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Mahindra & Mahindra Ltd. (M&M Ltd. or Mahindra), a mobility products and farm solutions provider, is the flagship company of the Mahindra Group, an Indian multinational federation of companies operating in over 100 countries around the globe with a presence in multiple industries. In order to take action on combating climate-related risk and contribute to the ambitious emissions reduction target set by the Group, M&M Ltd. decided to set a robust Science Based Target (SBT) in line with the climate goal from the Paris Agreement. During the target development process the company assessed and set science-based greenhouse gas (GHG) reduction targets, considering their direct (scope 1) and indirect (scope 2 & 3) emissions. Mahindra evaluated their emissions based on the Science Based Target Initiative (SBTi) methodology, using primary collected data and data from Sphera’s LCA databases GaBi, to get robust insights for their entire value chain.

Lumileds, a company committed to a culture of quality, responsibility, and sustainability, was facing a challenge. Their custom-built, end-of-life software for the monthly data collection for health & safety and quarterly data collection for environmental data was no longer adequate. They needed to replace it with a state-of-the-art sustainability solution to track, monitor, and report against their sustainability targets. The new solution needed to be easy-to-use and intuitive for data collectors and data admins. Furthermore, the implementation had to be completed before the next reporting window.

Rafter, a company that operates a cloud-based platform for colleges and universities to make educational content more affordable, effective, and accessible, was facing challenges in maintaining a competitive position in the market for online book rentals. The company was bottlenecked by a nightly ETL execution that took up to 10 hours and caused slowdowns during the day. Excel and an existing data visualization tool couldn’t handle analytics at scale. In addition, most of the Rafter staff was dependent on data analysts for writing routine queries, causing further delay before their questions could be answered. These limitations hampered critical activities across the business such as finance, inventory management, pricing and campaign management, and operations.

Infectious Media, a UK-based company pioneering the use of programmatic advertising, faced a challenge in accessing real-time data for campaign performance analytics. The company's fast-paced business required optimization specialists to adjust campaigns while they were still in flight. Initially, account managers wrote their own SQL queries, but mistakes often led to interruptions for data analysts. When the company shifted to having the analytics team write every query, it created a bottleneck that caused unacceptable wait times for reports and distracted data analysts from critical consulting and optimization work. The company needed a new analytics solution that offered flexible, self-service data exploration and reporting. Additionally, Infectious Media used two different databases: MySQL for billing and client reporting, and Google BigQuery for user-level data and algorithm research. The analytics team needed a solution that could provide access to both data sources and allow them to query both directly with the speed and flexibility required to optimize campaigns in flight.

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”.

Hipcamp, an online platform for discovering and booking campsites, was facing challenges in accessing comprehensive data to understand their customers’ experience. The company was using a set of special-purpose analytics tools to track web events and visualize behavioral trends, each with its own database. This resulted in inconsistent data across the different tools, making it hard to understand exactly where the data conflicts came from. To access financial data, engineers had to write manual SQL queries and it was impossible to handle common exceptions such as partial refunds and promotions. The company’s marketers were unable to identify unique visitors across multiple devices or tie anonymous visits to customer transactions. This meant that they couldn’t accurately manage the customer acquisition funnel, and were left to guess what was working and where they should improve.

Grand Rounds, a healthcare company, was established to connect patients with the most qualified experts for their medical needs. The company works with firms of various sizes, from 1,000-member firms to Fortune 50 employers, in over 120 countries. They provide expedited access to top physicians for better health outcomes. However, Grand Rounds faced a challenge in managing and accessing its vast network of data. They needed a partner to help build a scalable, external dashboard to provide its customers with valuable reporting.

DigitalOcean, a developer-focused cloud computing platform, was facing challenges in managing and utilizing their data effectively. As the company grew, their product evolved to cater to larger teams of developers working on more complex projects. However, they realized that there was a wealth of data across the company that wasn’t being properly incorporated into the decision-making process. They were initially doing every single analysis in an Excel spreadsheet, which was not scalable or efficient. They needed a solution that could centralize their data and make it more accessible for decision-making.

Casper, a small industry disruptor in the mattress industry, was facing a challenge with scattered and siloed data across files. The underlying technology was not efficient or scalable, and Casper needed a tool to take them to the next level. The business user front end was also impacting company decision making. Analysis was siloed in files and people couldn’t do much more with it other than look at the data. Querying across sources was a huge task and no one place held all the information. It was everywhere.

Counsyl, a high-tech genetic testing company, was struggling with financial modeling and optimizing operational workflows. The finance and billing departments were particularly challenged with billing medical services to health insurance companies, which made predicting revenues and cash flows difficult. The company initially tried using their CRM software for reporting, but it wasn’t a good fit for their industry-specific requirements. As a result, the finance team had to manage multi-gigabyte Excel files with complex nested pivot tables and VLOOKUPS, which took hours every morning to manage and often resulted in frozen machines. Additionally, they had to pull developers and software engineers away from their work to get the raw data needed for their analysis.

Urban Airship, a mobile growth company, was facing a challenge in managing and analyzing a growing data pool while helping some of the world's best-known brands grow with mobile. Their clients had a growing demand for data and Urban Airship needed a data platform that was not only ideal for its customers but also for their product team. Flexibility was an important factor as Urban Airship's users are mobile marketers and mobile product owners, with a broad spectrum of technical backgrounds. They started to look at ways to provide user-level intelligence, along with the ability to slice and dice the data they collected in their product.

Blue Apron, a pioneer in the meal kit delivery service industry, faced challenges in managing its complex operations. The company needed to source ingredients at the right time, quality, and price, pack orders efficiently and in exactly the right proportions, and ensure meal kits were delivered to the customer fresh and on time. As data volumes grew and queries became more complex, it became difficult to scale their existing data warehouse hosted on another cloud provider. The only options were choosing ever-larger server classes and increasing storage throughput by purchasing a higher number of provisioned IOPS. This led to a need for a more efficient and scalable solution.

DogBuddy, an online dog-sitting and home dog boarding community, generates a torrent of data about the parties on both sides of the transaction—owners and sitters—as well as particulars about the dogs. Add to that the enormous amount of clickstream data coming from the website, the app, and online ad campaigns, and you’ll find a potential business intelligence headache as well as a rich lode of potential insights. The marketing team struggled to make sense of reports from disparate data sources that couldn’t always be combined, often with different metrics for each data source. This created a barrier to building consensus about what was or wasn’t working well in ad campaigns. Delays in getting the data also impacted the team, because they often had to wait for the technical team to assist in building reports for them, and because the reports came in fixed, manual formats like Excel, it was hard to share data in meaningful ways.

Deliveroo, a food delivery service, was facing challenges with its initial BI tool as the company expanded. The tool lacked the necessary security, user management, and automation to support the company's growth. Business users were unable to make agile decisions as they had to wait for technical staff to write SQL for each report they requested. Code management became time-consuming for the data team as each dashboard had a unique code base. Changes in the underlying data architecture required updating the code in about 100 different locations. Additionally, variable demand based on the day of the week or time of day could put unpredictable strains on the data warehouse capacity.

Kiva.org, a nonprofit that connects lenders with low-income entrepreneurs and students worldwide, was facing a data bottleneck. Despite having a data-driven culture, accessing data was difficult for their non-technical business users. They either had to learn SQL and request access to the data or write a specific question for a data engineer, which was a slow and discouraging process. As Kiva continued to grow and acquire lenders and borrowers from across the globe, the data analytics team knew that they needed a business intelligence tool that could scale with them. They needed a tool that could meet their various reporting and analytics needs.

Guidewire saw a market opportunity for industry-specific business intelligence software, but it wasn’t practical to build a solution from the ground up. Guidewire customers relied on the SurePower Innovation® enterprise suite for managing their day-to-day business operations, but they had to use third-party tools for business intelligence. They struggled to make sense of limited canned reports after enduring delays from data transfers, and the reports weren’t customized for the insurance business. Guidewire stepped in to close the gap by adding insurance-specific business intelligence features to the SurePower Innovation® suite. From a technical standpoint, 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 Guidewire public cloud strategy, and integrate seamlessly with the SurePower Innovation® platform.

King, a leading interactive entertainment company, is in a fast-paced, competitive market where the key to success is making games that are consistently fun to play. The company has always relied on analytics to help developers create the perfect balance between simplicity and difficulty. However, they wanted to improve their ability to deliver the right insights to the right people at the right time. Before Looker, a jumble of tools used by individual studios mixed imperfectly with centralized BI services, so queries could produce inconsistent results. At a point where time was of the essence, BI complexity could make modifying an existing query a sluggish process. The teams at King realized it was time to begin searching for a BI solution to better suit their needs.

The Economist Group, a longstanding media company, faced the challenge of adapting to the digital age. With the addition of apps, web content, social media, blogs, debates, and audio/video programs, the company struggled to tie together the customer experience across all these platforms. They had numerous disconnected operational data sources, each telling a different story about the customer journey. This left The Economist Group with few insights into how to maximize revenues, determine the best steps for moving the company and products forward, entice advertisers, and deliver exceptional customer satisfaction.

PDX, a market leader in pharmacy management software and services, faced challenges with limited access in the type of reporting they could run on their data. The company's existing system was text-based, and pharmacies had to load their own data into their own data warehouse. This setup was not efficient and did not provide a good visual representation of the data. PDX realized that these limitations would present issues down the road and started looking for a more efficient way to present data in a more concise way.

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.

Travix, a leading online travel agency, was struggling with handling the massive amount of business intelligence (BI) data generated from over 20 million unique searches. The existing BI solution, which consisted of several dashboard tools and databases, was hosted on servers maintained by several IT staff. By 2015, the infrastructure was straining under the pressure of so much traffic. It was costly and time-consuming for the operations team to maintain the data centers. Travix needed a new scalable, cloud-based, high-speed solution to handle both batch processing and streaming data jobs. The company also wanted to build on its data-driven environment and improve agility to stay competitive in the high-volume, low-margin online travel business.

Handshake is a platform that connects students, universities, and employers for job and internship opportunities. However, managing the complex user journeys in this dynamic three-sided marketplace is a challenge. The company needs to understand how students engage with employers, how employers discover and engage with students, and how universities assist their students. Furthermore, Handshake aims to democratize data, allowing users to have direct access and gain the insights they need. This requires managing a large amount of data, including click-stream data, application data, service data, and third-party tool data. As a startup, Handshake does not have the resources to build a complex software stack to handle this.

Ultra Mobile, a young company in the competitive telco space, captured vast amounts of data that needed to be curated and analyzed to create value. However, the underlying infrastructure didn’t support what the teams needed. The dissemination of information was laborious and clunky. There weren’t enough resources or access to adequate tools to do any kind of reporting or analytics. Analysts spent their time in a vicious cycle of ‘lather, rinse, and repeat’ process with their data. With every change or update, the process was repeated in its entirety — creating bottlenecks, chaos and poor productivity.

SkinVision, a mobile app for early detection of skin cancer, was facing a data challenge. With over a million users and growing rapidly, the company had hit a data wall in terms of volume, complexity, and ease of access and analysis. This was due to the multitude of disparate data sources and the geographical separation of their business users in the Netherlands and their IT team in Romania. As a result, SkinVision constantly faced discrepancies in how the various teams viewed, used, and interpreted their ever-expanding pool of data. Without a single source of truth, concerns grew around data definitions, accuracy, reliability, and consistency between teams.

Yieldify’s predictive marketing technology interacts with over 1 million people on eCommerce sites daily, generating a tremendous amount of event data. This data has to be combined with transaction data to answer key questions about website performance. The company uses two different models for charging clients—either commissions on online sales or fixed-cost campaigns—and it works through affiliates as well as directly with brands. In the midst of all this complexity, the sales and account teams need accurate revenue tracking and attribution, both for internal team management and for reporting ROI to clients. Before Looker, Yieldify’s data was distributed across siloed sources: Salesforce for CRM data, Hubspot for marketing, a commissions database, a campaign database, and more. On top of that, the affiliates had their own separate data systems for tracking and billing. Reporting across all of these sources forced the BI team to use time-consuming manual processes to aggregate data and write queries, and there were some types of reports they couldn’t create at all.

GetYourGuide, a travel experience booking platform, was facing challenges with its open source Relational Online Analytical Processing (ROLAP) cube. The tool required basic database skills to use, limiting the number of employees who could utilize it. As the company expanded, data volumes and the number of employees needing insights increased, resulting in sluggish query performance and an increase in time-outs. This led to an overburdened Data Platform team, as they were inundated with ad-hoc requests for insights. The team realized that they needed a new solution that could scale with the company's growth and make data accessible to all team members.

Kollective, a company that offers a cloud-based, smart peering video distribution platform, was facing challenges with its legacy system. The system was slow, inflexible, and incapable of delivering analytics that could unlock the full value of the massive amount of data generated from numerous sources such as grid servers, directory servers, APIs, Kafka Streams bin logs, with multiple data types including JSON. The company needed a solution that was flexible, fast, and scalable to meet the growing demands of its rapidly onboarding new customers. The legacy system was only capable of providing a retrospective on specific events that had occurred, but no real analytics. If a customer wanted to get any sort of custom metric out of it, a tech engineer could get back to them in six months.

Commonwealth Care Alliance® (CCA) is a not-for-profit, community-based healthcare organization that provides care for high-cost, high-needs individuals. When the COVID-19 pandemic started, CCA knew their members, who live with complex medical, behavioral health and social needs, would be faced with new challenges and require an enhanced level of support and care. CCA also knew the best way to understand, prepare, and protect their members was through the use of timely and reliable data. The challenge was to quickly build monitoring dashboards and infuse COVID-19 information throughout other existing clinical dashboards to allow CCA's clinical workforce to understand how their members were being impacted, their potential risk, and the best next steps to help each member.

Car Next Door, an Australia-based car-sharing service, was relying on a homegrown data analytics solution. However, as the company grew, the internal development team was spending a disproportionate amount of time servicing data requests rather than focusing on product development. The company was facing issues with inconsistent metric definitions and data accuracy. Users typically worked with static data in spreadsheets, leading to human errors that were often undetectable, causing distorted results. The company was looking for a solution that would make data easily accessible to the company at large and provide real value across multiple aspects of the business.

eMoney Advisor, a provider of technology solutions and services for financial planning, was dealing with multiple data silos and managed several products across its internal teams and business units. It was difficult to achieve the unified, client-first mindset they were committed to with their previous tech stack. In order to gain insights into and improve their client experience even more, they felt migrating to the cloud and leveraging integrations between tools was an important and inevitable investment. They also wanted to deliver more accessible and actionable insights across the organization for their internal users, whom they value as critical customers of their data and analytics program.