Medline Industries, a global manufacturer and distributor of healthcare products, faced the challenge of developing a self-service analytic environment that would provide workers with an accurate view of every aspect of the business. This included inventory management, plant capacity, labor, and finance. The company needed to streamline business processes and deliver analytics and audit tools to ensure ongoing efficiency and profitability. The challenge was to track thousands of products and dozens of distribution centers as part of a worldwide supply chain, which involved a lot of moving parts and an extraordinary amount of data.

Service NSW, a government initiative in New South Wales, Australia, was conceived as a 'one-stop-shop' for citizens needing to interact with state government departments. However, executing on the program's broad vision provided a thorny challenge for the organisation's IT team. Delivering new digital services required that manual back-end processes be automated; doing so required access to data spanning over 40 government departments and agencies. The sensitive nature of the data involved made security an imperative, complicating things further. Recognizing the need for a connectivity platform that could not only handle complex data integration, but also orchestrate, expose, and govern data access through APIs, Service NSW adopted MuleSoft's Anypoint Platform to anchor the program.

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.

Deporvillage, Spain’s top online sports retailer, was facing challenges in managing its website which features multiple images of the more than 8,000 seasonal products it offered. The company’s engineering team bore the cost of using Amazon S3 to store and serve a growing number of product images. They needed to ensure all images were responsive and optimized for size and speed, since mobile devices accounted for about 70% of its web traffic, and about 55% of all conversion was mobile. The company managed its website in six different languages and were needed to constantly add or remove images for high-turnover to items, or to address seasonal promotions.

The consulting firm was facing challenges with the quality of legacy data during the migration process to Workday. The poor quality of data was slowing down project delivery and taking valuable time away from the firm’s more critical objectives. The firm required a framework that would quickly identify and report data issues. Reconciliation of data in Human Resources was crucial for compliance with legal and regulatory concerns. The firm was looking for a solution that would simplify the data migration process and allow consultants to recoup time they were spending on the manual work.

The LEGO Group, a global toy manufacturing company, has a comprehensive risk management program. The company's approach to risk management is proactive and extensive, and it was recognized as a recipient of the 2015 RMM Recognition Program. The company's risk management program is based on three core processes: an elaborate risk identification method, proactive risk management processes for key business projects, and workshop-based reviews of key strategies and complex initiatives.

Q2 Holdings, a company that develops innovative financial services solutions for banks, credit unions, and emerging financial services providers, was seeking a monitoring solution that was easy to maintain, simple to use, and adaptable to new technologies. Rapid innovation is key to Q2’s mission, so the firm also needed monitoring to support its Nomad cluster manager and scheduler for developers using microservices and batch workloads. For banks and credit unions, fast, secure transactions are critical. Therefore, a reliable and efficient monitoring solution was crucial for the company's operations.

ICON Health & Fitness, a leading name in the fitness industry, was facing a challenge with high abandonment rates across its web properties. Despite attracting millions of visitors to its brand websites each month, the company's online retail performance indicated surprisingly high abandonment rates. The team had difficulty pinpointing the exact reasons for the low conversion rates. They realized that in order to grow ICON’s direct-to-consumer retail business and drive revenue via online channels, ICON needed to implement a real-time engagement strategy that would identify the visitors most likely to benefit from help during their buying process, reducing overall site abandonment and successfully convert prospects into satisfied, long-term customers.

Bankwest, a subsidiary of the Commonwealth Bank of Australia, is a bank established in 1895. It is leading the way in digitally transforming their customer experience and their contact centre. They have been a client of LivePerson for over 6 years and are constantly adapting and driving adoption through web chat, mobile chat, and now In-app and Web Messaging. With a clear digitally focused strategy, having Messaging and Conversational design both playing important roles within that space, Bankwest puts the customer first and strives to offer a world-class experience. They saw the market evolving, their customer’s preferences changing, and strived to lead in the contact centre space. They rolled out In-app Messaging across iOS and Android, then replaced their web chat offering with authenticated asynchronous Web Messaging.

Dow Chemical, a leading producer of plastics, chemicals, and agricultural products, was faced with the challenge of managing its environmental reporting across hundreds of facilities in the United States. The company had been using multiple legacy systems for regulatory reporting, but these systems were no longer sustainable, varied between sites, and required duplicate resources for support. The need for regulatory reporting compliance and technology replacement led to the initiation of the Dow Environmental Reporting Project in 2004. The project aimed to provide a cost-effective, high-quality, multi-media environmental reporting solution.

Kuwait National Petroleum Company (KNPC) is a global leader in oil refining, gas liquification and supplying petroleum products to local and international markets. It operates three major refineries with 6,000 employees producing an average total of 900,000 barrels per day of distilled gasoline, diesel, and bitumen products. The company has a goal to be considered as the leaders in safety, health and environmental performance in the petroleum industry. To achieve this, KNPC has taken a proactive approach to environmental, health and safety (EHS) compliance and sustainability, focusing especially on the critical role of information management. In 2003, KNPC was one of the first companies in the Middle East to implement a centralized, enterprise-level software platform for air emissions tracking and reporting. However, the company faced challenges in promoting efficiency in current EHS information management work processes and leveraging EHS data for strategic decisions.

HNI Corporation, a global leader in workplace furnishings and residential building products, was in the midst of a planned five-year transformation from seasonal bulk orders by big distributors to customized orders by dealers, individuals, and enterprises. This required a refactoring of the management of the supply chain by taking control of the data from ordering systems, ERP, and fulfillment systems. The COVID-19 pandemic and disrupted office and work-from-home environments forced HNI to speed up changes to how it does business, requiring a solution with flexibility and speed as a cornerstone for transformation. The data science and analytics team at HNI needed a platform that could scale with them, minimize cross-functional dependencies, reduce time-to-pipeline production, and refocus on the logic versus the infrastructure.

HotelTonight, a last-minute travel service, collects large amounts of accurate data on hotel inventory, then connects available rooms to consumers at the exact moment the need arises. As a fast-paced business dealing in highly complex data, HotelTonight needed on-the-spot data analytics. Sales, product management, and account management staff had logical questions. What is the right price point for a given hotel in a given region? How many rooms are likely to be available during a particular event? The answers lay in the company’s database, but couldn’t be accessed through any natural-language discovery process. Most of HotelTonight’s data is transactional. Before Looker, they used a MySQL database to track variables such as pricing, hotel availability, rooms secured, and customer activity. They could only analyze data by running manual queries and reports, or through a complicated Ruby on Rails document management system—both time-consuming and inflexible processes that required engineering support.

ThredUp, an online consignment shop, faced challenges in managing its complex data environment. As a large-scale aggregator of used one-off clothing items, they had a huge number of SKUs and a very broad supply chain. The company's distribution center collected information as clothes were inspected, itemized, categorized, packed, and shipped. By analyzing that data and integrating it with sales and marketing data, company management could discover which types of inventory were most valuable, how best to ensure quality, and how to increase transactional volume. However, their existing data management system lacked the power and flexibility to grow with the business. They needed a business intelligence system that could provide full, real-time analytics to anyone with an account login and would enable users to collaborate on iterative exploration of a shared data repository.

Kiip, a mobile rewards network, needed to monitor large amounts of dynamic data about app users and their in-app activities. The company's success depends on their ability to target audiences with prizes at specific moments within the app environment. Accurate data on user demographics, reward redemption patterns, and other consumer behaviors enables them to provide app users with an enjoyable experience, which results in strong advertising performance. However, the makeup of those users can shift dramatically as mobile apps rise and fall in popularity. At the same time, different apps appeal to different types of users, and the nature of user activity within an app may also change due to variables such as time of day, app promotions, and external events. Prior to working with Looker, Kiip faced several challenges on the data analytics front. Each time the business team wanted detailed information, they had to formulate a request, send it to a data analyst, and wait for a report to answer their questions. This meant that the company’s analytics team was over-burdened with query-writing and report management, instead of focusing their talent on growth initiatives and innovations around data. And because Kiip had no central data repository, the company also found that shared information wasn’t consistent across all office locations.

Simply Hired, one of the world's largest online job search engines, manages a significant amount of data to operate its search engine. The company captures user-generated data from every transaction, resulting in a massive amount of data that both business and technical staff want to analyze continuously. They rely on this data to improve search algorithms, optimize website and other channel interfaces, link cost-per-click and sponsored listing costs to market-driven auction rates, and strengthen online campaigns through regular A/B testing. Data analysis is also essential to predicting trends and identifying new business opportunities. Before Looker, Simply Hired engineers handled analytics by pre-aggregating data from multiple data sources and importing it into MySQL. Users couldn't see the connections between different data sets or understand what was going on at a high level in the business. Only people who knew how to write SQL could write queries, blocking an entire population of business users from direct data access.

ISCS identified a market opportunity for industry-specific business intelligence software, but building a solution from scratch was not practical. Their customers, who were scattered along the adoption curve for analytics software, needed a flexible solution that could be used by anyone from novice business users to sophisticated data analysts. The software also had to make it easy for ISCS developers to build new and customizable features for companies with special requirements. The ideal BI solution had to eliminate data silos to offer 360-degree views of all the data, display interactive data visualizations, and support data exploration in real time. The tool also had to be scalable, provide fast performance, align with the ISCS public cloud strategy, and integrate seamlessly with the SurePower Innovation® platform. Lastly, the company wanted to minimize the time to market, which strongly tipped the build-vs-buy analysis in favor of “buy”.

Werkspot, an online marketplace for home improvement services, faced the challenge of balancing supply and demand. With over 12,000 service professionals and two million jobs posted on its platform, analyzing data from various sources became a daunting task. The company's BI team was overwhelmed with pulling reports, building dashboards, and handling ad-hoc requests, leading to a two-week turnaround time for business users to get the insights they needed. Werkspot needed a data analytics and visualization tool to streamline this process and provide real-time, actionable insights to its employees. The company also wanted to avoid hiring additional analysts and instead focus on more valuable data engineer and data scientist roles.