Discover ways to improve unstructured data governance. Keep data structured for easier usage, and ensure that information is used effectively and efficiently.
Businesses must regulate their data in order to keep it clean and organized for future usage. They may concentrate on data governance for their record systems and structured data, but what about large amounts of unstructured data, such as images, videos, digitized hardcopy documents, and continuous text messages from social media?
Businesses must take numerous proactive steps to strengthen unstructured data governance, including selecting trusted sources and defining user access limits. However, there are various constraints that may impede successful unstructured data governance.
Big Data Governance Issues
There are various obstacles to the big data governance of unstructured data due to its nature and the complications required in guaranteeing its quality, security, and compliance.
- Lack of intrinsic organization: Because unstructured data lacks a stable schema—predetermined categories or labels—defining a standard structure for analysis, governance, categorization, and data retrieval is challenging.
Data security and privacy: When gathering data from various sources, the unstructured data may contain sensitive information that must be identified and safeguarded against illegal access, use, and disclosure in order to comply with requirements such as the CCPA or GDPR.
Contextual understanding: Recognizing context from text, photos, or videos can be difficult, perhaps leading to misinterpretations. - Limited expertise: Relying on data scientists to set up standards and procedures for data might result in problems including inconsistent data practices, security flaws, and compliance challenges.
So, how can we strengthen the governance of unstructured data, which today accounts for nearly 80% of corporate data under management? Here are five approaches to dealing with the issue in the workplace:
Top 5 approaches to increasing unstructured data governance
1. Use reliable data sources
The data that organizations develop and accumulate is trustworthy, but most organizations also obtain data from outside cloud sources as they establish an aggregated data repository for analytics.
How do you know the data from these third-party sources is reliable? You don’t until you vet the data supplier, understand where the data extraction was performed and secured.
For example, if you work in a sensitive area like healthcare, you’ll want to verify that data about individual patients has been anonymized to comply with privacy regulations.
Checking vendor governance standards to ensure they match your own should be a routine exercise conducted before entering into any contract. Prior to signing a contract, you should also seek the vendor’s most recent IT audit to examine recent governance and security performance.
2. Create unstructured data policies for user access and permissions
Structured data has strict regulations in place for user access and rights, but unstructured data may not. Access to unstructured data should follow the same principles as structured data.
In other words, access to unstructured data should be restricted to users who need it. There are likely to be tiers of permission within the category of access, with some people having more access to data than others, based on job function or role.
These user access decisions should be made in collaboration with the IT and end-user departments. At a minimum, there should be annual reviews, and protocols should be in place so that when an employee departs the organization, access is quickly withdrawn as part of the separation process.
3. Secure All Data
The fundamentals of data security include trusted networks, strict user access controls and monitoring, perimeter monitoring that looks for cracks and potential breaches, and user behaviors that adhere to security best practices (such as not sharing passwords or avoiding copying data to portable thumb drives). If data is housed on hardware at the enterprise’s edge, that hardware should be physically caged and protected whenever practicable, with access restricted to those who are allowed.
Most of these standards and practices apply to structured data, but not necessarily to unstructured data, such as Internet of Things data.
Unstructured data should be subject to the same levels of security principles and practices as structured data.
4. Make use of logging and traceability.
When it comes to big data, robust recording and traceability software should be always at work. Who or what is gaining access to the data? When and where does the data get accessed? If an issue emerges, what event precipitated the problem?
Logging, tracing, and (in the future) observability all reduce the time spent on problem resolution and are essential to security.
5. Dispose of bad data
As raw and incoming large data floods in, bad data should be deleted as an initial data cleaning technique. There is a lot of terrible big data out there, whether it’s unnecessary documents, IoT streams with as many device handshakes as useful information, or redundant social media threads.
The data preparation process that is part of data import should delete this data so that it never takes up storage space. Big data repositories should also be renewed and examined on a regular basis, with obsolete content removed.
Using AI technologies to handle unstructured data
When compared to structured data, unstructured data is typically more difficult to handle and analyze for insights, which is one of the reasons it is not commonly employed for business intelligence. AI technology can speed up the process of indexing, tracking, mining, analyzing, and deriving insights from unstructured data. AI-enabled solutions provide numerous capabilities for dealing with unstructured data:
- Natural language processing (NLP): It allows you to automatically extract data from unstructured data using a variety of approaches and techniques such as sentiment analysis, named entity identification, topic extraction, and language translation.
- Image and video recognition: AI systems that use object recognition and classification technologies can recognize items, people, and scenes in photographs or videos, allowing for better visual data analysis.
- Speech and audio analysis: Users can transcribe and analyze audio recordings of spoken content such as customer service calls, conversations, and interviews using speech and audio analysis capabilities.
- Recommendation system: Businesses can use AI tools to scan unstructured data and offer tailored suggestions based on customer feedback, which can be used to improve their products and services, enabling business growth and improving customer satisfaction.
When looking for a data governance solution, look for one that adheres to unstructured data governance guidelines. This type of technology will assist you in enforcing consistent standards throughout your firm. It will encourage adherence to industry norms and data protection legislation, as well as provide data quality verification, giving your data long-term worth.
Remember that there is no one-size-fits-all solution for data governance. The optimal data governance technology for your company is determined by your data requirements and preferences.
