What is Data Governance?
Data governance is a set of procedures, responsibilities,
rules, standards, and measurements that assure an organization's effective and
efficient use of information to achieve its goals. It defines the
processes and responsibilities that assure the quality and security of data
utilized within a company or organization. Data governance specifies who can
perform what actions, on what data, in what circumstances, and with what techniques.
A well-crafted data governance plan is essential for every organization that
deals with big data, and it will illustrate how your company benefits from
uniform, standardized processes, and responsibilities. Business drivers
emphasize what data in your data governance plan needs to be carefully handled
and the advantages expected from this effort. This approach will serve as the
foundation for your data governance structure.
For example, if ensuring the privacy of your beverage
company's data is a business driver for your data governance plan, customer
data will need to be maintained securely as it travels through your
organization. To guarantee compliance with applicable government regulations,
such as the GDPR, retention requirements (e.g., a history of who modified what
information and when) will be defined.
“Governance allows organizations to use
critical data to drive
the organization”
-Dick Tyler
What
makes Data Governance the most important factor in Beverage Industry?
The
Economist released an article in 2017 headlined “The world's most precious
resource is no longer oil, but data.” Since then, we've seen a growth in the
number of machine learning platforms, as well as a significant increase in the
need for data scientists who can use this data to make better decisions. Beverage companies have been gathering all kinds of data, wrangling it together, and
running it through algorithms or putting it into visualization tools to obtain important
insights to stay competitive.
Companies
are moving beyond depletion data and integrate retail, and e-commerce data and
are gaining a complete picture of their supply chain and their customers'
buying behavior in a three-tier distribution system (supplier, distributor,
retailer) that we see in the beverage industry and other fast-moving CPG
industries that we see at Asperity. While this data will provide answers, will
the answers are correct? If terrible, inaccurate, out-of-date, or inadequately
harmonized data is utilized as an input, one can only anticipate bad,
erroneous, or out-of-date results. So, what is the answer? Data science and
machine-learning is useful, but without data governance, it is impossible to
achieve consistent data accuracy. Data governance may be defined as the people,
procedures, and technology required to manage and safeguard a company's data
assets in order to ensure that corporate data is usually comprehensible,
correct, comprehensive, trustworthy, secure, and discoverable.
According
to the description above, the primary goal of data governance is to guarantee
that data is accessible, useful, standardized, accurate, trustworthy, and safe.
To achieve this goal, a company must deploy the necessary people, processes,
and technology. To create business rules/definitions and confirm they're right
implementation inside an organization, data owners/stewards must be identified.
This goal can be difficult to achieve
inside a three-tier distribution structure. Data must be merged, cleaned, and
harmonized among various parties before being made available to the right users
across numerous companies in the proper format and at the appropriate time. To
acquire trust in the data, users from many companies are seeking insight into
the process to understand where the data came from, how it was transformed, how
current the data is, is the data correct, and what impact the data must be
restated with. In summary, customers want data to be validated as current and
correct. With
that as a backdrop, With that in the background, what all platforms should the company
feature in their three tire system, also how it helps them to leverage data as a
competitive asset?
As we can see there is a system that is scalable also which
can be extendable. In addition, there is increasing the value of data and also
there is prevalence in the quality of data, they should look for systems that
are always open, also which provides visibility into their process and can
certify who much the data is accurate which gains the user trust and helps in
leveraging of data.
In data to provide certification and confidence in the data, there
should be visibility into:
·
All constituents' data submission status
·
The accuracy of the data supplied by all constituents (current and
historical submissions)
·
As the data travels through the process, it is validated.
·
The chain of custody for all data supplied, from intake through end-user
consumption.
·
All standardization/harmonization rules (product/store mappings) that
have been applied to the data are defined.
·
When necessary, data restatements are made, as is the status and impact
on end-user data.
·
The timeliness with which standardized/harmonized data is delivered to
end-users.
While this visibility may appear to be an apparent need
of any data system, many (both purchased and custom programmed) do not supply
it. The system's primary goal was to acquire, process, and deliver business
data to end-users. Visibility into the process was given little attention.
Companies have attempted to bolt on this sort of openness, but only those built to be open from the start can give the necessary level of
transparency that users want to trust the data.
As stated in the outset of this essay, data has evolved
into an organization's most important asset. However, data, like oil, must be
refined and processed with the proper controls, processes, and monitoring in
place to guarantee that the machines that use the resource can trust the
quality of the output and exploit it as a competitive advantage. Good data
governance is completely dependent on data quality.
Conclusion: The most significant result is that data governance, as part of an enterprise data management
approach improves corporate data quality. We notice that businesses are
gaining more control over their data and, as a result, the quality is improving
structurally.
**Thank you for reading. Please don't forget to leave comment and share with friends**
Author - Saurabh Bagul [21st June, 2021]
Keyword: Data science and machine learning, Good data governance, data governance in beverage, data scientists
Reference
:
Youtube.com. 2021. Before you continue to YouTube. [online] Available at:
<https://www.youtube.com/watch?v=nGqnhVolddo> [Accessed 23 June 2021].
Aperity. 2021. Why Data Governance is Necessary in the Beverage Industry – David Palmer -
Aperity. [online] Available at:
<https://aperity.com/why-data-governance-is-necessary-in-the-beverage-industry-david-palmer/>
[Accessed 23 June 2021].
Roadmaster, 2021. Topbraid Data Governance, HD Png Download - kindpng. [online] KindPNG.com. Available at:
<https://www.kindpng.com/imgv/TiJJiJb_topbraid-data-governance-hd-png-download/>
[Accessed 23 June 2021].
Well done, Saurabh! Fascinating article. You have mentioned that data is being the new oil, and I totally agree with your point. Data governance helps business organize and understand the power of data they have in hands—otherwise, it is worthless.
ReplyDeleteImplement data governance is fundamental for business and a considerable step throw success. Sometimes, all the data is under the resposibilty of IT. Although, using data governance IT creates a framework to make the data governance programme repeated and scaleable, which all the stakeholders should be involved.
In general beverage industry retain a lot of data from customers, employees, partners. Data governance is the best way to create processes to understand all this data better.
This article seems to be interesting for me because the amount of data research has done is fantastic and i agree with your point that data has evolved into an organization's most important asset. However, data, like oil, must be refined and processed with the proper controls, processes, and monitoring in place to guarantee that the machines that use the resource can trust the quality of the output and exploit it as a competitive advantage.
ReplyDeleteGood data governance is completely dependent on data quality and maintaining that data is quite difficult as in today's data security is quite unsafe.
It is rightly said “The world’s most valuable asset is no longer oil, but data.” Everyday there is an increase in the machine learning platforms and in data scientist. With increase in competition companies is grounding all the data which are available in the market and running algorithms to run the data to gain some insights which can be helpful for their companies. Usually, it is a three-tire system which includes suppliers, distributors, retailers which is used in beverage industry and other CPG industry they are looking to integrate results and deplete data which is really helpful for the companies.
ReplyDeleteWell done, very well researched. When we think of Data governance, we think of GDPR. As per my research, basic thumb rule of data governance is Data must be brought together in order to be governed. As part of master data management, bringing data from silos into a controlled environment allows it to be easily accessed by personnel who can derive value from it. And, I'd mention safety. When you have all of your data in one place, it's critical to keep track of who has access to it. Data governance improves data security and audibility. The final major advantage is uniformity. Data quality and enrichment, as well as the normalisation of reference data, should work hand in hand with governance. Having consistent drop-down options, for example, makes evaluating, reporting, and making decisions easier. World’s biggest beer company Budweiser maintains the single and centralised data base, which makes the company govern and know the customer better and also helped them to know the customers review on competitor brand like corona.
ReplyDeleteThat was a worth reading article.
ReplyDeleteTotally agree to your context and in addition governance delegated authority and restrict freedom. In this situation, we require the Publisher to transmit the data set in the most efficient manner possible for all Consumers. We do this by looking at the value chain from all of the Consumers' perspectives and allocating responsibilities (and hence expenses) between the Publisher and the Consumers in order to optimize the total value generated by all of the data set's uses.
Governance is reflected in an organization's Enterprise Architecture as standards, patterns, and rules, and it is evaluated as part of the software engineering process, such as at phase gate reviews.
Making laws that no one observes incurs only costs with no advantages, and hence produces negative value.
Constraining the Publisher can lower costs by limiting the types of interfaces available, limiting the backward compatibility requirements for an interface, or limiting technology possibilities. Constraints, on the other hand, frequently increase the Publisher's costs by requiring a schema transformation, improved data quality, or higher availability, for example. These enhancements assist consumers in lowering the cost of data usage. The enhancements also eliminate job duplication across all Consumers, resulting in an increase in total value.
- Mohit Jain