The business of an organization is driven by the fuel called Data. Humongous amount of data needs to be handled diligently by an organization to extract meaningful insights. Data being extracted both internally and externally must go through some form of refining and governance to manage risks and reduce the cost. The effective and efficient use of data is enabled by ensuring a collection of standards, metrics, policies, and processes. This is collectively called Data Governance.
Data governance is an efficient strategy followed by an organization which not only leads to a better understanding of the data, but also, improves the quality of the organization’s data and the data management. It is advantageous as its centralized policies and systems help in reducing the IT costs, data standards allow better cross-functional decisions and communications. Data governance also makes compliance standards easier to maintain.
Let us talk about data dictionary and how it is used in data governance.
A data dictionary (also called Data Definition Matrix) is a collection of detailed information about the business data, like, standard data definitions of data elements, its meanings, and allowable values. A data dictionary acts more like a bridge between the business and the technical teams. Data dictionary is used as a tool that allows communication between business stakeholder requirements and technical teams.
Data dictionaries are of two types:
Active data dictionary and passive data dictionary. Active data dictionaries are created within databases and any change in the host database is directly reflected here too. Whereas, passive data dictionaries are created separately and enabled mainly for storing information. Passive data dictionary needs to be synched on a regular basis.
The key aspect of using a data dictionary is its efficient way of organizing and maintaining a comprehensive list of data which can be easily searched. Redundancy of the data is reduced, and data integrity is maintained across multiple databases, while establishing a relational information between databases. The business stakeholders are actively involved while creating data dictionaries, so there is a clear definition of all the terms. Though data dictionaries are more of a technical concept, it is best practice to involve the business stakeholders so the data system can be more specific and cater to the needs of the business, which serves the actual purpose of creating it.
A solid data dictionary provides the base for a sound data governance for an organization. A robust data governance model enables easy data accessibility, data confidence, data understanding, data activation and data delivery. Since a data dictionary acts as a centralized metadata, the efficient maintenance of data dictionary governs the efficiency of data governance.
Once the metadata repository that interacts with lot of other databases is efficiently managed as per the business stakeholders, the data efficiency is to its maximum, thus reducing the time and effort of the data analysts and business stakeholders in just organizing the data for drawing using insights. In the absence of a data dictionary, analysts end up spending way too much of their time on just collecting, cleaning, and organizing of data from various databases, leaving them very little time to analyze the data further. If this major step has been taken care of by using a data dictionary, it frees up a lot of time and thus organizations can focus on utilizing the data extensively. Business reports can also be created using data dictionaries. Databases can also be periodically upgraded smoothly since it is a built-in aspect of data dictionaries. Data documentation is also made super simple with data dictionaries as they are responsive to change. Since the data is so well organized, it makes it easily accessible to the everyone and hence more meaningful insights can be drawn faster.
Few of the cons of using a data dictionary are that it is very time consuming, cumbersome. Since the entire data is at located at one place, one must learn the entire metadata even for a small function that is restricted to his department. Data analysts who are having a tight delivery schedule would not find it suitable to read through the entire data dictionary documentation before approaching the data, nor to update the documentation. Though data dictionary is a must for organizations with huge data, it can be easily skipped for smaller organizations like start-ups. Overall, it serves as one of the best tools to achieve data governance.
There are few efficient data dictionary tools like Collibra and Alation in the market. These tools help in finding and collaborating huge volumes of data. The tools are built in such a way that it prioritizes the business needs and delivers trusted data, also adopting data governance by facilitating compliance and managing risks. The main key in data governance lies in organizing and understanding the data and the tools take care of the same.