Schroders, a UK-based investment management firm with over $939.2bn worth of assets under management, faced a significant challenge in integrating third-party data sources with internal data to create comprehensive environment, social, and governance (ESG) models. These models are crucial for understanding the companies in which Schroders invests, as the firm aims to ensure its investments are directed towards well-governed, socially, and environmentally compliant companies. The inability to effectively integrate these diverse data sources hindered the firm's ability to produce timely ESG views of its portfolios, which in turn impacted the ability of fund managers to manage ESG risk and make informed investment decisions.

MultiChoice Group, a leading entertainment company, was facing a significant challenge in managing and integrating its geographically distributed data. The company operates in various countries in Africa and maintains relationships with different service providers, each operating in different countries and providing data in a variety of formats. This diversity made it difficult for the data and analytics team to integrate and process the data using traditional methods. The team was spending over 80 hours per month on data preparation, and the data required for reporting was almost a month behind schedule. The company needed a data integration solution that could connect to all kinds of data sources, integrate the data through a single service layer, and enrich data on the fly.

Trustpage, a company that provides B2B SaaS companies with a dynamic Trust Center, faced a significant challenge in their integration strategy. They needed to ensure that their customers' sales teams could securely request and share documents within their CRMs. Additionally, they had to ensure that all teams of their prospects could effectively leverage Trustpage with the tools they were already using. This created an urgency to turn around these integrations quickly. The team began searching for solutions to offload the engineering work for their integration strategy. They needed a solution that met their security requirements, saved substantial engineering resources on each integration, was feasible for their integration use cases, and provided reliable technical and account support.

Caring Pharmacy, a leading pharmacy chain in Malaysia, was facing a significant challenge in bridging the gap between its offline operations and online platforms. As a predominantly brick-and-mortar business, the company recognized the value in the rich offline data generated by its physical stores. However, integrating this offline data with the online data from its digital channels and generating actionable insights was a complex task. The company's goal was to leverage its offline data for personalized marketing on digital channels and activate offline data in recommendation models, A/B tests, and chatbots to empower users to effortlessly locate products. The urgent need for digital transformation and integration led to Caring Pharmacy's partnership with Appier.

Shin Kong Life Insurance, a Taiwan-based company with over 60 years of history, was looking to leverage FinTech and AI technologies to enhance its big data analysis and prediction capabilities. The company aimed to build comprehensive customer profiles, target high-potential customers, improve advertising effectiveness, provide personalized interactive marketing, and deeply engage with its user communities to increase brand stickiness. The challenge was to effectively integrate AI technologies into their existing systems and processes to achieve these goals. The company also wanted to expand its customer base and increase sales success rates while ensuring precise customer acquisition.

SK Rent-a-Car, a leading long-term car rental company in Korea, faced challenges in driving conversion and maintaining customer engagement. The company needed to move beyond generic messages to attract its consumers’ attention with personalized engagement and boost satisfaction and conversion rate. The process of renting a car was perceived as tedious by customers, leading to a high bounce rate due to the long application journey. The company needed a solution to engage with on-site customers on a real-time basis to keep them on the website and encourage them to complete the application process.

Since 1992, LoyaltyOne has operated the AIR MILES Reward Program, generating $70 billion in revenue across their network. Despite having billions of data records from collectors and retail partners, AIR MILES struggled to foster stronger touch points with its customers due to a lagging legacy infrastructure. This outdated system prevented the company from gaining a holistic view of their customers, thereby hindering their ability to improve retention and customer lifetime value. AIR MILES wanted to leverage the petabytes of customer data generated by 558 million transactions per year to enhance the collector experience and enable their partners to better engage and drive revenue. However, their existing technology was unable to support this vision. The company's data scientists couldn't access data older than 5 years, and complicated queries limited what they could do with the data they did have access to. Pipeline development alone took 3 to 4 months, stifling their ability to deliver innovative solutions to market in a timely manner.

Aktify, a company specializing in conversational AI, aims to help its clients convert their customers through AI-driven SMS conversations. The company's goal is to make these AI agents as effective as possible, which requires drilling into massive volumes of data to find overlooked insights. However, Aktify faced challenges in managing its complex data dependencies, which hindered data democratization. The company needed a way to allow different teams to interact with data as per their needs. For instance, while a data scientist might need raw data, an executive team would prefer pre-aggregated data for quick decision-making. The company also sought to minimize the risk associated with data transformations and to make data management more simple, flexible, and cost-effective.

Al-Futtaim, a major economic driver in the Middle East, operates and distributes a wide range of global brands, generating a wealth of data across these brands. However, their legacy systems struggled to provide a unified view of their customer due to data silos and the inability to scale efficiently to meet their analytical needs. Business intelligence was slow, forecasting was not scalable and was limited to classical algorithms, and customer insights were too generalized to affect outcomes faster. Their data teams were unable to maximize the promise of their data due to memory and CPU constraints, outdated libraries, and inflexible tooling. Data analysts had to wait four to six hours for small amounts of data to process, which slowed both time-to-insights as well as time-to-market of new ideas. The extra time, effort, and resources required to redesign, implement, test, and launch data projects added unnecessary costs, created a greater divide between business units, and ultimately slowed innovation.

AT&T, a leading communication service provider, was facing challenges with its legacy on-premises architecture. Despite having 182 million wireless customers and handling 10 million transactions per second, the company was struggling to stay ahead of fraudsters. The existing infrastructure was complex and failed to deliver the innovation required for an optimal customer experience. The company was using rule-based technology for fraud detection, which was reactive rather than proactive, making it difficult to stay ahead of sophisticated fraud attempts. The process was not only protracted, inefficient, and resource-heavy, but also expensive. AT&T also struggled to gain real-time insights and automation necessary to optimize dispatch. The company could not unify data points to match a technician’s troubleshooting skills to the customer issue and location, leading to increased operational costs and a negative impact on customer experience.

Encore Glass, a company that transitioned from recycling bottles to importing new glass and creating custom packaging for customers, faced a significant challenge in managing its transportation operations. The company had identified experienced carriers to work with, but lacked the means to efficiently manage the relationships with these carriers. The sales and customer service teams were responsible for finding their own transportation solutions, including booking trucks, which was a time-consuming process. Furthermore, they often purchased transportation at a significant premium to market prices. The lack of a dedicated transportation team stood in stark contrast to Encore's commitment to customer service, which included fulfilling even the smallest customers' needs with exactly the right bottles, delivered just in time to be filled. The challenge was particularly acute when serving small winemakers in remote areas, who often lacked sufficient storage space.

Faurecia, a French tier-one auto parts supplier, was grappling with the complexity of its North American transportation network. The company was seeking to simplify its network, reduce costs, and improve service. The challenge was to find reliable carriers who could deliver their high-value automotive freight on time. The truckload carriers that run automotive freight tend to specialize in it and offer dedicated capacity solutions that can become embedded on very regular, repetitive lanes. This made it difficult for non-asset transportation providers to break into those verticals. Furthermore, the specific requirements of the commodities might make them unsuited for digital freight brokerage.

Titan Supply Group, a laminate-flooring importer based in the Pacific Northwest, was facing significant challenges with its less-than-truckload (LTL) shipments. The company, which imports its products from overseas and distributes them to flooring retailers across the United States, was grappling with the common LTL industry issues of damaged and lost freight. This was not only causing financial losses due to the need for restocking damaged goods and filing damage claims, but it was also damaging the company's reputation. The LTL industry's unreliable service, unexpected fees, and poor operating service at the dock were causing significant operational issues for Titan Supply. The company needed a solution that would eliminate these issues and allow it to maintain its stringent schedules and meet its customers' strict delivery requirements.

Road Rescue, a Texas-based company that manufactures a pothole-repair product, was facing a significant challenge in its operations. The company's VP of Operations, Andrew Blades, was frustrated with the amount of time it took to create accurate quotes for customers. This was a critical aspect of the business, as their customers included city and state governments managing large-scale road repairs, big box-stores buying wholesale, and home improvement retailer sites. Any inaccuracies in the quotes could lead to unnecessarily high shipping costs, eating into the profit margins of the orders. Additionally, the labor costs were high due to the hours employees spent entering data.

Siemens was facing a significant challenge in bridging the growing skills gap in the manufacturing industry. According to a Deloitte study, 77% of manufacturers were anticipating difficulties in attracting and retaining workers, with an estimated 2.1 million jobs expected to remain unfilled by 2030. Siemens was actively seeking innovative solutions to expedite training and reskilling. The company was particularly focused on providing resources for electrical apprentices, electrical contractors, and distributors through Siemens University, the SIschool program, and VR training initiatives. However, the traditional channels of delivering this content, such as classroom instruction, textbooks, videos, and virtual reality, were not sufficient. The Covid-19 pandemic further complicated the situation by disrupting the global supply chain, leading to longer lead times on fully assembled goods. Distributors needed a faster, cheaper way to get products to their customers.

Companies, regardless of their size, often struggle with keeping their employees motivated towards shared goals. The challenge lies in creating a culture where teams are invested in company goals and are constantly working towards streamlining processes. The lack of a problem-solving culture can lead to disengaged employees, inefficient workflows, and increased waste and rework. This can ultimately impact the bottom line, reducing the value delivered to customers. Additionally, many companies have a wealth of processes and information locked in tribal knowledge, which is not transferable and cannot be improved upon. This presents a major risk, as without a standard, there is no benchmark to improve upon.

For nearly a century, a leading manufacturer of construction and mining equipment, diesel and natural gas engines, industrial gas turbines, and diesel-electric locomotives has been striving to digitize and standardize its processes to minimize waste and control machine setups and changeovers. However, the company faced significant challenges in achieving this goal. The existing documentation was limited, making it difficult for frontline workers to share feedback to improve operations. The company was using Excel to create and store process information, which often contained over fifty pages of information that workers had to read and retain. This method also lacked the ability to gather and implement frontline feedback, leading to missed opportunities for operational improvements. The company also faced inconsistencies in production, competency gaps among employees due to job shadowing as a primary training method, and safety concerns. The introduction of new automation technology also posed a challenge as the company needed to ensure that standards were properly distributed to frontline operators.

Silgan Plastic Food Containers (PFC), a global leader in high-barrier plastic packaging, faced several operational challenges. Despite being an industry leader, the company struggled with issues related to transparency and management of tasks. The documentation process was entirely paper-based, leading to a lack of clarity about who was responsible for what tasks and when they were completed. This lack of transparency affected operational results. Handing over responsibilities was also a challenge, especially when team members were absent. Missed tasks were difficult to address due to these issues. Additionally, Silgan was required to conduct one major audit and several smaller ones each year. However, the paper-based filing system made locating necessary documentation a time-consuming and difficult process.