Why Big Data Enhances the Need for Enterprise Information Management

Many EIM (enterprise information management) or data management programs do not live up to their potential, and the arrival of big data makes the need for enterprise information and data management even more significant.

EIM refers to the collection of disciplines — including data governance, data quality, business intelligence and data warehousing, data modeling, metadata management and master data management — that enable the understanding and usage of information and data as an asset to the enterprise.

EIM is a mature field, but the arrival of big data has made the need for enterprise information and data programs absolutely essential within the organizations. The reasons for the critical need for EIM as a success factor for any company aspiring for success with their analytics and big data initiatives are:

Should a company choose one or few components of EIM before committing to big data strategy and then incrementally adopt the rest of EIM? Or, should a company focus on EIM before committing to big data?

For success, both appear to be necessary. The enterprise information management program doesn’t have to be large, but it must continuously progress and be sustained to be of any value to the company, especially when combined with an analytics or big data initiative.

Here are seven major points to keep in mind when considering an EIM solution for big data:

EIM initiatives need a continuing effort. They usually have recurring costs and need experienced staffing and management. It is also essential that the EIM program be started and maintained for the right reasons. Determining the right business goals is a fundamental necessity.

These goals should be the ones the company will value for a longer period of time, such as shared data managed collaboratively, data accessibility, data quality. Over the life of the program, the goals may be defined but for a successful implementation, they must always relate to the current business objectives. These statements can be applied to big data initiative as well and the level of effort for the two programs will be the single most significant factor to consider when planning the approach.

Relevant business goals provide the requirements that are valuable for technology, processes, and data, and this statement applies at a deeper level when discussing analytics and big data.

The business needs align various EIM initiatives into a cohesive program (business intelligence/data warehousing, enterprise data architecture, data quality, data governance and metadata), and provide the focus and define the scope. Eventually, every company will choose the components of EIM it requires to address initially.

The order in which the components of EIM are addressed must be driven by the business requirements. If the organization program has decided to implement only a few components of EIM with its analytics or big data effort, chances are that the choices will include business intelligence, master data management of few areas, data quality, and data governance.

EIM tries to integrate various perceptions about the business and its utilization of information and data, making any EIM program an essential element of a successful analytics or big data initiative.

For shared understanding of the usage as well as meaning of data, which could be stated as a major goal of any analytics program, the EIM programs must be shared. This approach leads to the establishment of a data governance program within the enterprise.