As you can witness, data is the most valuable asset of any organization in this modern age. It is being created at a really rapid pace and also the key for decision making processes in a lot of businesses, which can help them boost the value.
It is also undeniable that big data is an invaluable source of insights. It enables companies to optimize everything they are working on, from targeting the right customers to operating supply chain processes. Being capable of identifying, collecting, processing and analyzing data to capture meaningful insights has become the most important thing to set up agile companies whose aim is to survive in the business era driven by data nowadays.
Moreover, dealing with big data also leads to some challenges for the first time. Most companies still do not have enough skills and knowledge in order to construct a strong business data culture, build a data plan and consider their approach to information and knowledge management. It is highly recommended to know the right data management principles on which your company can identify what you are in need to approach big data.
Craft a data management strategy
Enterprise data management means how a company defines, integrates as well as retrieves their data in order to gain accurate and consistent insights. As big data is keeping on expanding and evolving, identifying and making use of data gets more and more difficult and data management is a really difficult task.
The first data management principle is to take a strategic approach to data management, which is important for the development of a company’s data initiatives.
An ideal data management strategy asks for creating customized data management system. More precisely, a data management system is a framework which effectively integrates all the software, hardware, workflows, and culture defining data management in a company. As a result, companies need to identify which processes, resources and technologies are the most suitable for their data strategy.
What is more, in order to generate relevant results, data management asks the companies to pay attention to the data precision, granularity and meaning. Your data strategy should include such details as which data your company should use, which data sources should be considered and which are not, how and where to store and secure data, how to make sure the quality of data and how to manage the documentation related to the data management system.
Define ownership and stewardship
Data management often comes with lots of data sources and various organizations. Actually, data analytics has become so valuable when a wide variety of data sources are integrated together. Data integration enables companies to capture meaningful insights and generate profit.
Simultaneously, the integration of different sources would lead to a lot of challenges to estimate the value of data. actually, data does not come with any intrinsic value yet the insights generated from the data can be worth huge sums if they are combined with other sources.
In this case, companies should remember that good data management will require defining who owns the data and who can access it. If the data ownership is not clear, important data governance problems will evolve. Ownership means the legal rights and responsibilities. It demands on full managerial and financial control over data, such as the right to remove it when it is unused or too costly to keep.
Moreover, ownership is often assigned to the organization who commissioned the data, instead of the company gathering it. Nevertheless, proprietary rights may relate to one particular data item along with a merged data set or a value added dataset. Similarly, when company owns a dataset while other companies own the data, data governance would become a complicated task.
If you want to promote a collaborative approach to data management and avoid creating knowledge silos, you should be sure that ownership and stewardship are clearly identified for any specific project and staff.
In order to boost data access, data should be stored in the right system, such as a data lake or a data warehouse, according to your requirements. Moreover, companies should make sure each dataset will be documented properly and controlled so as to assist employees in identifying, controlling and using data in an efficient manner.
That is the reason why we need metadata. Metadata is the GPS system which enables your staff to navigate easily through data and take control over any modification in the data bases.
It is highly advised to use the metadata to make sure everything is well managed. Metadata is all the data that relates to your data. It will keep track of how data is gathered, verified, reported and analyzed. It would provide the users the insights regarding content, characteristics and use of every data while offering information about the content and accessibility of data.
To make sure that metadata is used properly, each company should build business procedures and boost a careful approach to metadata. Identifying and documenting your datasets is rather effective to make sure you are not gathering copies of the old data.
Make sure data quality
Data is now driving the decision making processes of companies. Thus, it is truly important to make sure that decision makers could rely on the data they are working with. Setting up data quality standards can help make sure datasets could satisfy the needs of those approaching them.
Data quality standards will set up the minimum demands that data must have. Various projects may come with various standards depending on the goal they want to achieve. Data quality entails the need of being defined consistently across the whole company. Thus, it is highly recommended to define some basic standards that can be applied to everyone working in the company and test data against those standards regularly. One tip to make sure the right level of data quality is to manage the data life cycle in a careful manner.