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.

Futureplay, a fast-growing mobile games studio, was struggling with scattered data and no single source of truth. They were using a variety of tools, resulting in multiple sources of truth. This made it challenging to understand how players were interacting with content and to make data-informed decisions. For instance, to validate revenue streams, they had to confirm if the number of installs charged by the user acquisition networks matched the number of players using the game. The manual process of bringing data together from multiple tools and consolidating definitions meant that most employees weren’t able to fully leverage data to improve the user experience and optimize their own budgets and effort. Futureplay needed a solution that would help more users across the company access, understand, and trust data.

GoCardless, a rapidly growing payment platform for businesses, was facing challenges in accessing accurate and reliable data. The company was using a combination of traditional internally developed business intelligence tools and third-party SQL clients that required manual queries. Non-technical business owners had to submit tickets to the analytics team, who wrote code to extract insights. This process was time-consuming and inefficient. The finance team spent four days at the end of each month running revenue numbers, which delayed critical visibility into revenue trends and frustrated executives. The product team also lacked easy and rapid access to product usage data that could help drive a roadmap for accelerated company growth.

SoundCommerce, a data platform for retail brands, was facing challenges in accessing and taking action on the data they needed to efficiently and effectively deliver above and beyond the competition. They were under pressure to offer the best customer experience, which required understanding customers and supply chain. Their previous solution put too much administrative burden on their technical team for creating, maintaining, and customizing a shared and responsive data model, and a growing library of reports and visualizations. The prior stack lacked the power and features that consumer brands and retailers need to make fast decisions at scale. In addition to product challenges, the SoundCommerce product team worried about the risk of potential vendor lock-in down the road — both for their commercial product and for their customers’ data.

Sunrun, a leading provider of residential solar power, was facing challenges in managing their growing volumes of data across installation operations, installed systems, customer operations, and sales. The company was using a legacy data stack that required IT and data team support for almost every internal data request. This reliance on IT and the data team drained time and resources with ad-hoc requests, changing requirements, and backlogs of reporting requests. Moreover, the data pipelines and infrastructure weren’t scaling to meet either data growth or increased demand for data access. The data team struggled to respond to changing data sets or new sources of data as quickly as the business demanded, and Sunrun's legacy Oracle data warehouse was not equipped to scale across growing analytics demands or unlock predictive insights with ease.

Vivino, the world's largest online wine marketplace, was facing challenges with data management. The company was using a plethora of spreadsheets owned by different stakeholders in different time zones, leading to inconsistent and inaccurate data. This was particularly problematic for the eCommerce side of the business, where data was being used to influence strategy and decision-making as the team scaled globally. In addition, technical users who ran their own database queries needed help making sense of conflicting results from queries run without consistent data definitions across the organization.

Moderna, a research-driven organization, has always relied heavily on data for its operations. However, the company faced challenges in accessing actionable insights from its data. The majority of employees relied primarily on Excel for data analysis, with some researchers utilizing Spotfire Desktop. These tools required significant manual work and set a high barrier to entry. This manual process led to data silos across the organization, limited opportunity to further explore data, and created issues of consistency resulting from various and conflicting versions of the same report. The company needed a solution that would improve self-service and exploration, maintain data quality and consistency, and ensure the new tool would be cost-effective and integrate with the tools Moderna already had in place.

Businesses require critical equipment to be running at peak efficiencies and utilization in order to realize return on capital investments. In the digitally connected world, businesses face higher operational risks due to unexpected failures and quite often have limited insights into the root cause of problems. Common industry challenges are: Costs of unproductive time, Lower asset utilization, Risks of operating outside of manufacturer specifications, Regulatory compliance risk.

Snowe, a home goods retailer, was facing challenges with managing an overwhelming amount of data from disconnected sources and too many tools. The company was using Excel, Google Sheets, and MS Access for data reporting, but these manual mechanisms could not scale with the company’s growing needs. The team was bogged down with laborious, time-intensive processes. Other solutions on the market were either too expensive or required advanced SQL knowledge. Snowe needed a cross-functional platform that could extract meaningful insights from their data while providing companywide utility and transparency, without breaking the bank.

PeopleDoc, a leading provider of SaaS HR platforms, needed a solution that could meet its complex requirements while also complying with the legal framework set out by GDPR. The company aimed to optimize communication and streamline administrative processes between HR departments and employees. However, they lacked a system that could handle complicated data modelling, provide access to real-time accurate data, ensure highly confidential HR information was secure and offer an intuitive user experience.

TIER Mobility, a Berlin-based startup offering e-scooter ride-sharing services, was facing challenges with its initial tech stack. The company's rapid growth and expansion into new cities required a scalable business intelligence solution. The existing system, which used Alooma for data loading and open-source BI solutions for the modeling layer, was inefficient and lacked the power to provide deeper insights into the performance of the fleet. The BI team was spending most of their time writing and updating SQL queries, which was not sustainable for a fast-growing business.

Conrad Electronic, a B2B sourcing platform, was looking for a way to provide its marketplace suppliers with valuable data insights on their products. The company wanted to maintain its high-quality service and ensure that marketplace providers could easily manage their products. The COVID-19 pandemic had caused demand peaks and the company needed a solution that could help suppliers respond to customers’ needs quickly. The company wanted a solution that could provide visibility into key metrics on their product portfolios.

Charlotte Tilbury, a rapidly-growing global luxury beauty and skincare brand, was seeking to advance its data analytics processes while scaling up its ecommerce platform to meet growing customer needs across channels. The company needed a unified approach to analytics and reporting that would provide access to real-time, critical data insights across the business, from ecommerce to supply chain and finance.

ACME Truck Line, a leader in the truck transport of equipment, materials, and supplies throughout the United States, was facing challenges with its invoice processing standards. The company was asked by a major oil and gas industry customer to shift from paper to electronic invoicing. However, the traditionally paper-based truck transport industry had varying invoice processing standards from customer to customer. Before deploying the Actian solution, ACME had to enter all of its invoicing data twice: once in its own accounting system and once in its customer’s e-invoice system. This 'double-entry' practice doubled the risk of errors, delays and rework in the invoicing process. It also had an adverse impact on the company’s customer onboarding process, making it unnecessarily cumbersome and complex and hindering support of high-volume customers.

Bucap, a leading Italian company providing outsourced document archiving and management services, needed a highly flexible and scalable system to allow quick and easy customization of the applications to respond to business requirements. The company handles huge quantities of data which must be managed and stored in systems compliant with the requirements of high availability, accessibility and security. Bucap also needed the system to be scalable, reliable, highly available and easy to maintain, in order to be able to support all the business processes of storage, indexing, retrieving, and displaying of the documents at the service level agreement committed with the customers.

Egide Informatique, a Paris-based company providing a comprehensive solution for real estate management, was in search of a reliable, high-performing database that would not require maintenance by a database administrator (DBA). Their software encompasses the commercial, residential, and social housing and addresses the needs of property administrators, co-ownership trustees, and property managers through its modular set of features. They have a wide-ranging base of 500 customers who can have anywhere from one to 200 workstations each, with some not even having an IT system in place. The challenge was to find a database solution that could cater to this diverse customer base, ensuring high performance and reliability without the need for constant maintenance.

The Office of the Revenue Commissioners in Ireland, responsible for the assessment and collection of taxes and duties, required high availability and high performance for its mission-critical computer systems. It had adopted the Ingres database and the OpenROAD application development toolset in 1993 and had constantly upgraded to the latest versions to ensure a stable, manageable, and economical platform. However, the organization faced challenges in improving transaction processing as well as better batch and checkpoint performance. It was essential to move mission-critical systems to Actian Ingres database without any impact on live services. The organization had many databases in multiple Ingres installations, all of which support mission-critical applications either in production or at various stages of development. It was essential to have minimal downtime during the upgrade and to ensure that all applications, especially its large-scale batch applications, continued to run smoothly.