What are the three types of information management?

The three main types are: - Transactional: Handling day-to-day data like sales and inventory. - Analytical: Using data to identify trends and make decisions. - Collaborative: Sharing and managing information among teams and stakeholders.

What are the 5 keys of information management?

The five key principles are: - Availability: Ensuring information is accessible when needed. - Integrity: Maintaining the accuracy and consistency of data. - Security: Protecting information from unauthorized access. - Compliance: Adhering to relevant laws and regulations. - Cost-effectiveness: Managing information efficiently and affordably.

What is a management information system (MIS) with examples?

A Management Information System (MIS) is a computer system that helps managers make decisions by providing them with relevant information. Some examples include: - Customer relationship management (CRM) system: Tracks customer interactions and sales data. - Enterprise resource planning (ERP) system: Integrates all aspects of a business, like finance and human resources. - Decision support system (DSS): Analyzes data to assist with complex problem-solving.

Data Data Quality Knowledge Management

Best 6 Real-Life Enterprise Information Management Examples

Hazal Şimşek

updated on Aug 6, 2025

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With global data volume projected to hit 149 zettabytes by 2045, organizations have more data than ever. However, 60-70% of enterprise data remains unanalyzed, limiting its potential value as ML stats show. Understanding enterprise information management examples can help businesses turn raw data into actionable insights, improving decision-making and efficiency.

Explore top enterprise information management examples to help organizations develop better EIM strategies:

The visual summarizes top 6 enterprise information management examples

What is enterprise information management?

Enterprise information management (EIM) refers to organizations’ strategies and technologies to manage and leverage their information assets. EIM helps companies:

Manage information
Leverage big data generated
Understand and transform the enterprise platforms
Automate and improve business processes
Improve efficiency.

One reason could be business leaders and analysts lacking familiarity with EIM. Exploring top enterprise information management examples helps tackle this issue.

1. Data governance

Data governance is the practice of managing and maintaining data in an organization.

Data governance helps organizations:

Ensure compliance
Standardize data systems, standards and procedures
Improve data quality
Decrease data management costs
Reduce errors
Enhance operational efficiency.

Data governance is a key aspect of information management since it involves:

Standardizing data definitions

Data governance can help to establish a common understanding of key data concepts and definitions across the organization. This ensures that everyone uses the same terminology and data structures, which helps eliminate confusion and inconsistencies in data management.

Defining data ownership and stewardship

Data governance can help define clear roles and responsibilities for managing data across the organization. This includes assigning data owners and stewards responsible for ensuring that data is accurate, complete, and secure.

Establishing data quality standards

Data governance can help develop standards that define how data should be assessed, measured, and maintained. This ensures that the organization’s data is reliable and trustworthy, which supports effective decision-making.

Ensuring compliance with regulations and standards

Data governance can help to ensure that the organization’s data is managed in compliance with relevant rules and standards, such as data privacy laws. This helps to reduce the organization’s risk of non-compliance and associated penalties.

Real-life data governance example

Engie, a utility company, faced siloed data across global operations when pursuing carbon‐neutral goals. It implemented a data intelligence platform to unify governance across its divisions. Engie standardized definitions and data sharing across the organization and achieving:

Improved agility and data control via an enterprise-wide governance framework,
Saved time and cost through unified data sharing and standardized definitions,
Standardized data language and processes, enabling trusted, self-service analytics.¹

2. Master data management (MDM)

Master data is the core data which underlies business objects such as customers, locations or products. Master data management collects data from different systems to create a well-defined single master record for all departments. This helps ensure that everyone in the organization utilizes the same data and eliminates inconsistencies and errors.

Master data management and information management are closely related since MDM provides the foundation for IM initiatives. Without effective MDM, the underlying data may be inconsistent, inaccurate or unreliable, reducing the successful implementation of information management.

For example, an organization has multiple departments responsible for managing customer data sets, such as contact information, purchase history, and preferences. Each department uses its data management system to store and maintain this data, resulting in redundant, inconsistent, and inaccurate customer data across the organization.

Real-life MDM examples

Holiday Inn Club Vacations (HICV) is a travel/hospitality company that had fragmented customer records across multiple systems, hindering personalization. It implemented a cloud MDM tool with governance and quality tools, resulting in:

Consolidated data from 7+ systems and unified 350K+ member profiles in ~4 months,
A 360° view of every customer, enabling targeted personalization and loyalty initiatives,
An automated, cloud-native MDM/Governance solution for ongoing data quality and agility.²

3. Business intelligence (BI) and analytics

Business intelligence (BI) and analytics involve using data to gain insights into business operations for data-driven decision making. BI and analytics include collecting, analyzing, and visualizing data from multiple sources to identify trends, patterns, and opportunities.

Both BI and Analytics require effective information management practices to be successful. Information management guarantees that data used for BI and analytics are accurate and reliable.

For instance, an organization can apply BI and analytics to data from CRM, , ERP, and SCM systems by centralizing it in a data warehouse using information management systems. This allows the organization to create dashboards, reports, and visualizations that highlight key insights and trends.

Real-life BI examples

Walmart’s finance department struggled with “trillions of records” and siloed reports, making analysis slow and difficult. To solve this, they implemented a business intelligence tool and created a single, shared data model for all finance systems. This way, Walmart could:

Centralize financial data into a common semantic model, giving all analysts and managers easy access,
Eliminate manual reporting work and reconciliations, drastically cutting analysts’ data-gathering time,
Provide executives and teams a single source of truth, enabling faster, drill-down analytics.³

4. Content management

Content management involves managing unstructured data such as documents, images, and videos. Content management ensures easy access, search and usage for organizations’ content by organizing and storing content and providing search and retrieval capabilities.

Content is a key component of an organization’s information asset. Without effective content management, information assets would be inefficient.

For example, an organization can benefit from content management to categorize its content based on document type, department, or project. This way, data becomes easier to search, use and manage.

Real-life content management examples

An insurer overhauled its claims and policy document management by deploying a content management tool. It centralized all policy and claims documents in one system, automating routing and approvals. As a result, the insurer could:

New policy submission time slashed from 2 days to 20 minutes, yielding ~$1.5M/year in savings,
Centralized document access for 1,500 users (200 locations), improving claim handling and accuracy.⁴

5. Information security

Information security protects sensitive data from unauthorized access, disclosure, and destruction. This includes implementing security controls such as encryption, access controls, and monitoring.

Information security and information management are closely related concepts since they are both about protecting and managing an organization’s information assets. In this respect, information security measures are critical components of information management.

Suppose a firm aims to use its sensitive data stored in a database (e.g. personal, financial, and confidential business information) while protecting it from unauthorized access, use, and disclosure. The company can implement:

Access controls to ensure that only authorized users have access to the data.
Encryption to prevent data breaches,
Information management to standardize data formats and establish clear data ownership and stewardship.

Real-life information security examples

Cal Poly Pomona’s sprawling campus (84,000 devices, 100+ buildings) overwhelmed its old alert system. It deployed an information security tool to centralize logs and threat intelligence, ensuring:

Centralized logs from ~84,000 devices into QRadar, narrowing alerts to ~20–40 actionable items per day
Provided real-time intrusion detection so most threats are caught and handled quickly (e.g. identifying and fixing compromised endpoints in a week).⁵

6. Enterprise search

Enterprise search helps employees find data from one or multiple databases in a single search query. The searched data can be in any format, from anywhere inside the company, such as:

In databases
Document management systems
E-mail servers
Physical documents

Enterprise search engines use search technologies such as natural language processing, machine learning, and semantic analysis to improve search accuracy and relevance.

Information management practices are essential to ensure the effectiveness of enterprise search. Information management provides relevant information in the search index so that search results are accurate and complete.

For example, imagine an organization storing a large amount of unstructured data across multiple repositories, such as file shares, email systems, and SharePoint sites. The data includes documents, emails, and other files related to different projects, departments, and teams. If users try to search through these multiple repositories, they would have wasted time and effort.

But with effective information management, the organization can ensure easy, accurate and complete search in its enterprise search system. As a result, they can boost productivity, efficiency, and better decision-making.

Real-life enterprise search examples

Dexcom’s research team had thousands of reports scattered in SharePoint, making it nearly impossible for the company’s 20+ teams to find insights. They launched a searchable enterprise knowledge repository. This way, Dexcom democratized its research by enabling self-service search, dramatically improving knowledge sharing and decision-making. Some results achieved include:

Created a Bloomfire-powered “Insights Vault” to centralize market research data across the company.
90% of users expressed satisfaction with the platform’s search; 84% found it easy to locate needed information
81% of users said the repository helped them better understand customers, indicating more data-driven insights company-wide.⁶