Simplify And Automate Your Data Governance Strategy

Data Governance is a method of defining and implementing a set of rules, roles, and responsibilities that work together to sustain and promote the value derived from the stored datasets your company holds. In simpler terms, data governance aims to maximize the value of your datasets while simultaneously reducing the risks associated with storing them.

Through years of data governance consulting, the team at Trianz has identified the four main principles of a good data governance strategy. They are:

  • Metadata Management

  • Lifecycle Management

  • Data Ownership Trails

  • Regular Qualitative Assessments

All of the above work together to improve accessibility to insight, while also facilitating the proper management of sensitive information so that you can adhere to regulations like CCPA and GDPR.

Why is Data Governance Important?

Data Governance is becoming increasingly important for businesses due to the exponential growth of data generation across the web. Having the ability to categorize, filter through, and store information in a secure manner is paramount for adherence with increasingly stringent data protection laws. Here at Trianz, we highly recommend working with a data governance consulting partner to ensure full regulatory compliance.

There are three main principles to an effective Data Governance strategy:

  • Accessibility – It is incredibly important to accurately index information in databases, to allow for efficient querying. Most businesses have a problem where they lack structured data rather than raw data. You create structured data through categorization and tagging of information, which subsequently creates value and insight.

  • Certainty – Without ongoing validation, updating, and deletion of relevant datasets, you cannot be confident that your insight is real. Validation is essential, as it identifies discrepancies that invalidate insight. For example, when statistics are composed of outdated information, any insight becomes invalid due to data uncertainty.

  • Utilization – Even if you perfectly index your data, and ensure it is entirely valid, there is no value unless you act upon the insights the data creates. These insights should be discussed regularly across the business, helping to shape your corporate strategy going forward.

How do you begin automating Data Governance?

Thanks to our numerous partnerships with industry-leading service providers, Trianz is the ideal partner if you’re looking for data governance strategy consulting services.

There are many aspects of your Data Governance services that can be automated via digital agents. This has the added benefit of removing humans from the equation, reducing the risk of both error and data misuse.

Here are some examples of Data Governance automation:

  • Data Input – Rather than relying on humans to correctly input information, you can enlist artificial intelligence and machine learning.

    For example, this could be used for verifying identity documents, where computer vision, coupled with AI, would extract information and input into your databases. For businesses that rely on identity verification, this could significantly reduce administrative workloads. You could also program the AI to delete the image once it has been verified, so you aren’t storing sensitive ID images on your network. If the AI fails to read the document (or lacks confidence due to a low-quality image), you could then enlist a human, making this an excellent way to reduce overall workloads.

  • Data Migration – You may have multiple databases, or maybe you’ve merged with another company and need to integrate their datasets with yours. This process can be entirely automated, thanks to machine-learning and AI.

    The AI can perform real-time validation against the two datasets you want to merge, identifying potential matches and discrepancies as you progress through the migration process. Any incomplete entries will be flagged at the end for manual processing, greatly reducing administrative workloads for your IT team.

  • Removal of duplicate entries – Keeping your database entries tidy is crucial for maintaining fast query times. Even with a strict data input policy, you are bound to encounter duplicate entries from time to time.

    You can automate the scanning and de-duplication of entries in your database using AI. These scans can be performed at regular intervals, maintaining database performance as your datasets grow over time. You could also repurpose this to identify misuse of your services, such as multiple user accounts despite policies that limit users to a single account, thus protecting your business interests.

Automated Data Governance with Trianz

Trianz is a leading Data Governance consultancy firm with a comprehensive understanding of effective data management strategy. We’ve worked with hundreds of businesses, overcoming thousands of issues, so you don’t have to.

Our extensive knowledge and experience means you can start governing your data effectively, as we help you jump over the unexpected hurdles. We have our fingers on the pulse of the business intelligence and data analytics industries, allowing us to curate a tailored solution suited to your business.

Get in touch with the Data Governance team at Trianz, and take the first steps towards protecting your data in an increasingly hostile digital world.

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