Small, focused analytical efforts can be of great benefit to an organization. They are often used to serve a specific need, such as build a pilot solution to prove value for a larger future effort, or provide an enhancement to an existing solution. Have you ever been challenged by what should be a short, quick, analytics project that turns into a time and cost nightmare? Unfortunately, one area that many organizations struggle with is the ability to take a small, reasonably sized analytics project and deliver it in the estimated time frame. Let’s take a look at some of the key criteria required to realize the rewards a small project can deliver.

One of the most vital functions of any MME is to provide the technical architecture and processes to manage corporate knowledge. All corporations strive to become more intelligent. To attain a competitive advantage, a business needs its IT systems to manage more than just their data; they must manage knowledge (that is, meta data).

Today’s data warehouse environments offer users a variety of information sources. Architectures can contain data staging areas, operational data stores, enterprise data warehouse, data marts, and other data store types. Each of these stores offers different opportunities for information investigation and delivery depending on the point in time and state of the data. In many cases, all information delivery has been redeployed to the data warehouse environment to offload the transactional systems allowing these systems to focus on business processing, not delivery.

In my seminars and conference presentations, I routinely ask the question: Who invented the Entity-Relationship modeling technique? With very few exceptions, I get the answer: Dr. Codd. Wrong. The origin of data modeling predates the relational database management systems (RDBMS) by several years.

Organizations considering a new approach to enterprise data management frequently wonder about the current state of their data management efforts. They don’t know where to start, what should be repaired, what should be left intact, and how to align the changes and continuing tasks into a cohesive practice. In this confused state, organizations embark on less productive and sometimes conflicting paths, making data management improvement a synonym for “failed initiative”.