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August 2, 2017

Why Big Data Enhances the Need for Enterprise Information Management

Savaram Ravindra

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:

  • The focus of EIM on enterprise governance of mission-critical data with standards and policies makes it possible for the company to utilize that data for decision making. Achieving a high level of data quality enables the company to to trust the data analytically and operationally.
  • The organization of commonly utilized data into MDM (Master Data Management) structures to allow cross-sharing and decrease possible errors and redundancy.
  • Business intelligence and data warehousing strategies that are founded on EIM standards and best practices to enable the business units to share analytical data as required without worrying about the differences in metadata or technology challenges.

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:

Level of Effort


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. As part of analytics and big data efforts, the establishment of a formal data governance program is the single most significant element because it will create the standards and policies for managing metadata and data, by the business units for business use. Most analytics programs without a formal data governance program do not prosper.

Few data governance approaches may incorporate designing governance program for the organization. Most will start at a project or business-unit level. In business unit projects the business data stewards that perform the data governance activities are concerned with the data completeness, correctness, cleanliness, and alterations in usage and definitions, especially for analytical data if the program is focused on a big data initiative.


In an EIM initiative, one of the most important point to remember is that cultural or organizational concerns must not override the particular requirements for which the EIM program is planned. Maintain the solid development program’s iterative nature and make sure that the scope remains manageable, within an enterprise focus.

Iterative data management development can have relatively low risks and will allow the continuation of program in spite of any financial concerns with the eventual result of an enterprise approach to data management. This is highly significant if the initial focus of an EIM strategy was the beginning of an analytics and big data initiative. Ultimately, the rest of the company will see the EIM’s value and will want to include EIM into operational data. That is good, but it also may indicate that it’s time to begin other EIM initiatives.

Conceptual Data Model

One essential point for a successful EIM is the development of an enterprise conceptual data model. This model doesn’t require a major effort but its benefits are demonstrable. Very few successful EIM programs do not have a feasible enterprise conceptual data model and its associated metadata. The analytical initiatives require a conceptual data model for showing the subject areas that comprise the data of the company and how those areas are related.

Experienced Project Management

With an EIM program focus, the experienced project management is another essential success factor. EIM is a program and as such needs program management skills and EIM program manager requires a solid understanding of each element of EIM and its relationship to the big data program.

Build on Success

Though an EIM program is complex when seen as a single unit, it can be made simpler with regard to each of the points made here, mainly when the EIM program is coupled with another initiative like big data and analytics. Accept the complexity of the enterprise but focus on each element for each business unit, developing the program in manageable portions, till the team reaches its stated goals.

Thus, with big data, EIM will make sure that your data is understandable, accessible and of high quality and it delivers decision-ready actionable information across the company and that is stated through the above seven points.

About the author: Savaram Ravindra was born and raised in Hyderabad, popularly known as the ‘City of Pearls’. He is presently working as a Content Contributor at and He is also an Author at His previous professional experience includes Programmer Analyst at Cognizant Technology Solutions. He holds a Masters degree in Nanotechnology from VIT University. He can be contacted at [email protected]. Connect with him also on LinkedIn and Twitter.