Vitality, a company focused on making buildings smart by analyzing IoT data for energy savings and risk mitigation, faced a significant challenge as it grew. The company's original plan involved building basic analytics into its platform. However, as the company expanded, customer demands for analytics quickly outpaced what Vitality could build in-house. The company realized it needed to purchase analytics capabilities to stay relevant to its customers. These capabilities needed to be seamlessly integrated into its proprietary platform, matching the look and feel of its software, and delivering an industry-leading user experience. Additionally, Vitality saw a huge potential for future growth by augmenting its powerful algorithms with industry-leading embedded analytics.

Profusion, a data science and marketing services company, was grappling with the challenge of slow and reactive reporting for its clients. The company wanted to transition its clients from relying on manual, Excel-based reporting to a more proactive, real-time optimization. A specific challenge was presented by a London creative agency, which required Profusion to develop a solution for reporting ticketing data to its client, an international live show production company. The agency had two requirements: an intuitive interface to communicate financial investment and return through different marketing channels, and the ability to query a single customer view of its customer and extract this data for use within its other business tools. The existing process was labor-intensive, with information only available sporadically or on the client’s request.

Interfolio, a software service provider for higher education institutions, faced a significant challenge as its user base and data volume grew. The company needed a modern data strategy that could unify internal data, streamline reporting processes, and be flexible and scalable enough to serve as an embedded solution within the Interfolio platform. The primary challenge was selecting a BI platform and a cloud data platform that could handle multiple data sources, model complexity, and enable straightforward dashboard creation. The company had been using a competing BI vendor and an in-house solution for managing their consumer-based SaaS metrics and reporting, and for managing reporting data for quarterly business reviews. However, as data sets grew exponentially, these solutions were no longer performant or scalable.

TLC’s approach to organizing high-quality data for analysis and distribution across the company and to clients was both labor-intensive and extremely time-consuming. Client data wasn’t being retained in any real meaningful fashion with data analysis and reporting was mostly done manually and managed in extremely complex Excel spreadsheets.On top of this, report delivery was done on a monthly basis by email and the sheer size of the spreadsheets was becoming problematic. Their data was triple the size of what any cloud-based system could handle and the need for superior reporting and analysis in a timely fashion to maximize fundraising was imperative.

MindMax, a company that partners with universities to increase enrollment, particularly among continuing education and adult learners, faced a significant challenge in scaling its customer base. The company's legacy analytics and BI solution required manual extraction of data from disparate sources, including Salesforce, Google Analytics, Facebook Ads, LinkedIn, and Marketing Automation systems. This process was time-consuming and inefficient, making it difficult to create meaningful reports and dashboards that incorporated data from all these different sources. The company's VP of Technology, Brian DiScipio, and Senior Business Analyst, Kiersten Warendorf, recognized the need for a cleaner, faster way to empower their customers with data-driven insights. They knew that the full automation of BI and ETL through the creation of a modern data pipeline and stack was crucial for the company's growth.

When CEO Andrew Vignuzzi joined Magellan Vacations, his priority was to enable everyone at the company to access, manipulate, and draw insights from their data quickly. The company needed a BI solution that could provide real-time feedback for agents on sales closings, destination performance, and other metrics. Additionally, the solution had to be user-friendly enough for non-technical users to create their own reports and drill down into the data. The dashboards needed to be agile and scalable without requiring major infrastructure upgrades. The personalized, phone-based service of Magellan Vacations made it difficult to track standard sales metrics like closing rates, commissions, and bookings by destination automatically. The company had already trialed a leading in-memory technology, but its performance was sub-par, requiring specialist IT resources and consultants to work with the tool's proprietary scripts. This tool did not meet their needs, prompting the search for a better solution.

As an organization with many different projects, departments, and moving parts, Amadip-Esment collects vast amounts of information from various sources, including human resources, financial ERP, operational software at restaurants and printing units, and specific software systems containing sensitive data related to persons with disabilities. The team had been manually collecting data and arranging it in Excel pivot tables, which was labor-intensive and limited in analysis. They needed to increase the efficiency of organizing and managing these data pieces and required a core BI platform for managerial reporting and customized data queries. Above all, they needed a system that could bring all their disparate data together for analysis.

WeFi’s database team had been manually running SQL queries, but they struggled to generate the reports that gave the management team crucial feedback. WeFi needed to perform advanced analysis on large amounts of data in three categories: the behavior of millions of WeFi users, including retention activity and data acquisition activity; the performance and activity of wireless networks to which its users are connected; and the activity records of active clients. The average table sizes for these categories were more than 5 million rows, 70 million rows, and 500 million rows respectively.

The operational data at OHT consists of over 20-million records in a 100GB MySQL database. Lior knew that they were collecting all the information he needed to get insights, but he simply couldn’t get to it. Transaction data was coming in pretty fast and, in order to continue to be an industry leader, he needed a way to get a 360 degree view of his business as fast as possible. Lior had various ad-hoc and separate solutions running to try and achieve the reporting and analytics the company needed, including manual analysis and home-grown software. He would often rely on someone from R&D to extract reports or would end up manually doing reporting in Excel, which would take weeks. These efforts were taking significant resources, both human and computer, to try and get the reports that were needed. Many of their analytics requirements were not being met at all, which was leading to a lot of frustration within the company.

Act-On, a software company, faced a significant challenge in managing data from over a dozen web tools used for various business activities. Each tool provided unique BI analytics, making it difficult to identify a single source of truth. The company needed a solution that could integrate these tools seamlessly and provide real-time, actionable insights to improve customer experience and operational efficiency. The complexity of integration, unknown costs, and the need for a tool that could prompt team action were major concerns.

With its multiple systems, Avatrade has been generating and gathering large amounts of data for years. Former CTO with a strong technical background, Mr. Lee Levenson, currently VP Operations of AvaTrade, took it upon himself to search out a better Business Intelligence solution for his company. “Our goal was to give a single view from different angles to different people that previously had taken three or four windows from different systems, with business analysts having to export reports into CSV or Excel to generate beforehand. We wanted to replace the need to manually mash all the data together,” explained Levenson. This huge quantity of data, spread over multiple platforms, meant that getting any report done was laborious. “R&D was writing queries, and making very simple reports for whoever needed them before. As is typical in any developed solution, when a report had to be changed or a new report had to be done, it went back to the R&D queue. These requests had to be prioritized. At times this was a huge bottleneck for us,” said Levenson. It was very important to the company to find a tool that would be cost effective, and quick and relatively easy to deploy. A key factor in choosing a BI software was that it be almost exclusively driven by the business user: meaning that anyone in the organization could create their own reports or drill down in dashboards without having to keep running to R&D for every question. “What we wanted from a tool,” summarized Levenson, “was the wow factor. We wanted people to look at it and say wow, where has this been all my life?”

The company’s marketing, operations, and finance departments all collect large quantities of data. The performance of various marketing channels (social media, television ads, influencer outreach, etc.) would generally be stored in spreadsheets, in addition to operational and financial data. As the business is global and data is coming in multiple formats from a variety of systems, the data was not uniform — it needed to be standardized before analysis. In the beginning, the company was working with a ‘data dump’ — a webpage with the relevant numbers, which could not be filtered or drilled into. As Crowd Media grew, so did their data and number of data sources. Suddenly, they were integrating Redshift DB, MySQL, and connecting to various APIs from Facebook Ads and App Annie in addition to their question/answer database. Ian wanted to generate more detailed reports on a daily basis that could be easily filtered by any user. At first Ian used Excel, but it soon became clear that a more robust system was needed.

Feros Care faced significant challenges with their manual and time-consuming reporting processes. The organization needed to store and compare historical data, present KPIs visually, and manage a variety of datasets from different sources. Their existing methods were error-prone, resource-intensive, and often outdated by the time reports were completed. Additionally, annual reporting requirements placed a heavy burden on senior management. Feros Care sought a Business Intelligence (BI) tool to alleviate these issues, streamline their reporting processes, and enable data-driven decision-making. They evaluated several BI vendors, including Microsoft BI Stack, IBM Cognos, Tableau, and Qlikview, but found that these solutions required extensive customization, consulting, and mature data warehouses, which were not feasible for their needs.

When a food retailer launches a new item, many people are involved in the process: chefs, suppliers, procurement teams, taste testers, marketing, focus groups, legal teams, manufacturers, shipping companies, etc. Moreover, Gap Systems’ clients manage hundreds, even thousands, of product launches a year. That’s a lot of moving parts - including mountains of data that needs to be managed and interpreted to ensure a successful launch. Smartflow was intended to track these disparate items, so you know who is doing what at any particular time. Except, this wasn’t how customers were using the tool in reality. As a result, clients weren’t getting the value or insights they needed. They would say things like, “It seems our legal department is slowing us down, we need to do something about that,” when in fact, they couldn’t really show either way whether legal was the problem. It was all based on instinct or assumption, rather than the hard data they had at their fingertips. Evidently, choice wasn’t working. Customers needed high-end reporting embedded into the product to make Smartflow a success.

Juwai.com faced significant challenges in managing and processing their multilingual big data. They used multiple data sources in both Roman and Chinese scripts, and their datasets contained billions of rows. Processing such large datasets with Excel and internal scripting was intensely difficult, leading to manual reports generated by IT that were often out of date. The company needed more flexible and timely reporting to keep up with real-time developments. Additionally, manually adding data led to human errors and inconsistencies, which could only be dealt with reactively. The reliance on IT for report generation also placed a heavy burden on the department, further slowing down the process.

EREA’s clients, primarily in the retail sector, were overwhelmed by the large amounts of data generated in their ERP systems. These datasets often contained billions of rows, making it difficult to analyze and derive actionable insights. The time and resources required to process this data were substantial, and clients were struggling to make sense of it all. EREA needed a powerful BI tool that could handle massive volumes of disparate data and scale across the entire Latin American region. They also required a solution that could be easily customized by non-technical consultants to meet specific client requirements.

Anaqua, a leading provider of intellectual property (IP) management software, faced significant challenges with their existing reporting system. The system was rudimentary, time-consuming, and static, making it difficult for end users to utilize dashboards effectively. Clients were increasingly demanding better analytics and a more intuitive way to visualize their data. Additionally, security concerns were paramount, as many clients opted for On-Premise solutions and needed assurance that their sensitive data would remain secure and isolated from other clients.

The hotel industry faces significant challenges with scattered and inconsistent data sets from multiple sources, making it difficult to centralize IT and gain meaningful insights. Property management businesses often use on-site, Windows-based hardware that requires dedicated maintenance personnel, further complicating data integration. Bahadour Moussa, a Technology Evangelist, recognized the need for a BI tool that could store, clean, and prepare data before visualization, enabling hotels to analyze guest behavior and enhance their experience. The search for a suitable BI tool led to the discovery of Sisense, which met the criteria of ease of use, attractive UI, and the ability to connect to complex data sources without requiring ETL work.

BrightStar Care faced challenges in visualizing large volumes of data spread across multiple systems, including their home-grown application ABS, accounting software, help desk software, survey systems, and Microsoft Dynamics. They needed a solution to quickly answer business questions without rebuilding the entire pipeline for each new analysis. Additionally, they wanted to distribute their dashboard solution globally, allowing franchisees to access and visualize their own data easily. The complexity of the data and relationships between sources made it tedious to analyze, and they required a BI tool that offered self-service capabilities, agility for ad-hoc queries, visually attractive presentations, and a low cost of ownership.

Doron Gill, VP R&D at FairFly, needed to make day-to-day decisions based on a rapidly growing database. He wanted to combine this data with several other sources, including MixPanel and a CRM, without relying on R&D resources. After researching several BI tools, Doron realized he needed a tool that was easy to use for non-technical business managers. He was concerned that if R&D or IT had to manage data requests daily, it would not be sustainable. Doron and his colleague Ami Goldenberg were impressed by Sisense's ability to be used by business users without R&D involvement and its intuitive data visualizations.

Gentex, a leading supplier of personal protection and situational awareness systems, was struggling with an outdated ERP database that was over 30 years old and heavily customized. The company needed fast and accurate operation planning metrics such as projected revenue, opportunity forecasts, and expense reports. However, incorporating modern technical tools into their ERP environment for data analysis was limited and costly. The system had millions of records, and they needed a solution that could quickly process this data and deliver actionable intelligence.

When Ric Lumbard, Executive Director at ICN, stepped into his position, he realized that a central visual point for data did not exist. As a broadband carrier, ICN needed a visual system to be able to monitor the overall health and performance of the organization. Director Lumbard explained that state agencies tend to focus on monitoring the legal aspects of operations, but measuring the performance of operations is not always given the same attention. Director Lumbard was looking for a solution to raise the importance of performance awareness by providing visual cues and monitoring that allowed the staff to easily and quickly see a dashboard with important performance indicators. Another focus point for ICN was that the data to be analyzed was scattered across numerous sources–from a variety of databases to spreadsheets–in multiple servers and applications. The operations perspective needed a way to combine that data, maintain a single repository of truth, and visually analyze the data without sifting through dozens of pages of spreadsheets and columns.

Orion’s platform gathers and analyzes data on client investments, allowing firms to view their overall performance, as well as identifying weak or strong points in their business strategy. This presents its own hurdle, though. There is a LOT of data to wade through: 51 terabytes of it, in fact. Finding a BI tool that could handle this volume without sacrificing granularity was not going to be easy. Before implementing a BI tool, Orion used a manually built, flexible, and customizable reporting platform for operational reporting. So far, so good - except, by the time they generated each business metrics report and sent it to the client several weeks after the month ended, it was already out of date. Plus, the data was static, so clients couldn’t delve in to check the details or context. They only had headline figures, giving them an idea of overall performance. If they wanted to analyze this in any way, they’d have to request a special data query. This could take a day to develop. Orion realized that the company needed to take the leap from business metrics to business intelligence. Their customers needed a platform with better visualizations and direct access to accurate, up-to-date data, in order to make informed business decisions. Orion had executed a proof of concept by integrating an Excel interface into their API to get a feel for what customers wanted. The first approach was to create a dimensional model of the data, push it to firms using an SQL Server and teach them how to connect data to their current data visualization tool. However, Orion customers found dashboard-building to be too complex. Often, they didn’t yet know or understand what they wanted to get out of their data. Clearly, they would need a solution that was ready to deploy out-of-the-box and accessible by all users - not just those with IT expertise.

Over the last 15 years, the National Healthcare Service (NHS) of the UK increased their spending from 70 billion British pounds to 150 billion British pounds. In order to improve their efficiency and cut costs, the NHS created dedicated internal organizations to measure performance of organizations against each other, and to publish and distribute the data in performance reports. The problem was, the healthcare data was very complex - big, scattered and siloed - limiting the reports to focus on one area of measurement or one organization per report, and were published as static PDF documents. Users were unable to compare to other organizations or integrate across different sets of data, giving them an isolated view of their performance. Data regarding each area, such as clinical performance, cost performance, and staff and patient surveys were reported in separate and heavy 50-page plus documents that required time and research to see a bigger picture across platform. That’s where Res Consortium came in with the goal of providing performance dashboards that showed the data across platform in an intuitive way. In the past Res Consortium was producing dashboards using Excel with protection keys to protect sensitive patient data, but started looking for a BI platform that could move the dashboards to a web-based environment as well as to more efficiently and quickly handle the amount of complex data typical in healthcare.

FCTG faced challenges in pulling data together for a simple and user-friendly report to track daily sales and performance across the company. It would take up to three hours each morning to create this report, which the sales team used to influence and plan their daily targets. There was a need to improve efficiency and minimize any lost opportunities, and thus, it was necessary to develop an end-to-end solution that would be able to replace manual reporting, and allow 80 business users to receive daily reports at the click of a button. Prior to Sisense, the nSight brand didn’t exist and Flight Centre’s finance team was responsible for generating all of the company’s reports. As a part of the finance team for three years, Graeme proposed the idea to implement a BI solution, as he noticed analytics were getting more and more difficult with the company’s rapid expansion. He looked at three different BI software companies hoping for a solution that could automate daily reporting in a cost-effective way, be easily managed by people who may have little to no technical knowledge, and also allow business users to flexibly manipulate the data to immediately answer questions they had. Sisense checked all three boxes.

Michael says data streams included CSV, Excel, or Salesforce along with donations over time, who gave how much, when they gave it, and in what fundraising program. What may have looked like a very straightforward, neat, and organized Excel document from a foundation, actually possessed a great deal of insight that could be derived from it if the data was manipulated strategically. Michael’s goal was to dig into what donations amount to loyalty, how contributions come in, renewal rate, weight of attrition, and much more. On top of this, because CESM is looking at time series data and is able to look at time analysis, like seasonality or cycles of giving, there were even more insights to be discovered. However, after many days of work, all Michael could say was, for example, 'in 2006, your renewal rate was 60%.' Looking at data that was static, he was unable to go further without putting a lot more effort into it. It was time to find a better solution, and that is where Sisense came in.

Advocate’s old system had problems with speed and scalability. It could take multiple hours to produce an extract, often timing out during the process and failing. On top of this, it was very slow to refresh, was dated, and most importantly, could not handle their growing amounts of data.

The Arizona Department of Health Services faced significant challenges in quickly analyzing data from newborn screenings. The process was labor-intensive and relied heavily on Excel spreadsheets, making it difficult to identify trends and quality issues in a timely manner. This delay in data analysis could lead to serious health consequences for newborns, as early detection and treatment of disorders are crucial. The department needed a more efficient and user-friendly solution to manage and analyze the data effectively.

Bold collects a huge volume of data, currently 60TB, and actively analyzes 2TB. They provide subscription-based services, including resume builders, cover letter builders, interview prep, job postings, and worker postings. Each subscription has different frequencies and levels that need to be tracked. They wanted to see which subscription types were getting renewed the most, which products were being purchased the most, and the most effective model for connecting employees to employers. Their existing tool for visualizing transactional data was not meeting their needs. Balaji Jayapal, Head of BI and Big Data, sought a better way to manage their 2TB of transactional data and visualize it effectively.

Casumo employees were not able to create their own business reports without the assistance of the data team and lacked information about crucial departmental KPIs. Emanuele decided it was time to move beyond time-consuming manual reporting, creating a forward-thinking approach to company data with standardized reporting and a centralized BI system. This would allow company employees to successfully integrate their own data sources and develop easily understood business reports, complete with data drill-down and ad-hoc reporting. After discovering Sisense online, Emanuele decided to move forward with a free trial. He was immediately impressed with Sisense’s ability to quickly connect to its Amazon Redshift database and crunch data into the Elasticube. The performance was significant, allowing him to generate useful reports on the very first day. Sisense gave Emanuele an end-to-end solution for ETL and reporting. He found that with just one hour of introduction to Sisense for his users, they were able to build their own dashboards and start getting the insights that they needed to effectively do their job, with minimal support from his staff.