There Is A Connection Between Metadata Management and Analytics: What Is It Called?
Metadata Management is a group of activities that manage data for better use and results. Thus, this practise is all about setting up strict roles, responsibilities, policies, and processes to make sure that data-driven information is available, accessible, sharable, and maintainable across an organisation so that it can be use and analyse in the best way possible in daily business. On the other hand, metadata Management is part of Data Governance, which is a well-plan, systematic process to put the right controls in place to manage and administer company data(data science in malaysia).
Metadata management
Metadata, according to Forrester Research, either describes business data or provides a context for business data and its processes, services, rules, and policies, which are all link to business data and its metadata. Besides, metadata can be of different types, such as technical metadata, business metadata, or operational metadata, each of which serves a different purpose. Metadata Management vs. Master Data Management: An Overview says that metadata does research (what, why, where, how, and when) on data. In that way, it helps the people who work with the data to understand what they do with it. All of these business requirements are important in the context of this article, because Business Analytics can’t happen without taking into account at least one of them. The article “Fundamentals of Metadata Management” talks about the main parts of metadata. People who work for any kind of business can benefit from Metadata Management because it helps them use enterprise BI. In order for enterprise BI or analytics to outperform the competition and find new opportunities, data definitions must be consistent, data relationships must be clear, and data lineage must be easy to follow. Metadata Management can do all three things.
With a Data Warehouse, you can do business analytics.
In a typical data warehouse, the place where metadata is store is very important. Definition: The data warehouse is define by how it stores and retrieves data as well as how it looks and how it is organise. This information is very important for business analytics because it is easy to find and doesn’t require a lot of extra work. Data Analytics and Metadata Management are two things that go hand in hand with each other. When the analytics are done with big data, which is about 80% unstructured data, the benefit is more clear. If the Data Management structure isn’t handle well in a situation like this, a business could lose a lot of market share because of bad analytics. This is because Metadata Management is very important for BI or analytics with big data. When “data categorization and organisation” is better, it takes less time to make a choice. It talks about why big data needs metadata management and how to use it. It says there are five good reasons why metadata is important for the success of big data analytics.
Data lake
You can’t use metadata to help you quickly find and get to the information you need when your company has a lot of different types of data troves, like a database or a lake. The article “Data Lakes and Big Data Analytics: The What, Why, and How of Data Lakes” gives a good reason why a data lake is a good place to store multi-structured data, but without Metadata Management, the solution isn’t done. In this video, Data Science Central explains why Metadata Management is so important for the success of big data analytics. Having metadata in large amounts of semi-structured or unstructured data can make it easier for people to quickly find what they need. It was always there in a data warehouse or a data lake, even if they didn’t use it. Also, Metadata Management can help you apply the same business rules to all of your company’s data. In other words, metadata makes important business information more clear, consistent, and easy to find. Social Media Today says that when there aren’t enough qualified data experts in the company, more and more businesses are hiring outside companies to handle big data analytics.
To keep your data safe, Metadata is very important to have.
Data Governance sets rules about who can see and use the data, who owns and controls the data, and who is in charge of being a Data Steward. This makes Data Management a completely “auditable and accountable” process. Metadata Management and Data Governance will work together to make sure that the right controls are in place for the company’s data. Metadata gives different descriptions, definitions, and tags to classify, categorise, and organise data. In this case, the article Data Management vs. Data Governance: Improving Organizational Data Strategy helps people understand the difference between Data Management and Data Governance. Without Metadata Management, businesses won’t be able to give “timely” and “trustworthy” information. The people, policies, and processes that make up Metadata Management are just as important as the data itself. The Data Virtualization Blog makes a strong case for the need for Metadata Management in data virtualization tools, and it makes it very clear why. The author of this post thinks that in order for a data virtualization application to work, it needs to be able to go from capturing data to making it available to other people. Besides that, a recent webinar helps people understand data dictionaries and data catalogues, as well as how metadata fills these data stores. The webinar also talks about how Data Governance is use with metadata. Source: data science course malaysia , data science in malaysia