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Table of contents
1.
Introduction
2.
Data Governance
2.1.
Visibility👀
2.2.
Unvetted Employees🙎‍♂️ 
2.3.
Auditing Big Data Process
2.4.
Identifying the Key Stakeholders
3.
Frequently Asked Questions
3.1.
What is data governance?
3.2.
What is the risk of not having data governance?
3.3.
What problem does data governance solve?
3.4.
What is an Organisational structure?
3.5.
Which organizational structure is best for large companies?
4.
Conclusion
Last Updated: Mar 27, 2024

The Data Governance Challenge

Author Rajat Agrawal
0 upvote
Master Python: Predicting weather forecasts
Speaker
Ashwin Goyal
Product Manager @

Introduction

Data governance is essential to the company regardless of the data sources or how they are managed. Firms in the traditional realm of data warehousing or relational database administration may have well-understood guidelines regarding how data must be protected. For example, it is vital to keep patient data private in the healthcare industry. As long as names, Social Security numbers, and other personal information are hidden; you may be able to keep and analyze patient data. Unauthorized individuals must not be able to access private or restricted information.

Big Data

Let’s learn about data governance in-depth.

Data Governance

Big Data Governance is the process and management of data availability, usability, integrity, and security of data used in an enterprise. It includes all the steps from storing the data to securing it from any mishap. It is not just only about technology.

It is very likely that big data sources are unsecured and unprotected and include much personal information. You will most likely study a lot of data that will become irrelevant to your organization during the initial processing of this data. As a result, you don't want to spend time and money protecting and governing data that you don't intend to keep. However, your firm may be subjected to unanticipated compliance responsibilities if sensitive personal data goes across your network. It may be safer to undertake the initial analysis in a "walled" internally yet segmented environment or the cloud, for truly exploratory data, with unknown contents.

Finally, once you've decided to dig further into a subset of that data so that the results may be included in your business process, you'll need to put up a system for applying governance rules to that data.

Some of the issues that are faced during the incorporation of unvetted sources into your environment are:-

✍️ Determine who has initial access to new data sources and who has access once the data has been reviewed and comprehended.

✍️ Understand how this data will be separated from that of other businesses.

✍️ Recognize your role in putting the facts to work for you. If the data is privately owned, you must ensure that you abide by any contracts or usage guidelines. Some information could be linked to a vendor's usage contract.

✍️ Recognize where your data will be stored physically. You may add information about clients or prospects from countries with strict privacy regulations. To prevent breaking the law, you must be informed of the specifics of these sources.

✍️ Understand how your data should be handled if moved from one location to another. Will you use a cloud service to store some of this information? What guarantees does that service make about where the data will be stored and how well it will be protected just because you have a security and governance procedure for your traditional data sources doesn't ensure that your staff and partners apply it to new data sources. You must evaluate two main issues: data visibility and the trustworthiness of individuals who work with the data.

Visibility👀

While your business analysts and partners may be excited to use these new data sources, you may be unaware of how they will be used and controlled. In other words, you might not be able to control your visibility into resources that aren't under your control. This situation is particularly inconvenient if you need to confirm that your provider adheres to any compliance standards or legislation. This is also true if you utilize a cloud service to manage your data because the storage can be very cheap to manage.

Unvetted Employees🙎‍♂️ 

Even though your organization does a thorough background check on all of its workers, you now have faith that no malicious insiders work in other business divisions outside of IT. You must also presume that your cloud provider's staff has been thoroughly vetted. Insiders are responsible for about half of all security breaches, so this is a legitimate issue (or by people getting help from insiders). If your firm leverages these new data sources in a distributed fashion, you'll need a strategy for dealing with internal and external risks.

To solve the above two problems, we can audit the big data and identify the key stakeholders.

Auditing Big Data Process

📕You must be able to show internal and external auditors that you are following the regulations that support the business's operations. 

📗You'll need a mechanism to demonstrate logs or other proof that the data you're working with is safe and secure. 

📕You'll have to explain where the data came from. Will you be able to validate the results to reduce the company's risk? You may have to show that you've archived the data you're utilizing to make decisions and run your firm.

📗This procedure may be well-managed for your traditional databases and data warehouse, but it does not include your unstructured significant data sources. Although external auditors may not scrutinize the accuracy of data warehouse–based data from external big data sources, your internal process will demand that these sources be well-synchronized. 

📕The data warehouse, for example, will have a well-defined set of master data definitions, whereas huge data sources may lack documented metadata. As a result, external data sources must be managed so that metadata definitions are defined, allowing you to have a uniform set of information across all of them. This is where thinking through the process can make the difference between success and failure in business.

Identifying the Key Stakeholders

Big data is often linked to specific business initiatives, one of its properties. For example, the Marketing department wants to take advantage of the massive amounts of data provided by social media sites like Facebook and Twitter. Operations teams will want to use RFID data to manage their supply chain. The Human Resources department will monitor what employees post on social media platforms to ensure that they are not breaking any internal or external rules. To ensure that privacy requirements are not broken, a medical claims department should keep track of the regulations governing how patient claim information within health insurance data is managed. These stakeholders may work for the same firm, so everyone must understand the rules and the infrastructure to secure the organization.

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Frequently Asked Questions

What is data governance?

Big Data Governance is the process and management of data availability, usability, integrity, and security of data used in an enterprise. It includes all the steps from storing the data to securing it from any mishap. It is not just only about technology.

What is the risk of not having data governance?

Without guidelines, you risk non-compliance with privacy standards, personnel who refuse to provide data for fear of misuse, and people who lack the necessary information to execute their jobs.

What problem does data governance solve?

Issues like data visibility, quality, security, and other related problems can be solved using Data governance.

What is an Organisational structure?

The structure addresses how jobs are allocated, how employee hierarchies are structured, and how personnel is organized or grouped into departments or particular positions. When a company starts, it is common to implement an organizational structure.

Which organizational structure is best for large companies?

Functional organizational structures are suitable for large firms with multiple departments and those that must fulfill stringent deadlines.

Conclusion

In this article, we have extensively discussed Data Governance Challenge and how to solve those challenges.

If you want to learn more, check out our articles on IAM Security StandardData Warehousing ToolsData Virtualization Use Cases, and Encapsulation. If you want to learn more about how virtualization is connected with big data, you must refer to this blog here. You can check out our blogs on Top 100 SQL ProblemsInterview ExperiencesProgramming Problems, and  Guided Paths.

Happy Coding!

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