Enterprise Information Management (EIM)

Definition of EIM

• EIM is a set of business processes, disciplines, and practices that manage information created through organizational business processes.
• It treats information as an enterprise asset, supporting business strategy execution and analyzing performance using leading and lagging indicators.

Gartner's Definition of EIM

• EIM is an integrative discipline that structures, describes, and governs information assets.
• It improves efficiency, promotes transparency, and enables business insight.

Complexity of Business Environments

• Enterprises today deal with complex business environments that require real-time and complex information management.
• Effective Information Management is crucial for managing enterprises in a global context.

Types of Data Managed

• Structured data (e.g., sales data, customer data)
• Unstructured data (e.g., customer satisfaction forms, emails, documents, social network sentiments)

Significance of Managing Information Assets

• Enterprises face a deluge of information due to global operations and complex business models.
• Making sense of data is crucial for deriving actionable intelligence.

Key Driver of EIM

• Support for the enterprise's business strategy and objectives, including profit, revenue, cost optimization, etc.

Importance of EIM Strategy

• Details integration, governance, and management of data across the information lifecycle.
• Facilitates seamless access to and sharing of information across the organization.
• Ensures data integrity, security, and compliance with regulations.
• Improves data management processes, reduces errors, and optimizes resource usage.
• Empowers decision-makers with timely, accurate information for informed choices.

Barriers to EIM

• Lack of consistent data definitions across departments or systems.
• Lack of consistent business rules and jargon across functional and business units.

EIM's Scope

• Encompasses managing enterprise information assets, including structured and unstructured data.
• Provides actionable insights into operations and performance management.
• Involves information exchange in collaborative commerce among enterprises, supply chain partners, and suppliers.

Key Dimensions of EIM

• People: Requires individuals in key roles.
• Process: Relies on data management processes enforcing key principles.
• Technology: Utilizes tools supporting data extraction, transformation, cleansing, and storage.
• Infrastructure: Requires supporting infrastructure for technology implementation.

Pillars of EIM Strategy

• Business Intelligence: Uses data to understand how the business is doing.
• Data Integration: Brings different types of data together so they can be used together.
• Master Data Management: Keeps important data consistent across the company.
• Information Governance: Makes sure information is managed properly and securely.
• Information Quality: Ensures that the data used is accurate and reliable.
• Data Architecture: Designs how data is stored and organized.
• Enterprise Content Management: Handles documents, files, and other unstructured data.
• Information Security: Keeps data safe from unauthorized access.

A Brief History of EIM

• In the 1990s, EIM began with a focus on structured data management, mainly through data warehouses for decision support systems.
• The understanding expanded to include unstructured data, leading to the inclusion of ECM practices within EIM.
• With the rise of big data solutions, EIM evolved to manage unstructured data like sensor data and social media sentiment analysis.

The Lifecycle of Enterprise Information Management

• Enterprise information assets have a lifecycle: creation, storage, analysis, archival, or destruction
• Key stages in the information lifecycle:
• Creation/Receipt: data is generated at the point of origin within the business process
• Distribution: data is distributed to relevant consumer applications
• Consumption: data is used for reporting, analysis, and business processes
• Disposition/Archival: data is stored in cheaper storage options, eventually becoming obsolete and marked for archival or destruction
• Destruction/Retire: data is removed from storage systems to maintain data relevance

Enterprise Information Lifecycle Management (EILM)

• EILM involves applying policies based on business classification of data for effective information management
• It aims to identify and classify data elements based on business criticality and nature
• EILM encompasses policies, processes, practices, and tools used to align the business value of information with the most appropriate and cost-effective IT infrastructure

Data Classification

• Data classification is a component of EILM, helping enterprises understand available data types and ensure appropriate protection and compliance
• Benefits of data classification include:
• Data compliance and risk management
• Optimization of data encryption needs
• Better control of disaster recovery and business continuity needs
• Enhanced metadata management
• Appropriate data security controls and access based on data criticality

EILM Adoption

• EILM adoption trends are around 30-40%
• Driven by government laws like HIPAA, which dictate varying retention periods for patient records
• Organizations need an EILM strategy as part of their information management strategy due to regulatory requirements

EILM Strategy Steps

• Data classification of data assets based on business value
• Assessment of data security needs based on data classifications
• Definition and implementation of policies concerning data type and storage mechanisms

EILM Implementation Steps

• Analyze/Categorize: classify data assets based on business value and technical usage patterns
• Develop Strategy: develop data aging and retention strategy considering business needs and data classification
• Implement Strategy: implement data aging and retention strategy along with data security needs
• Monitor: monitor data archiving and retention processes to ensure compliance and efficiency

Actors in the Stages of the Enterprise Information Lifecycle

• Data Integration Teams: define data synchronization and replication jobs for distributing data to consuming business processes and applications
• Infrastructure Architect: define offline storage media for storing semi-active data and collaborate with data stewards to identify data assets for offline storage and those marked for retirement or destruction

Components of Enterprise Information Management

• Governance: establishing policies and procedures for managing data effectively
• Data Quality: ensuring the accuracy, consistency, and reliability of sourced data
• Integration Patterns: structuring the exchange of different types of data
• Data Architecture and Models: designing the framework and structure for organizing and accessing data
• Master Data Management: managing consistent, uniform sets of data across systems
• Information Warehousing: storing and managing large volumes of data for analysis
• Information Delivery: providing access to data and insights for users
• Metadata Management: managing information about the data to facilitate its use and understanding
• Big Data Components: handling and analyzing large and complex data sets