Enterprise Information Management (EIM) is gaining momentum. Organizations are hard pressed to find ways to adequately manage their data without affecting production systems and operational processes. With organizations requiring an overall view of their organization, and many initiatives focusing on lowering expenses, the utilization of data becomes the backbone of many of these projects. Although organizations see the benefits of dashboards, scorecards, and other front-end applications, the fact remains that these solutions are only as valuable as the data sources and quality of the data behind them.
The value of cleansed, valid data cannot be underestimated. The example of executives of an organization running individual reports only to realize that each one sees a different view of the organization with disparate data when in a planning meeting highlights the one key issue of not having a data management initiative. Without a coherent set of data that is validated and defined across the organization, each unit may have their own view of the organization. And although each unit may see the world through their own eyes and come to different conclusions, the fact remains that separate distinctive revenue, expenses, or other figures means that there is no clear picture of the business.
Part 1 of this article provides an overview of information management, how it ties into other enterprise data-related initiatives, and how this market will continue to change and mature over the next few years. Part 2 looks specifically at the Business Objects/SAP view of EIM (Enterprise Information Management) and is based on an interview with Philip On, the Director of Product Marketing for their EIM division.
The Basics Of Information Management
Whether it is called enterprise information management, data management, or information management, the general understanding is that managing information across the organization includes the concepts of data governance, data integration, data quality, and master data management. Consequently, much goes into an enterprise-wide initiative. On a simple level, the goal of EIM is to provide organizations with common data definitions, an understanding of how information interrelates, and its value to disparate departments and business units across the organization.
Obviously, this is not easy to achieve. Data governance is the term used that requires organizations to develop a cohesive set of data definitions, standards, and processes that help manage an organization’s data maze. For organizations with mature data integration and data quality initiatives or for organizations that implement MDM, data governance is the next step towards overall enterprise information management.
Unfortunately, out of the plethora of organizations, only a few are mature enough to successfully manage their information across the organization. For others, the data management process might start with one or various business units. Additionally, for organizations that understand the value of managing their information to help provide cleansed and trusted information to meet compliance, to perform audits, or to simply provide a single view of revenues and expenses, it becomes important to start the process with one business unit at a time. Traditionally, master data management (MDM) solutions have enabled organizations to start with customer or product entities creating a single view of what a customer or specific product within the organization.
Enterprise information management takes MDM and other data related initiatives to the next level by enabling organizations to manage their data across sources, ensure a level of quality at each stage of the integration process, and enable organizations to govern the various processes that are associated with the various data points.
How organizations start is a different question. Although many organizations desire an overall solution right away, the fact remains that slow and steady will usually win the race when it comes to data management initiatives. It becomes important to start within one business unit or data entity and develop the processes that surround the data as well as cohesion across the organization that includes the agreement of stakeholders regarding definitions of customer, product, etc. and how the data interrelates across the organization.