Comcast, a multinational mass media company, was facing challenges in building models on complete production datasets to improve the accuracy of their predictive analytics. They needed a solution to run models on complete production datasets as opposed to relying on sampling. The company was also looking for ways to operationalize and scale data science to match the company's volume of operations. They were dealing with issues such as extracting and integrating real-time production data that comes in different formats, using heavy computation to transform raw data into usable data sources, providing timely responses to a great number of prediction requests and continuously updating models with the latest data to keep predictions accurate.

Property Guru, a leading property management company based in Singapore, handles a large volume of listings and had looked to leverage AI and machine learning (ML) for multiple use-cases - image moderation, predicting churn, forecasting credit, measuring performance of listings. They realized early-on in their development that they needed machine learning techniques to manage user data, user retention and ensure the customer experience on their app lives up to their reputation. Doing this manually was not scaling so there was a real need to automate their ML process.

AIMIA, a global leader in customer engagement and loyalty solutions, faced challenges in predicting customer churn and detecting fraud due to the lack of relevant datasets and the steady evolution of fraudulent behavior. The development of machine learning techniques in the face of these challenges was difficult. There was a need to increase the agility of model development, build newer use-cases quickly, iterate on them faster, improve overall trust in AI by making the results of machine learning algorithms transparent to business stakeholders, as well as benchmark the performance of the models already in production.

Transporting fruit from the farm may take weeks, so Hortifrut had to predict the quality of produce upon arrival. Not being able to do this accurately can impact customer experience and revenue loss. But getting such predictions accurately can be a difficult task given the complexity of the distribution channel, weather data, variety of datasets, shipping times and more. If traditional machine learning methods and toolkits were used, it could easily take months to build accurate predictions that can be reliably deployed. This may also require hiring additional data science talent on the team, hence requiring additional time and budget to achieve the aforementioned business goal.

Finding the right specialist is the first step to receiving the right care. However, consumers are not equipped to navigate the complex and confusing healthcare system. It can be challenging for patients to discover which specialist they should approach for different health situations and, even with a referral from a primary physician, it can still be a long process until they find the right specialist who can accurately treat them while also providing a satisfactory patient experience. Finding the right match between patient and doctor can solve major problems and save lives.

Data is everywhere at Nationwide and the company wanted more from the data they had to find new ways to provide more personalized service and response to their members. To achieve this goal, Nationwide needed an AI platform that allowed them the flexibility to easily work in a complex data environment to rapidly explore data and quickly prototype new models.

The lack of liquidity in bond markets has traditionally made predicting bond prices challenging. In order to provide higher transparency and find the most consistent and accurate prices to help traders become more confident as price makers, MarketAxess relied on machine learning to develop Composite+, a proprietary algorithmic pricing engine for corporate bonds that leverages a range of proprietary and industry data sources, with updates up to every 15 seconds.

Visión Banco, based in Asunción, Paraguay, provides financial services to small and micro-sized companies in its home country. The bank was looking to expand its services and offers to customers, easily determine credit risks, and do so with accuracy and speed. It also wanted to enhance its practices by implementing predictive analytics, such as to predict customer payment default or churn. However, the bank was facing challenges in scaling these operations without a new tool or plan. The data science team first hired an external consultant who developed a model using IBM SPSS Software, a process that took a year. Then the team started using open source tools R, H2O, and Openscoring.io, which allowed the data scientists to deploy models in Predictive Model Markup Language (PMML) format—an industry standard for data models. Yet predictive analytics were still taking considerable time and effort.

Jewelers Mutual, a leading provider of insurance for jewelers and consumers in the United States and Canada, recognized the need to invest in analytics, AI, and machine learning to improve overall customer experiences. Their business relies on effectively protecting their jeweler customers’ businesses and providing personal insurance directly to consumers with innovative customer experiences. They collected data from losses, customers, and multiple other sources which weren’t tapped into before. They started their AI journey a few years ago by implementing Gradient Boosting Machine, and then moving to an AutoML solution from DataRobot. However, they realized that they needed greater transparency of a solution and needed to have an explainable AI component.

Airvantage, a South African cellular telecommunication provider, was facing a challenge with its Prepaid Airtime Advance System (PAS). The system, which advances airtime, data, or mobile money to subscribers, was using a series of static business rules to make airtime advance decisions. However, these rules were overly cautious, leading to a high number of false negatives - refusing credit to desirable customers. Airvantage needed a solution that would allow them to accurately assess the risk of advancing credit to their subscribers, without unnecessarily declining potential customers. They sought a data science toolkit that could help them build a more accurate risk model, and after evaluating over 30 toolkits and platforms, they decided to use H2O Driverless AI.

The insurance company was facing a significant challenge with claims fraud, which is estimated to cost the industry $80 billion annually in the United States alone. The existing process for detecting suspicious claims was entirely manual, relying on the judgment and experience of professional claims examiners. This approach was not scalable for a growing business and was time-consuming due to the need to pull information from multiple systems. The company had consolidated data from various sources into a Hadoop data store, which included a mix of structured and unstructured data. However, Hadoop lacked the capability for sophisticated predictive analytics, and extracting the data to an analytic server was time-consuming.

Senko Group Holdings, a large integrated logistics service provider, was facing challenges in manpower planning due to the shortage of workers in Japan. The company needed to maintain a high level of customer service despite the inability to cut operational tasks. The logistics staff was burdened with the task of predicting shipment volumes from their warehouses, a task that was time-consuming and complex. The company initially tried using R and IBM SPSS for AI-based shipment volume forecasts, but found them challenging for the logistics staff to use for actual operations. The structure of the model was complicated and required a great deal of effort for creating models as well as for applying feature engineering.

Paypal, a global payments platform, was facing a significant challenge with customer churn. The company's previous approach to identifying churn was based on specific time increments, marking a customer as churned if they hadn't used the platform within that period. However, this method was not fully accurate and impacted the effectiveness of Paypal's marketing efforts to win back customers. The company needed a more precise way to predict if and when a customer would churn and the reasons behind it. This information was crucial for the operational teams to develop new programs aimed at customer retention.

Informa, a large company with about £1 billion in revenue, is the world's largest events and conferences business. The company delivers in-depth proprietary market intelligence, real-time news content and analysis, bespoke consulting services, industry events, and specialist online training in nine industry sectors. However, the company faced a challenge in delivering data from spreadsheets and databases to customers easily and cost-effectively. The traditional method of delivering slides and spreadsheets required customers to build their own reports from the data. Furthermore, customers were not fully aware of the extent of data that they could leverage from Informa.

PepsiCo’s Collaborative Planning, Forecasting, and Replenishment (CPFR) team faced the challenge of reconciling disparate data from various sources, including warehouse inventory, store inventory, and point-of-sale inventory. Each customer had their own data standards, which didn’t correspond with each other or PepsiCo’s system. This made data wrangling a challenge and reports could take months to generate. The team primarily relied on Excel for analysis, creating large quantities of messy data. And the team had no efficient way to spot errors, leading to potentially costly outcomes. For example, a missing product from a report could result in inaccurate forecasts and lost revenue. The CPFR team needed a way to wrangle large quantities of disparate data. At the same time, the team needed a visual analysis tool that could help them make the most of PepsiCo data.

Rosenblatt Securities, a New York-based firm that provides institutional investors with advice and trade execution services, was looking for a way to improve its pre-trade and post-trade analysis. The firm wanted to be able to perform derived analytics on hundreds of different fields and visualize the data quickly and simply. They wanted to provide their traders and clients with insights on when to buy or sell a security. The firm was also looking for a tool that could handle large amounts of structured and unstructured data, including time series data.

Mercer, a global consulting, outsourcing, and investments company, was looking for tools to improve the graphical capabilities of their dashboards. The existing in-house products were not keeping up with the times. The company was transitioning from a P&L-centric business to focusing more on clients and client profitability. This shift required dealing with a much larger dataset and providing leadership with a high-level view of the data, directing them where to focus. This need necessitated a more interactive, graphical tool rather than a simple PDF listing of information on a report.

Alcatel-Lucent Shanghai Bell, a telecommunication and information enterprise in China, was facing challenges in analyzing network data. The company's professional services team was using Excel spreadsheets to sort data and manually chart and present the information. This process was time-consuming and the end deliverables did not provide many insights. The team couldn't see the full perspective of a communication network when they analyzed network data. Equipment assessments could take up to three months. The team needed a tool that could help them accurately identify the need for skills upgrades on the team and ensure that they are deploying staff with the right skill-sets to each on-site job.

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.

amaysim, a leading Australian online-led Mobile Services Provider (MSP), faced challenges in analyzing and obtaining insights from the vast amount of data it collected. The company had over 10 billion call data records, with this number growing by more than 20 million call data records daily. The data was complex and came from multiple sources including Livechat, Zendesk, call data records, Google Analytics and more. The previous BI methodology required coding which could not easily scale to business users. Insights could only be sought by one or two individuals within the organization, limiting business gains due to resource constraints and the time-intensive process. The low productivity levels of the current solution prevented the team from fully realizing the value of their data.

Spil Games, a company that publishes and distributes mobile games to over 100 million monthly users, was struggling to extract insights from its massive volumes of data. The company wanted to build dashboards based on multiple data sources quickly and easily to drive data-driven decisions on a daily basis. The data they wanted to learn more about included everything from game loading times and search engine advertising optimization, to user demographics. At the time, Spil Games already had a BI tool in place for dashboards, but it was inefficient and time-consuming, taking 44 clicks simply to update one dashboard.

Pacific Northwest National Laboratory (PNNL) was asked by the Department of Homeland Security to participate in a pilot program. The goal was to pilot an analytical application for the Immigration and Customs Enforcement (ICE) department that would leverage analysis techniques used for unstructured text analysis with those used in structured data analysis. PNNL’s primary objectives over the project were threefold: Provide a new capability for structured and unstructured data analysis, Evaluate/characterize ICE data, and Be deployable within eighteen months. PNNL wanted to address the question of mixed data: information typically found in structured forms with annotations, case files, reports, etc. They wanted to ensure data sharing and interoperability and to facilitate data exchange between specialized tools.

Blastrac Manufacturing was facing a challenge with its reporting method. The company did not have a consistent reporting method in place and, consequently, preparation of reports for the company’s various needs was tedious. Blastrac’s analysts each spent nearly one whole day per week extracting data from the multiple Enterprise Resource Planning (ERP) systems, loading it into several Excel spreadsheets, creating filtering capabilities and establishing pre-defined pivot tables. These massive spreadsheets were often inaccurate and consistently hard to understand, and they were virtually useless for the sales team, which couldn’t work with the complex format. In addition, each consumer of the reports had different needs—while some thought the reports were lacking detail, others thought there was too much.

Ernst & Young's Fraud Investigation and Dispute Services (FIDS) practice works in a broad spectrum of providing analytical services related to identifying factors related to fraud and forensics. They analyze all types of fraud that can be coming from all types of different data sets, whether or not those data sets are transactional data in form. They also try to uncover elements of unusual patterns or unusual behavioral activity within unstructured data sets of text data – such as emails, instant messages, or SMS text messages that are exchanged in an organization from employee to employee. The challenge was to analyze these large volumes of data and present the results in a way that is easily understandable by a variety of individuals across an organization—from analysts and end users right up to C-suite professionals.

AOL, a leading-edge web services company, was facing challenges with its data not being integrated with the corporate data repository. The company was dealing with a massive amount of data, with tens of millions of searches daily, each generating between 20 and 40 rows of data. This resulted in 400 to 800 million records every single day. The company was operating on a push model where the Business Intelligence (BI) team would have to manually pull and send out reports to those who needed them. This process was time-consuming and inefficient.

Kwantum Institute, a market research firm serving major global automotive manufacturers, was struggling with its survey analysis and reporting process. The existing process, which involved a combination of Excel pivot tables, charts, and VBA macros, was not only time-consuming but also produced static and lengthy reports. As a result, clients were losing interest and missing potentially important findings and results. The company needed a survey analysis and reporting tool that would not only improve its internal operational processes but also increase the quality of its customer offerings by structuring results visually. In addition, Kwantum required a solution that was able to access its massive ChoiceMonitor database (up to 200 million rows of data) without impacting the integrity of its other systems. Finally, given the ever-changing market conditions in the automotive industry, Kwantum’s clients required reports and dashboards that offered real-time information as well as the ability to further interact with the data to a degree that static Excel tables don’t allow.

Ferrari and Maserati of North America were facing challenges in their data analysis process. The process of generating and sharing reports for retail sales analysis, service and repair orders, and monthly expenditures was time-consuming and involved manually entering data into Excel files and distributing that static information via email. This method was not only inefficient but also did not allow for real-time interaction with the data.

SuperData, a market intelligence research provider specializing in the video game sector, was struggling with an Excel-based product that couldn't efficiently handle the data from over 40 million video gamers each month. The reports generated were multi-spreadsheet and required customers to dig for insights. The company needed a solution that could handle large data volumes and present insights in an easily digestible format. They also needed to ensure data security, as the data was gathered directly from game companies and shared anonymously. The company initially tried an open-source business intelligence solution, but it didn't meet their needs.