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Data Governance includes people, processes, rules, regulations, policies, and standards that are required by a company to achieve its data-management goals. While the business team is usually in charge and helps create the processes required, the technology team helps automate these processes and breathe life into them. At a macro level, it is a part of political discourse and international relations whereas at a micro level, it is a part of how companies plan out their data-strategies.
Data governance usually takes into account multiple factors such as:
a). Ensuring data accessibility to different stakeholders
b). Ownership of data
c). New Ideas to add more data sources
d). Ensure data security and maintain access controls
e). Have data cleaning and data processing pipelines in place
f). Conform to rules and compliance requirements related to data access and storage
Effective data governance makes sure that every aspect of the data handled by the company is managed through a series of processes, makers and checkers, data-owners, and control mechanisms. It also ensures that the privacy, integrity, availability, and cleanliness of the data is maintained as it gets accessed and updated by multiple teams.
Fig: Pillars of Data Governance
Companies today have an opportunity to gather massive amounts of data from numerous sources. They can fetch data from machines using IOT technology or use internal data – that is data generated by customers, clients, and processes. The Data Science team can also tap into external sources of data and make use of web scraping solutions.
Fig: Multiple Data Sources
While handling data from multiple sources, care needs to be taken before aggregating the data, and companies need to check for data validation at all levels to minimize the risks. Large quantities of data do not automatically ensure the success of a company which is why companies need data discipline through data governance.
The Data Governance team of any company usually includes members of the key verticals such as Technology, Business, Quality Assurance, and Compliance. The team r works on the critical requirements which include, but may not be limited to:
a). Policy approval
b). Creating a data advisory panel
c). Allocation of owners for required data products
d). Data corrections and data normalizations
e). Rule engine or frameworks
f). Data infrastructure
The requirements for data governance usually start with the business team. For instance, let us take the use-case of a company that provides micro-loans to college students. For such a business, there might be a requirement to store certain financial data of those who apply for loans. The information needs to be masked in a way such that different teams and members have limited access to data-points that they need to work on.
Once the Business team comes up with the requirements, it needs to be validated by the compliance team. The changes should help the company meet its statutory requirements during an audit. Once the requirements and validations and additional information are added to it, the Technology team would usually build a solution.
The solution would be in two parts – a) the actual code that needs to be written to mask the data, b) the infrastructure setup that would be needed on a cloud platform like AWS. Once the changes are built, they will need to be tested by the Quality and Assurance team and re-validated by the Compliance team before they go live.
When multiple teams are a part of a single solution, one of the major obstacles is terminology. For an eCommerce company, the warehouse team may believe that when a product is “shipped”, it means that it has reached the warehouse, whereas the delivery team may believe that the term stands for “out for delivery”. Common terminologies need to be defined to enable everyone to stay on the same page while working on data governance problems.
Data Governance and Data Management may seem like synonyms but aren’t. In most cases, however, data management is a result of a part of data governance. Data management deals with different aspects related to handling and storing the data. This may include setting up cloud-infrastructure and maintaining it while keeping the costs in check. It would also involve cleaning and processing data from multiple sources so that those who access the data can use it in a plug and play format. The data management team would work on some specific requirements on a day to day basis, such as:
a). Data normalization and formatting
b). Data pipelines and ETL workflows using services like step functions
c). Data cataloging using services like AWS Glue
d). Creating and updating a one-stop data lake
On the other hand, the data Governance team would define the policies and compliance requirements that need to be met when the Data Management team works on any of their projects. The base architecture of the data streams that need to be designed first, would also need to conform to the standards set by the Data Governance team.
In short, the Data Governance team would set processes and rules for everything related to data in a company whereas a Data Management team would usually work upon applying those rules and processes and setting up the infra requirements.
Data governance may be difficult to implement in the short run but it is like a fruit-bearing tree that keeps giving results once it is established. It can help boost the efforts of the Data Science and Analytics team and also help in managing risks and staying compliant:
a). With data governance in place, you would have a standard set of rules that anyone working on a data science project and requiring access to the company’s data streams can follow. This would, in turn, reduce the need for multiple levels of communication and decision making
b). With set goals and requirements, costs associated with data management would go down and cost-saving would be on the higher end. This is applicable especially when a company has tons of data on its hands but has proper storage, archival, and access methods
c). Data-driven activities would be more transparent and this would enable companies to provide answers to stakeholders or auditors faster
d). With proper guidelines in place, the company can aim for more external sources of data to enrich the current data sources and also create broader market studies
e). A Data Governance team can provide faster resolutions for all data related issues that may be faced by the product or tech, or encountered by the Compliance team
f). Improved monitoring and logging mechanisms will ensure data security and enable companies to gain the trust of customers. With multiple data hacks occurring across the globe in recent years, data safety can be the reason for you losing all your customers, even if your product is a bang for the buck
The rise of data governance was due to the struggles faced by companies in the aftermath of multiple cyberattacks and loss of public trust. Today, such external breaches, increased regulations, and cost savings make data governance a must for companies both big and small, that dabble in data. Recent regulations like the California Consumer Privacy Act (CCPA) and the General Data Protection Regulation (GDPR) increase the incentives for companies that build their data-infra on the pre-defined standards.
Just like Rome wasn’t built in a day, creating a Data Governance team and building the framework for the entire company to follow might not be feasible in a short period. The reason behind this is that you would need participants from different teams to come together and analyze the data that the company will be using, the sources of the data streams, the purpose that it will be used for, and the users who will access the data.
Web scraping is the biggest source of external data for industries alike, due to the almost infinite amount of information available on the web and the real-time data updates. However, fear of litigation and compliance requirements create roadblocks in the path of using web scraping as a data source. Having a standardized data governance rulebook and a team that can come up with the “to-do-list” every time a new source is added can help you stay on the right side of data-laws.
If you liked reading this blog, we are sure you might like to read the Difference between Data Normalization and Data Structuring. Make sure to leave us your valuable feedback in the comments section below.
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