Sigma Computing’s Embedded Analytics Make Data-Driven Insights More Accessible And Actionable For External Customers

Sigma Computing commissioned Forrester Consulting to interview eight representatives and conduct a Total Economic Impact™ (TEI) study to better understand the benefits, costs, and risks associated with Sigma Computing.¹ This abstract focuses on three interviewed representatives who embedded Sigma Computing’s analytics in external customer-facing applications to deliver value to their organizations.

Forrester spoke with the following three decision-makers about their experience with Sigma Computing’s embedded analytics:

• The director of analytics of a professional services company with revenues under $10 million and less than 500 employees.
• The CEO of a privately held technology company with revenues under $10 million and less than 500 employees.
• The SVP of data and analytics of a technology company with revenues between $100 million and $500 million and less than 500 employees.

All interviewees’ organizations were in the business of providing analytics and insights to customers in different industries. One organization was a startup that had no other data visualization tools before adopting Sigma. The other two organizations recently modernized their data architectures, consolidating enterprise data in a cloud data warehouse environment, and found their older-generation business intelligence (BI) tools were incapable of scaling to the degree needed in the new environment. The interviewees recognized the need for a more powerful analytics solution that could securely leverage their cloud infrastructures while delivering a more engaging and impactful experience to their external customers at scale.

Sigma Computing’s cloud-based BI platform emerged as a solution for these organizations. No-code integration made it easy to integrate dashboards and visualizations into existing applications, and Sigma’s customizable design features and AI capabilities enabled the interviewees’ organizations to create tailored exploratory experiences that catered to their customers’ needs for up-to-date market insights based on rapid processing of massive datasets. 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. The interviewees noted how these quick turnaround times made their end customers happy.

INVESTMENT DRIVERS FOR embedded analytics

Before adopting Sigma, the interviewees noted their organizations struggled with several challenges in their legacy environments, including:

• Information silos. Prior to organizing their enterprise data within a single cloud data warehouse, one of 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 unique to each siloed platform.
• 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. The SVP of data and analytics of a technology firm said: “[The legacy tool] is powerful, but in order to modify anything within [it], you need somebody who knows SQL [structured query language] or something similar. Moving to Sigma opened up the tool to a much larger audience.”
• Operational inefficiencies. Legacy BI tools were difficult for internal users to learn, which made them overly dependent on data engineers and analysts for their enterprise insights. According to the director of analytics at a professional services firm: “A lot of our operations team’s time was spent pulling custom reports. … It was an arduous task.”
• Poor performance under scale. Two of the interviewees observed that their organizations’ legacy BI tools could not handle the larger scale of the cloud data warehouse environment. According to the CEO of a technology company: “We wanted our platform to be unlimited scale. We work with a lot of customers that have massive volumes of data. What we needed was technical scalability.”
• 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 faster.”

SIGMA COMPUTING Features

The interviewees’ organizations chose to invest in Sigma for several reasons, including:

• Performance at scale. Because the interviewees’ organizations were staking their value proposition on embedded analytics, performance under scale was a key selection criterion. The CEO at a technology firm said: “It came down to a technical decision. There was no other visualization platform that gave us the scale that Sigma could give us.” This was echoed by the director of analytics at a professional services firm, who told Forrester, “We have tons of data in the form of billions of rows, which is built into insights using Sigma’s embedded analytics.”  
• 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 SVP of data and analytics at a technology firm explained: “What happened with [the legacy BI tool] was that people would download data, create an Excel file, and pass it on to someone else, who would create their own Excel file, and soon you have 10 different versions of the data. We avoid that now using Sigma. It’s very helpful in the sense that we have that source of truth.”
• Build versus buy decision. The CEO at a technology firm told Forrester, “We went through a process to understand what products were out there and [what it would take] to build our own front-end visualization capability, and it came down to the fact that we didn’t want to rebuild something that somebody had already built.”

Key Results For sigma’s EMBEDDED ANALYTICS CUSTOMERS

The results of the investment in Sigma for the interviewees’ organizations include:

• Improving data analyst productivity. The CEO at a technology firm called out several aspects of Sigma’s feature set that contributed to 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, their 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 extracts. Our engineers probably operate 10 times faster on Sigma than they would on any other tool.”
• Improving end-user productivity. One technology firm provided performance data used for revenue forecasting and strategic planning purposes. Sigma’s reports enabled this company’s clients to drill deeper into the data to optimize spending. According to the organization’s SVP of data and analytics, “Sigma embedded in [our data cloud platform] enables faster turnaround on insights, which drives more business.”
  
  The interviewee also noted that Sigma improved end-user productivity inside their organization as well: “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.”
• Improving time to market for new business. Another technology firm used Sigma as an embedded front-end visualization capability in turnkey applications for customers in retail, consumer packaged goods, and media/advertising industries, plus new use cases planned for the insurance space. Use cases involved integrating huge volumes of data easily in the billions of rows.
  
  According to the CEO: “Our ability to deliver what we deliver at the speed with which we deliver is a competitive advantage. In some instances, we are taking processes that would take companies nine to 12 months [to build] and shortening those processes to a matter of days. That right there is the value proposition to our customers: Don’t go build something yourself. We already have it, and it’s built by an industry expert.” They continued, “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 director of analytics of a professional services firm agreed that Sigma’s platform contributed to faster production cycles: “Sigma is an incredible tool. I’ve seen changes with our product team in weeks rather than months for product feature development.” This interviewee also 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.”
• Saving costs after retiring or reducing the user of legacy BI platforms. Retiring legacy BI solutions and moving analytics to the cloud resulted in significant cost savings for the interviewees’ organizations. For the SVP of data and analytics at a technology firm, Sigma’s licensing structure compared favorably to that of their prior BI solution: “Our [internal] cost structure has definitely improved 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.
• Growing customer success. All three organizations acknowledged the role Sigma played in their growing success with customers. According to the CEO at a technology firm: “We have multiple customers who started with three to five users and, within the first three months of engagement, expanded that number five-fold. … We see higher user counts because our customers introduce or expand the usage on their side when they see how fast the front-end is, in combination with the results that the applications themselves are delivering.”
  
  The SVP of data and analytics at another technology firm remarked: “We get regular testimonials from our service desk tickets about how happy users are with the turnaround time for investigation and how we are on top of everything we do. All of that is possible because we have all these different data cuts readily available to dive into and create the report they are looking for.”
  
  The director of analytics at a professional services firm was more cautious in ascribing success to Sigma but was positive nonetheless: “It’s hard to directly attribute this to our reporting, but our general client satisfaction is through the roof. We just went through the process of doing a customer test action survey, and it blew out the benchmarks.” The interviewee added: “We’re growing very fast. If you look at just revenue and customer satisfaction, the proof’s in the pudding. Our clients are really happy with our reporting portal compared to what was there 18 months ago.”

Appendix A: Endnotes