The pharmacy chain, with over 9,500 stores across the US, 20,000 suppliers, and 1 million products, generated 17 billion records of transaction-level data each day. They wanted to analyze two years of supply chain data to drive their business decisions. However, they faced several challenges in analyzing the continuously growing supply chain data. Their data was coming from a wide variety of internal and external sources and living in multiple applications such as Netezza, Oracle, Excel, and more. Despite consolidating it on an on-premise data lake, they faced several challenges in analyzing the continuously growing supply chain data. Some of the key challenges they faced included hundreds of billions of records from 50+ sources, inability to scale up to billions of rows, slow response times kept business users waiting for days to get insights, and diagnostics were difficult as they could not drill down to granular details.

A leading multinational investment bank and financial services company operating in 50 countries wanted to establish an executive-level global view of its financial data to support Management Discussion & Analysis (MD&A) for their C-suite. Their executives needed a single dashboard where they could measure the company’s performance on a comprehensive set of real-time measures and drill down to the lowest levels of granularity instantly. They also wanted to enable quick ad hoc analysis for 700 analysts located across the globe. Their reporting requirements were quite complex, and they needed a single view across all international businesses. It was difficult to use their existing BI environment to model complex financial KPIs and build reports on massive financial records. With no tolerance for latency, it became challenging to meet the high expectations and aggressive timelines from the executive team.

The investment bank arm of a global financial institution was struggling to limit its daily risk exposure across its entire business. The risk analytics team found it difficult to assess their daily trading positions across various asset classes - foreign exchange, equities, fixed income, and other special products. They were unable to see how one asset class risk affected another. The bank set out to deliver a daily single consolidated view of its risk position to drill down into individual transactions. However, they faced several challenges: With over a billion risk points a day, they were struggling to create a consolidated view of risk across their assets. It was impossible to correlate risks across asset classes to understand trends. Analysts were unable to drill down into their data to understand complex transactions. Risk analysis was always late and deficient.

The global telecommunication company was facing several challenges with their existing MicroStrategy BI tool. As the volume of their data increased massively, and more dimensions, metrics, and measures got added to their MicroStrategy reports, response times increased from seconds to several minutes, even for smaller and less complex reports. Transferring billions of rows of raw data from the data lake to MicroStrategy for computations caused the application to time out in most cases. Disconnects between the data collected by different departments such as marketing, finance, product development, and others made cross-functional analysis extremely difficult. Building cubes on MicroStrategy consumed 50% of resources, resulting in prohibitive costs. They also faced inconsistent response times on MicroStrategy queries and an inability to meet increasing business requirements as they could not accommodate additional dimensions and measures in reports without impacting performance.

The Land and Buildings Department at a top ten U.S. university was facing significant delays in accessing analytics, which was reducing revenue and negatively impacting their budget. With over 300 buildings to manage, the department generates work orders within Oracle E-Business Suite (EBS) for maintenance tasks. However, the existing BI tool was slow in pulling data from EBS, causing delays in work completion. Technicians would sometimes wait up to 15 minutes for a response to basic queries, eating into their billable time.

The leading apparel and footwear brand faced challenges in fine-tuning its material forecasting based on consumer demand patterns. With an extensive network of factories servicing global stores, it was difficult to estimate the exact quantity and type of raw materials for different manufacturing locations. The existing BI architecture did not allow them to analyze more than 18 weeks of data. They were pulling source data from Amazon S3 to Snowflake, building aggregates on Azure Analysis Services (AAS), and then performing analysis on Excel. This led to multiple points of failure and each hop had an associated cost. They were hitting the limits of AAS in terms of processing that could be done and missed SLAs due to high data volumes during the holiday season. As data volumes rose, Excel reports would often freeze/crash.

The grocery chain wanted to use their data to understand their customers better and maintain their market-leader position. They decided to align their merchandise mix and store inventory to match each customer’s specific needs. With hundreds of stores, thousands of products, and daily visitors, the store had details on almost 140,000 transactions per day and wanted to use this data to support their business decisions. However, their current environment could not handle the data scale and complexity, making it impossible to conduct Year-over-Year, much less Month-over-Month analysis. They resorted to writing complex queries using Impala to fetch data from their Cloudera platform. But when they tried to join tables with more than one billion cardinalities, Impala usually timed out.

The pharmacy store chain, with over 20,000 national and international suppliers, was struggling with the efficiency of its supply chain network. The company had billions of records from over 50 tables and sources, making analysis difficult. There was no single source of truth as data was decentralized and coming from different sources. Reporting was time-consuming as teams had to compile reports manually and could send them only once a month to top 200 suppliers. There was no way to provide secure, self-service access to massive volumes of supply chain data to external users/suppliers.

The telecom company was facing several analytical challenges. The time-to-insights was too long for ad hoc analysis, they first had to write, tune, and optimize their queries and then run them to get the required reports. As massive datasets were joined at runtime, queries would take 10 to 30 minutes to return, and complex ones would take even longer. As the business grew, it became difficult to meet the aggressive timelines of its users. In addition, though their data platform allowed them to retain 15 months of data, it was very challenging to analyze across multiple months due to the volume and complexity of their data.

The e-waste recycling company was facing several analytical challenges on their Snowflake / AWS / Tableau platform. They were experiencing severe degradation in query performance while analyzing multiple years of data in Snowflake. There was no semantic layer to define consistent data models and standardize KPI calculations. The company was also dealing with unpredictable querying costs on their Snowflake data warehouse, with bills running very high at times. They found it difficult to model their data and deal with multi-level hierarchy and one-to-many joins between facts.

The US-based financial technology services company was struggling with creating and automating a single consolidated view of all its data for hundreds of users. Despite having uniform data, reporting was siloed due to the inherent limitations of the analytical environment. They initially attempted to build MicroStrategy intelligent cubes from data residing in Google BigQuery. However, there were limits on the amount of data each cube could hold. As a result, they were forced to split their data and create separate cubes for combinations of geographic regions and data sources. This led to an inability to get a single consolidated view of their data as they were using 20 different cubes for core reporting. The time to publish the cube, even for a single data source, exceeded 6 hours. Changes in data or incremental refreshes required full reprocess downtime. Reprocessing took over 24 hours, and users could not access the system for this duration. They maxed out the capacity of their Google environment. Despite running on a large server, there was no room for additional data. They were spending massive amounts on cloud computing.

The multinational computer software company wanted to consolidate the data from different customer touchpoints and create a 360-degree view of the customer interactions with its products and services. They faced several challenges in analyzing the enormous amount of data being generated. A wide variety of customer data from sources such as call centers, web interactions, customer churn, marketing, purchase, and product usage made it difficult to get a consolidated view. Disconnects between the data collected by different departments made cross-functional analysis difficult. Non-standardized reporting between business units, with over 1000 analysts reporting on 80 customer metrics collected from more than 20 source systems. Different business units used different BI tools and were reluctant to adopt new tools.

The telecommunications company had access to a massive amount of session data from live TV viewing, DVR, video-on-demand, pay-per-view, set-top box usage, and other streaming devices. However, it was difficult to connect and get useful insights from this data. The existing infrastructure worked well for small datasets, but as data volumes grew, they started facing challenges in processing and deciphering it. The company was unable to leverage the massive volumes of viewership data from millions of subscribers stored on Hadoop to improve the viewer experience and gain a competitive advantage. New or ad hoc queries took hours or even days to return, making them unusable for decision-making. Slow responses to queries made it impossible to analyze data over extended periods to understand trends or recognize patterns.