GigaOm Key Criteria for Evaluating Data Warehouse Solutionsv5.0

1. Executive Summary

Data warehouses store and process data to provide meaningful business insight. They are foundational to many organizations’ data management strategies. In contrast to transactional or operational databases (optimized to handle large volumes of record-by-record transactions), data warehouses are specifically optimized for analysis of historical, or aggregate, data.

Data warehouses consolidate information from all of an organization’s internal and external data sources and store it in a central repository. Specific design choices allow them to facilitate fast analytics over large volumes of data, including a dimensional model, or star schema, massively parallel processing, columnar storage, vector processing, single instruction multiple data (SIMD) operations, and data compression. Data often must meet strict quality requirements to be loaded into the data warehouse; thus, in addition to helping organizations centralize, organize, and derive meaningful insight from their data, data warehouses also play an indirect role in ensuring data quality.

Data warehouses form the cornerstone of many organizations’ data stacks, and are important to all personas and levels of the business. Data engineers cleanse, structure, organize, and model the data. Database administrators monitor and maintain the data warehouse on the back end. Business users, business analysts, and data analysts access the cleansed and organized data in the warehouse through client applications, such as business intelligence (BI) applications, to extract meaningful insights about the business. This insight is presented in reports, visualizations, and/or dashboards, answering executive and C-suite questions and forming the basis for strategic decisions of the business.

Business Imperative
To remain competitive, regardless of industry, an organization’s management and C-suite need to make smart, timely, and informed decisions about the operations and strategic direction of the business. However, relational databases, optimized to store the record-by-record data generated from individual business transactions, are typically not also designed to perform analysis on large volumes of that data. Organizations can’t afford to let data sit when potentially critical and competitive information can be extracted from it. But they also can’t afford to struggle with the poor performance, unacceptable response times, and the incomplete view that can result if operational systems are forced to perform analytical functions they were never designed to handle.

Data warehouses are platforms purpose-built to solve this problem. Since their development in the late 1980s, they’ve existed to enable the data analysis that forms the foundational support for business decisions. Data warehouses assemble a historical, curated, comprehensive collection of the organization’s data into a database that’s specifically architected to perform analytics over that data. The insight thus derived reveals an organization’s current and past performance, uncovers patterns that can lead to predictions about potential future performance, and overall informs both the day-to-day operations and the long-term roadmap of the business.

Sector Adoption Score
To help executives and decision-makers assess the potential impact and value of deploying a data warehouse solution, this GigaOm Key Criteria report provides a structured assessment of the sector across five factors: benefit, maturity, urgency, impact, and effort. By scoring each factor based on how strongly it compels or deters adoption of a data warehouse solution, we provide an overall Sector Adoption Score (Figure 1) of 4.4 out of 5, with 5 indicating the strongest possible recommendation to adopt. This indicates that a data warehouse solution is a credible candidate for deployment and worthy of thoughtful consideration.

The factors contributing to the Sector Adoption Score for data warehouses are explained in more detail in the Sector Brief section that follows.

Figure 1. Sector Adoption Score for Data Warehouses

This is the fifth year that GigaOm has reported on the data warehouse space in the context of our Key Criteria and Radar reports. This report builds on our previous analysis and considers how the market has evolved over the last year.

This GigaOm Key Criteria report highlights the capabilities (table stakes, key features, and emerging features) and nonfunctional requirements (business criteria) for selecting an effective data warehouse solution. The companion GigaOm Radar report identifies vendors and products that excel in those decision criteria. Together, these reports provide an overview of the market, identify leading data warehouse offerings, and help decision-makers evaluate these solutions so they can make a more informed investment decision.