With the emergence of distributed architectures such as Big Data and Cloud, which enabled siloed systems and data, metadata management is now essential for managing the information assets in a company.
We can find myriad definitions of metadata, but these can often refer to different use cases and, as such, can sometimes be incomplete and difficult to relate to. Here we give a comprehensive overview of metadata management and, more importantly, show how your business can benefit from managing the metadata.
#What is metadata
Metadata is structured reference data that helps to sort and identify attributes of the data it describes. It is also sometimes referred to as "data about data". In simpler terms, metadata is essential information about data that makes it easier to find, use and reuse particular blocks of information. As the complexity of data increases, metadata gets new definitions. There are many distinct types of metadata that includes:
- Descriptive: helps describe and enable a particular asset's discoverability. For example, a streaming platform would have the video and metadata that explains it, such as title, genre, duration, list of performers, directors, and more.
- Administrative: the information about the administration of a particular resource, such as when it was created, updated, permissions, and more.
- Structural: contains information about data types that the dataset contains, versions, relations, and more.
However, in broader business terms, we can split the metadata into technical and business metadata.
For example, technical metadata can provide information on the format and structure of the data, such as data models, data lineage, and access permissions.
Business metadata, on the other hand, defines standard business terms such as table and column definitions, business rules, data sharing practices, and data quality rules.
#Why is metadata management important
Metadata management is vital because it allows you to bring further context to understand, aggregate, group, and sort data.
You can also solve many data quality problems by addressing metadata. Quality metadata makes everything you do more accessible, from internal communications to planning new applications to making better decisions.
Businesses today generate massive amounts of data and consume it at a high rate. Metadata management provides a clear and rich context to both scenarios. It decides what data to produce and consume while ensuring the data remains a valuable business asset.
More context in the data sets
Metadata is the data that describes and gives context to data sets. Context is a key to data discoverability, even for the most seemingly straightforward business terms.
Let's take an example of how different teams define the concept of "customer" or perceive this related data.
Sales: They will likely use the term customer as the company as a whole rather than people data. As a result, the sales team is less concerned with where the customer data is stored and more about whether they can access it through their dashboard to move prospects through the funnel.
IT: For them, the term customer may represent new customers onboarded for the professional services organization. It can also represent customers who haven't renewed their maintenance contract for customer service. Considering IT is mainly concerned with the technical aspects of managing metadata.
Compliance: This team views customers and their related data strictly on a people level and is concerned with who has access to customer data and how it is stored and managed.
Metadata management is a critical element of data governance that allows users to mine value from the data they have at their disposal. It also enables teams to classify data sets and red flag sensitive or confidential information to avoid breaching existing privacy regulations.
The increased importance of higher-quality data
As more organizations capitalize on data, using and structuring data becomes as important as keeping up with the competition. Businesses that don't take advantage of data risk staying behind. The better the data quality, the more you can take out of it.
For example, technologies such as AI and automation have enormous potential, but their success depends on data quality. For example, machine learning alone requires large volumes of accurate data.
High-quality data is becoming so integral to business operations that, rather than treating it as separate from other functions, many industry leaders integrate it into everything they do.
The growing complexity of data
The main problem is that while data is becoming more complex to understand, organizations are demanding broader use in many business scenarios.
Satisfactory thick pie charts and upwards trending graphs that used to adorn every corporate presentation are no longer enough. Instead, an executive of today needs solid proof that their reaction has a positive, quantifiable effect.
#Use cases for metadata management
A business glossary is a list of business terms and their definitions used by companies to use the same terminology across the organization. It is an integral part of data governance. Its job is to ensure that everyone in the company speaks the same language.
For example, one department may use the term "customer" to refer to another company, while another may use it for an individual. Additionally, marketing and sales processes may have the definitions of a Lead, User, MQL, SQL, SAL, and similar, and having these precise, can be crucial for inter-departmental alignment.
With a business glossary in place, you can avoid those discrepancies by providing uniform definitions for every business term company-wide.
A business glossary relies on metadata that assigns meaning or semantic contents to data. In this way, a business glossary is a product of data governance initiatives.
A business glossary that lists relationships between acronyms, terms, approved standards, and synonyms maps this data to a central data catalog so users can easily find it.
Data policies and rules
There are several advantages of using business policies and rules as metadata.
- Maximum flexibility
By documenting business rules as metadata elements, companies can quickly change rules as policies, guidelines, strategies, and environments change. Software component code doesn't need to change. The only thing that changes is the content of the business rule tables.
- Reduced system maintenance
When you don't have to change software component code every time you change a business rule, you automatically reduce the system maintenance. You don't need to re-code, re-compile, re-integrate, and re-implement components. Instead, you change entities in a logical model, automatically generating modified tables in a database entity.
- Simplified system design
You can develop software components much more simpler when rule-based processing logic is limited to evaluating the content of one or more database tables.
- Rules can change without affecting implementation
With business rules modeled and implemented as metadata, changes in the walls have little impact on installed software components. Changes also have little effect on component design, development, and implementation. As a result, systems built from these components can reflect the most current business requirements.
Data profiling and quality
Data profiling is analyzing and summarizing data so you can better understand how your data is relevant and valuable. Data quality, on the other hand, refers to identifying errors within your data and correcting those errors so that your information is as accurate as possible.
Data profiling and data quality are inseparable because profiling is the first step in improving the quality of your data. Data profiling provides:
- Better data credibility
Data profiling software can help eliminate duplicates or anomalies when the data is analyzed. In addition, it identifies valuable data that could impact business choices and quality problems that persist within a company's system.
- Predictive decision making
You can use profiled data to prevent small mistakes from becoming big problems. It can also help you reveal possible outcomes for new scenarios. For example, data profiling helps create an accurate MRI of a company's health to inform decision-makers better.
- Proactive crisis management
Data profiling can help you identify and address problems before they appear.
Metadata management is a critical element of data governance because it deals with many core issues that governance initiatives are designed to address. These include a lack of standardization, ambiguous data ownership, unidentified data quality rules, data security, compliance concerns, communication issues, and more.
If managed correctly, metadata can provide solutions teams can employ to tackle these issues.
For example, you can use metadata to mitigate risks because it allows businesses to categorize data so users can determine whether the information is sensitive or confidential.
Compliance in the data government context refers to the measure that data governance teams take to ensure a company follows all relevant data privacy regulations.
Every organization that deals with user or customer information must guarantee that sensitive data is protected.
However, storing the data in a secure location is just part of being compliant. Apart from protecting data from 3rd party threats, businesses must ensure that only people with adequate permissions can access it.
Compliance is, in many ways, a data management issue. It ensures that only correct metadata is collected and visible to the right users.
In most cases, the repository for collecting metadata is an integral part of a data warehouse. The data warehouse is defined based on its "data definitions, schema, views, hierarchies, locations, and content". This information becomes useful during business analytics, as it eliminates a lot of excessive labor associated with data explorations.
The benefit is even more significant when the analytics is conducted with big data, which is 80% unstructured data. If data management in such a complex scenario is not handled correctly, a company may lose a significant market share due to analytics errors.
This is why metadata management is critical for Business Intelligence (BI) or analytics with big data.
When data is spread across an organization in different data troves such as data warehouses, data lakes, or silos, nothing works like metadata to quickly search and access required data.
While some digital TV users are content with one service, most viewers browse multiple apps and services to find something to watch.
As a result, streaming guides that aggregate multiple streaming services have become increasingly popular. These guides help you find the content you want to watch without scrolling through each subscription.
Metadata management is vital to sharing content via these aggregated guides. In the context of streaming guides, metadata management is the appropriate allocation and organization of entertainment metadata to allow users to find and identify content quickly and conveniently.
Good metadata practices are also beneficial for streaming platforms, as they improve the stability of the product for subscribers.
For example, as Telenor expanded its streaming platform, the company needed to scale up the database to continue delivering a high-quality digital TV experience. Ultimately, the Telenor team chose Hygraph to provide the same power by adding an abstraction layer for content, native localization, and schema builder.
#How Hygraph fits as a metadata management platform
Hygraph allows you to pull data from distributed data sources, provides the UI for easy metadata editing, and provides a single GraphQL API for all the data to be used on any frontend.
A Federated Content Platform such as Hygraph acts as an enabler for content editors to add, edit and review the metadata regardless of where you keep the other information as long as it has a REST or GraphQL API to pull this information to Hygraph. From there, you can distribute it to practically any frontend.
Metadata management helps businesses maintain a competitive edge by making more informed decisions based on relevant customer data. In addition, it improves collaboration between teams and departments while enabling users to access high-quality and trusted data to deliver accurate insights.