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How to handle AI in your data governance framework

Using AI for data governance, is very important. See what the current trends are below:

If you are utilising AI or machine learning in your enterprise business, you might be wondering how to incorporate AI into your data governance framework. In this article, we explain how a robust governance framework plays a pivotal role in ensuring AI's efficient, compliant, and above all, ethical use in your data practices.

How AI is impacting data governance

Data governance in the era of AI is a pressing concern. A strong data foundation is a prerequisite for the successful delivery of AI projects. As AI tools revolutionise our work and inform business decisions, we need to manage the opportunities with clear guidelines and practices.

The need for transparent processes in how data is incorporated and used within any AI systems you employ is paramount. For AI to function as intended, it requires access to data managed with stringent data governance practices. There is a direct correlation between AI governance and robust data governance.

Trying to keep on top of technological advances

According to Ray Kurzweil, technology will change so fast that it's impossible to know what the future holds. He famously said: We’re entering an age of acceleration. The models underlying society at every level, which are largely based on a linear model of change, are going to have to be redefined. Because of the explosive power of exponential growth, the 21st century will be equivalent to 20,000 years of progress at today’s rate of progress. Organisations have to be able to redefine themselves at a faster and faster pace.”

Many organisations seek to avoid uncertainty and are struggling to come to terms with how AI will impact their workplaces, culture, and activity. Some are delaying facing the consequences of this new frontier. Others are experimenting but without a structured plan in place.

The United Nations has even weighed into the conversation about the future of AI, by observing "the necessity of integrating data governance more prominently into the conversation on AI, thereby fostering a more cohesive and effective approach to the governance of this transformative technology."

Data governance provides a way of being aware of and responsive to ethical standards, social expectations, and legal requirements. This will only continue to become a critical issue as organisations expand their use of AI and increase their data analytics capabilities.

What is data governance?

Data governance involves establishing processes, practices, and standards to ensure data is consistently handled, managed, and available. AI capabilities are inherently linked to your data governance approach. If your AI tools are accessing good data, you will obtain accurate recommendations and results.

A robust data governance framework is indispensable if you want ethical and accurate AI data. It not only helps you lay a solid foundation but also enables you to strive for a continuous loop of improvements as data is enriched with the addition of more data records.

AI is just one part of data

Effective governance for AI requires a broader and overarching data governance system that ensures all data is handled effectively through development, deployment, use, and deletion. Data governance principles apply to any technologies that collect or handle data. In this way, AI is just one part of your broader data framework.

Data governance provides a set of principles and guidelines that apply to new or evolving technologies. AI governance, on the other hand, looks solely at AI and is concerned with the implications of the use of AI rather than the more holistic principles contained in a data framework.

Data governance enables responsible AI

AI systems use data to learn and improve. Good quality data governance equips AI tools with data responsibly, including through a framework that dictates what data is available and how it can be accessed. Data governance strategies ensure that the data AI access is of high quality and accurate. Data standards in the framework clarify how data can be dispersed and accessed, providing interoperability between big data and smaller sets of records.

Challenges with AI in data governance

Many organisations are starting to hold large data sets and are experimenting with data warehousing. However, the recent explosion of AI, particularly machine learning technologies, are making their way into businesses, posing significant challenges. A solid and clear data governance model and strategy is not just the key to a firm foundation; it is an urgent necessity in this evolving landscape.

AI enables you to obtain and assess highly detailed levels of data. This can enable improved customer segmentation. For example, traditional analytics would be interested in characteristics and geographic locations, such as how old you are, where you live, and how much you earn.

Data issues

Most AI tools used in business assess large data sets, which are often held in multiple tools. If a robust data governance framework has not been adopted, there is a risk that the AI's data is not clean or consistent enough. If staff members have been working without data guidelines and a straightforward approach for record generation, there is a high chance that AI will be rendered unable to operate effectively. Reliable infrastructure is also required to support this exponential data growth.

While many tools are improving the way they handle, identify, and disregard anomalies, there will always be a need for a strong, reliable, unbiased, and complete baseline data set.

The more robust the data is, the earlier it is to establish reliable prediction models and approaches. Not only does quality data give you better insight into what is happening, but it also gives you a more accurate insight into what is likely to occur in the future.

Risk management, compliance, and privacy

A good data governance framework outlines any risks related to data privacy, security, and compliance and the measures in place to mitigate risk. Data governance can give you the confidence that your data has been collated with individual consent.

More than 64% of consumers in a 2021 study said they mistrust companies in at least one industry to protect their personal data and privacy online. As the role of AI changes, data sets containing private or sensitive data can be consumed unexpectedly. There is a risk that sensitive information should be handled in confidence. AI may, in effect, be able to "launder" data.

Security in AI models is still evolving. It can become more difficult to manage access and determine who has accessed what data and when. When used effectively, AI can help ensure that sensitive data is handled properly. However, if it is not set up with the correct parameters, you run the risk of data breaches.

Furthermore, many businesses allow customers to have their data or records deleted or destroyed upon request. If an AI tool has generated content, ideas, or outcomes based on data you have been asked to destroy, there are ethical challenges and risks, as well as the need for duplicated effort and repeated data queries.

Systems that help achieve data governance

There are many options when it comes to tools and applications that will help you achieve a high level of compliance and good data governance. You can even use AI to help ensure your data governance processes are followed. AI can scan for gaps in your security and identify any phishing, ransomware, or spyware. AI can also improve your data governance efforts by automating anomaly detection or classification tasks.

Find the right tool, reshape your processes and systems to operate effectively and in accordance with legislation and regulations and reap the rewards of AI - without sacrificing your data governance objectives.

Pimcore for Master Data Management

Pimcore is a comprehensive open source tool that supports data governance by providing robust tools for data management, including data classification, data lineage tracking, access controls, and data quality monitoring. It helps enterprise businesses ensure that their data is properly managed, secured, and compliant with regulations. Pimcore's data governance features enable businesses to establish clear policies, processes, and standards for their data assets, promoting transparency and accountability across the business.

Pimcore can use AI in a compliant and structured way to help you achieve outcomes in line with you governance framework. Examples of how Pimcore can use AI include:

Data assessment and cleansing
Automated product classification
Product search and discovery
Personalised product recommendation
Product Content Management
SKU matching
Dynamic pricing
Demand forecasting
Image recognition and tagging

We are Pimcore partners and have seen how this product facilitates rapid innovation for small businesses who want to scale. Contact us to learn more about Pimcore’s flexibility and “connect anything” architecture.