Top 5 Data Management Principles to Consider

Businesses today need to get ahead of the game for the purpose of growth and sustainability. But one very important element that is needed to be able to do that is data. Decisions that are data-driven increase the chances of being in favor of the company. But, in today’s date, data is available in abundance. This raw data needs to be cleaned, sorted, analyzed and then put to use. This process is imperative for the drafting of master data. According to data governance, there are certain regulations that have control over the usability of data to prevent it from being misused. And the foundation of all that is data management. Data management is the base on which you will build the house of your data, which will be used for the advantage of your organization.

After data collection preparation comes the management of your data. Management of your data includes obtaining, retaining, and accumulating, securing and processing it to make it useful as well as reliable for the users. It becomes imperative to organize and categorize this big data. It is a resilient process that makes the data consumable. There are a few data management guidelines that can be used and implemented by the users.

Below are 5 basic principles of data management

  1. Develop a strong strategy to manage the data

To get your data management process off the ground, the first thing you need to do is design a framework. This framework will act as data management guidelines. This will allow systems and data integration. All of this will result in the formation of a strategy. Now, this strategy has to be really sturdy and personalized to be effective. Since the data is ever-growing and constantly expanding, the strategy has to be flexible. Effective data management will require your strategy to be able to answer various what, how, when, and where questions. The reliability and sustainability of the data cannot be achieved by cutting corners. There has to be a well-built blueprint to show the roadway of integration and advancement.

  1. Possessing, Controlling, and conserving the data

After putting in place the strategy, the next thing to do is identify which data is relevant and which is not. The irrelevant data needs to be discarded, while the relevant and useful data needs to be owned. Possessing the data means getting its ownership, which means you legally have rights over the said data elements or datasets. Before consuming any data, it is necessary to first have its possession. Then comes, controlling the owned data. Now that you legally have the data, you need to be able to have control over it. The regulatory norms in data controls are taken care of by data governance. Finally, you need to conserve, preserve, and store this data. Why? Because the data will have no value if it is not safe and lacks integrity. It has to be clear and ready for progressive use.

  1. Make effective use of Metadata

The first thing to understand here is, what exactly is metadata? The data that describes another set of data is called metadata. Like, a line or a paragraph which will tell you the basics of deep data. Metadata really comes handy when data retrieval or a quick review of data is needed. During your school time, you must have used those stickers on your books that had information like your name, class, division, subject, and school. That allowed you or anybody else to easily understand what was in that particular book. That is a type of metadata. There are different types of metadata, all providing the key information of a lake of data, but all designed differently, depending upon the type of data that needs to be described. Metadata helps in the prevention of the purchase of redundant data. Developing a catalog that contains all the data your organization has, will result in easily pulling out information when needed.

  1. Total Quality Management of Data

You cannot expect a tailor to stitch a suit for you if you only provide him with whatever cloth you have and no measurements. He will require a suitable material of cloth, your accurate measurements, and your expectations with references. The same is the case with data. For actually getting the result you wish to achieve, you need to set a benchmark and have quality data that can be used for effective decision making. Quality data can be achieved by following the data and analytics principles. To maintain the quality, regular audits and tests are to be done. A thorough screening process will help in managing the total quality of data. Any data won’t be useful, specific, relevant data will be.

  1. Make data itself a principle

When you make data a principle in itself, it will lay the foundation for acquiring your vision from the ground up. Integrate data in as many processes as possible, it will only enhance the quality of the process. Like the first principle stated, that a flexible strategy will make it simpler to adapt and have access to the information when required. This will prove to get the ball rolling in the court of the stakeholders. After all, those are the ones who are instrumental in the overall development and implementation of data in processes. Use the data correctly. You can have all the data in the world, but it will be of no use if you don’t even know how, when, and where to use it. Make use of data in such a way that it creates value for everyone using it.


These principles of data management can be sewn into any organization that wants to properly manage their data. Data management is as important as data itself. So, organizations need to understand the stance at which their firm is and the right momentum to integrate the basic principles of data management. To prevent from drowning in the oceans of data, build streams of data management systems.

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