Data Governance Report: Benefits, Tools, and Company Examples

This report delves into the critical concept of data governance, emphasizing its significance in the contemporary business landscape where data is a valuable asset. It outlines the various benefits of data governance, including enhanced decision-making efficiency, improved data quality through standardization, and easier data accountability. The report further explores the tools used for data governance, such as Talend, IBM Data Governance, and Cloudera. It then presents case studies of Audi AG and IBM, demonstrating how these companies have successfully implemented data governance strategies using tools like Cloudera and IBM InfoSphere Master Data Management, respectively. The report highlights the impact of these tools on data management, customer relationship enhancement, and overall business performance. The conclusion underscores the importance of data governance for modern organizations and the ongoing efforts to strengthen and improve it.

Running head: DATA GOVERNANCE
Data Governance
Name of the Student:
Name of the University:
Author note:

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Introduction:
The process of management of data in an enterprise is regarded as data governance. For
proper management of data, it must be secured, usable and available whenever required along
with its integrity. One must also account if the data quality is poor and follow the procedures set
by the governing body under good data governance program. In this assignment we will discuss
about some of the benefits of the data governance along with some of its applications (Koltay,
2016).
The benefits of data governance:
With the technological advancements the entire system of information has been digitalized.
The flow of the information system has increased as well. Thus managing the data that is being
delivered to the system has become more and more challenging for the business organizations
(Keil et al., 2019). With the process of data governance this problem can be solved. Some of
the benefits of the data governance includes the following:
 Under sound data governance system, the decisions made and process of decision making
gets efficient.
 In today’s world, data is a valuable asset and thus must be treated accordingly through
inspection, maintenance and improvements.
 Data governance makes understanding and accountability of data much easier as it shows
us all the assets of data analysis and also helps in assigning specific tasks to specific
people.

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 Data governance helps in improving data quality by standardization of data. Data quality
is basically the usefulness of data and data governance helps in locating data and the
person responsible for it. This helps in improving its quality.
Tools used for data governance:
In order to implement data governance in the system business and companies needs to
undertake certain tools. Here are some of the tools that can be used for governing data.
 Talend
 IBM Data Governance
 Informatica
 Truedat
 OvalEdge
 A.K.A
 Alteryx
 Datattoo
 Datum
 Cloudera Enterprise (Mahanti, 2018)
 Collibra
All the above mentioned tools have been used by the companies all over the world for
improving their data management (Fleckenstein & Fellows, 2018). For the purpose of this
assignment we have chosen some Audi AG and IBM Company as the organization which have
been benefited by the usage of these tools.
Audi AG:

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Audi AG has implemented Cloudera Technology for its data governance. This technology is
used for the configuring, installing, optimizing, tuning and ultimately running Hadoop for
processing a large scale of data in the system. The tool is extremely fast and efficient and helps
in enhancement if the security with its alert and monitoring management and functionality.
Variety of data can be managed with the help of this technology.
One of the most talked about technology of the contemporary world is the Big Data
Analytics. Cloudera makes use of the technology for the management of the data. Audi is a huge
company and the flow f data in the company is greater as well. With the increasing demands for
the management of the data Cloudera is the best solution of the company and not only that the
technology is effective for the all the other companies which have a huge amount of information
to deal with.
Before the implementation of the technology, the company faced numerous issues for
managing the data. As the company is known globally and has a huge base of customers, it was
extremely challenging for an organization to optimize the data manually. The problem of the
data management was solved post implementation of the Cloudera technology.
It was impossible for the company to improve the user experience without understanding
their activities online. The customer information were unavailable due to the security issues.
Moreover, the system of backing up was not efficient.
After the implementation of the technology in the organization, apart from the governance of
the data, the customer relationship have being enhanced. The Big data analytics that is used by
the Cloudera technology helps in monitoring the customer activities and provides improved
suggestions (Brous, Janssen & Vilminko-Heikkinen, 2016). Big Data helps in the

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management monitoring and optimization of the data which helps in maintaining the integrity of
the data.
IBM:
IBM makes use of its own data governance technology known as the IBM InfoSphere
Master Data Management. It is an information servers which helps in the management of the
data that is put as input in the system or the servers. The technology also helps in the integration
of the data along with Cleansing, Monitoring, Understanding, Transforming and Delivering the
data in a proper format. The Inforsphere is an entire server family developed and owned by IBM
(DeStefano, Tao & Gai, 2016).
This technology is developed IBM for the purpose of the integration of the data. With the
advancement in the technologies that have occurred in the past few decades, the availability of
the data on the server has increased considerably. For the companies like IBM the entire process
of operation is over the internet, servers and databases. Thus the technology is chosen by the
organization for the benefit and the development of the company and advanced data
management.
Before the implementation of the technology, the business factors like the how the
company will be benefited with three usage of the technology, how it will affect the revenue of
the entire business, were determined.
One of the major risk that has been faced by the organization before the implementation
of the Infosphere was lack of agility in the system. With the limited agility in the company, the

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process of development decreased. The agility issue resolved once the Infosphere came into
action.
IBM-InfoSphere consists of Information analyzer which helps in the quicker analysis of
the data that helps in the process of decision making, IBM-InfoSphere FastTrack for quacking
tracking and integrating data, Metadata Workbench, Data Architect and numerous more tools
which not only helps in maintaining the integrity of the data but also ensures security and helps
in the process of decision making as well. The company has benefited considerably after the
implementation of the InfoSphere tool. Not only IBM, but also companies like, New York Life,
Landstar System Inc., DMI and many other companies are using the technology and quite happy
with the results that are obtained.
Conclusion:
Hence, it can be concluded from the discussion that data governance is one of the key
components used by the business organizations now a days. There are numerous benefits of data
governance and Information Technology based companies are putting considerable efforts for
making it stronger and better with each passing days. Almost all of the business giants have
either created their own data governance system or have made use of the available data
governance tools for the implementation in their esteemed institutions.

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Reference
Brous, P., Janssen, M., & Vilminko-Heikkinen, R. (2016, September).
Coordinating decision-making in data management activities: a
systematic review of data governance principles. In International
Conference on Electronic Government (pp. 115-125). Springer, Cham.
DeStefano, R. J., Tao, L., & Gai, K. (2016, June). Improving data governance
in large organizations through ontology and linked data. In 2016 IEEE
3rd International Conference on Cyber Security and Cloud Computing
(CSCloud) (pp. 279-284). IEEE.
Fleckenstein, M., & Fellows, L. (2018). Data Governance. In Modern Data
Strategy (pp. 63-76). Springer, Cham.
Keil, M., Culnan, M., Dinev, T., & Xu, H. (2019). Data governance, consumer
privacy, and project status reporting: Remembering H. Jeff
smith. Information Systems Frontiers, 21(6), 1207-1212.
Koltay, T. (2016). Data governance, data literacy and the management of data
quality. IFLA journal, 42(4), 303-312.
Mahanti, R. (2018). Data Governance Implementation: Critical Success
Factors. Software Quality Professional, 20(4).