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.

foodpanda faced significant challenges with their existing data warehouse, which was unable to efficiently handle terabytes of complex data from multiple sources. The limitations included a lack of data mining functions and the inability to affordably process large volumes of data. Additionally, foodpanda aimed to centralize data to promote transparency and democratization, reducing employee reliance on the BI department. They needed an intuitive, self-service BI solution to shift the focus from data collection to insights and strategy.

Quentin Villon, a non-technical data analyst at Qualifa, faced significant challenges in managing and utilizing the vast amount of data collected by the company. Internally, he relied on Excel to manually build activity reports, a process that took two hours daily and resulted in a cumbersome 20MB spreadsheet. This method was prone to errors, with broken formulas and other issues, making it a nightmare to manage. The reports were also delayed, going out a day after the activity took place, which meant they could only acknowledge performance rather than influence it. Generating commission reports was even more time-consuming, taking two days each month. Externally, creating campaign insight reports for potential clients was a labor-intensive process that required several days of manual effort. These reports were crucial for client acquisition but were not reusable, adding to the inefficiency.

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.

Paylogic considers itself a next-generation technological company, and its organizational structure and operations are highly automated. Instead of employing a standard monolithic ERP system (e.g., SAP), Paylogic has built its IT infrastructure using corporate-environment open-source standards. Automation is approached from a decentralized perspective based on this open-source IT backbone. The company uses different software packages for different functions and ties them together to form an enterprise collaboration platform. The result is impressive: Paylogic enjoys no-compromise IT functionality and flexibility with extremely low software costs. It was into this environment that the company sought to integrate a reporting and analytics tool. The company’s operational and historical data was consolidated into a homegrown data warehouse, based on a MySQL database. Paylogic was looking for a powerful and flexible business intelligence solution that would easily integrate with its existing system, which would not require rebuilding the data warehouse, which would allow non-technical business users to quickly create new reports and analytic queries and which would be relatively inexpensive.

Fiverr needed quick insights on growing data. The company wanted to connect data from MySQL with data from Google Docs, Spreadsheets, and Analytics to better track user actions on their website and mobile app. As users increased, so did Fiverr’s data needs, making it larger and more complex with millions of rows a day from various sources. Despite using an internal big data system based on Hadoop, the data complexity made it difficult for the team to build reports and dashboards quickly. Fiverr’s senior BI director, Slava Borodovsky, emphasized the need for real-time results due to the dynamic nature of their data. The product department relied heavily on BI to determine their product roadmap, making the need for data more urgent as departments began understanding its impact on their success.

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?”

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.

Casting Network’s data was being stored but not really seen, leaving the Sales and Business Development departments tracking KPIs manually. As the company started to expand internationally, the need to aggregate different data sets and evaluate the business on a global scale became even greater. Needing to access Google Drive documents, multiple Quickbook files and 35 SQL databases comprising over a billion rows of data, Nitika had her work cut out for her in pulling together a solution that met Glen’s directive.

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.

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.

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.

ProMarket required timely and accurate reporting and analysis of key metrics, such as sales, inventory, profit by product category, spoilage, and optimal order quantities for each store. The company was struggling to process very large amounts of data (over 40 million rows) from its centralized database. The data processing, transformation, and analytics were extremely time- and labor-consuming, making it impossible to generate some of the analytics required by management. Business partners of two leading BI vendors demonstrated their solutions to ProMarket and provided implementation proposals. However, ProMarket selected Sisense due to its ability to fully meet their requirements, faster customization of reports and dashboards, impressive data processing speed, and lower total cost of ownership.

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.

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.

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.

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.

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.

With the vast amount of data that CTSI was pulling in each day through millions of invoices and bills, they wanted to find a system that could visualize this data for their customers and provide a place where they could track key trends in the shipping industry. They were not able to provide any kind of deep view into transactions and wanted to offer their customers the chance to see what was going on with their bills on a day to day basis. But taking it one step further, they needed a platform that their customers would actively sign into in order to track those trends. For Todd, getting their services personnel on board and regularly checking the data was a must in order to provide the best analytics and data information.

With over five-billion records to analyze (and growing), Gianmaria's IT group had reached the limits of manual Excel reporting, realizing that Excel was not a scalable solution for its growing data size and scope. Analyzing Kamernet’s website data was fast becoming a burden on an already taxed IT department. Nearly twice a week, IT staff had to manually extract data from an SQL database, analyze the data, and transform the data in Excel reports. However, Excel reporting once again, proved limited. Employees were not able to arrive at quick, intuitive insights, and were having trouble visualizing their data, primarily reporting on revenue, website subscription data, and market-share information.

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.

Trupanion faced challenges in managing and analyzing large volumes of data across multiple departments. The company needed a solution to track real-time performance, optimize marketing opportunities, and build accurate financial reports. Existing in-house solutions were time-consuming and prone to inaccuracies, leading to a need for a robust BI tool that could be easily used by non-technical users and deployed quickly.

Sonny explains that before Sisense, company resources would be invested in reigning in data and wrestling with the complicated process of aggregating, processing and delivering it to the client. “We would build a bunch of pivot tables in Excel on numerous tabs, and then we would give people an import function that would import the raw data so that they could see the dynamic reports in Excel. But there were a number of problems, for example Excel would limit the amount of rows in a report, or the report was slow, or people just didn’t know how to use it.” Sonny also mentions that another significant problem was the task of distributing the data reports to thousands of different clients. To keep the reports current and updated, clients had to manually re-import the data, and eventually customization requests demanded even more time and resources, per client. “Overall, there just wasn’t control over what was happening. On top of that, if I had to update the report configuration, I had to send out thousands of new Excel files that had all the pivot tables defined in them. And ultimately every customer would need us to modify their pivot tables. It was just a nightmare.”

Ignite Media processes massive amounts of data, maintaining approximately 3 TB of transaction, demographic, and media performance data. They had been building all their reporting internally using PHP and .Net, but it was becoming increasingly difficult to scale. Writing new reports from scratch to follow a 'hunch' could take weeks, making it impractical to test new ideas. The company had valuable data but lacked the resources to fully leverage it. Mazda Ebrahimi, the VP of Application Development, sought a solution that would allow them to produce results faster and more easily without sacrificing their intellectual property.

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.