Ensuring data is accessible, consistent, reusable, trustworthy, and safe is largely dependent on data governance. Upgrading systems like artificial intelligence and machine learning raises additional issues in data governance maintenance.
Systems using AI/ML operate differently from conventional systems with fixed records. Returning a value or a transaction status for one transaction is not the goal. A machine learning and artificial intelligence system sort through petabytes of data to find solutions to potentially complex questions.
Furthermore, there are various internal and external sources from which data can be gathered, curated, and stored. These sources may or may not adhere to the governance norms of your company. To guarantee accuracy, it is then necessary to make sure AI/ML systems are trained on reliable data.
These are just a few issues that companies and their auditors deal with as they search for solutions and concentrate on data governance for AI/ML.
Why do AI/ML Systems Need Data Governance?
The IBM Global AI Adoption Index 2022 states that the adoption rate of AI is 35% worldwide and widespread in a few nations and industries. The handling and quality of the underlying data are crucial given how quickly AI and ML technologies are being adopted to spur creativity and decision-making.
The fact that AI and ML systems are more advanced than conventional computer systems highlights how important data governance is. For AI/ML systems, a strong data governance architecture is required for two key reasons:
Dynamic structure: AI/ML systems are dynamic, constantly evolving, and learning from both organized and unstructured data, in contrast to traditional data systems.
Volume and variety of data: An AI/ML system's performance is closely correlated with the quantity and diversity of the datasets it uses for training and learning.
How do AI/ML Systems and Data Governance Interact?
AI/ML systems can handle large amounts of data asynchronously and simultaneously. This enables faster and more effective data processing by feeding the processor with data in numerous threads.
But this also adds a layer of complexity. An AI/ML system's main objective is to sift through enormous datasets in search of answers. This can involve anything from patterns in e-commerce data to forecasting future trends based on historical data. The whole output may be impacted by tainted or biased data from one source, rendering the conclusions untrustworthy.
To guarantee that every data thread is accurate, pertinent, and bias-free, it is crucial to include strict data governance throughout the process.
Data Governance Implementation Challenges for AI/ML Systems
Organizations must overcome several data governance obstacles when managing and integrating data for AI/ML systems.
1. Combining data from multiple sources
Ensuring consistency becomes a major challenge when businesses collect data from numerous sources, each with its governance requirements. Data errors, redundancies, and mismatches may arise from this diversity.
For efficacy, data must be harmonized to present a whole picture. The challenging process of integrating data into a single format involves cleaning, converting, and normalizing the data. Ensuring the enormous datasets utilized by AI/ML systems are relevant and correct is crucial to prevent flawed models.
2. Preserving the quality of the data
Since clean, correct, and current data is essential to the functioning of AI/ML systems, it must be ensured. Inadequate data quality can result in inaccurate model insights and forecasts.
For instance, biased predictions can result from low data quality. Another excellent illustration of how an ML trained on ten years' worth of resumes in 2014 created a prejudice against female candidates is the now-discontinued Amazon recruiting process. AI/ML systems may be ensured to use only the highest quality data by implementing a data governance structure, which can aid in removing any biases or inaccuracies.
3. Recommendations
The fact that some AI/ML models employ private training data may make it difficult for businesses to understand and trust the recommendations. Misuse or misinterpretation is possible in the absence of understanding of the decision-making process.
To ensure fairness in model outcomes, it can be helpful to identify and correct these biases by understanding what training data the model uses and that strict data governance is followed.
4. Data security and privacy
Managing large amounts of processed data necessitates ongoing attention to detail regarding security and legal compliance. Increased security and compliance risks associated with larger data volumes necessitate compliance with numerous international data privacy and protection laws.
Data leaks, tampering, and unauthorized access are just a few of the disastrous outcomes that can result from data security lapses. Additionally, it might erode confidence in the AI system and result in legal ramifications that harm a business's brand and cause financial losses due to dwindling sales or fines from regulators.
A data governance strategy uses encryption techniques, periodically checks data access, and proactively ensures data security conforms with the data protection laws.
How AI/ML Systems Can Benefit From Data Governance?
The future of data governance in AI/ML isn’t only about managing data but also ensuring it’s leveraged responsibly and effectively. As the landscape of AI/ML evolves, so does the importance of robust data governance. Organizations must be proactive, adaptable, and equipped with the right tools to navigate this terrain.
1. Make sure the data is accurate and consistent
To facilitate communication and blend in with data from other sources, it is necessary to standardize data when integrating it from internal and external transactional systems.
This allows them to communicate data with other systems, and many systems come with prebuilt application programming interfaces. Businesses can employ ETL tools, which move data from one system into a format that another system can read if APIs are not readily available.
2. Data should be functional
Useable data encompasses more than simply data that is accessible to customers. When information becomes outdated and loses value, it should be deleted. However, business users and IT need to agree on when to execute data cleansing.
This will have the form of data retention policies. AI/ML data ought to be removed when the data no longer matches the model after an AI data model modification.
3. Trustable data
An AI/ML system that was previously very effective may become less efficient. We call this model drift. This can be verified by routinely comparing the outcomes of AI/ML with historical data and current events. It is imperative to rectify the AI/ML system if its accuracy is shifting from the available data.
Although data scientists have techniques for measuring model drift, business professionals may most easily assess for drift by comparing the performance of AI/ML systems with past performance.
Wrapping Up
To summarize, it is evident that firms seeking to maximize the value of their data while maintaining accuracy and ethical standards must integrate data governance with AI/ML technologies. Comprehending data governance is becoming more important than ever as AI and ML take on a more central role in innovation and decision-making.
Going forward, businesses can leverage data engineering services to get more value out of their data and to improve their data management processes.
Ensuring data quality, protecting privacy, and integrating disparate data sources are meaningful tasks. However, companies may sidestep these obstacles and increase confidence in their AI/ML systems by implementing strong data governance policies.
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