In connection with the automation of business processes and the use of information technologies for an ever growing number of activities, data are becoming one of the most valuable assets that companies possess. The ability to manage data and use information contained in data is directly related to a company's ability to perform its business activities.

In order to use data effectively, it is necessary to create structures, rules and processes similar to those used for administration of other types of assets - Data Management, including, among other things, the following areas:

Data Architecture Management

If we want to manage assets, we must know the demands placed on such assets' structure. It is necessary to ensure the collection of requirements for the data architecture, creation of a general data architecture and maintenance of the data architecture so that it is always up to date.

Data Quality Management

Quality has the greatest impact on the value of data and information. Therefore it is necessary to find a way to measure quality, regularly evaluate it and set up corrective mechanisms to improve it.

Metadata Management

In order to manage data, we must be able to describe the type of data we have and the structure thereof. Metadata serve this purpose. If we have well-described systems at the metadata level, we are able to effectively perform an impact analysis, which gives the organisation the possibility to simply estimate how expensive potential system modifications will be and makes it possible to analyse the data throughput of the systems from their acquisition up to financial reports.

Master Data Management

There is a group of data for which uniform maintenance throughout the entire organisation is very important (organisational structure, product catalogue, various types of codebooks, etc.). This often involves structures that are handled individually by each application. Therefore, each change is organisationally demanding and can lead to numerous errors. It is necessary to ensure that changes are the same and synchronised. The introduction of uniform administration and automatic distribution of these data substantially reduces the number of errors and makes it possible to define a uniform "truth".

Data Development Management

It is important to approach the development of individual applications throughout the company from the standpoint of a uniform conceptual model, so that the same approaches are used for designing physical models in order for test data to be sorted and degraded from production using a uniform method.

Data Security Management

The more complicated an organisation's infrastructure, the more difficult it is to maintain and manage access to data in order to ensure that such access corresponds to the security classification of the given data and the roles of individual users. It is necessary to ensure the definition of access rules and adherence thereto, other rules for security auditing and processes for resolving security incidents.