Tyson Foods, a global food giant, aimed to deliver self-service data analytics to its 144,000 employees. However, the company faced a significant challenge due to its fragmented data spread across diverse platforms. The primary goal of their analytics modernization journey was to better connect their data. With massive amounts of disparate data moving across data lakes, it was a challenge to navigate this information effectively. The business was stuck in an analog experience and needed to pursue a more scalable and flexible data strategy to stay competitive and successful.

Cardinal Health, a multinational healthcare services company, had a fragmented set of systems due to a series of acquisitions. This made managing and accessing data complicated. The company needed to forecast six months in advance to make sound business decisions, but business analysts across the pharmaceutical, corporate, and medical business lines didn’t have easy access to data for analytics and reporting. In addition, many business systems analysts (BSAs) were using shadow IT or unapproved applications to track metrics. As Cardinal Health moved into the future, the company needed to simplify its data landscape to streamline data and analytics access throughout the organization.

Wayfair, a fast-growing ecommerce retailer, was facing challenges with its existing analytics infrastructure. The company was using Microsoft SQL Server Analysis Services (SSAS) for mission-critical analysis, but as the business grew, this solution was not scalable enough. Wayfair decided to modernize their analytics infrastructure and move to a cloud platform, specifically Google BigQuery. However, they needed to ensure that the transition didn't disrupt the hundreds of business analysts who relied on SSAS for their daily operations. The company also wanted to maintain a hybrid on-premises/cloud environment for a time to ensure business continuity.

Toyota, an international automotive company, was faced with the challenge of consolidating 35+ constituent North American companies into a single structure. This required a transformation of their data warehousing and analytics architecture across the business. The IT department was tasked with creating a semantic layer that supported high performance analytics that could be leveraged by all business analyst teams. Prior to the project’s implementation, analysts would often need to wait weeks for manual data engineering to take place. This delay hindered their ability to provide actionable insights on key business questions. The company's backend infrastructure was partly to blame for the slow query response time, as data was siloed across thousands of individual data marts.

As the top online retailer in the Netherlands and Belgium, bol.com has grown massively in a short amount of time. As the company scaled, the data began evaluating alternatives to their overloaded Hadoop cluster that was taking too long to run some jobs. At the time, the company’s analysts were using Platfora for data preparation and visualization. Shortly after the go-live, Platfora announced its acquisition by Workday and with that the discontinuation of the product. With this as a catalyst, bol.com began looking for a new solution to support their BI and analytics program. Self-service was a top priority for the bol.com team. As they looked for new technology partners, they wanted to integrate a semantic layer solution that could cover all data assets, now and in the future. Further, they wanted to ensure compatibility with whatever BI and analysis tools they may use in the future.

A Fortune 50 retailer launched an initiative to modernize their analytics infrastructure with the primary goal of increasing the flow of data-driven insights that could lead to improved margins, optimization of product mix and better inventory management. Their challenge was to enable better analytics at scale while ensuring efficiency and consistency across a broad audience of data consumers. With thousands of users performing analytics using a diverse set of legacy platforms, including SQL Server Analysis Services (SSAS), Teradata, and Hadoop, the existing infrastructure was expensive and could not scale at the rate of their business. To empower their users, the data team needed a scalable semantic layer solution that could serve the needs of internal users as well as suppliers that rely on a shared view of inventory. The solution needed to scale, needed to support security and access control policies, and needed to support the organization’s migration from on-premise legacy data platforms to a cloud data warehouse.

EverQuote, a large online marketplace for insurance, was facing challenges with its in-house custom OLAP solution. The system, which was over ten years old, had several bottlenecks that prevented many use cases and suffered from poor query performance. As the company grew, it also found it difficult to scale self-service analytics to non-technical employees. The company needed a modern data architecture that could democratize data analytics for all.

When COVID-19 hit the US, public health officials scrambled to collect and analyze as much data as possible about the disease to better understand and share its impact with the public and Centers for Disease Control (CDC). The Boston Children’s Hospital (BCH) team introduced CovidNearYou.org as a way to crowdsource self-reported symptom data so that public health officials could derive insights about the spread of the disease across North America. Their challenge was to be able to quickly query all the data being collected from a variety of sources and visualize the results so that their research partners could identify patterns and stop the spread of the disease. It was a manual, time-consuming process.

PT Bank Rakyat Indonesia (BRI) was facing challenges with data analytics. They had performance issues when processing and visualizing their data. They also had storage and load time issues caused by moving data from the data lake into an RDBMS for visualization. Additionally, they had an issue with the processing time it took to provide data for queries. If they gave the wrong instructions, they would have to create, build and run each job over again. This was an inefficient and expensive way to provide executives with the analysis they use to make business decisions.

The Company’s operational data was siloed and difficult to access. Users wanted to leverage modern BI tools such as Tableau and Excel, and were being forced to extract data from databases and work with local copies. This “pump and dump” strategy of extracting data and working locally meant analytics were scattered across employees’ desktops, data governance and accuracy problems were endemic and the Company struggled to maintain one single version of the truth for their analytics. The Company developed strict criteria for their intended data solution: Ease of Use, Tool Agnostic, Disruption Free, and Single Source of Truth.

Rakuten, a shopping rewards company, had moved from their initial SQL database in 2014 to an AtScale-powered Hadoop solution in 2018. However, this wasn’t sufficient and they soon began to experience a resource crunch based on the sheer size of their database. Rakuten's existing architecture meant that business users didn't have the computing resources necessary to work with large datasets. This led to competition between business units for hard disk access, memory, and CPU time. The internal team was frustrated with the competition for resources, and the operational overhead and associated hardware and electricity costs also meant the solution was no longer cost-efficient. That, coupled with the continuous processing demands on storage infrastructure, forced Rakuten to consider new solutions for their data needs. They knew they needed more processing capability and flexibility to continue serving their customers effectively.

Affinity Federal Credit Union (AFCU), a large member-owned credit union, was looking for opportunities to better leverage their data assets to improve service to their more than 185,000 members. They had been relying on legacy analytics infrastructure tools like ModelMax or Dundas BI, which required too much manual effort and slowed down decision-making. AFCU had been partnered with a Credit Union Service Organization (CUSO) that provided analytics-as-a-service, but this approach was slow and uncontrollable, often getting in the way of decision making and making it difficult to grow internal understanding of data. AFCU realized they couldn’t remain reliant on an outsourced analytics team and legacy processes to unearth insights from their data. It was time to transition to a modern, self-service BI program to allow faster, data-backed decision-making at scale.