but found incomplete for its needs.
Flexibility
The value of flexibility is unique to each customer. There are multiple scenarios in which a customer might implement Dagster+ Pro and later realize additional uses and business opportunities, including:
-
Envisioning and executing new
possibilities. Interviewees said deploying Dagster+
Pro enabled their organizations to dream bigger. The VP, data &
analytics platform, for a retail organization said, “This has freed us to
focus on the future: building new architectures, anticipating emerging use
cases, and boldly expanding into new territory.” The same interviewee
described several high-impact, data-intensive corporate initiatives that
increased their organization’s revenue and profit margins and said they
would have been much more difficult or simply infeasible before the
organization deployed Dagster+ Pro.
The senior engineer manager, data, for a media company described a group of analytical use cases that requires looking at user activity across multiple data sources to understand what drives improved retention. They noted the company had not been able to support datasets at that level of complexity prior to deploying Dagster+ Pro. - Gaining agility by expanding the pool of data pipeline builders. Several interviewees noted that with Dagster+ Pro, individuals in a wide range of roles (including some outside of the formal data engineering team) can build data pipelines. The AVP of data analytics at a consumer financial services organization said: “We now have a small number of highly technical folks on the business side, like data scientists and data analysts, who also can act as data pipeline builders now. In the past, they would sometimes write scripts on their machine to automate something, but now they can do it on a production-level platform and tie in with other workflows. Because Dagster+ Pro makes it feasible for additional people to build pipelines, you have more flexibility in how you get the job done.”
- Using Dagster+ Pro for additional use cases. Interviewees said that after deploying Dagster+ Pro, interviewees’ organizations expanded their use of it to more use cases and expect to continue to do so. The head of data engineering in the food industry deemed it fundamental for running any of their organization’s analytics programs. The AVP of data analytics in consumer financial services said they anticipate their organization will integrate Dagster+ Pro with additional operational use cases and internal systems to help automate more of its frequent and sizable processes.
- Leveraging Dagster+ Pro’s capabilities more completely. Interviewees said that after initial deployment, their organizations continued to discover new ways to leverage the functionality of Dagster+ Pro. They said they anticipate further capitalizing on Dagster+ Pro’s functionality over time and gaining even greater value from it. The senior engineer manager, data, in media mentioned further automating their organization’s approach to data quality by setting up alerts in the user interface that would indicate if something went wrong with any of several dozen datasets used in a number of places. They also mentioned starting to use Dagster+ Pro to orchestrate the organization’s significant use of data for machine learning and to parse data warehouse query logs and tie those to Dagster+ Pro assets and jobs run to determine how often an asset was run and how much that cost.
- Capitalizing on new Dagster+ Pro capabilities as Dagster introduces them. The interviewees said they anticipate using enhanced and new features and additional integrations as Dagster+ Pro continues to evolve. The head of data engineering in the food industry noted Dagster’s work with AI integrations and said their organization’s data science and machine learning teams focus on such integrations.
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 |
|---|---|---|---|---|---|---|---|
| Gtr | Dagster subscription costs | $27,778 | $111,694 | $134,033 | $147,145 | $420,650 | $350,640 |
| Htr | Third-party services | $6,708 | $40,250 | $40,250 | $40,250 | $127,458 | $106,804 |
| Itr | Internal labor | $179,124 | $111,927 | $111,927 | $111,927 | $514,905 | $457,470 |
| Total costs (risk-adjusted) | $213,610 | $263,871 | $286,210 | $299,322 | $1,063,013 | $914,914 | |
Dagster Subscription Costs
Evidence and data. Dagster subscription costs are based on the number of seats purchased, the volume of consumption fees (Dagster credits) incurred, and a platform fee. One Dagster credit is consumed each time a pipeline is run or a data asset is updated. The platform fee is based on revenue, number of employees, number of seats, and number of Dagster credits.
Pricing will vary. Contact Dagster for likely costs specific to your organization.
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
- The composite pays for 35 seats in years 1, 2, and 3.
- Because the composite continues to build out new use cases in years 1 to 3, its consumption fees increase year-over-year.
- The composite buys 5 million credits in Year 1, 10 million credits in Year 2, and 15 million credits in Year 3.
- The number of data assets increases over several years as the composite organization identifies additional use cases with which to use Dagster+ Pro.
Risks. These costs may not be representative of all experiences because Dagster+ Pro subscription costs will vary based on:
- The number of Dagster+ Pro seats required.
- The volume of Dagster credits required (which will vary significantly depending on the nature of an organization, the maturity of its data management efforts, the number and type of its use cases and data assets, how frequently a pipeline is run or a data asset is updated, and more).
- Organization-specific factors that determine the platform fee.
Results. To account for these risks, Forrester adjusted this cost upward by 5%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $351,000.
Dagster Subscription Costs
| Ref. | Metric | Source | Initial | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|---|
| G1 | Cost of Dagster+ Pro seats | Composite | $10,360 | $31,080 | $31,080 | $31,080 | |
| G2 | Consumption fees (Dagster credits) | Composite | $9,003 | $54,020 | $71,040 | $81,030 | |
| G3 | Platform fee | Composite | $7,092 | $21,275 | $25,530 | $28,028 | |
| Gt | Dagster subscription costs | G1+G2+G3 | $26,455 | $106,375 | $127,650 | $140,138 | |
| Risk adjustment | ↑5% | ||||||
| Gtr | Dagster subscription costs (risk-adjusted) | $27,778 | $111,694 | $134,033 | $147,145 | ||
| Three-year total: $420,650 | Three-year present value: $350,640 | ||||||
Third-Party Services
Evidence and data. Most interviewees’ organizations used Dagster+ Pro in hybrid mode, meaning Dagster hosted the orchestration services but the organizations ran Dagster+ Pro workloads on centralized cloud infrastructures using external services to manage cluster configuration.
Third-party services costs varied widely for the organizations depending on the hosting environment they selected (e.g., on-premises, hybrid, private cloud, public cloud) and the SLAs and other compliance requirements for each of those options, including the number of jobs run, whether there are high-availability requirements, and other factors.
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
- The composite deploys Dagster+ Pro in hybrid mode.
- During the composite’s technical deployment of Dagster+ Pro and subsequent migration of data assets, it consumes third-party services at a rate that averages 50% of what it pays when in full production.
- After the composite has Dagster+ Pro is in full production, it pays $35,000 each year for external services to manage cluster configuration and run Dagster+ Pro workloads.
Risks. These costs may not be representative of all experiences because the cost of third-party services can vary based on:
- Whether the organization deploys Dagster+ Pro in hybrid or fully cloud mode.
- Whether the organization’s cloud hosting is public or private and the SLAs and other compliance requirements for each of those options.
- What the organization chooses to orchestrate.
- The complexity of the organization’s data workloads.
- The number of data pipelines and how frequently each is run.
- Whether there are high-availability requirements.
- Prevailing rates for third-party services.
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 $107,000.
Third-Party Services
| Ref. | Metric | Source | Initial | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|---|
| H1 | Third-party cloud consumption fees | Interviews | $5,833 | $35,000 | $35,000 | $35,000 | |
| Ht | Third-party services | H1 | $5,833 | $35,000 | $35,000 | $35,000 | |
| Risk adjustment | ↑15% | ||||||
| Htr | Third-party services (risk-adjusted) | $6,708 | $40,250 | $40,250 | $40,250 | ||
| Three-year total: $127,458 | Three-year present value: $106,804 | ||||||
Internal Labor
Evidence and data. Interviewees said their organization’s implementation typically included several weeks of technical deployment (hybrid mode), several months of migrating existing data assets to Dagster+ Pro, and training and change management for staff who would use Dagster+ Pro. Data platform team members typically got up to speed on Dagster+ Pro through their involvement with technical deployment and by reviewing Dagster’s documentation and training videos. Training for data pipeline builders typically involved a brief internal training session followed by on-the-job self-education and mentorship. Data consumers did not need training since they do not directly interact with Dagster+ Pro. Dagster informally assisted the interviewees’ organizations, but the organizations did not retain professional services providers to augment their internal resources.
Although most interviewees’ organizations completed their data asset migration during an initial implementation period, other organizations may take a phased approach to data migration that may extend over several quarters or more than a year.
On an ongoing basis, primarily data platform engineers supported end users and determined how to optimize their use of Dagster+ Pro by further leveraging its existing functionality and its new capabilities as they were introduced.
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
- Data platform engineers, data pipeline builders, and IT staff spend a combined total of 1,400 hours on initial technical deployment and data asset migration and a combined total of 1,040 hours on management and support in each subsequent year.
- During implementation, eight data platform team members each spend 15 hours in training. In each subsequent year, they each spend 2 hours in training.
- During implementation, 25 data pipeline builders each spend 10 hours in training. In each subsequent year, they each spend 2 hours in training.
- The blended fully burdened hourly compensation for a staff member trained on Dagster+ Pro is $88.
Risks. These results may not be representative of all experiences because the costs of internal labor can vary based on:
- The prior state and overall maturity of the organization’s operations related to orchestration and overall data management.
- The scope and complexity of the organization’s Dagster+ Pro implementation and the broader data transformation efforts that may affect the deployment.
- The experience and capabilities of the organization’s data engineering staff.
- Whether Dagster+ Pro is deployed in full cloud mode or hybrid mode.
- The volume of existing data assets to be migrated and the organization’s pace and timing for that migration (e.g., migrating up-front versus over time).
- The number of data platform team members and data pipeline builders trained on Dagster+ Pro.
- The extent to which the organization invests time to expand and evolve its use of Dagster+ Pro’s full capabilities.
- The extent to which the organization invests time to explore and implement additional use cases for Dagster+ Pro.
- Prevailing local compensation rates.
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 $457,000.
Internal Labor
| Ref. | Metric | Source | Initial | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|---|
| I1 | Combined total IT time required for initial technical deployment, asset migration, and ongoing management and support (hours) | Interviews | 1,400 | 1,040 | 1,040 | 1,040 | |
| I2 | Data platform team members trained on Dagster+ Pro | Interviews | 8 | 8 | 8 | 8 | |
| I3 | Time each trained data platform team member spends in training (hours) | Interviews | 15 | 2 | 2 | 2 | |
| I4 | Data pipeline builders trained on Dagster+ Pro | Interviews | 25 | 25 | 25 | 25 | |
| I5 | Time each trained data pipeline builder spends in training (hours) | Interviews | 10 | 2 | 2 | 2 | |
| I6 | Blended fully burdened hourly compensation for a trained team member | TEI methodology | $88 | $88 | $88 | $88 | |
| It | Internal labor |
(I1+(I2*I3)+ (I4 *I5))*I6 |
$155,760 | $97,328 | $97,328 | $97,328 | |
| Risk adjustment | ↑15% | ||||||
| Itr | Internal labor (risk-adjusted) | $179,124 | $111,927 | $111,927 | $111,927 | ||
| Three-year total: $514,905 | Three-year present value: $457,470 | ||||||
Consolidated Three-Year Risk-Adjusted Metrics
Cash Flow Chart (Risk-Adjusted)
Total costs Total benefits Cumulative net benefits Initial Year 1 Year 2 Year 3-
The financial results calculated in the Benefits and Costs sections can be used to determine the ROI and NPV for the composite organization’s investment. Forrester assumes a yearly discount rate of 10% for this analysis.
These risk-adjusted ROI and NPV 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 | ($213,610) | ($263,871) | ($286,210) | ($299,322) | ($1,063,012) | ($914,914) |
| Total benefits | $0 | $1,794,799 | $2,049,664 | $2,049,664 | $5,894,127 | $4,865,516 |
| Net benefits | ($213,610) | $1,530,928 | $1,763,455 | $1,750,342 | $4,831,115 | $3,950,602 |
| ROI | 432% | |||||
| 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
-
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.
-
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.
-
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.
-
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 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.
2 Source: Margins by sector (US), NYU Stern School of Business, January 2024.
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