The Benefits of Data Governance for Food Industry Businesses

Food industry businesses today are collecting more data than ever before, therefore proper data governance must be upheld to ensure the data being collected and stored is compliant to current data regulations, security protected and a credible source.  

What exactly is Data Governance?

Data Governance involves processes, policies and standards that ensure company data is secure, compliant, and confidential over the whole organisation, not just customer data but employee, supplier, manufacturing and marketing data, which in the food industry would include raw ingredients, ingredient suppliers and recipe development. In the Journal of Securities Operations & Custody, The MDM Institute perfectly summarised the value added to datasets in their definition of data governance.

"The formal orchestration of people, processes and technology to enable an organisation to leverage data as an enterprise asset." (Chakravorty, 2021)

Data governance processes include distributing data to employees and granting access to confidential data. The main objective here is data usability.

"Data must be readily available to those with a legitimate business need." (Chakravorty, 2021) 

Universal data storage systems also categorise data by how often it is needed to improve access. Successful implementation of data governance will ensure that all data stored is compliant with current government regulations and industry requirements. Overall it leads to a better understanding of the organisations customers which opens up opportunities for better customer targeting.

The Benefits of Data Governance for Food Industry Businesses

It can improve data usability as data governance standards ensure that employees are able to easily find and understand the data they have been given, how it was collected and how to use it within their role. This would integrate best practices for data management throughout the organisation. An up-keep which is required is storing metadata which can help to explain the purpose of the data set in question, as the descriptions of the original piece can give clues to its context.

Data governance also addresses data security which can help to protect private and confidential data and ensure that access is granted to the appropriate personnel. A key benefit of data governance is that it ensures data quality with processes put into place to reinforce the following.

"Data should be collected only if it has known and documented uses and value." (Chakravorty, 2021)

Quality checks also make sure that data is reliable, accurate, consistent and therefore of the correct quality. This can help to avoid out-of-date or corrupt data being used which could interfere with decision making. The data integration process groups smaller datasets together to create larger data sets that can create a view of an organisation in one place which can provide clarity. 

The benefits of proper data governance are abundant and more important than ever for big data collection and storage. Whether you are working in a global business or starting up your own company, in the food industry or not, I hope this has helped you to understand data governance and it's benefits.

Author: Elizabeth Duffy 


#DataGovernance #DataGovernanceFramework 
#DataGovernanceProcesses #DataGovernanceBenefits 

References:

Chakravorty, R., 2021. Common challenges of data governance. Journal of Securities Operations & Custody. Winter2020/2021, [online] 13(1), p.29, 36. Available at: <https://web.b.ebscohost.com/ehost/detail/detail?vid=5&sid=d5067f65-d67d-428b-8383-e13c99f8a4e0%40pdc-v-sessmgr03&bdata=JmF1dGh0eXBlPXNoaWImc2l0ZT1laG9zdC1saXZlJnNjb3BlPXNpdGU%3d#AN=148044598&db=bth> [Accessed 5 February 2021].

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Comments

  1. Any business, and in this case restaurants must consider the major data protection legislations out there, such as GDPR as a part of their own data governance.

    ‘’General Data Protection Regulation’’ applies to any processing done of personal data.
    To define what information is privy to GDPR, it is any information that relates to or identifies an individual, any genetic descriptions, physiological, cultural or social are also a factor subject to GDPR.
    To specify what is included as identifiable qualities, it is personal details of customers or employees, personal details such as: names, ID numbers, location, and personal descriptions. For the food industry the most common type of personal data that will be collected is that of customer names and contact details.

    GDPR is not the only data protection legislation to consider CCPA ‘’ California Consumer Privacy Act’’ have given consumers the right to know what information organisations have collected about them, the reasons they are collecting it and who also has access to their data.
    Understanding and implementing strong data governance programs is the only way to create processes within your business, that are compliant with the latest data protection laws.

    Business’s should strive to not just meet data protection laws, but to create trust within the customers that the business is handling their personal data in a responsible and reliable way.

    Author
    Deirbhile Coyle

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  2. Author: Jamie Carty

    There are four main data governance models.
    Model 1 is one that’s decentralised for individual access. This is most beneficial to independent business owners who create and collect their own data purely for their own use.
    Model 2 is decentralized for team access. This is also based around one business, but in this case, the business owner shares data with several employees within the organisation. If an organisation has several working spaces, this can be a good system to provide accessible information to all relevant employees.
    Model 3 is centralized data governance. Here, a business owner or executives control the master data, and grant access accordingly to departments who request it based on their level of authority.
    In Model 4 we see somewhat of a hybrid of the previous models. Here, a business owner still controls the master data, but employees create their own datasets to contribute information to the company. Larger organisations tend to benefit most from this as is allows them to streamline data to management teams

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