Data Governance and Its Importance

Running head: IMPROVING DATA GOVERNANCE
IMPROVING DATA GOVERNANCE
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Table of Contents
Data Governance and its Importance...................................................................................2
What technology did they implement?................................................................................2
Why did they choose that technology?................................................................................3
What business factors were evaluated prior to implementation?........................................3
What information deficiencies existed prior to implementation?........................................4
What benefits were gained after implementing the technology?.........................................5
Do the benefits associated with the technology outweigh the costs?..................................6
References............................................................................................................................7

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Data Governance and its Importance
Data Governance is merely required for assuring that most of the organization's assets are
managed in proactive and efficient way. It is mainly done for managing the trust and
accountability with respect to organization data. Most of the organization around the globe
generate a huge amount of data which are related to client and internal processes (Alhassan,
Sammon & Daly, 2019). One of the biggest aspect of data governance is the ease with which the
sharing of data is available. The main concept behind the implementation of data governance is
all about need and reuse of data again and again. By making use of data management tool
sharing and reuse of data is done frequently. Data stand out to be as one of the internal asset of
organization. Complete misuse of data or any deliberate kind of activities will ultimately led to
serious kind of issues (Kerber, 2018). Data governance tools aims to provide complete data
security from both internal and external members.
What technology did they implement?
Amazon makes use of data ingestion tools so that it can support the whole mechanism of
bringing data into the current ecosystem. It has been designed for moving the data from the
original source to first data store like staging area. Some of the common steps are collecting data
from different sources, ingestion of data stream, routing of data stores (Paskaleva et al., 2017).
Most of the ingestion tools come up with some functionality related to data governance. It is
merely used in wide degree where there is awareness of governance.
Zomato makes use of data collecting, where it focus on managing the inventory data sets.
Catalog tools stand out to be helpful in managing and managing the metadata which is an

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important understructure in the current ecosystem (Brous, Janssen & Vilminko-Heikkinen,
2016). Data catalog aims in connection people with data which is required for complete
evaluation, understanding, and acquisition of new data. The main reason of using data catalogue
is all about helping data governance for accessing the datasets.
Why did they choose that technology?
Amazon makes use of data ingestion tool that merely supports different methods for
bringing data into the current analytical system. Most of the primary governance function is
merely inclusive of open source tools in comparison to some of proprietary tools (DeStefano,
Tao & Gai 2016). Primary governance capability is merely implemented for the ingestion tools
which can capture the ingestion metadata.
Zomato choose data cataloging so that it can easily manage the inventory of data sets.
The main task of catalogue tool is all about collecting and maintaining the meta-data. Data
catalogue helps in collecting people with different kind of data for understanding, evaluation and
lastly acquiring (Kerber & Frank, 2017). The main task of data cataloging is all about helping
most of the data consumer, more specifically the self-service customers, to easily access the
needed datasets.
What business factors were evaluated prior to implementation?
A list of business factors were evaluated before the implementation of technologies in
both the countries like

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Strategy: Business strategy stand out to be best foundation required for successful
working of a firm (Lee, Zhu & Jeffery, 2017). Most of the leader need to criticize the asses if the
employee below management have equal change of understanding the strategy.
Management: Management team is completely selected and assembled for different
kinds of employee which completely fits for internal culture and goals of organization (Hwang,
Lin & Shin, 2018). Much of the attention is being paid if the characteristic completely match the
competencies.
Internal standard of Organization: Employee are able to carry out most of the duties if
most of the guidelines are with respect to internal standard and processes (Soomro, Shah &
Ahmed, 2016). It is very much important to understand the employees understand it along with
understanding of relationship between different processes.
Suppliers: Suppliers aim to play a key role in order to achieve better customer
satisfaction. This stand out to be very much important in evaluating standard and different
principles which meet certain standard and level of service.
Internal Communication: Considering most of the organization, it is very vital to open
friendly environment for both kind of communication that is horizontal and vertical (Eroshkin et
al., 2017). Much of attention is paid for speed up the information and feedback flow which
highlight information quality.
Information System: There are large of information system which makes the day to day
of routine work much easier. In some cases, it is seen that real-time data within the firm is not
available at the instance of importance decision.

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Quality: There is need for ensuring that most of product or quality of service is always
for changing environment of business (Paskaleva et al., 2017). This stand out to be biggest
challenge for most of the firm.
What information deficiencies existed prior to implementation?
In both the organization, there is a list of problem that existed like poor data quality, data
inconsistency, improper planning of business, and bad revenue in both the organization. Data
governance can be stated as proper management of data so that it can validate as per the
requirement of user (Brous, Janssen & Vilminko-Heikkinen, 2016). In addition, it also focus on
standard or rules that are required for carrying out business. With every passing year, both the
organization are growing at a rapid rate and each of system process a large number of
transaction. This aims in creating huge amount of data sometimes like customer, vendor,
payment and debits. At the instance of data entry, there is always a chance of either duplicity or
incorrect kind of data (DeStefano, Tao & Gai 2016). This merely leads to big kind of disaster for
taking up decision along with implementing new kind of business strategies. Data governance
aims to control data quality along with providing trusted data so that business can depend on
critical decision.
What benefits were gained after implementing the technology?
Data ingestion can be defined as the way in which data is being obtain or imported.
Importing of data is inclusive of certain process which are required for data need for analysis.
Data ingestion can be defined as the flow of data in between two or more system (Kerber &
Frank, 2017). It merely results in smooth and independent kind of operation which completely

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result in complete independence or even automation. In Amazon, data ingestion can provide
certain number of benefits like
 Automation of data ingestion enhance the time –to market goal.
 It helps in increasing overall scalability
 Complete refocuses on required work
 It helps in mitigating different kind of risks.
Data stand out to be the disruptive force which is being encountered for world-wide
business. It merely focus on the ability to combine data from various kind of sources. This is
mainly done so that it can leverage the growing value of details in the provided data (Lee, Zhu &
Jeffery, 2017). There are mainly seven benefits of data catalog in the present world.
 Democratization of data
 Personalized data for intensive data users
 It completely support Amazon-ification of data
 Boarding of both new and trusted data
 Providing complete view of data.
Do the benefits associated with the technology outweigh the costs?
There are large number of benefits which are associated with both the technology. There
are some list of governance function found in open source tools in comparison to proprietary
tools. Complete interoperability along with catalog and tools for penetration stand out to be
valuable feature for getting benefits of machine leaning (Hwang, Lin & Shin, 2018). Both
Amazon and Zomato stand out to be big corporation which can a financial take the burden of
both the technologies.

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Data catalogue is all about managing cost related to data set of inventory. Catalog tool
stand out to be best tool needs to maintaining metadata.

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References
Alhassan, I., Sammon, D., & Daly, M. (2019). Critical success factors for data governance: a
theory building approach. Information Systems Management, 36(2), 98-110.
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.
Eroshkin, S. Y., Kameneva, N., Kovkov, D., & Sukhorukov, A. (2017). Conceptual system in the
modern information management. Procedia Computer Science, 103(C), 609-612.
Hwang, Y., Lin, H., & Shin, D. (2018). Knowledge system commitment and knowledge sharing
intention: The role of personal information management motivation. International
Journal of Information Management, 39, 220-227.
Kerber, W. (2018). Data governance in connected cars: The problem of access to in-vehicle
data. J. Intell. Prop. Info. Tech. & Elec. Com. L., 9, 310.
Kerber, W., & Frank, J. (2017). Data Governance Regimes in the Digital Economy: The
Example of Connected Cars. Available at SSRN 3064794.
Lee, S. U., Zhu, L., & Jeffery, R. (2017). Design Choices for Data Governance in Platform
Ecosystems: A Contingency Model. arXiv preprint arXiv:1706.07560.

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Paskaleva, K., Evans, J., Martin, C., Linjordet, T., Yang, D., & Karvonen, A. (2017, December).
Data governance in the sustainable smart city. In Informatics (Vol. 4, No. 4, p. 41).
Multidisciplinary Digital Publishing Institute.
Soomro, Z. A., Shah, M. H., & Ahmed, J. (2016). Information security management needs more
holistic approach: A literature review. International Journal of Information
Management, 36(2), 215-225.