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Unilever’s IT team faced numerous challenges, due to their infrastructure size, which encompasses 1,000 applications, 10,000 interfaces, and 500 IT projects per year. Unilever had multiple global teams and was looking to find more efficient solutions to deploy new products and services.

HSBC, one of the world's largest banks, was facing the challenge of technological disruption in the financial services space. This disruption brought new regulations, higher customer expectations, and increased competition. To stay ahead, HSBC needed to become the disruptor, which required the use of APIs to securely unlock data from thousands of applications and make core banking services available to internal and third-party developers. The bank aimed to improve the customer experience and open new revenue channels by launching an API developer portal, increasing customer loyalty, and partnering with third-party platforms to deliver new HSBC products and services.

BP, one of the largest energy companies in the world, is facing the dual challenge of meeting the increasing global demand for energy while reducing emissions. The key to achieving these goals is to leverage digital solutions, big data, and advanced technologies. The BP IT&S team is tasked with accelerating the pace of technology delivery, securing data access, and reducing dependencies on costly and time-consuming legacy systems. The team needed to modernize legacy systems to speed up access to applications and data, shift the role of IT from simply delivering technology solutions to enabling the business to take advantage of digital technology, and develop a Center for Enablement (C4E) to drive the adoption of BP’s API strategy.

Legal & General (L&G) is the United Kingdom’s largest provider of individual life assurance products and a top 20 global asset manager. The insurance industry has changed dramatically with increasing pressure to reduce costs and engage customers through digital channels. To remain competitive, L&G’s insurance arm, General Insurance (GI), has a goal to become a market leader in providing digital access to insurance. However, developing digital experiences for customers and advisers requires connectivity between various systems, surfacing claims, policy, billing, and other data in a quick and scalable manner. But behind the scenes, the company’s IT systems were connected via point-to-point integrations, which exacerbated operational inefficiencies and forced teams to reinvent the wheel each time they needed to develop a digital experience or release a new product or service.

Australian workers' compensation insurance company, icare, protects 3.6 million people across 326,000 businesses and 193 government agencies. The company needed to improve its customer experience by delivering a digital, machine learning-driven system that makes submitting and processing claims quick and easy. However, icare's customer data were siloed in SaaS and legacy systems, creating a disjointed, slow process in which customers had to fill out paperwork and visit multiple websites to submit claims, choose a policy, and more.

Tic:Toc, an Australian fintech company, was launched with the aim of transforming the traditional home loan process. The company wanted to eliminate the inefficiencies in the home loan approval and fulfillment process, allowing customers to easily submit loan applications online and receive instant decisions. However, the challenge lay in the complexity of the traditional process. For instance, validating a property purchased via a home loan involved multiple steps and parties, making it a time-consuming process. To provide customers with a seamless, instant home loan application experience, Tic:Toc needed to integrate data from various sources, offer real-time document generation and home loan decisions, and deliver their new product to market quickly to gain a competitive advantage.

New South Wales Health Pathology (NSWHP) is an Australian statewide health organization that provides services to various local government bodies. The organization operates more than 60 laboratories, 150 pathology collection services, and conducts over 100,000 clinical tests per day. However, NSWHP's IT systems were siloed, making it difficult to deliver on key initiatives that would improve patient outcomes, maximize taxpayer benefits, and build a foundation for change. The organization sought to efficiently integrate data to deliver time-critical initiatives, including digitizing pathology results to speed up access to patient data and make clinical decisions faster, reducing cost and effort associated with transportation and testing, and building a foundation to rapidly respond to change and emerging, urgent demands, including COVID-19.

The Indiana Department of Child Services (INDCS) manages over a quarter million child cases per year, requiring the collaboration of over 4,000 staff members. The department needed to digitize its processes to scale with the case volume, streamline staff member collaboration, and improve the entire child services journey. The objectives were to modernize legacy systems, implement a national electronic system to exchange case data with courts, families, and other state agencies, create a single view of the over 20,000 children in its care, and ensure caseworker and child health safety by creating a comprehensive view of health data in response to COVID-19.

RBC Wealth Management, a division of RBC Capital Markets, was facing challenges in delivering a world-class customer experience. The company, which has over $379 billion in total client assets and more than 2,000 financial advisors operating in 179 locations across 42 states in the US, needed to further embrace digital to remain competitive. However, achieving these goals proved difficult as RBC needed to unlock critical customer and financial data in siloed legacy systems and integrate that data with modern cloud and on-premises applications across the organization for a single customer view. The company's objectives included automating and connecting siloed legacy systems to digitize paper-based onboarding processes, building a single customer view and improving financial advisor productivity by integrating legacy data with cloud and on-premise systems, and launching a client service portal that streamlines the customer experience.

Revolution Beauty, one of the fastest-growing beauty brands in the UK, was facing challenges with its eCommerce platform. The website could not keep up with the brand’s pace of continuous innovation and new product releases. It was difficult and time-consuming to update and add pricing, images, product descriptions, and other critical information. To maintain market leadership, Revolution Beauty also needed to integrate its eCommerce platform with on-premises applications for a single customer view — enabling customer service reps to resolve inquiries faster and deliver a seamless customer experience.

AXA Luxembourg, part of the AXA Group, aimed to become the #1 preferred insurance company by embracing digital transformation to outpace competition and better serve their policyholders. However, the company faced challenges in leveraging their data due to the need to connect different homemade systems on-premises and external systems in the cloud. The custom-coded integrations made it costly and slow to connect systems, apps, and data. The company aimed to reduce operational costs, eliminate manual labor by automating key business processes such as claims management, create a single customer view of policyholders to resolve their queries faster, and build an architectural foundation that enables the team to launch future customer innovations more quickly through reuse.

Ahold Delhaize, a global food retail group, is striving to stay ahead of the competition in the rapidly evolving retail world. The company is implementing an omnichannel strategy, blending the best of brick-and-mortar, delivery, and pick-up. However, to future-proof its business, Ahold Delhaize needed a way to quickly incorporate new technologies and respond to changing consumer demands.

Salesforce, a global leader in CRM, has grown rapidly over the years, acquiring over 70 companies. This growth has resulted in thousands of systems and massive amounts of data. The company had leveraged MuleSoft's Anypoint Platform well before acquiring the company. After the acquisition, Salesforce initiated an effort to adopt API-led connectivity to better integrate systems and data, aiming to provide connected experiences to their 150,000 customers and 49,000 employees. The company wanted to move away from point-to-point connectivity to unlock and integrate critical data across the enterprise, create a single view of their employees, automate manual HR processes, and integrate Salesforce customer accounts with the accounts of acquired companies to build a 360-degree customer view for sales teams.

WatchBox, a leading company for buying, selling, and trading pre-owned luxury watches, wanted to scale its business internationally via eCommerce. The company's success relied on exposing its inventory to as many customers as possible for quick resale. This required creating a powerful eCommerce experience that not only allows for rapid expansion but also pulls critical inventory data from legacy and homegrown systems. The objectives were to create an eCommerce platform that allows for rapid expansion into new regions, unlock and unify data from legacy and homegrown systems, and build standardized eCommerce processes that support quick inventory turnover.

Invesco, a global asset management firm, was facing challenges due to the existence of over 200 siloed IT systems. These systems were preventing business users from quickly accessing valuable customer and market data. The lack of data access was also impeding the development process at Invesco, as teams had little transparency into other team’s work or projects currently in development, leading to inefficient and repetitive processes. Invesco needed to enable data sharing between multiple business units and 1,700 tech employees worldwide.

SMCP Group, a French luxury retailer, aimed to build a single view of the customer across its in-store and online channels to better understand shopping habits, increase touchpoints with customers, and provide a more personalized experience. The company also wanted to deepen relations with wholesalers by allowing real-time data exchange. However, SMCP's aging IT infrastructure, underpinned by legacy systems, made it difficult to unlock critical data, preventing them from building a single view of the customer and sharing data across different functions and with partners. The team was forced to manually batch upload information such as orders to disparate systems of record at regular intervals throughout the day. These manual uploads were not only time-intensive but led to data duplications.

Liberty Holdings, a South Africa-based financial services and insurance group, was facing a challenge due to its outdated and inefficient claims process. The company had its claims, customer, and underwriting data information stored in multiple monolithic back-end systems. This resulted in a suboptimal insurance claims process that required claims processors to manually extract data from seven different systems and enter it into a spreadsheet. They then had to log into several other systems in order to process a claim. The operation was slow, prone to human error, and did not provide the level of support that Liberty’s customers expected. Liberty needed to digitally transform its operations, modernizing legacy systems to streamline the claims process and provide a better customer experience.

The Brain Imaging Data Generation and Education Center (BRIDGE) at Carnegie Mellon University (CMU) and the University of Pittsburgh (Pitt) has been an early adopter of BIDS' (Brain Imaging Data Structure). BIDS is an increasingly adopted standard of data organization that allows researchers to more easily share neuroimaging data and software tools across the broad range of research conducted by users scanning at their facilities. The BRIDGE Center leadership sees this technology for standardizing (i.e. organizing, annotating, and describing) data as an important facilitator for replicable analyses and advancing research collaboration to speed discovery. The need for efficient data practices became more evident when the Center decided to purchase another 3-Tesla MRI system in 2019, a decision that would greatly increase the amount of data acquired at the Center.

The Dana and David Dornsife Cognitive Neuroimaging Center (DNI) at the University of Southern California (USC) was facing challenges in distributing data to researchers across the university community. The data from scans conducted at the center had to be transferred to DVDs and delivered to individual researchers or pushed onto the researcher's own DICOM server. These manual processes often resulted in delays and quality concerns. Additionally, Dr. Jonas Kaplan, Assistant Research Professor of Psychology at USC's Brain and Creativity Institute and Co-Director of the DNI, was seeking a new platform to manage his lab's data. He wanted a solution that would allow him to access and curate his data quickly and accurately, while also providing a secure mechanism for sharing data with collaborators and students.

The Yassa Lab at the University of California, Irvine (UCI), led by Dr. Michael Yassa, was facing several challenges. They were struggling with managing multi-center collaboration involving the collection of large data sets, quality control, analysis, and submission to NIH databases. The growing data and analytic complexity were impeding data reuse and scientific reproducibility. They were also looking for ways to best support and collaborate with other labs in the UC Irvine community. The lab was involved in a multicenter collaboration studying biomarkers of Alzheimer's disease in Down syndrome, which required secure sharing and processing of a variety of data.

Stanford Medicine’s Brain Stimulation Lab is working on solutions for treatment-resistant depression, a condition that affects 5% of adults worldwide. The lab is studying the use of Repetitive Transcranial Magnetic Stimulation (rTMS), a therapy that involves activating or inhibiting the brain directly with electromagnetic fields. The lab's work is growing, and so is their need for smart data management. The lab originally used the Flywheel platform to store raw and reconstructed data and applied its basic tools for reconstruction and quality control. However, when they wanted to perform analysis, researchers were still downloading data to a static lab PC. This process was time-consuming and made it difficult to track data provenance.

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.