What are successful enterprises doing for the governance of their information assets?

Many companies are evolving towards the implementation of innovative information management solutions, such as new business intelligence models or automated data services. However, while changing the way they capitalize their information, they are facing new challenges in looking for their approaches to be successful and pursuing the expected return on this asset.

The first step of these companies is to     recognize 4 dimensions in the implementation of their business strategy: Human resources, processes, technology and information. Each requires special handling and techniques. Proper handling is vital to achieve strategic objectives.

The challenges faced by these companies to leverage their information are related to several issues of the existing information culture. Today's most relevant challenges include:

• It is not clear enough, who is responsible for data in the organization: Is the systems area responsible for it? Are the business areas responsible for it? Is the management responsible for it?
• How can I ensure that the same meaning of data is understood by everyone?
• How can I build trust on data I am using?
• How can I ensure that the data we use in different areas is consistent?
• How can I ensure that the quality of data is kept over time?
• Who defines the quality of data?
  

We’ve found that one of the main factors that enable the use of information is the ability of organizations to define and communicate clear distinctions among stakeholders, and these must be reflected in the implemented solutions.

The most effective approach to communicate these distinctions to the business, technology, and management areas is to define the governance elements of the most relevant information issues.

We use here the government concept in its proactive, more than administrative meaning, to show that companies which have succeeded in the profitable management of their information have the following things in common: design practices, prediction, and information’s behavior follow up, addressed from different perspectives including, quality, data relationships model, semantics or meaning of data, processes data goes through (for example, integration, standardization, life-cycle, etc.)

On the other hand, we have today several methodologies and tools that have evolved to support the actions required for these kind of initiatives. Their usage, in the framework of an efficient data governance model, enables organizations to use information in profitable ways and achieve outstanding results, even as a competitive advantage.

Today’s known practices to assure information assets as a strategic value-generating pillar include:

• The establishment of a data – or information – governance model that addresses the management and business areas requirements with specific services.
• The creation of organization's relevant topics competence centers such as, data integrity, data quality, data modeling, information exploitation, non-structured information management, etc.
• Formalization and proper treatment of information's lifecycle.

We can see progress on these concepts in some industries, for example in the banking and in the financial industry overall.

This particular industry, is trying very hard to integrate customers’ data in order to implement the concept of a sole database (Data Hub) that enables a business area to have full visibility of customers’ behavior in other areas of the same organization.

Other solutions that have been implemented are being used to monitor the quality and behavior of information at all times, to comply with regulations and to ensure the profitability of this information.

These kinds of efforts are streamlining the business actions of companies in this industry, to the extent that some institutions are using these practices as their competitive distinctive.

Other interesting example is the public sector. Today this sector is pursuing a radical change in the orientation of its services, going from an agencies-centered model to a citizens-centered model.

A fundamental element to become citizen-oriented is a comprehensive understanding of it. For this purpose, the different agencies are working hard to clean, integrate and classify the citizens' register they manage.

Some agencies have already established specific responsibilities to ensure the coherence, quality, and safety of the data they manage.

Many agencies are digitizing, organizing and classifying the files associated to the registers they manage, which allow them to highly improve the quality and timeliness of the services they are offering to the citizenship.

To address this reality, several practices have been developed that help organizations to speed their information management improvements. Among the most important we can list the following:

• Non-structured content management.
• Data integration solutions.
• Design and implementation of data or information governance models.
• Data quality models.
• Information lifecycle management (ILM).
• Data modeling and data classification techniques.
• Data export platforms (portals, data warehouses, and intelligent electronic libraries).

Several important benefits can be achieved:

• Extended visibility of relevant information for each area.
• Error and inconsistencies reduction caused by manual processes and paper use.
• Loss of information prevention.
• Fast access to information.
• Enhanced security on information access and modification.
• Reliability and accuracy in auditing and follow up processes.
• Information quality assurance over time and alignment to business processes.

In Intellego we have developed several specialized professional practices to help our customers achieve their strategic goals by leveraging their information assets.

These practices are organized as follows:

• Data governance.
• Master data administration.
• Data integration and standardization.
• Data quality.
• Data cleaning and migration.
• Information lifecycle.
• Non-structured content consumption.
• Integration, classification, and organization of non-structured content.
• Non-structured content exploitation.
• Non-structured content solutions.
• Portals.
• Knowledge management.
• Collaboration.

José Manuel Fernández
Content and Data Management Director
Intellego

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