In the current competitive business environment, where data is widely seen as the market’s major differentiator, enterprises are astutely learning ways of handling data as a highly valued asset and the management of it as an investment.

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).

5. Information Topology and Architecture

One of the greatest challenges that organizations deal with is the ever-increasing number of sources and volumes of data. The need for integrated heterogeneous data (structured, unstructured, etc.) across disparate systems and the need for a mechanism to reduce latency in data exchange and analysis have added another layer of complexity to EIM.

The organization should define and adopt an EIM architecture to support its long-term functional and nonfunctional requirements. Parameters like data growth, data integration strategy, concurrent usage, real-time data exchange, etc. should be considered while defining the EIM topology. The EIM architecture should be flexible to adopt future changes and offer maximum component reusability.

6. Tools and Technologies Standardization

A majority of large global organizations lack standardization of tools and technologies. Their various business and IT groups use different tools to meet needs that are similar. The use of heterogeneous tools/technologies creates fragmented technology architecture and inflates the IT total cost of ownership for the organization.

To optimize IT expenditure, the organization should look for opportunities to standardize and adopt common tools/technologies. This will help in reducing the overall support and maintenance cost. In some cases, it may be difficult to consolidate to a single technology suite, and the organization can adopt standard tools for each category of services.

For example, one U.K. pharmaceutical company has taken an initiative to standardize tools and technologies across its business groups and geographies. This improved license utilization and reducing the overall support and maintenance cost. In the long run, the uniform tools and technologies backbone will have a positive impact on the EIM programs and TCO optimization.

7. Infrastructure Consolidation

In the past, organizations have grown with siloed and fragmented IT applications, which led to a duplicate and inefficient environment with ineffective utilization of IT infrastructure. The organization incurred heavy costs on procurement, support and maintenance of the distributed IT environments.

Technology advancements like cloud or grid computing, parallel processing and balanced configuration unit have minimized the need for replicated hardware and software stacks and offer an on-demand scalable environment with optimal resource utilization. Data size is no longer a bottleneck for application performance.

These advancements open an avenue for the organization to consolidate its EIM infrastructure and create a shared infrastructure environment.

As a first step toward the infrastructure consolidation, companies can consolidate their existing servers and create a virtual environment to be used on an on-demand basis. This will improve the overall server utilization and load balancing.

8. Master Data Consolidation

A majority of organizations have master data distributed across many heterogeneous applications, which hinders an organization’s ability to get a 360-degree view of master information. This negatively impacts organizational growth and sustainability.

As a part of an EIM initiative, the organization should define a roadmap for master data consolidation and management, and offer business units a single true view of party, product and other master entities. A business-driven, ROI-linked MDM initiative with a phased implementation strategy will be the right approach to move forward.

For example, an MDM program may be required to provide an integrated view of vendor, supplier, dealer and product data in an enterprise resource planning system. In the case of the North American manufacturing company discussed earlier, executive support for MDM could be gained when the benefits of the order-to-cash system hinge on complete and accurate master data in the ERP application.

9. TCO Optimization

Intense competition and the global price war have considerably impacted companies’ profitability. In addition, the increasing IT budget and cost of applications management has created a threat to the sustainability of many organizations.

Organizations are looking for effective and innovative ways to reduce the TCO of applications. Business process outsourcing and cloud computing, software as a service, platform as a service and infrastructure as a service reduce the risk and cost of ownership of applications. The organization can explore possible usages of open source software to reduce software license cost.

10. Data Fusion

Organizations have legitimate needs to integrate unstructured, semistructured and structured data and derive meaningful analytics out of it. Linking data from various source types and formats, and understanding the meaning and interrelationships between this data, can enable businesses to deliver more proactive services to their customers.

As a part of the EIM initiative, organizations should define the right strategy and framework, and adopt the necessary infrastructure to support various data sources. There should be a seamless mechanism to integrate structured, unstructured and semistructured information.

For example, an electronic manufacturing company embarked on an initiative to integrate data of different types and formats from disparate sources and perform reporting and analytics to derive meaningful insights from enterprise data. This initiative has been taken to improve the post-sales supports services and other business key performance indicators.

Growth of data volume, siloed data repositories, poor data quality and lack of proper data governance have created limitations on the organization’s ability to use its data as an enterprise asset and derive meaningful information out of it. It limits the organization’s agility and results in a loss of competitive edge and productivity.

Bring it Together

An EIM initiative can help in overcoming these issues. EIM will offer a logical orchestration of various information management applications and provide a consolidated enterprise-level view of data sets and business rules. It brings a standard protocol for data definition, integration, exchange and management, and well-defined governance and stewardship.

The journey of EIM is not easy, and it is full of surprises and hurdles. Organizations are advised to have proper due diligence before they embark on an EIM initiative. Adopt an agile strategy for implementing different pieces of EIM, starting with sweet spots like data quality management, master data management and data governance.

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