Data governance

The Five Pillars of Unstructured Data Governance

Data availability is a common problem, especially in India; they are rare for NLPs in regional languages ​​or government datasets. But government data and organizational data are usually present in large volumes, untapped and wasted. Also, unstructured data does not have defined and consistent fields. It may not even contain numbers or text, making it difficult to mine the data for data science and machine learning projects.

Recent digital transformation initiatives have emphasized the importance of cleaning and structuring this data to derive business value from it. But to ensure accurate and secure insights from the data, it is important to manage it properly. Data governance practices include understanding business data and ensuring it is free from potential risks while generating insights. While governing unstructured data can be tricky, it’s not impossible. Analytics India Magazine has outlined some key tips and tools to improve unstructured data governance.

Establish governance guidelines/Develop governance model

Unstructured data often contains important and sensitive information in the same way structured data does, making it important for organizations to treat unstructured data with as much care. The key step in any data governance is to create governance guidelines outlining data ownership and access, how data will be processed, assigning tools to stakeholders, and controlling data. This ensures data privacy and compliance within the organization.

The “Unstructured Data Governance: Classification and Data Protection Enabled by Microsoft” at the Edgile 2021 conference in collaboration with Microsoft discussed some key governance techniques for unstructured data, suggesting developing a governance model. The “government pyramid” they operate in has the operations department at the bottom, tactical data in the middle, and strategic data at the top. The operations department generates 80% of the data to understand how the data will be used, then investigates tactics for outputting unstructured data into spreadsheets or files to put into other systems. This helps to understand the risks of having data between systems, which later the strategic governance committee will check to ensure that the security tools are in place and that there has not been a breach. Finally, the leadership will decide the future of this data.

Simple labeling scheme

It is important to keep the labeling scheme as simple as possible to ensure usage and adoption of unstructured data. Data is classified into four categories at Microsoft according to its risk taking; public, internal, confidential and restricted. These are further referenced for use cases. Data is labeled for internal use only, only for receiving and for business unit/business unit teams in a confidential and restricted setting.

Validate each data source

Businesses usually have tons of personal organizational data to mine, but that’s not always enough. Most companies also acquire data from external sources for a more holistic data repository. When it comes to external data sources, ensuring that the data is reliable is critical, which creates the biggest governance challenge. The first step is to clarify the corporate values ​​and governance standards against which any new supplier will be reviewed. They should also consult with the legal team on these regional policies and regulations that must be followed. These include factors regarding the data provider, where the data was acquired from (to ensure that the data is both reliable and legal) and how the data was prepared. Organizations can also take additional steps to verify the data source through their recent customers and IT audits.

Analyze data quality

Once the data source has been verified, organizations should perform their own data quality test. This is because the analytics-based business solutions that the company will use rely heavily on the quality and validity of the data. If the data is wrong, the product will also be wrong. Therefore, companies need to ensure that data quality is not hampered when exposed to new procedures and systems. Businesses can determine data quality based on sources, precision, meanings, number of empty values, consistency, amount of dark data, and time to value.

Secure the good data and eliminate the bad data

Once data quality has been derived and organizations have good and bad data, the next step in governance is to secure the good data while eliminating unnecessary information. Organizations should secure unstructured data as they would structured data. Some common techniques for securing unstructured data, including the use of trusted networks, perimeter monitoring, data encryption, and assigning data to an owner, can help identify areas vulnerable to breaches and fix them. to secure ; furthermore, ensure traceability in user logins to know who has access and control of the data.

Bad data must be eliminated in its entirety. In fact, experts suggest that it should be removed in its raw form during the data preparation part of the process. Physical data can be disposed of through sanitization or digital shredding.