Although human civilization has made massive developments in the course of its learning, a few basics still stick around. Learning a language and building a vocabulary form the foundation of elementary education for most of us. Building a vocabulary is a continuous life-long process that provides us with a reference for better communication. If we draw comparisons to the blooming data-analytics industry, we find several instances where a similar reference needs to be built for all data to be utilised better. This creates the need for metadata catalogues in the present data scenario.
What are metadata catalogues?
Data lakes are now an integral part of any data-driven organization. This collects structured, semi-structured and unstructured data in one place. Organization of this data is hence, vital for the smooth functioning of all operations in such firms. Metadata exists at the nexus of all data management processes. It provides information of the data and can improve the efficiency of all operations by :
1 – providing shorthand-representation and reference information for all data available in the environment.
2 – creating summaries of data, thus making working on particular instances of the same simpler.
3 – providing accurate and basic references to data at any level since it can be created and updated manually.
Organizations and data consumers use metadata to report their data holdings, create data requests, categorize and prioritize datasets and even analyse and discover data trends that would otherwise not be visible due to lack of organization.
Metadata can be classified into the following categories:
1.) Business metadata: This encompasses business rules pertaining to the organization and definitions of data files and business terms.
2.) Technical metadata: Technical metadata holds information about the various containers of data (structural metadata), description of the data content (descriptive metadata) and all facets of data management (administrative data).
Why create Metadata Catalogues?
Metadata is a result for growing demand of intelligent data solutions and applications. The power of metadata can be maximized by linking it to specific use-cases that maximize business value and create a fit-for-purpose user-experience. Data scientists and analysts require ‘fast’ data. Delays can be caused in procurement and pre-processing of data, due to a centralized IT infrastructure. Metadata catalogues can help them design a self-service automated process using different technologies and tools, which can pull data automatically as per the requirements specified.
Metadata management also aids in data security. You can easily define the accessibility and availability of datasets across multiple organizational hierarchies using metadata catalogues. Compliance with industry regulations can also be defined within the metadata, thus ensuring minimal violations. Besides security and ease of access, metadata catalogues can also link complex databases to various tech-nodes simpler. The boom in the IoT sector brings in a variety of complex and unstructured data to the lakes. Maintaining a catalogue for the same and using supporting algorithms can help one organize the data, process it into a convenient format and even analyse it in an effective and easier way.
Maintaining a catalogue can also help firm provide context to their data content. Searching from the vast pool of data using metadata is expected to give us contextual relevance along with the data. In addition to the various benefits mentioned above, metadata catalogues can also help in predictive and prescriptive analyses. Running forecasting algorithms on past data using the metadata tags, might improve the accuracy of the results. Dark Data defines the data locked away in silos across organizations. Scanning these data-repositories can create a metadata-catalogue that can in turn, be used to provide much greater transparency, explore more data domains, and understand relationships and sensitive data.
A few tips for creating a well-defined metadata catalogue
Metadata is as important as the data itself. To design it, one needs to invest a structured-thought-process. Before creating a metadata catalogue, it is important to think of the impactful decisions and questions that the organization would want to answer. Data for each of these questions will differ. Data for projecting inventory requirements will be very different from the data being used to analyse cross-selling opportunities among the various lines of business. Metadata catalogues if well-designed can make storing and pulling data easier for various needs.
The core attributes and sources of data should be enlisted in the most accurate and exhaustive manner. Forming a dictionary of data pools across enterprises will require a reliable index that can be followed by everyone. Since data changes and grows rapidly, one should expect regular updates being made to the catalogues. Being agile to the developments will help us utilize the data better. A lot of organizations have some resources who have the maximum expertise and knowledge about the data. Identifying the key experts can thus, help us put a well-structured metadata infrastructure in place.
Data brings a lot of questions along with it. The existing systems can use the data to answer these. However, it requires a lot of effort and time in organizing and maintaining data appropriately for the same. Metadata catalogues can help create a healthier relationship between the man and machine. If used properly, they can help one improve the quality of the data over a period of time. They can provide clarity on the usability of the data and help one design better algorithms for analyses. Having said that, metadata catalogues are vital to growing enterprises that operate on a data-driven framework.