An organization usually functions with the use of many internal and external sources that generate heterogeneous (structured, unstructured, etc.) and voluminous data. The constant challenge faced by an organization is how to effectively manage enterprise data, taking into account the emerging needs of cross-functional data exchange, which introduce yet another layer of complexity to data management as a whole.
To improve the value and usability of data as an enterprise asset while dealing with data dependencies, organizations require enterprise information management. EIM is a logical coming together of strategy, architecture, processes, technology and resources (human and infrastructure) for the effective integration, management and dissemination of enterprise information. In other words, EIM is the art of managing data and converting it into valuable information.
The Key components of EIM are depicted in the framework in Figure 1 (see PDF at the end of the article).
This EIM framework encompasses an end-to-end information management lifecycle including data acquisition and integration, data transformation and consolidation, data quality and master data management, content management, performance management, and BI reporting and analytics. Data governance and stewardship improves overall data transparency and management, and provides strategic and tactical direction for data management and dissemination. Commercial off-the-shelf tools and technologies can be leveraged to realize the EIM initiatives of an enterprise.
Key Considerations of EIM
Organizations embark on comprehensive EIM programs to meet the growing demands of data coming from various stakeholders. The key intent of these programs is to offer effective management of enterprise data, and provide quality data in a timely manner with better governance and control. EIM is considered as a platform to reduce data redundancy and inconsistency, and link common information together (e.g., master data, metadata, structured and unstructured data, etc.).
Each organization has its own unique limitations, challenges and dependencies in defining its EIM strategy; there are, however, some common issues and considerations that many organizations face. Organizations should devote attention to the following key points while defining their EIM strategy:
1. Data Management Strategy
The lack of a data management strategy hinders the organization’s ability to identify and prioritize data issues and results in a loss of competitive edge. The business suffers due to data issues and this has significant impact on the organizational revenue and credibility.
The organization should have a compelling vision and strategy toward data management. The EIM strategy should be aligned with the corporate data strategy and should include the technical and business directions for the critical data sets of the organization.
For example, a manufacturing firm in North America is in the process of transforming their business applications to improve upon the order-to-cash cycle. The EIM strategy and landscape has been defined to support the various levels of information integration and exchange. The different components of EIM (like data quality management, master data management, metadata management, enterprise information integration, etc.) have been defined to support the order-to-cash transformation. The close-linked EIM and business transformation initiative has paved a path for schematic business transformation.
2. Data Quality Strategy
Poor data quality results in incorrect business decisions, poor customer service and loss of productivity and profitability. Fixing data issues across different applications and connecting the data elements to give a homogenous view is one of the significant challenges to the EIM initiative.
The EIM program should have a framework for continuous analysis, measure and improvement of enterprise data quality. It should support data analysis, standardization, cleansing and consolidation. Linking and enrichment of the internal data elements with the external (third-party) data should be the part of data quality strategy.
For instance, an insurance company in India has embarked on a customer relationship management initiative to increase the “share of wallet” by cross selling and up-selling. Correct customer name and contact details were mandatory to support an effective campaign. The company defined a comprehensive data quality management strategy to correct and consolidate customer information to support the immediate and long-term needs of CRM. There is a plan to initiate similar data quality initiatives in other departments and connect each of these initiatives under a common EIM framework.
3. Enterprise Data Governance
The growing complexities of the data management environment, the necessity of compliance to external and internal regulations, inorganic growth of the enterprise through mergers and acquisitions and the increasing trend of process outsourcing require a specific focus toward enterprise data governance.
To deal with these challenges, the organization should embark on a data governance program and define the standards, policies, processes, guidelines, framework and technology for management, usage, distribution and protection of the enterprise data. An effective data governance program helps reducing risk and ensures regulatory and legal compliance. The EIM program should be closely coupled with the enterprise data governance initiative.
As an example, a pharmaceutical company in the U.K. has embarked on an EIM journey to support multiple transformational initiatives within the organization. A data management competency center and a data governance council have been established to improve the overall data transparency and management. A common setup of policies, processes, architecture, framework, tools and technologies has been defined to support the DMCC and DGC functions. The schematic approach for data governance has further reinforced the EIM journey for the organization.
4. Enterprise Data Services and Integration
Data integration and exchange across the disparate applications has been one of most difficult challenges of the EIM program. Most global organizations function with rudimentary custom-built point-to-point interfaces, which in turn add limitations to the data exchange services.
As a part of the EIM initiative, the organization should adopt an enterprise standard for data integration/exchange with a common architecture and topology. Usage of EAI tools and a service-oriented architecture strategy help reduce the latency and complexity of the environment. Prebuilt reusable services will help achieve a schematic approach for data integration with optimal utilization of infrastructure (for example, metadata management services, data analysis services, data quality management services, master data management services, etc).
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