Data at the current state can be defined in the following four dimensions (four Vs).

The volume of data is an important measure needed to design a big data system. This is an important factor that decides the investment an Enterprise has to make to cater to the present and future storage requirements.

Different types of data in an enterprise need different capacities to store, archive, and process. Petabyte storage systems are a very common in the industry today, which was almost impossible to reach a few decades ago.

This is another dimension of the data that decides the mobility of data. There exist varieties of data within organizations that fall under the following categories:

• Streaming data:
  • Real-time/near-real-time data
• Data at rest:
  • Immutable data
  • Mutable data

This dimension has some impact on the network architecture that Enterprise uses to consume and process data.

This dimension talks about the form and shape of the data. We can further classify this into the following categories:

• Streaming data:
  • On-wire data format (for example, JSON, MPEG, and Avro)
• Data At Rest:
  • Immutable data (for example, media files and customer invoices)
  • Mutable data (for example, customer details, product inventory, and employee data)
• Application data:
  • Configuration files, secrets, passwords, and so on

As an organization, it's very important to embrace very few technologies to reduce the variety of data. Having many different types of data poses a very big challenge to an Enterprise in terms of managing and consuming it all.

This dimension talks about the accuracy of the data. Without having a solid understanding of the guarantee that each system within an Enterprise provides to keep the data safe, available, and reliable, it becomes very difficult to understand the Analytics generated out of this data and to further generate insights.

Necessary auditing should be in place to make sure that the data that flows through the system passes all the quality checks and finally goes through the big data system.

Let's see how a typical big data system looks:

As you can see, many different types of applications are interacting with the big data system to store, process, and generate analytics.

Before we try to understand the importance of Metadata, let's try to understand what metadata is. Metadata is simply data about data. This sounds confusing as we are defining the definition in a recursive way.

In a typical big data system, we have these three levels of verticals:

• Applications writing data to a big data system
• Organizing data within the big data system
• Applications consuming data from the big data system

This brings up a few challenges as we are talking about millions (even billions) of data files/segments that are stored in the big data system. We should be able to correctly identify the ownership, usage of these data files across the Enterprise.

Let's take an example of a TV broadcasting company that owns a TV channel; it creates television shows and broadcasts it to all the target audience over wired cable networks, satellite networks, the internet, and so on. If we look carefully, the source of content is only one. But it's traveling through all possible mediums and finally reaching the user’s Location for viewing on TV, mobile phone, tablets, and so on.

Since the viewers are accessing this TV content on a variety of devices, the applications running on these devices can generate several messages to indicate various user actions and preferences, and send them back to the application server. This data is pretty huge and is stored in a big data system.

Depending on how the data is organized within the big data system, it's almost impossible for outside applications or peer applications to know about the different types of data being stored within the system. In order to make this process easier, we need to describe and define how data organization takes place within the big data system. This will help us better understand the data organization and access within the big data system.

Let's extend this example even further and say there is another application that reads from the big data system to understand the best times to advertise in a given TV series. This application should have a better understanding of all other data that is available within the big data system. So, without having a well-defined metadata system, it's very difficult to do the following things:

• Understand the diversity of data that is stored, accessed, and processed
• Build interfaces across different types of datasets
• Correctly tag the data from a security perspective as highly sensitive or insensitive data
• Connect the dots between the given sets of systems in the big data ecosystem
• Audit and troubleshoot issues that might arise because of data inconsistency

Having very large volumes of data is not enough to make very good decisions that have a positive impact on the success of a business. It's very important to make sure that only quality data should be collected, preserved, and maintained. The data collection process also goes through evolution as new types of data are required to be collected. During this process, we might break a few interfaces that read from the previous generation of data. Without having a well-defined process and people, handling data becomes a big challenge for all sizes of organization.

To excel in managing data, we should consider the following qualities:

• Good policies and processes
• Accountability
• Formal decision structures
• Enforcement of rules in management

The implementation of these types of qualities is called data governance. At a high level, we'll define data governance as data that is managed well. This definition also helps us to clarify that data management and data governance are not the same thing. Managing data is concerned with the use of data to make good business decisions and ultimately run organizations. Data governance is concerned with the degree to which we use disciplined behavior across our entire organization in how we manage that data.

It's an important distinction. So what's the bottom line? Most organizations manage data, but far fewer govern those management techniques well.

Let's try to understand the fundamentals of data governance:

• Accountability
• Standardization
• Transparency

Transparency ensures that all the employees within an organization and outside the organization understand their role when interacting with the data that is related to the organization. This will ensure the following things:

• Building trust
• Avoiding surprises

Accountability makes sure that teams and employees who have access to data describe what they can do and cannot do with the data.

Standardization deals with how the data is properly labeled, describe, and categorized. One example is how to generate email address to the employees within the organization. One way is to use firstname-lastname@company.com, or any other combination of these. This will ensure that everyone who has access to these email address understands which one is first and which one is last, without anybody explaining those in person.

Standardization improves the quality of data and brings order to multiple data dimensions.