Swedbank, a leading bank in Sweden, Estonia, Latvia, and Lithuania, was facing challenges in analyzing its business performance and making data-driven decisions. The bank was using traditional tools like Excel and PowerPoint for data analysis and presentation, which were not efficient and interactive. The bank needed a solution that could speed up the analysis process, provide interactive visualizations, and facilitate collaborative problem-solving. The bank was also looking for a tool that could blend data from different sources and provide new insights during discussions.

The strategy team of a global technology company approached Bridgei2i to help improve its market forecasting processes. Historically, the client organization relied on a global team to prepare the overall market forecast. This involved multiple, static reports primarily delivered in Excel spreadsheets and .CSV files. And since the insights were not regionspecific, each regional team developed its own forecasts as well, using local reports. The lack of a unified view of data across the organization created confusion around the true numbers. The client asked Bridge i2i to build a solution—using 100MB of data— that would deliver a single version of truth for its market share/size forecasting.

SYSTEX Corporation, a leading Taiwan-based IT services provider, was facing challenges due to disparate systems of data collection and analysis that did not 'talk' to each other. Employees were using many different types of data analytics software and business intelligence systems based on work function and operational requirements. This led to inefficiencies and difficulties in standardizing reporting. The company leadership wanted everyone in the company to be able to work with data like a data scientist.

Nanyang Polytechnic, a leading institution of higher learning in Singapore, offers diploma courses in Business Intelligence & Analytics and Business Informatics. These courses aim to equip students with data analytics skills that are in high demand in today's workplace. However, the school faced a challenge in finding a data analytics tool that was easy to use and did not require extensive coding knowledge. They wanted a tool that would allow students to spend more time working with data and less time learning the tool itself. The school also wanted a tool that had the analytical depth necessary for the curriculum.

American Family Insurance (AmFam), a private mutual insurance company, faced challenges in providing data support to its local and regional agencies. These agencies, organized as independent contractors, are a crucial part of the AmFam data ecosystem, utilizing corporate data to optimize their sales and customer service contributions. However, the existing system was siloed and offline, making it difficult for agencies to access and analyze data. AmFam sought to improve the delivery of analytics to its agents, particularly around product availability, performance metrics, and expected business results. The challenge was to replace the siloed data sources and offline workflows with a secure portal that integrates insights into existing user interfaces (UI).

PT Bank Rakyat Indonesia (BRI), one of the largest banks in Indonesia, was facing a significant challenge in its data analytics process. The bank, which is data-driven in its approach, was struggling with a centralized system where data analytics was considered a task for the IT department only. Business teams had to put in a request to IT every time they needed a new report or dashboard, and then wait up to four weeks for a response. The reports and dashboards were prepared using complex queries and coding, which often led to requests for changes and further delays. In some cases, the dashboard was no longer required by the time it was complete. BRI wanted to enable faster delivery of data to business teams and build a foundation for self-service analytics.

Basware, a global provider of SaaS solutions, helps businesses digitize their financial processes to drive compliance, facilitate better process automation, and optimize overall data quality. The company supports over 2,500 customers across more than 175 countries. However, the challenge was to provide these customers with a complete overview of their financial data, from procurement-to-payment (P2P), via a single, central platform. The goal was to enable customers to make faster, more informed decisions and streamline payment processes. The ongoing COVID-19 pandemic further emphasized the need for organizations to have complete visibility of both direct and indirect spending, making accurate analytics essential.

Princeton University's Office of Development, responsible for initiating and nurturing donor relationships, was struggling with time-consuming, error-prone manual reporting processes. Staffers had to dig through several different reports to answer questions, a process that was not only time-consuming but also prone to mistakes. Viewing data in Excel made it difficult to notice results that didn’t quite make sense—data entry errors or outliers that could indicate significant risks or opportunities. The major gifts team each managed a pool of approximately 200 prospects—far too many people to rely on memory to manage contacts. With no dedicated tools to visualize overall engagement, the office relied on large list reports and manual review of individual records to make sure prospect and donor relationships were adequately tended. The research analytics team was responsible for reporting high-level campaign metrics to university trustees and other top executives, but this was another time-consuming, manual effort.

Vertex Services, a global distributor of flooring products, faced challenges in accessing and sharing data stored in disparate sources due to its worldwide operations. The company did not have a centralized data warehouse, but rather diverse, discrete applications located across the globe. Many systems, including accounting and order processing, were duplicated or split between the US and Hong Kong offices. Vertex also operates a number of different portals through which partners share data; data from this partner portal was maintained in its own discrete database. This added time to the decision-making process. Another challenge was that the primary analytics and reporting tool for the company was Microsoft Excel, which did not lend itself to the iterative questioning process that leads to true insight. As a result, often decisions were made based on assumptions or educated guesses.

Audience Audit, a company that provides unique research to agencies, wanted to make its services more accessible to a wider range of clients. This required lowering the cost of its projects and delivering results more quickly without compromising the quality of the research. The company's projects involved designing and delivering targeted online surveys, performing statistical analysis of the data, and creating customer-facing presentations to communicate the findings. This process was time-intensive and manual, and any changes to the data required updating everything individually. Furthermore, the owner of Audience Audit, Susan Baier, had to work through the data herself to parse out key insights and share its story with her clients. The challenge was to find a way to make the work more accessible to agency clients, which would require major changes.

The French Red Cross, with its 54,000 volunteers and 18,112 employees, provides a range of services across more than 1870 locations in the country. However, the organization faced a significant challenge in terms of data analysis. They had no means of easily analyzing or making sense of all the data arriving in the organization. The focus had been on transactions and support, but the emphasis shifted to data analysis, leading to the launch of a new business intelligence architecture. The new model comprised a decision support system, data integration, and reporting. The organization needed an intuitive, user-friendly business intelligence tool that enables dynamic performance and results in real time.

Swedish Medical Center (Swedish) was facing challenges in understanding and communicating physician and hospital performance across various areas. Traditional reporting solutions were proving to be cumbersome to implement and expensive to maintain. The healthcare provider was looking for a way to drive improvements in quality care, improve patient experiences, and enjoy substantial savings through process improvements and improved efficiencies. The organization was also preparing for value-based reimbursement and wanted to improve other areas like operating room efficiency, physician engagement, and financial performance.

Lucky Voice, a leading U.K. provider of karaoke services, was facing challenges with its outmoded booking system for reporting. The company was in need of a solution that could introduce live reporting on karaoke bars performance throughout the business. The developers were spending a significant amount of time on report generation, which was slowing down the company's progress. The company was looking for a solution that could provide real-time, detailed reporting to meet the increased demand due to the company's growth.

China Eastern Airlines, the second-largest carrier in China and the ninth-largest worldwide, was struggling with data analysis. The airline had a vast amount of data to share with management, sales, pricing, and other departments. However, the existing system, an Oracle database and IE browser, was slow and made it difficult to utilize the data effectively. The airline needed a tool that was not only easy for IT to use but also for business people.

HomeRepair, a company that repairs residential property damage claims on behalf of one of Australia’s largest general insurers, was facing increasing competition. The company needed to understand its performance metrics quickly to retain and build its market position. HomeRepair had the majority of its data stored in GClaim, a web-based repair administration and management solution. However, the company could not use that information in a timely manner due to the lack of an in-house IT department. HomeRepair management relied on IT vendors to run queries and reports from the SQL database, a process that was too slow for the company's needs. HomeRepair management was able to pull some data out of the SQL database and into Excel pivot tables, but the resulting reports were not very helpful. To truly understand productivity and cost-effectiveness, HomeRepair would need to look at data stored in other sources in concert with its GClaim data.

Ray White, the largest real estate company in Australia, was looking for a way to improve their market share and make their agents more effective. They wanted to provide their agents with better information about themselves and their offices, so they could then provide that information to their consumers, making them better agents overall. They also wanted to differentiate themselves in the marketplace by providing unique, high-quality visualizations in their reports. However, they faced challenges in terms of making this information easy to use and accessible for their agents, many of whom were not very analytical.

Celsius International is a B2B data marketing company that specializes in managing multicountry data and lead generation programs. They provide multinational corporations with database management and analytics, including creating, enhancing, maintaining, and hosting B2B multicountry databases. The company operates in a specialist niche, providing data and campaign management services in Europe, Middle East, and Africa. However, their old methods required days of human intervention before they could actually start analyzing data. This delay in data analysis was a significant challenge for the company as it affected their efficiency and the quality of service they provided to their customers.

US Auto Parts, a leading Internet retailer of aftermarket auto parts, was facing challenges in analyzing their sales across the various dimensions of their business. The process was tedious and sometimes overwhelming. They relied heavily on their IT department to build reports—a process that was neither flexible nor fast. They needed a reporting and analysis tool that could be managed by the analytics team, with limited IT involvement. The tool also needed to empower business units to do their own analysis.

FiberTower, one of the largest alternative backhaul providers in the United States, was facing challenges in analyzing market data to ensure successful offers to the large cell phone companies it serves. The traditional method of data analysis was time-consuming and inefficient, often taking weeks to generate well-scrubbed maps and recommendations for decision-making. The company was also dealing with large and occasionally impure data sets, making it difficult to visualize and understand the information being considered for decision-making. Furthermore, the company was looking to extend its markets to areas that were traditionally unattractive, requiring a nationwide analysis of potential opportunities.

DePauw University in Indiana underwent a massive administration system upgrade. The school needed to extract student metrics, enrollment, peer groups, and financials from their Oracle database and visualize it in a way that would aid in strategic planning. The data also needed to be accessible to people who don't work with it regularly. Another challenge was the production of a large strategic indicator report that took weeks to compile, bringing together disparate reports from various departments and divisions.

Camelot Strategic Marketing and Media, one of the largest privately-owned strategic marketing and media services companies in the U.S., was looking for ways to differentiate their services. They wanted to present data in meaningful ways to show clients how they’re “moving the needle” and driving business. The company was facing the challenge of growing data in the industry. Their biggest challenge was taking performance data from one system of record and tying it back against cost data. They needed to bring this data to life and optimize the media on a daily basis, and then review that in a rolled up manner very efficiently with the client.

The Market Research Department of Amica Insurance was tasked with collecting and analyzing marketing and sales data, particularly in relation to the company's advertising and direct mail campaign calls. The department needed to sift through tens of thousands of call records to identify actionable information for senior management. The challenge was to find an application that would enable marketing analysts to identify important trends, relationships, and outliers in the data. The application needed to be compatible with existing tools such as Excel, Access, PowerPoint, and SPSS. The primary evaluation criteria for the new tool were powerful visual analysis features, ease of use, and quick deployment without the need for servers or IT support.

The Financial Services Administration Department of the Region 4 Education Service Center recognized a need to develop meaningful ways to analyze and visualize state-mandated information submitted by its member districts. The information is contained in ASCII text files that are difficult to use or analyze. Region 4 developed a process to bring multiple years of this data into one cohesive database. However, the significant amount of time needed to build these capabilities into their products was extending their time to market, affecting not only their internal resources and productivity but their customer satisfaction rates as well. The missing piece in this process was the ability to interact with the data in a meaningful way. Region 4 needed a tool for analysis and reporting that was visual, easy to learn and use by school administrators, didn’t require servers or IT support, and was affordable.

Pearson, a leading education company, was facing challenges in monitoring and measuring their training metrics. The Human Resources department was responsible for keeping track of corporate training programs, which included training over 30,000 people via e-learning on critical subjects like plant safety and data security. The existing system of using Access and Excel for reporting was time-consuming and inefficient, taking up to 30% of Martin Keiser's time. The format also made it difficult to identify trends and outliers.

Eteamz, a publishing platform for sports teams and leagues, was struggling with understanding conversion rates on their site. They had a wealth of data, but making sense of it was a challenge. They needed a way to consolidate all their data in one place and create multi-dimensional charts. The process of doing this in Excel was time-consuming and inefficient. They wanted to understand the relationship between their free and paid memberships and identify which funnel was generating the most conversions. They also wanted to track and respond to any decline in conversions quickly.

ThoughtStorm, an investment portfolio company, was struggling with evaluating large amounts of data to come up with business strategies. They had a team of SQL programmers, analysts, and statistical experts, but the process was slow and inefficient. The analysts often didn't understand the strategic and management considerations of the financial experts, and the financial experts didn't have time to run complex statistical software. This was causing ThoughtStorm to lose time and ground, which was unacceptable to the company's co-founder and managing director, Mike Princi.

Stanford University's Computer Graphics laboratory was facing a significant challenge when they received a complaint about a rogue FTP server running within the stanford.edu domain. The machine had produced very high traffic volumes to off-campus locations, and the laboratory manager was asked to investigate. The data used in the analysis consisted of Cisco NetFlow records from a Stanford router. Network flows are highly granular; flow endpoints include a time-stamped summary of source, destination, protocol, and number of packets and bytes. Each source or destination consists of an IP address and a port. The primary challenge was to find and implement an analysis application that supported this type of investigation. The application needed to support Microsoft SQL Server, be able to quickly sift through millions of records of data, and be able to manipulate and display many dimensions of the data simultaneously.

Oco, a provider of business intelligence solutions, wanted to enhance its service by offering sophisticated data visualization capabilities. The company needed a solution that could integrate with its existing data warehouse and provide powerful, easy-to-use reporting and analytics for its clients. Oco's clients span various industries, including retail, consumer packaged goods, and industrial manufacturing, and the company wanted to provide a tool that could present data in many different formats to cater to these diverse needs.

Cornell University needed a reporting tool that would allow its college deans to better track Key Performance Indicators (KPIs). The university receives tens of thousands of applications every year and needed a way to access this information quickly and easily. The university also wanted to meet the needs of both highly sophisticated technical workers and hundreds of other employees who rely on the software everyday to answer important questions but whose jobs don’t revolve around data management.

The Operations Department at First American Corporation was responsible for collecting, shaping, and analyzing sales performance data across all segments, products, and geographies. They were using Excel pivot tables, but they were slow, inflexible, and difficult for people to learn. Also, the graphics being generated from Excel didn’t effectively communicate the critical insights to executive management. The primary challenge was to find and implement an analysis application that supported First American’s wide-ranging requirements. The Operations Department was charged with reporting monthly sales performance trends to senior managers categorized by channel, market segment, and product mix, analyzing price points by product then recommending pricing strategies that would positively impact revenue and simplify current customer pricing plans, and investigating dozens of sources of marketing, customer, and financial data to drive strategic initiatives and uncover trends and themes previously not known.