Applying DevOps-style test automation to projects can guarantee a high level of data quality in any business intelligence / analytics initiative. Introduction According to a CIO.com article, Gartner Group recently stated that between 70 and 80 percent of business intelligence initiatives are initially considered failures but many resume due to their importance to the organization. As businesses create (and need) more data than
Data extraction, transformation, and loading processes enable many activities in information technology projects. Understanding the concepts and practices of ETL is essential for all data and technology professionals Introduction Data extract, transform, load (ETL) is a process of copying data from one or more sources into a target system which is usually designed to represent the data differently from the
Preparing a data warehouse testing strategy can ensure the successful development and completion of end-to-end testing of any data warehouse, data mart, or analytical environment. Introduction Organizations need to learn how to build an end-to-end data warehouse testing strategy. This is most often necessary because the success of a data warehousing project is highly dependent upon the team’s ability to
An Overview of Data Warehouse Testing Data warehouse and data integration testing should focus on ETL processes, BI engines, and applications that rely on data from the data warehouse and data marts. Introduction There is little that casts doubt on a data warehouse and BI project more quickly than incorrectly reported data. It is crucial that data warehouse project teams do all in their power to deliver reports with high
Introduction Today’s digital world and fast paced business environment demands pervasive, timely analytics. Organizations must deliver speed, agility and ease of use for data access and analytics. In response, business intelligence (BI) and analytics teams are shifting to a more agile, self-service model that enables greater user access to data and the ability to create and perform individualized reports and
Investing in new sources of data can help to unravel complex customer behavior and improve key analytical insights Introduction Big Data and Analytics are all around these days. Most companies already have their first analytical models in production and are thinking about further boosting their performance. Far too often, they focus on the analytical techniques rather than on the key
Every organization’s analytics strategy should be connected to its data strategy; and every organization should have a data strategy that supports its business strategy Introduction Like the lyrics in the children’s song Dem Bones, “the hip bone’s connected to the thigh bone”, your data strategy is connected to your analytics strategy – or it should be! Further, your analytics strategy
Data warehouse / business intelligence / analytics environments require business and IT cooperation for successful architecture and design Introduction In working with many large corporations across a variety of industries, it is clear that we all face the reality of re-architecting data warehouses, whether we want to admit it or not. Since data warehouses are business driven (rethink your strategy
Standards are different from guidelines. Standards are firm and must be followed. Successful data warehouses use standards Introduction Many dog owners give their dogs what they consider to be commands. They are really more like guidelines. (“Boscoe come!… pause, pause, pause… Well I guess Boscoe is busy with his chewy toy and doesn’t want to come just now.”) A number
Organizations can find increased value in business intelligence and analytics efforts through the use of various data delivery strategies. Introduction Like many organizations, your company probably has invested significant time, effort, and money into establishing your data warehousing and business intelligence environment. You’ve built the databases and the ETL programs to move, clean and integrate data from multiple sources into
