What is Metadata?
Metadata is the term used to refer to data that describes other data. In a more concrete explanation, Metadata are the data that describe the structure of and some meaning about other data. Usually, Metadata have these three characteristics:
- They are data that is extremely structured and describes characteristics of other data, such as the content, quality, information, and other attributes.
- It has differentiations that will depend on the rules included in the applications to determine the internal structure of the data.
- It can be classified following different criteria such as their content, variability, utility, etc.
The importance of Metadata
With the rise of Big Data, IoT and Cloud Computing in general, metadata has acquired extreme importance. Good management of metadata allows working easily with the vast amounts of data growing exponentially and most of the time Metadata hides the key to translate unstructured data into purpose-oriented and structured insights. However, it is indicated that few organisations really understand metadata, and fewer understand how to design and implement a metadata strategy.
According to a report published by IDC, which was sponsored by EMC, metadata is one of the fastest-growing sub-segments of enterprise data management. But there is still a significant gap between the growth of Big Data and the growth of Metadata. The result is that metadata isn’t keeping up with the rapid rate of Big Data projects. Without metadata, companies are losing out on analyzing and interpreting Big Data and the subsequent insight it offers to propel their business.
Metadata allows us to better understand data, as it includes the information required to determine for example what set of data exists for a particular geographical location, for example, or what set of data is relevant for a specific application. Metadata also allows us to create and maintain data consistency, which is super relevant considering many companies still have issues in just defining a common term such as ‘customer’.
Metadata management is vital for any business. There are numerous benefits of managing metadata on the data strategy.
Better search and analysis
Metadata helps to search and locate data. Good management of metadata also enables the analysis of the data journey, from the source, facilitating the documentation, transformation, analysis, and reporting.
Data standardization improves the quality of our data and the relevance of the resulting insights. With good metadata management in a centralised and integrated way, we can achieve a complete view of the data lifecycle and gain better control of the data integrity throughout the journey.
Consistency of data
Instead of having all loose data, with metadata you can have consistent labeling. Making it easy to navigate and ensure the consistency of data. Metadata are maintained so that they can be assessed by IT personnel and users. Metadata includes software programs about data and rules for organizing summaries that are easy to index and search. This ensures quality, consistency, and helps to better locate data.
Better data integration
Metadata is key for data integration. Having a shared repository for metadata for IT and Business we will be facilitating data governance and improving data integration.
Metadata management provides visibility and control and helps to keep track of changes. That’s especially valuable in complex environments.
Good management of metadata helps to protect critical business data in case of changes and enables compliance. Metadata Management is helping security and risk professionals be ahead of risk scenarios by classifying data according to risk and security needs.
Thanks to good metadata management the quality and integrity of our data will be increased, resulting in better and more reliable reports. With the use of Metadata, you can more easily understand the relationships between different types of data. This will make it easier to analyze, leading to better reports.
More agile development
Metadata can also help to squeeze the development time for certain applications. Having good metadata management will facilitate any future project based on our data.
Types of Metadata
These are the Metadata related to the form and structure of the dataset, the schema or the type of data. Technical Metadata is metadata describing technical aspects of IT systems, which designers and developers use to build and maintain them. Examples of technical metadata include descriptions of database tables, columns, sizes, data types, database key attributes and indices, and technical data transformation rules.
Operational Metadata is made up of operational reporting and statistics, such as logs, timestamps, transaction counts and volumes, or system performance and response time. Simply, it describes the events and processes that occur and the objects that are affected when you run an operational job.
Business Metadata captures information related to the meaning of the data to the end-user, making the data easier to find and understand. Business Metadata is metadata about the business terms, business processes, and business rules. Business metadata provides the semantic layer between a company’s systems (operational and business intelligence) and its business users. It provides users a roadmap for navigating all the data in the enterprise by documenting what information is available and, when accessed, provides a context for interpreting the data. It is invaluable for making sound business decisions.
Future trends in Metadata
Based on Gartner’s latest report on the Metadata market one of the levers leading the growth of the Metadata market is the need for more innovative solutions offering automation capabilities (machine learning and semantic enrichment). The global growth of Data projects in organizations increased the need for better data access and governance, and to that end, good Metadata management is key.
Gartner points out the following two big trends in the evolution of Metadata:
- Extensive use of Metadata: from database technologies to data integration tools, the use of Metadata is being now used in automation of database optimization, data preparation flows, automatic detection and implementation of data governance rules, etc.
- Adoption of algorithms: the increase of algorithm use will require metadata vendors to be involved in the model development, providing insights about the data that might be relevant to ensure that algorithms cannot be biased.