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Neurologists treating patients with traumatic brain injury (TBI) have long faced a significant challenge: determining which patients with mild or moderate head injuries are at increased future risk of developing neurological problems such as dementia, mood disorders, and Parkinson’s disease, and which are not. Both in classification and outcome assessments, TBI scores are often exclusively symptom-based, and therefore too general to catch some brain injuries and prognoses. To improve the diagnosis, treatment and rehabilitation of patients with TBI, Dr. Geoffrey Manley, Vice Chairman of Neurological Surgery at the University of California, San Francisco, set up the Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACKTBI) study 10 years ago. Today, 19 institutional partners in the TRACK-TBI NETWORK collect more than 3,000 data fields per subject, including outcome measures assessed at four time points post-injury: medical imaging, biospecimen samples, proteome test results and genomic information.

The University of Texas Medical Branch (UTMB) was facing challenges in realizing greater value from its imaging assets and collaborating more efficiently within and outside the system. The COVID-19 pandemic further amplified the need for efficient and remote collaboration on imaging research. UTMB wanted to leverage its imaging assets more fully as its radiology archive grew and it went live with digital pathology. The hospital aimed to create data sets and make them available for AI researchers. However, without a clear way to organize the process, this posed a significant challenge.

Commonwealth Bank was looking to enhance its AI capabilities to provide more personalized and relevant experiences for its customers. The bank wanted to decrease the time to prototype new models from weeks to just 2 days. The bank was also looking to scale machine learning across the entire organization, including data scientists and business users. The bank was seeking a solution that would allow it to better help customers find personalized and relevant offers to save money while they shop across platforms like Little Birdie, Karta, CommBank Rewards, and Klarna, while at the same time driving sales for merchants.

Consumer debt was rising in Brazil, and it was becoming challenging for both consumers and debtors to find solutions. The target market of debtors and the ensuing volume of data to be analyzed were both large and therefore, challenging. Acordo Certo had a small team of data scientists, who were using traditional methods to collect and analyze data, as well as to build and deploy predictive and scored models. However, due to the large database of customers, there was a need to increase the agility of model development and accuracy, as well as improve overall trust in AI by making the results of machine learning algorithms transparent to business partners, such as retailers and banks.

G5, Inc., a leading marketing optimization company for the real estate industry, was facing a challenge with its lead generation process. The company found that only 14% of its call leads were productive, resulting in low job satisfaction, high turnover for leasing agents, and low conversion numbers. G5 wanted to solve this by using machine learning to identify stronger leads that would more likely result in sales. However, the company didn’t have dedicated data science resources to create the needed machine learning models. The implementation of machine learning could prove to be time consuming, expensive, and a barrier to innovation.

Capital One's mobile app is a popular platform for customers to perform transactions, with up to 5,000 customers logging in every minute. This high volume of usage means that even small outages need to be identified and resolved quickly to prevent service disruptions. The bank's technology operations group monitors all critical systems and platforms and sets up alerts based on company policies. However, setting up alerts for volume anomalies, such as a drop or spike in transaction volume, proved to be a challenge. Traditional methods of calculating volume anomalies were not scalable and required a lot of coding, development, and oversight to manage.

Cross-channel attribution, revenue optimization, and forecasting are among the biggest pain points in brand marketing today. Marketing teams need to see a complete picture of their effectiveness but are often limited to partial data in spreadsheets that take months to analyze. As a result, CMOs and analysts often throw darts in the dark when forecasting the outcome of their marketing investments - severely restricting an organization's ability to reach more customers and grow. Organizations are often buried under an avalanche of data, which is often collected on an automated basis, as a byproduct of everyday operations. The process of moving from 'insights' to 'prescriptive action,' as a result, becomes a major challenge.

Kaiser Permanente, an integrated healthcare system, was facing a challenge with patients who undergo an unplanned transfer to the Intensive Care Unit (ICU). These patients experienced significantly worse outcomes than those directly admitted to the ICU. They represented about a fourth of all Kaiser ICU admissions, a fifth of all deaths in the hospital, and about an eighth of all of the hospital days. The patients who experienced an unplanned transfer to the ICU experienced two to five times the mortality of patients who are directly admitted to the ICU, and they would stay in the hospital an average of 8 to 12 days more than patients who are directly admitted to the ICU. The challenge was to identify these patients ahead of time who are likely to crash and be rushed to the ICU, and intervene before the deterioration.

Cisco, a multinational technology company, faced challenges in building models to deliver accurate predictions about customer propensity to buy across its extensive product portfolio. The company was struggling with speed and scalability challenges associated with analyzing an exploding amount of information about buying patterns. The company had to recreate all its predictive models from scratch every quarter, a process that took more than four weeks. To avoid even longer processing times, models were trained on relatively small samples that were rarely larger than 100,000 cases. The company was limited to standard techniques and was unable to test competing algorithms such as ensembles, grid search, and deep learning.

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.