The Total Economic Impact™ Of Sigma Computing
Cost Savings And Business Benefits Enabled By Sigma
A Forrester Total Economic Impact™ Study Commissioned By Sigma Computing, November 2024
Deriving insights from complex enterprise data was once the domain of data engineers and scientists with deep knowledge of enterprise semantics, specialized query languages, and statistical methods. However, to scale up in today’s business environment, leaders must find new ways to democratize data and insights, enabling both internal users and external customers to access and query enterprise data for actionable insights. Sigma’s business intelligence (BI) platform sits atop the cloud data warehouse, enabling organizations to leverage AI at scale.
Sigma Computing is a cloud-based BI platform for data exploration and visualization using the power of cloud data warehouses. Its spreadsheet-like interface, customizable design features, and AI capabilities allow users to interact and collaborate with large datasets in real time through a conversational interface. The platform supports the creation of data applications, allowing users to build dynamic workflows and dashboards combining data from various sources. This allows organizations to embed analytics into their existing processes.
Sigma Computing commissioned Forrester Consulting to conduct a Total Economic Impact™ (TEI) study and examine the potential return on investment (ROI) enterprises may realize by deploying Sigma.1 The purpose of this study is to provide readers with a framework to evaluate the potential financial impact of Sigma on their organizations.
Return on investment (ROI)
321%
Net present value (NPV)
$2.88M
To better understand the benefits, costs, and risks associated with this investment, Forrester interviewed eight representatives with experience using Sigma. For the purposes of this study, Forrester aggregated the interviewees’ experiences and combined the results into a single composite organization that is a global B2B company with $2.5 billion in annual revenue and 45,000 customers.
Prior to adopting Sigma, the interviewees’ organizations had modernized their data architectures, consolidating enterprise data in a cloud data warehouse environment to eliminate data silos. When these organizations used their older-generation BI tools in the new environment, they found these tools incapable of scaling to the degree needed. Moreover, these legacy tools required specialized knowledge of query languages, which limited their use to overburdened data analysts fielding demands from increasingly restive business users unable to effectively access data directly. The interviewees recognized the need for a more powerful analytics solution that could securely leverage their cloud infrastructures while enabling democratization of data at scale.
After adopting Sigma, the interviewees were able to transform work processes, shifting analytics workloads to end users who experienced shorter wait times for query results. This freed up analytics teams to work on more strategic projects, such as predictive analytics, and further streamlined workflows and data consumption. As a result, the interviewees’ organizations were able to make more informed business decisions, bring products to market faster, and improve satisfaction for those involved with enterprise data.
Key Findings
Quantified benefits. Three-year, risk-adjusted present value (PV) quantified benefits for the composite organization include:
- Gaining over $2 million in data analyst productivity over three years. The number of data analysts supporting end users with their data requests remains constant, but more of their work is shifted away from those tasks over time and their productivity improves thanks to Sigma’s fast performance and user-friendly interface, which encourages greater self-service by data consumers. These efficiencies grow over time as more users are introduced to Sigma.
- Saving 49,400 hours of end-user time in retrieving and analyzing enterprise data over three years. Sigma’s user-friendly interface enables data users at the composite organization to run workloads without relying on data team resources. Sigma’s ability to quickly process large datasets reduces time to acquire data, allowing users to mine for deeper insights and improving their decision-making.
- Improving time to market for new business by three months. The composite organization shortens its business creation processes through quicker iteration and delivery of insights. The value of this accelerated time to market adds up to nearly $475,000 over three years.
- Saving over $500,000 by reducing the use of legacy BI tools. The composite organization migrates 95% of users to Sigma over three years and reduces its legacy BI platform costs by a commensurate amount, including time spent to maintain the platform.
Unquantified benefits. Benefits that provide value for the composite organization but are not quantified for this study include:
- Performance and latency. Because Sigma is a cloud-native platform, it can process data and render reports and visualizations based on millions of records faster than other BI tools, delivering a better user experience to the composite organization.
- Security and governance. Because Sigma serves as a front end to the cloud data environment, replacing multiple legacy BI tools in the process, the composite organization avoids data proliferation problems. Sigma’s administrative controls also make governance easier, contributing to a more secure environment.
- User satisfaction. End users find Sigma’s spreadsheet-like interface simple and easy to use, which encourages further adoption and spreads productivity gains throughout the composite organization.
- Sigma service and support. The composite organization benefits from a close collaborative relationship with Sigma’s engineering and product development teams and takes advantage of Sigma’s training and documentation as it continues to drive user adoption throughout the organization.
Costs. Three-year, risk-adjusted PV costs for the composite organization include:
- Sigma licenses. In Year 1, the composite organization purchases licenses for the Sigma Platform, Pro licenses for its data analysts, and Essential licenses for 30% of the power users adopting Sigma in the first year. Other users receive Lite licenses at no added cost. The organization adds more Essential and Lite licenses in subsequent years as it rolls Sigma out across the organization. The costs of the platform and licenses add up to $313,700 over three years.
- Planning, implementation, and ongoing platform management. Initial setup of Sigma takes two months and involves five data architects working part-time over that period. Moving forward, two data engineers spend 10% to 25% of their time maintaining the Sigma platform. These costs total $256,400 for the composite organization over three years.
- Training. The composite organization provides 8 hours of initial training to its data analysts, and 2 additional hours of refresher training in subsequent years. End users receive 3 hours of training on Sigma as they are introduced to the platform. These activities cost the composite organization a total of $328,000 over three years.
The representative interviews and financial analysis found that a composite organization experiences benefits of $3.78 million over three years versus costs of $898,000, adding up to a net present value (NPV) of $2.88 million and an ROI of 321%.
Key Statistics
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Return on investment (ROI)
321%
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Benefits PV
$3.78M
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Net present value (NPV)
$2.88M
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Payback
<6 months
Benefits (Three-Year)
Data analyst productivity gains End-user productivity gains Value of faster time to market for new business Cost savings from retiring/reducing use of legacy BI platform
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 Sigma.
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 Sigma can have on an organization.
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Due Diligence
Interviewed Sigma Computing stakeholders and Forrester analysts to gather data relative to Sigma.
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Interviews
Interviewed eight representatives at organizations using Sigma 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 Sigma Computing 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 Sigma.
Sigma Computing 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.
Sigma Computing provided the customer names for the interviews but did not participate in the interviews.
Consulting Team:
Henry Huang
Caro Giordano
Drivers leading to the Sigma investment
Interviews
| Role | Industry | Revenue | Employees |
|---|---|---|---|
| Director of analytics | Professional services | <$10M | <500 |
| CEO | Technology | <$10M | <500 |
| SVP of data and analytics | Technology | $100M to $500M | <500 |
| Head of analytics | Technology | $500M to $1B | 1,000 to 5,000 |
| Director of enterprise analytics | Healthcare | $1B to $5B | >10,000 |
| Senior manager, analytics | Consumer goods | $1B to $5B | 1,000 to 5,000 |
| Head of data visualization | Financial services | >$5B | 1,000 to 5,000 |
| Program manager, data and analytics | Real estate | >$5B | 1,000 to 5,000 |
Key Challenges
Prior to adopting Sigma, the interviewees’ organizations had modernized their data architectures, consolidating enterprise data in a cloud data warehouse environment to eliminate data silos. When these organizations used their older generation BI tools in the new environment, they found these tools incapable of scaling to the degree needed. Moreover, the legacy tools required specialized knowledge of query languages, which limited their use to overburdened data analysts fielding demands from increasingly restive business users unable to effectively access data directly.
The interviewees noted how their organizations struggled with common challenges, including:
- Information silos. Prior to organizing their enterprise data within a single cloud data warehouse, the interviewees’ organizations struggled to manage multiple warehouses across different business units and application teams that each used different BI tools and ad hoc analytical processes. Data was extracted and passed around in spreadsheets, losing integrity. According to the head of analytics at a technology firm, “There were data duplication issues, lack of consistency, reconciliation issues, and a lot of confusion for the business.”
- Specialized knowledge requirements. While some traditional BI tools were considered powerful, interviewees complained that they required specialized knowledge of query languages which business users often lacked. According to the head of data visualization at a financial services company: “We had BI tools in place but with very different rates of adoption. All those platforms sold around self-service, but the learning curve was too high. Not every [business user] is going to learn SQL [structured query language], so how do we meet stakeholders where they are?”
- Operational inefficiencies. The steep learning curve of legacy BI tools in turn created operational bottlenecks as business users became overly dependent on costly data engineers and analysts for their enterprise insights. The program manager of data and analytics at a real estate firm said: “Only 5% of [our business users] knew SQL. People skipped over [the legacy BI tool] and went to the data team.” This behavior was confirmed by the director of analytics at a professional services firm, who noted: “A lot of our operations team’s time was spent pulling custom reports. … It was an arduous task.”
- Poor performance under scale. Several interviewees observed that their organizations’ legacy BI tools could not handle the larger scale of the cloud data warehouse environment. For the director of enterprise analytics at a healthcare organization, this was evident in poor refresh rates: “It was slow. It wasn’t leveraging the cloud.” Analyzing large volume of records was a problem for the program manager of data and analytics at a real estate firm, who said: “Everybody loves their spreadsheets and loves to have millions upon millions of records, but they would bring [the legacy BI platform] to its knees trying to do things like that. … [It] tried to be a self-service tool, but it’s not.” The interviewee added that users were unable to easily pull data from different sources together: “You couldn’t join tables inside a workbook. You had to create a dataset and use that dataset inside a workbook.”
- Cost. Cost concerns over instance fees also drove interviewees to look for other solutions as they sought to make enterprise data accessible to more users. The SVP of data and analytics at a technology firm recalled, “Performance was fine, but the cost was higher for [the legacy BI platform].” The interviewee later added, “Not only are we saving costs [with Sigma], but we are making data more accessible and access to it much faster.”
Solution Requirements/Investment Objectives
The interviewees recognized the need for a more powerful analytics solution which could securely leverage their organizations’ cloud infrastructures while enabling democratization of data at scale. The interviewees’ organizations searched for a solution that:
- Was easy to stand up with a short learning curve and low maintenance costs.
- Kept data in the cloud data warehouse.
- Bridged the gap for spreadsheet users who wanted to consume large-scale datasets and join data from different underlying sources.
- Could render results extremely quickly.
The interviewees’ organizations typically evaluated a handful of applications and often ran a pilot as proof of concept before making the selection decision. The program manager of data and analytics at a real estate company explained: “We started with our ERP [enterprise resource planning] system. We were able to expose tables in Sigma with very little data latency and use it as a data exploration tool to mine millions of records.” This was something they were unable to do with their organization’s legacy BI tool.
Reference checking was part of the process for the head of analytics at a technology firm, who stated, “Sigma offered customers who were coming from the same place as us, so we found those referrals quite valuable.” Sigma also provided case studies for the cloud data warehouse they were using, as well as insight on their product development plans: “When we saw what Sigma is capable of and what their roadmap was, we liked that we could see their vision and where they were headed, and we wanted to join the bandwagon.”
Once the decision was made, the interviewees’ organizations chose Sigma and began deployment. The director of enterprise analytics at a healthcare organization characterized their rollout process as strategic: “We started with a pilot of two groups, and they absolutely loved it. They showcased to their leadership the things they could build in Sigma without us, and that started getting more teams hearing about it and wanting to learn more about it.”
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 eight 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, business-to-business organization with 5,000 employees and $2.5 billion in annual revenue. The organization has operations in several countries, providing goods and services to a growing number of customers around the world. One-quarter of the workforce are users and consumers of enterprise data, a significant fraction (15%) of which are power users, supported by a team of 40 data analysts.
Deployment characteristics. After piloting Sigma in its cloud data warehouse environment, the composite organization begins using the solution in Year 1, following a two-month implementation period. The initial rollout covers 30% of the data consumer workforce and scales to 95% by Year 3.
Quantified benefit data as applied to the composite
Total Benefits
| Ref. | Benefit | Year 1 | Year 2 | Year 3 | Total | Present Value |
|---|---|---|---|---|---|---|
| Atr | Data analyst productivity gains | $340,704 | $1,022,112 | $1,192,464 | $2,555,280 | $2,050,367 |
| Btr | End-user productivity gains | $145,860 | $316,030 | $461,890 | $923,780 | $740,807 |
| Ctr | Value of faster time to market for new business | $90,000 | $200,850 | $302,357 | $593,206 | $474,975 |
| Dtr | Cost savings from retiring/reducing use of legacy BI platform | $100,821 | $218,445 | $319,266 | $638,531 | $512,057 |
| Total benefits (risk-adjusted) | $677,385 | $1,757,437 | $2,275,977 | $4,710,797 | $3,778,206 | |
Data Analyst Productivity Gains
Evidence and data. Interviewees noted Sigma drove data analyst productivity gains through a combination of factors. The more user-friendly, spreadsheet-like interface made it easier for data consumers to serve themselves, freeing up technical teams to work on other tasks and initiatives, including automations to further reduce or eliminate manual processes. Sigma users also experienced faster performance rendering reports and dashboards, saving time.
- The head of data visualization at a financial services firm told Forrester they were able to forgo hiring additional technical staff due to the self-service enabled by Sigma, stating, “Instead of our technical teams being bogged down in an endless loop of data access and volume issues, Sigma allows our business users to self-service in a curated data environment.”
- Doing more with same or fewer resources was confirmed by the SVP of data and analytics at a technology firm: “Even though my team is working the same hours that it did, it’s working on cleaning up the data and making it more streamlined. Creating new reports for stakeholders has been reduced 50%.”
- The CEO at another technology firm called out several aspects of Sigma’s feature set that contributed to the greater productivity experienced by their team: “They’re developing faster than they would in any other tool, simply because of Sigma’s features — their functionality, the scalability where they’re testing dashboards, and things like that — on billions of rows of data and they don’t have to wait for products to spin. We also don’t have to do any work to set up or provision cloud resources. We don’t have to do any extracts. Our engineers probably operate 10 times faster on Sigma than they would on any other tool.”
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
- The number of data analysts supporting end users with their data requests remains constant, but more of their work is shifted away from those tasks over time and their productivity improves thanks in part to Sigma.
- Sigma improves the productivity of the data analysts’ work by 10% in Year 1, 30% in Year 2, and 35% in Year 3. The efficiencies grow over time as more users are introduced to Sigma and data quality improves.
- Of the time saved, 70% is recaptured for work on other tasks and initiatives.
- The average fully burdened annual salary for a data analyst is $135,200.
Risks. Operational differences that may impact data analyst productivity gains include the number and compensation of data analysts involved in supporting end users with their enterprise data requests, and their prior state of efficiency. These time savings can be even greater for organizations leveraging Sigma’s features and automation capabilities more proficiently.
Results. To account for these risks, Forrester adjusted this benefit downward by 10%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $2.1 million.
Data Analyst Productivity Gains
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 |
|---|---|---|---|---|---|
| A1 | Number of data analysts supporting end users/consumers of data | Composite | 40 | 40 | 40 |
| A2 | Reduction in workload attributable to Sigma | Interviews | 10% | 30% | 35% |
| A3 | Fully burdened annual salary for a data analyst | Forrester standard | $135,200 | $135,200 | $135,200 |
| A4 | Percentage of time recaptured | Forrester standard | 70% | 70% | 70% |
| At | Data analyst productivity gains | A1*A2*A3*A4 | $378,560 | $1,135,680 | $1,324,960 |
| Risk adjustment | ↓10% | ||||
| Atr | Data analyst productivity gains (risk-adjusted) | $340,704 | $1,022,112 | $1,192,464 | |
| Three-year total: $2,555,280 | Three-year present value: $2,050,367 | ||||
End-User Productivity Gains
Evidence and data. According to interviewees, Sigma’s user-friendly interface enabled users to run workloads without relying on data team resources. Sigma’s ability to quickly process large datasets reduced time it took the interviewees’ organizations to acquire data, which was especially important for time-sensitive activities. Queries that used to take minutes or hours now took seconds to render. This allowed users to mine for deeper insights, which improved their decision-making. As a result, users grew more confident, efficient, and insightful with their analyses, evident by growing acceptance and usage of Sigma within the interviewees’ organizations.
- The head of data visualization at a financial services firm explained: “Users can run workloads without having to beg, borrow, or steal resources from the data teams to run them. It’s much more scalable.” The interviewee added: “The Input Table function in Sigma allows business users to enrich their own data so they can run quick ad hoc analyses immediately. They don’t need to get a database engineer to do it for them. It allows us to collapse the workflow.”
- The program manager of data and analytics at a real estate firm confirmed these efficiencies and their impact on decision-making: “Our operations teams can utilize data in real time rather than opening a ticket and waiting a few days for it. Monthly and quarterly reports used to take hours to run and now they get to the data in less than a minute. They do reconciliations to find issues, make adjustments, and, within a minute or two, see the impact of the changes. This is a game changer that enables the simplest of users to make better use of data to make better decisions.”
- The SVP of data and analytics at a technology firm told Forrester: “It’s saved our customer success teams and salespeople time. They’re able to respond to RFPs much faster. They’re able to get back to [clients] with data much faster. They have all been able to get what they need a lot faster.”
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
- One-quarter of the composite organization’s workforce are users and consumers of enterprise data, who spend 10% of their workday on average retrieving and analyzing data.
- Starting in Year 1, 30% of these users are introduced to Sigma and begin using it to run their queries. The number of Sigma users increases over time as adoption spreads.
- Sigma improves end user productivity by 10%, which leads to 7,800 hours in time savings in Year 1. These efficiencies grow over time as more users are introduced to Sigma.
- Of the time saved, 50% is recaptured for work on other tasks and initiatives.
- The fully burdened hourly rate for an internal data user is $44.
Risks. Operational differences that may impact end-user productivity gains include the number and compensation of users involved in retrieving and analyzing enterprise data and their prior state of efficiency. These time savings can be even greater for users leveraging Sigma’s features and functionality more proficiently.
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 $741,000.
End-User Productivity Gains
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 |
|---|---|---|---|---|---|
| B1 | Number of employees | Composite | 5,000 | 5,000 | 5,000 |
| B2 | Percentage of employees who are internal data users/consumers | Interviews | 25% | 25% | 25% |
| B3 | Number of internal data users/consumers | B1*B2 | 1,250 | 1,250 | 1,250 |
| B4 | Average percentage of workday spent retrieving/analyzing data | Interviews | 10% | 10% | 10% |
| B5 | Hours spent retrieving/analyzing data | B3*B4*2,080 | 260,000 | 260,000 | 260,000 |
| B6 | Percentage of users adopting Sigma | Composite | 30% | 65% | 95% |
| B7 | Percentage of time saved attributable to Sigma | Interviews | 10% | 10% | 10% |
| B8 | Percentage of time recaptured | Forrester standard | 50% | 50% | 50% |
| B9 | Fully burdened hourly rate for an internal data user/consumer | Forrester standard | $44 | $44 | $44 |
| Bt | End-user productivity gains | B5*B6*B7*B8*B9 | $171,600 | $371,800 | $543,400 |
| Risk adjustment | ↓15% | ||||
| Btr | End-user productivity gains (risk-adjusted) | $145,860 | $316,030 | $461,890 | |
| Three-year total: $923,780 | Three-year present value: $740,807 | ||||
Value Of Faster Time To Market For New Business
Evidence and data. In several cases, interviewees discussed how their organizations shortened their business creation processes through quicker iteration and delivery of insights. Three of the eight interviewees noted their organizations embedded Sigma in customer-facing products and services while other interviewees’ organizations simply benefitted from more rapid, data-driven decision-making.
- The senior manager of analytics at a consumer goods company told Forrester: “It’s allowed the business to move much faster. People don’t need to ask me about performance. They can see it for themselves and, if things aren’t going well, they can put together action plans much quicker. Whereas before, that sort of thing would take a day or two. Obviously, every day you wait is lost revenue.”
- The head of analytics at a technology company noted: “There are a lot of things that we have created today in the form of data products that would not have been as efficient as they are today. The amount and volume of data that we can host on Sigma, the latency it provides, and how fast it operates and how quickly you can explore data — it’s just crazy.”
- The director of analytics for the professional services firm said: “Sigma is an incredible tool. I’ve seen changes with our product team in weeks rather than months for product feature development.” This interviewee described how Sigma functionality underpinned their product offerings to make them more competitive: “Basic reporting on campaigns is pretty much table stakes. Where we take things further is with our mapping capabilities, which Sigma allows us to do. It’s certainly a lot better than what was available across the BI marketplace.”
- The CEO of a technology firm also called out the role Sigma functionality played in their products: “Sigma has been a key accelerator in our ability to bring our products to market. It would have taken us multiple years to build a visualization capability that delivered what we need to deliver for our customers, and Sigma delivers that out of the box. Sigma was a significant accelerator of getting our products to market.”
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
- The composite organization’s revenues are $2.5 billion in Year 1, growing 3% annually with a profit margin of 8%.
- For users of Sigma, time to market is reduced by three months, 25% of which is attributable to Sigma.
Risks. Organizational differences that may impact the value of faster time to market for new business include the organization’s size in terms of revenue, net margin and growth trajectory, and its ability to effectively drive new business development faster through insights delivered by Sigma.
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 $475,000.
Value Of Faster Time To Market For New Business
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|
| C1 | Annual revenue | Composite | $2,500,000,000 | $2,575,000,000 | $2,652,250,000 | |
| C2 | Year-over-year growth | Composite | 3% | 3% | 3% | |
| C3 | New business revenue per month | C1*C2/12 | $6,250,000 | $6,437,500 | $6,630,625 | |
| C4 | Number of months time to market is reduced by with Sigma | Interviews | 3 | 3 | 3 | |
| C5 | Reduction in time to market attributed to Sigma | Interviews | 25% | 25% | 25% | |
| C6 | Percentage of users adopting Sigma | B6 | 30% | 65% | 95% | |
| C7 | New business revenue recognized from faster time to market | C3*C4*C5*C6 | $1,406,250 | $3,138,281 | $4,724,320 | |
| C8 | Profit margin | Composite | 8% | 8% | 8% | |
| Ct | Value of faster time to market for new business | C7*C8 | $112,500 | $251,062 | $377,946 | |
| Risk adjustment | ↓20% | |||||
| Ctr | Value of faster time to market for new business (risk-adjusted) | $90,000 | $200,850 | $302,357 | ||
| Three-year total: $593,207 | Three-year present value: $474,975 | |||||
Cost Savings From Retiring/Reducing Use Of Legacy BI Platform
Evidence and data. Retiring the legacy BI solution and moving analytics to the cloud resulted in significant cost savings for the interviewees’ organizations. Interviewees appreciated Sigma’s flexible licensing model, which did not charge for viewer licenses. One interviewee noted that Sigma’s visualization capabilities were sufficiently versatile such that it saved them the need to purchase additional visualization tools.
- The head of analytics at a technology firm explained: “For my team, it was a huge relief because our infrastructure support costs for maintenance work went down significantly. … After the migration, we expanded our footprint 2.5 times in terms of data alone, and my team of data developers has only gone up by a couple of resources.”
- For the SVP of data and analytics at another technology firm, Sigma’s licensing structure compared favorably to that of their prior BI solution: “Our cost structure has definitely changed since moving to Sigma. … With Sigma, there are no viewer fees. Licensing costs only come into play when you need somebody to have more access than just reviewing the data.” The interviewee noted that their warehouse costs had also been reduced 20% to 25%, because queries were now rendered on [the cloud data warehouse] instead of being run through extract, transform, and load (ETL)/extract, load, and transform (ELT) processes needed for their legacy BI tools.
- The program manager of data and analytics at a real estate firm stated: “I’m not going to buy 15 different data visualization tools. I can’t afford it on my budget. Now, [Sigma gives us] different tools to fit all our different use cases. Sigma filled our data exploration needs perfectly.”
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
- The composite organization spends $370,000 in legacy BI platform costs, including hardware, software, and storage. It employs two architectural engineers who invest on average 20% of their time to maintain the platform.
- As the organization migrates users onto Sigma, it reduces these costs proportionate to the migration effort. The savings grow over time as more users are introduced to Sigma.
- Of the architectural engineering time saved, 70% is recaptured for work on other tasks and initiatives.
- The average fully burdened annual salary for an architectural engineer is $178,880.
Risks. Organizational differences that may impact the cost savings from retiring or reducing use of the legacy BI platform include the licensing and pricing models of those other BI platforms and the costs associated with their upkeep.
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 $512,000.
Cost Savings From Retiring/Reducing Use Of Legacy BI Platform
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 |
|---|---|---|---|---|---|
| D1 | Legacy BI platform costs (owned/leased hardware, storage, software) | Interviews | $370,000 | $370,000 | $370,000 |
| D2 | Number of architectural engineers managing existing infrastructure | Interviews | 2 | 2 | 2 |
| D3 | Percentage of time spent supporting legacy BI platform | Interviews | 20% | 20% | 20% |
| D4 | Fully burdened annual salary for a data architect | Forrester standard | $178,880 | $178,880 | $178,880 |
| D5 | Percentage of time recaptured | Forrester standard | 70% | 70% | 70% |
| D6 | Cost of maintaining legacy BI platform | D1+(D2*D3*D4*D5) | $420,086 | $420,086 | $420,086 |
| D7 | Percentage of users migrating from legacy BI platform to Sigma | B6 | 30% | 65% | 95% |
| Dt | Cost savings from retiring/reducing use of legacy BI platform | D6*D7 | $126,026 | $273,056 | $399,082 |
| Risk adjustment | ↓20% | ||||
| Dtr | Cost savings from retiring/reducing use of legacy BI platform (risk-adjusted) | $100,821 | $218,445 | $319,266 | |
| Three-year total: $638,532 | Three-year present value: $512,057 | ||||
Unquantified Benefits
Interviewees mentioned the following additional benefits that their organizations experienced but were not able to quantify:
- Performance and latency. Because it’s a cloud-native platform, Sigma was able to process data and render reports and visualizations based on millions of records faster than other BI tools used and considered by the interviewees’ organizations. Performance under scale was a key selection criterion, and several interviewees were especially pleased with Sigma in this regard. The head of analytics at a technology firm explained: “Now our BI is on the cloud. It’s a live stream of data from [our cloud data platform] to Sigma. There’s very little downtime or wait time. There are no issues with data latency. It’s a much better experience for the end user.” This was confirmed by the senior manager of analytics at a consumer goods company, who told Forrester: “Instant data is the most important outcome of using Sigma. Data processing time went from up to 10 minutes to 1 minute.”
- Security and governance. Interviewees had been frustrated by data proliferation problems in their previous BI environments and were happy that consolidating BI activities on Sigma avoided this because it served as a front-end to the cloud data environment. The head of data visualization at a financial services company explained, “The data never leaves [the cloud data environment] for the most part, and Sigma uses optimization techniques and native [cloud data] techniques, so everything is a materialization.” The interviewee also praised Sigma’s administrative controls, which made governance easier: “Access controls are great with Sigma. You can harden a SaaS application very easily in Sigma. You have SSO [single sign-on]. You can provision very quickly. After you have guest users, you have domain blocking. All of those processes are built into the platform itself.” Other interviewees confirmed that Sigma contributed to a more secure data environment. The head of analytics at a technology firm described sitting down with their security team, “The Sigma platform passed all the sniff tests in terms of architectural reviews and the ability it offers for self-service and automation and scale that we needed to get.”
-
User satisfaction.
Interviewees noted that end users found Sigma’s spreadsheet-like interface
simple and easy to use, which helped encourage further adoption and spread
productivity gains throughout their organizations. The program manager of
data and analytics at a real estate firm recalled: “The spreadsheet
interface was so important for our accounting and operations teams. It
really moved the needle for them. At its core, we are a financial company
that lives in spreadsheets.” The interviewee continued: “I would
characterize it as a game changer in terms of enabling even the simplest
business user to view, utilize, explore, and analyze data to make better
business decisions, to really understand where things are at, where they
could possibly go, and what that means. The data availability and ease of
use really are game changers for us.”
The head of analytics at a technology firm also acknowledged increased adoption of Sigma within their organization due to ease of use: “Since [users] find it simple and easy, they come back to what they need on a daily or weekly basis. Our usage numbers are improving constantly.” User satisfaction with Sigma was confirmed by the director of enterprise analytics at a healthcare organization: “We conducted a survey of users of Sigma. Everyone was satisfied with the current state. It’s all because of speed. They’re not waiting for us to do something for them.” -
Sigma service and support. Interviewees praised various aspects of Sigma’s customer service
and support. The CEO at a technology firm told Forrester: “The engineering
team at Sigma is the best we’ve worked with from a visualization standpoint.
They’ve shown a great deal of collaboration with no
hesitation.”
The director of analytics at a professional services firm confirmed Sigma’s collaborative spirit: “When I submit a feature request or feedback, it’s taken seriously, even though we’re a relatively small company. I see changes within weeks, not months. There’s a pretty unique collaborative approach that Sigma takes. I actually feel listened to compared to pretty much every other vendor that I work with in my tech stack.”
Other interviewees remarked positively on the training and documentation Sigma provided. According to the program manager of data and analytics at a real estate firm, “Sigma has fantastic documentation, probably one of the better documentation pieces compared to other software vendors.”
Flexibility
The value of flexibility is unique to each customer. There are multiple scenarios in which a customer might implement Sigma and later realize additional uses and business opportunities, including:
-
Expanding internal use cases.
Interviewees discussed their plans to expand use of Sigma to other
departments and business units, which often relied on migrating enterprise
data to the cloud to interface with Sigma. The senior manager of analytics
at a consumer goods company noted: “[Our expansion plans include] marketing,
potentially our POS data, potentially our merchandising data and inventory.
Ideally, it would be nice to use one tool instead of multiple
tools.”
For the head of data visualization at a financial services firm: “The next part of our journey is around user-generated data. How do we curate and catalog this data for future use because, at the end of the day, there’s going to be a lot of great insights from user-generated data. Whatever spreadsheet file they bring in, whatever CSV [comma-separated value] file they bring in, whatever ad hoc market research data they bring in, those are going to be important and useful for feeding our longer-term AI projects.” -
Creating and expanding external use cases.
The three organizations currently embedding Sigma in their
customer-facing applications looked for opportunities to add more value
while other organizations looked for ways to get started embedding Sigma in
external applications. The interviewees found Sigma to be a helpful partner
in collaborations. According to the CEO of a technology firm, “We are
rapidly developing and delivering additional application-build capabilities
for our customers, and the innovation on the Sigma side and their
willingness to listen to product ideas from us has been super critical to
the partnership.”
The head of analytics for another technology firm said: “Now is the time to create similar value for our customers through embedded analytics [provided by] Sigma. That’s what we’re working on now. Hopefully by the same time next year, we would have similar success stories for data monetization through our paired analytics services and not just create analytics but also a lot of thought-leadership product that you want to put over to the industry.” - Activating more of Sigma’s capabilities. The SVP of data and analytics at a technology firm noted that they hadn’t tapped Sigma’s AI and natural language capabilities yet but were planning to roll it out later in the year. They noted, “We are being very cautious about AI within our company, so we haven’t used [Sigma’s capabilities] a lot and I can’t speak to it [but] it’s in our roadmap for the second half of this year.”
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 | Sigma licensing | $0 | $102,854 | $128,911 | $151,246 | $383,011 | $313,675 |
| Ftr | Planning, implementation, and ongoing platform management | $81,675 | $39,354 | $78,707 | $98,384 | $298,120 | $256,416 |
| Gtr | Training | $23,920 | $62,905 | $129,393 | $186,318 | $402,537 | $328,027 |
| Total costs (risk-adjusted) | $105,595 | $205,113 | $337,011 | $435,948 | $1,083,668 | $898,118 | |
Sigma Licensing
Evidence and data. Sigma offers a modular pricing approach, so interviewees’ organizations only paid for what their organizations used. Interviewees liked that free “viewer” licenses granted casual users access to data via Sigma throughout their organizations. As the head of analytics at a technology firm put it: “Sigma has this flexible model where they don’t charge anything for viewer licenses. The reach and scale of any data published on Sigma is available to everybody in the organization as long as they have the right access and approvals to view the information.” Interviewees noted that Sigma licensing costs can vary, and licensing information includes the following:
- A Sigma Platform subscription is required.
- Pro is Sigma’s full use license for platform administrators and builders. It allows administrators to manage users, integrations, and connections, and lets data architects explore, transform, and model data using a full suite of analytical capabilities. It also allows license holders to find and share their insights.
- Essential is Sigma’s license for data deep divers. It enables power users to drill down into charts and unaggregated data, add filters, write data into Input Tables, and schedule reports and alerts.
- Lite is Sigma’s free license for internal data consumers. It allows users to consume content produced by Pro users and leverage predefined explorative capabilities, such as filters, sorting, and drilldowns, to arrive at deeper insights.
- A separate per-user license applies where Sigma is embedded in external products and services.
- Pricing may vary. Contact Sigma for additional details.
Modeling and assumptions. Forrester assumes the following about the composite organization:
- In Year 1, the composite organization subscribes to the Sigma Platform, Pro licenses for 40 data analysts, and Essential licenses for 30% of the power users who adopt Sigma in Year 1 (note: power users represent 15% of total users); other users adopting Sigma receive Lite licenses.
- The composite organization adds more Essential and Lite licenses in subsequent years as it rolls Sigma out across the organization.
- Sigma licensing is estimated for a composite organization having 5,000 employees.
Risks. Organizational differences that may impact the costs associated with Sigma licensing include the size and scale of deployment and the mix of license types deployed.
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 $314,000.
Sigma Licensing
| Ref. | Metric | Source | Initial | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|---|
| E1 | Sigma licensing costs | Sigma | $93,504 | $117,192 | $137,496 | ||
| Et | Sigma licensing | E1 | $93,504 | $117,192 | $137,496 | ||
| Risk adjustment | ↑10% | ||||||
| Etr | Sigma licensing (risk-adjusted) | $0 | $102,854 | $128,911 | $151,246 | ||
| Three-year total: $383,011 | Three-year present value: $313,675 | ||||||
Planning, Implementation, And Ongoing Platform Management
Evidence and data. Initial setup of Sigma took the equivalent of two to three data architects working full-time over two months for interviewees’ organizations on average. This experience varied depending on the scope and state of the cloud data and whether Sigma was embedded in external applications. Many interviewees’ organizations took a phased approach to adoption to facilitate change management. Ongoing maintenance was considered minimal and included mainly setting up new datasets and making enhancements to existing datasets.
- According to the senior manager of analytics at a consumer goods company: “It was relatively quick to implement. We stood it up and were reporting on it within 24 hours. Getting the business to use it took four to five more months.”
- The director of analytics at a professional services firm explained that they relied on the services of an external consultant to develop a customer-facing reporting portal with which Sigma was embedded: “The initial setup took a month or so, but it was pretty painless. The biggest hurdle was figuring out our [external] application.” Once set up, upkeep of the portal was easy. According to the interviewee: “Now that I have the reporting portal up and running, when I want to embed new things, I go into the portal and configure a few things. It takes me a few minutes and I am ready to go.”
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
- The composite organization chooses to use five data architects working on a part-time basis for two months to set up the Sigma platform and get it into a working state.
- The fully loaded monthly rate for a data architect is $14,850.
- Moving forward, two data engineers spend 10% to 25% of their time maintaining the Sigma platform over the first three years, including setting up new datasets and making enhancements to existing datasets.
- The average fully burdened annual salary for a data engineer is $178,880.
Risks. Organizational differences that may impact costs associated with planning, implementation, and ongoing platform management include:
- The amount, organization, and state of cloud data inside the organization prior to deploying Sigma the development of any new applications that Sigma would be embedded into.
- The amount and state of new data sources added to Sigma over time.
- The efficiency of the teams deploying and maintaining Sigma moving forward.
- Prevailing local compensation rates.
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 $256,000.
Planning, Implementation, And Ongoing Platform Management
| Ref. | Metric | Source | Initial | Year 1 | Year 2 | Year 3 |
|---|---|---|---|---|---|---|
| F1 | Number of data architects involved in planning and implementation | Interviews | 5 | |||
| F2 | Percentage of time dedicated to planning and implementation | Interviews | 50% | |||
| F3 | Time spent on implementation (months) | Interviews | 2 | |||
| F4 | Fully burdened monthly rate for a data architect | Forrester standard | $14,850 | |||
| F5 | Subtotal: Planning and implementation | F1*F2*F3*F4 | $74,250 | |||
| F6 | Number of data engineers | Interviews | 2 | 2 | 2 | |
| F7 | Percentage of time dedicated to ongoing management | Interviews | 10% | 20% | 25% | |
| F8 | Fully burdened annual salary for a data engineer | Forrester standard | $178,880 | $178,880 | $178,880 | |
| F9 | Subtotal: Ongoing management costs | F6*F7*F8 | $35,776 | $71,552 | $89,440 | |
| Ft | Planning, implementation, and ongoing platform management | F5+F9 | $74,250 | $35,776 | $71,552 | $89,440 |
| Risk adjustment | ↑10% | |||||
| Ftr | Planning, implementation, and ongoing platform management (risk-adjusted) | $81,675 | $39,354 | $78,707 | $98,384 | |
| Three-year total: $298,120 | Three-year present value: $256,416 | |||||
Training
Evidence and data. Interviewees remarked that the learning curve for Sigma was much easier compared to other BI tools, especially for business users accustomed to working with spreadsheets. Some of the interviewees’ organizations video-recorded their training sessions and provided those materials to subsequent new users. Several interviewees noted Sigma was very helpful in providing initial training at no cost.
-
According to the SVP of data and analytics at a technology
firm: “The learning curve is much less compared to any other BI tool. None
of the traditional BI tools have [a spreadsheet] look and feel. Anybody and
everybody who had a basic understanding of [spreadsheets] was able to hit
the ground running in being able to navigate within Sigma to get data. To
understand the more complex datasets or more complex ways to create like a
dashboard within Sigma, yes, there is a learning curve, but it’s not as
steep as other BI tools. It’s pretty intuitive. It’s click and play a lot of
times. As long as my team has datasets done correctly, the end user will be
able to put something together right away. If they want something more
advanced, I would say the learning curve is two to three weeks at that
point.”
The interviewee continued: “Sigma helps us with training. I also have a dedicated person on my team who does Sigma training videos and creates courses for all the new features that come out. It’s a little one-time investment but then that video is forever, available for anybody to learn Sigma on their own.” - The senior manager of analytics at a consumer goods company added: “The tough spots come with teaching your typical user, who is used to looking at static dashboards, how to more fully leverage the tool. When you go into Explore mode on Sigma, it opens up a lot of capabilities that can be overwhelming and a little bit tough to navigate, but that’s kind of typical for every BI tool and I think Sigma is one of the more welcoming and friendly tools.”
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
- The composite organization provides 8 hours of initial training on Sigma to the 40 data analysts.
- In subsequent years, the 40 data analysts receive 2 additional hours of refresher training, including coverage of new features and functionality.
- The fully loaded hourly rate for a data analyst is $65.
- End users receive 3 hours of training on Sigma as they are introduced to the platform.
- The fully loaded hourly rate of a data user is $44.
Risks. Organizational differences that may impact costs associated with training include the amount of time the organization needs for up-front planning and development of training materials, the amount of training and familiarization time needed by users, and 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 $328,000.
Training
| Ref. | Metric | Source | Initial | Year 1 | Year 2 | Year 3 |
|---|---|---|---|---|---|---|
| G1 | Number of data analysts | Composite | 40 | 40 | 40 | 40 |
| G2 | Hours spent for training per data analyst | Interviews | 8 | 2 | 2 | 2 |
| G3 | Fully burdened hourly rate for a data analyst | Forrester standard | $65 | $65 | $65 | $65 |
| G4 | Subtotal: Training costs for data analysts | G1*G2*G3 | $20,800 | $5,200 | $5,200 | $5,200 |
| G5 | Number of internal data users/consumers adopting Sigma | B3*B6 | 375 | 813 | 1,188 | |
| G6 | Hours spent for training per end user | Interviews | 3 | 3 | 3 | |
| G7 | Fully burdened hourly rate for an internal data user/consumer | Forrester standard | $44 | $44 | $44 | |
| G8 | Subtotal: Training costs for end users | G5*G6*G7 | $0 | $49,500 | $107,316 | $156,816 |
| Gt | Training | G4+G8 | $20,800 | $54,700 | $112,516 | $162,016 |
| Risk adjustment | ↑15% | |||||
| Gtr | Training (risk-adjusted) | $23,920 | $62,905 | $129,393 | $186,318 | |
| Three-year total: $402,536 | Three-year present value: $328,027 | |||||
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, 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 | ($105,595) | ($205,113) | ($337,012) | ($435,948) | ($1,083,668) | ($898,118) |
| Total benefits | $0 | $677,385 | $1,757,436 | $2,275,976 | $4,710,798 | $3,778,206 |
| Net benefits | ($105,595) | $472,272 | $1,420,424 | $1,840,028 | $3,627,130 | $2,880,088 |
| ROI | 321% | |||||
| 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: Supplemental Material
Related Forrester Research
Boris Evelson, AI Is Now A Core Capability Of BI Platforms, Forrester Blogs.
Tech Exec Primer To The Symbiotic Relationship Between Knowledge Graphs And GenAI, Forrester Research, Inc., August 2, 2024.
Next Stages Of Enterprise GenAI Infused BI Evolution, Forrester Research, Inc., July 25, 2024.
AI-Infused (Augmented) Business Intelligence Further Democratizes Enterprise Data, Forrester Research, Inc., February 23, 2024.
Appendix C: 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.
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