What is Data Management?

Trying to look into the field of data management, one stumbles across quite a few different ideas surrounding the question what data management is exactly, how to practice it in the most efficient way and how to maximize its benefits. Also: where does data management start and where does it end?


While you probably won’t find two organizations that deal with and manage data in the exact same way, there actually is some basic consensus regarding the question what should be kept in mind when managing data.

The basics of data management

Data management became a necessity when information and data was discovered as a production factor. With the beginning of the information society sometime during the 70s information became an asset for companies. Nowadays data is considered producible and can be gathered at every step along the user/customer journey. As simply producing more and more data doesn’t really benefit anybody, data management has the task of ensuring the usage of data is balanced from an ethical and economical point of view.


All things considered, data management shares lots of its main characteristics with strategic communication management: it includes the planning, modelling, monitoring and regulating of the information flow and the communication of information inside of organizations with the goal to reach strategic goals.


To reach these goals, the creation of a goal oriented way to process and communicate data is just as important as ensuring that teams whithin the organization can communicate and gather appropriate information from the existing data pool as freely as possible.

The four main responsibilities of data management

1. Assessing the need for information: all data and information necessary to reach a companies goals need to be identified and consolidated. Also the exact content of available information as well as its presentation and the time and context in which the information is needed should be precisely defined.


2. Planning the information offered: every available internal and external source of information should be identified and summarized in some kind of ‘information portfolio’.


3. Making needed information available: access rights need to be assured and considered from both a technical and legal perspective. Information has to be processed and prepared to be useful.


4. Organization of the supply of information: appropriate information needs to be matched with the according divisions and teams. Responsibilities for the maintenance of existing data should be allocated.






Being in control of your business data

There are certain aspects to data management that put organizations in control of their available customer or business data. Proper data management provides access to any kind of business data when and where it is needed and regardless of where it resides. It should also be enable teams to avoid ambiguity, conflicts and miscommunication. At the same time data management practices should make sure everybody conforms to organizational best practices regarding access to as well as storage, backup and retirement of data.


As data management is an end-to-end life-cycle starting at the creation of data and ending at its retirement, it should develop and execute architectures, policies, practices and procedures that properly manage the full data life-cycle needs of an enterprise.





Enhancing data security with good management

Since data management works with internal as well as external streams of data to improve such things as sales and marketing, product classification and decision making, one big factor to consider is the safety of the used data. Not only should data be stored and distributed in the safest possible way, but everybody working with the provided information needs to adhere to certain guides and rules concerning the usage.




Data governance ensures that data is handled and stored in the most secure way


Data governance is the part of data management concerned with things such as compliance, privacy and security etc. Its core can be split into:


• Data Quality: has all data that’s needed to reach strategic goals been gathered in the first place? Is the gathered data up to date? Is it structured in such a way that it can be processed? Are the access rights distributed accordingly?


• Data maintenance: that gathered data needs to be cleaned up. Mistakes need to be identified and corrected or eliminated and the information needs to be updated regularly.


• Data compliance: makes sure that the usage of data adheres to the law and ethical and moral guidelines as well as standards and guides defined by the company.


While it is important to meet these standards–in order to not be sued–data governance entails other benefits as well: processes concerning the access to data are standardized and a clearly defined set of rules concerning the work with the gathered data makes adherence to laws and guidelines easier for employees. Its main goal is the enrichment of knowledge inside of companies and the perception of standards while working with data. As it’s a part of data management, data governance is an ongoing process as well.


All in all data management helps organizations to ensure everybody has the appropriate access rights, recognize and avoid risks while working with data and utilize potentials and opportunities as well as lowering the costs of storing and managing it. What exactly is the best way to do this? Unfortunately the one solution to all of these problems has not been found yet, but we are working on a solution that helps companies manage their data as well as their gained insights.




We’re actually working on improving insights management within companies by developing a platform to store and evaluate data collaboratively. You can get more information and updates here
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