“If you can’t measure it, you can’t improve it” is a quote often attributed to management consultant Peter Drucker. Although the quote’s exact origins might go back to Lord Kelvin, who stated the same thing in a more circuitous way, the statement’s sentiment is as simple as it gets. In order to effectively improve an organization, you must first be able to measure and quantify data within that organization. This is a good concept to keep in mind when working with enterprise information management (EIM).

Over the past few decades, EIM has evolved significantly. Although the foundations of EIM go back to before the 2000s, EIM really took off when businesses recognized the need for a more holistic approach to data management. Once organizations got inundated by big data, they knew they had to find a way to handle the massive amounts of data flowing through their systems. They had to integrate data from disparate sources while also making that data available to users throughout the company. Today, EIM emphasizes a unified approach to managing the information assets of a company. It breaks down information silos and fosters collaboration across departments. This comprehensive approach would be impossible without artificial intelligence (AI).

Guiding Principles

In his article Guiding Principles of Enterprise Information Management, Dr. David P. Marco, an internationally recognized expert in data management, claims organizations need a “foundation framework”, which is a defined approach to make complex concepts understandable, manageable, and implementable. “Without a framework, the various EIM component projects fail to leverage the possible collaboration and benefits that are required to build an efficient and agile data management organization. As a result, the organization will be left without enhanced capabilities for information creation, capture, distribution, and consumption,” Marco argues.

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Enterprise Information Management architecture

Establishing an Enterprise Information Management Foundation Framework

In his article EIM Framework Components and Dependencies, Michael F. Jennings lays the components of an Enterprise Data/Information Management Framework. It includes:

  • Data Governance & Data Stewardship
  • Information Architecture
  • Metadata Management
  • Information Security Management
  • Information Quality Management
  • Reference and Master Data Management
  • Data Warehousing and Business Intelligence
  • Structured Data Management
  • Unstructured Data Management

Artificial intelligence (AI) can help with each step of the above framework.

Data Governance & Data Stewardship

By automating repetitive tasks such as data classification, anomaly detection, and data cleansing, AI strengthens data governance and data stewardship. AI methods like machine learning and deep learning can identify and correct dataset errors, thereby reducing inconsistencies and inaccuracies. This will lead to more efficient data management processes and allow human resources to focus on their strategic tasks.

AI technologies are automating various aspects of information management, such as data classification, categorization, and retrieval. Machine learning algorithms enable systems to manage large volumes of data with minimal human intervention, reducing the need for manual tagging and filing. 

Dr. Marco’s YouTube “Why is AI Governance Important”

Data governance frameworks integrated with AI streamlines the data management processes as well as ensures an organization maintains high standards of data quality, security, and compliance. As AI technologies continue to evolve, their role in enhancing data governance will likely expand. This will help organizations leverage their data assets more effectively.

Information Architecture

AI has the potential to revolutionize Information architecture (IA), which is, according to Jennings, the “domain responsible for the master blueprints that control semantic and physical integration of data assets across the enterprise, defining information products and the information supply chain (e.g., data models, enterprise models, data integration models, etc.).”

AI can automate the categorization of content by analyzing user behavior and context. This allows for a more intuitive organization of information, ensuring that users will be able to find what they need quickly and efficiently. By recognizing patterns in how users interact with content, AI can help designers place information where users expect it to be, which could improve the overall user experience.

AI can create personalized user journeys that adapt in real-time to a user’s current and past interactions. This ensures each user’s experience is unique and optimized for his or her individual needs, something which should enhance customer engagement and user satisfaction.

Metadata Management

For Dr. Marco, Metadata is an “enterprise resource contributing directly to improved information quality, reference & master data, and enterprise capabilities for data usage.” It is also “the fabric that connects all of the other components of EIM.” When AI is added to the metadata mix, it can optimize data handling and automate manual tasks. This should improve data quality considerably. AI can automate the creation, classification, and management of metadata as well as streamline workflows and reduce manual intervention. With AI, massive volumes of data can be handled more efficiently, allowing it to enhance metadata quality and integration throughout the data lifecycle.

AI can quickly detect anomalies as well as help with the deduplication and standardization of metadata. This will ensure the high data integrity that is essential for compliance and effective decision-making. By utilizing advanced analytics and natural language processing, AI can also provide contextual insights that enhance the usability of metadata. This should facilitate better data governance and operational efficiency.

Information Security Management

According to Jennings, “Information Security is responsible for protecting the privacy, confidentiality and competitive advantage of information assets.” AI augments data security by analyzing information access patterns and identifying suspicious behavior, such as unauthorized access attempts. When it finds security breaches, AI can inform all necessary partners of the issue. Machine learning-based systems can create a robust defense against data breaches by detecting cyber security threats in real-time. This proactive security monitoring helps safeguard sensitive corporation information and data while maintaining trust levels with stakeholders.

Information Quality Management

“Information quality defines and manages the health of information for an intended use, measured by key indicators such as accuracy, consistency, freshness and completeness for specific purposes,” says Jennings. AI systems can continuously monitor data flows and access patterns to ensure compliance with regulations. It can detect anomalies that indicate potential violations of data policies, triggering alerts and recommendations for corrective actions.

Information quality management boils down to four things — data accuracy, data consistency, data freshness, and data completeness. AI algorithms can be taught to spot data error patterns that often go unnoticed. Utilizing predictive analytics to study past data patterns, AI can forecast missing or unknown values in new datasets. This should help improve data accuracy. By analyzing trends and correlations, AI can fill in any gaps in data, ensuring more reliable datasets for analysis.

AI can help keep data consistent by transforming it into a common format. This process establishes data uniformity, which is crucial for effective data modeling, data analysis, and corporate decision-making. AI systems can continuously monitor data inputs and flag inconsistencies as they arise. The models must be built on up-to-date data to ensure accuracy.

AI can expedite the data update process, ensuring datasets contain and reflect the most recent information available to the company. This is particularly important in dynamic environments as data can quickly go out-of-date, grow stale, and then produce meaningless results. As for the completeness of data, AI can analyze existing data to predict any missing values in the data. This is particularly useful when data collection is incomplete.

Reference and Master Data Management

“Reference and master data management consolidates the capture, storage, synchronization, and usage of core business information across the enterprise,” says Jennings. Besides the automated data cleansing, anomaly detection, data standardization, and data normalization mentioned above, AI can help in master data management (MDM) by predicting missing or unknown values in a master dataset. By filling in gaps in the data, AI can improve the integrity and accuracy of MDM initiatives.

AI can automate the data relationship discovery process between different domains of master data. AI can map out how data moves across an enterprise, from source to application to model. This lineage information is crucial for understanding data provenance as well as ensuring full regulatory compliance. By uncovering these hidden connections, organizations can optimize their end-to-end business processes. This should help them gain a more comprehensive view of their data assets.

Data Warehousing and Business Intelligence

“DW/BI/analytics are responsible for the management of the data, technologies and resources required to support business intelligence, providing the business with answers to ‘any question’ ‘any time’ across all data subject areas through a secure environment,” says Jennings. From automating tedious tasks like data collection, data cleansing, and data preparation, to enhancing data analysis in real-time, AI can turn data into actionable intelligence. Utilizing natural language processing (NLP), a subfield of AI that enables computers to understand and manipulate human language, BI tools can interact with users utilizing normal, everyday language. BI and data visualization allows users to view their data in the way most people prefer to get their information — visually.

One of the strengths of ML and deep learning is that they can glean insights from massive amounts of data. AI modeling utilizes massive data sets of structured, unstructured, and/or semi-structured data. Cloud-based AI systems can use the power of scalability to reveal data insights in real-time. AI can uncover patterns and insights that normally go unnoticed by human users, but can be extremely valuable to a business.

AI facilitates advanced analytics through augmented data preparation and automated analysis processes. By monitoring massive datasets for trends, anomalies, and inconsistencies, AI provides insights that support better decision-making. This capability helps organizations identify growth opportunities and optimize their processes.

Structured and Unstructured Data Management

The oldest component of EIM, Jennings believes, is structured data management, which “is responsible for the management of physical database assets throughout the data lifecycle.” Similarly, unstructured data management “is responsible for the identification and management of content found in documents, images, video, and web pages,” claims Jennings.

Because of the massive deluge of data most businesses are facing, AI has become an indispensable part of data management. It can classify and tag unstructured data, such as documents, emails, images, videos, and other multimedia content.

AI-powered search algorithms can analyze both structured and unstructured data to provide users with contextually relevant search results. By understanding user queries in natural language, AI can improve the accuracy and efficiency of information retrieval.

AI can enhance data quality across an enterprise by automating data cleansing processes. In structured data, AI identifies and corrects errors, uncovers duplicates, and reveals data inconsistencies. With unstructured data, AI ensures the data complies with defined data quality standards. Techniques like machine learning can be employed to profile data and detect anomalies, ensuring that both types of data are reliable and usable.

AI aids in the integration of diverse data sources, ensuring that structured and unstructured data can work together seamlessly. By automating schema matching and data mapping, AI simplifies the process of creating a unified view of enterprise data, which is essential for effective decision-making.

Conclusion

In their book, Enterprise Information Management: The Next Generation of Enterprise Software, Barrenechea and Jenkins claim, “Information is only useful to us if we can understand it, and to understand it, we have to have access to it, put it in context, and compare it to what we already know. The way we make use of it is up to us, but the more information we have on which to base a decision, the better the decision.” It’s back to Drucker’s “If you can’t measure it, you can’t improve it.” Measurement becomes a part of an ongoing process of continuous data improvement.

AI is significantly transforming Enterprise Information Management by enhancing the efficiency, accuracy, and capabilities of data handling across an organization. It can help with each step of the EIM framework, including with governance and data stewardship, information architecture, metadata management, information security management, information quality management, reference and master data management, data warehousing and BI, as well as structured and unstructured data management. Overall, it is reshaping EIM by helping businesses to make more informed decision while enhancing operational efficiency.