We have had the privilege of working with exceptional teams and organisations since we were founded in 2019. As we mark our fourth anniversary this month, I would like to share one of the most common challenges our clients and partners face across all sectors, be it aerospace, healthcare, finance, food production, utilities and others. And that is employing inadequate or no metadata within their data standards.
Over 70% of the businesses we supported were not using metadata effectively (if any), which cost them collectively thousands of wasted hours, security issues, data maintenance problems and inefficiencies. Not to mention the loss of opportunities to discover hidden business-critical trends through augmented analytics, cutting-edge and transformative AI-based services, and much more.
So why is it so important? Let’s dive deeper into it without the technical jargon.
So What is Metadata?
In simple terms, metadata is information that describes the data collected and stored in a given database.
Sounds simple, right? Well, it is much more instrumental in large data models. However, modern data standards automatically expect any data-driven organisation to have a set of metadata that play the following crucial roles:
- Data Discovery – providing a way to understand and navigate the contents of an extensive database, making it easier to discover and access relevant information for a specific task or analysis.
- Data Governance – defining and enforcing business rules and constraints on the used datasets, ensuring that the data is accurate, complete, and consistent. This can improve the overall quality of the data models and reduce the risk of errors.
- Data Lineage – tracking the origins and transformations of data, providing a way to understand how the data has been acquired, processed, and used over time.
- Data Security – allowing teams to define and enforce access controls and security policies, providing a way to protect sensitive or confidential data from unauthorised access.
- Compliance – helping to maintain compliance with regulations and standards, such as GDPR, HIPAA, and SOC 2.
- Auditing – tracking data access by providing a way to audit and monitor data usage, detect potential data breaches, and investigate possible data misuse.
Thus, some may argue that databases without metadata can be deemed purposeless, designed only to store some datasets and files and use analytics to visualise the collected data. That is fine if this is all your team needs. But we would advise that, in a data-driven world, it is always a good practice to be prepared for the future and ensure that your databases follow modern and evolving data standards.
Indeed, your data models do not need to be large for your team to take advantage of having a clear metadata policy as part of your wider data strategy. Consider how combining your databases with metadata can establish the above benefits. Not to mention the ability to aggregate your datasets with third-party data sources, such as market research data, accelerate the delivery of business insights, and discover new trends that enhance decision-making processes and productivity for your organisation by severalfold.
Do AI Models Rely on Metadata?
While it is possible to build AI models and perform advanced and more dynamic analytics on databases without metadata, it tends to be significantly more complex, expensive, less accurate and prone to human errors.
If we are to consider the benefits of metadata described above, below are the potential outcomes of building AI models without metadata, particularly when involving large datasets:
- Data Preparation:
Without metadata, it may take more time and effort to understand the structure of the data and prepare it for use in an AI model. This significantly increases the time needed and costs of data preparation, which leads to delays in getting the model into production.
- Model Development:
Building a given AI model without metadata can be more complex and requires more development time and additional human and technical resources to achieve the desired level of performance.
AI models trained without metadata can be much more problematic to maintain over time. It would be more challenging, for instance, to understand how the model generates predictions or identify when it is no longer accurate. As you might expect, this further increases the costs of maintaining the model.
- Legal Compliance:
Since metadata is essential for data governance, it would be much harder to track and trace data provenance, lineage, quality, etc., without metadata in place. This is a drawback for organisations that must comply with regulations or industry standards. However, with the emerging AI regulations globally, all organisations will have the legal obligation to comply with these regulations.
Thus, with metadata, AI models have a much higher capacity to construct (or “understand”) the context of the datasets in question and build more accurate intelligence output.
Above all, metadata also plays a critical role in ensuring that AI models are developed and deployed in a responsible manner by providing information that can be used to identify and address potential ethical and bias issues with the datasets and the algorithms involved.
Metadata and Responsible AI
As a company dedicated to digital ethics, responsible AI is a key aspect of our work and research. Here are a few common examples of how the benefits highlighted above are applicable in building responsible AI models:
- Data provenance:
Metadata can provide information about where the data came from, who collected it, why it was collected and how it was collected, which can help to identify potential sources of bias or unfairness in the data.
As strong advocates of explainable AI, we use metadata to explain a model’s predictions, actions and data processes, which can help make the model more transparent and understandable to humans.
- Human in the loop:
Or what we like to describe as the human-centric models, where metadata facilitates human oversight of a given model by providing details about the model’s decision-making process and allowing humans to intervene when necessary.
- Compliance with Ethical Guidelines:
Again, this is part of the overall data governance aspects of metadata policies, which can also be used to ensure compliance with ethical and legal guidelines – examining how the original data was collected and processed and how the model is applied, particularly when interacting with end users online.
As we advise all of our customers, the key behind any successful construction of responsible AI models is to be proactive and transparent about the ethical considerations and risks involved in building AI-powered systems and to build in mechanisms for ongoing monitoring, evaluation and feedback to ensure that the models remain fair, unbiased and aligned with ethical principles. Without metadata, this task is practically impossible to achieve.
With all that said, it is worth noting that any AI-based or AI-generated output depends entirely on past learnings and accumulated data over time, which tend to lead to various assumptions and unintentional biases of the teams involved.
While metadata is the critical tool to set up a strong foundation for your data strategies, problems can arise when teams misinterpret the underlying inferences in the data and produce inaccurate and biased metadata without realising it.
That is why it is essential to combine as many data sources as possible, to maximise the accuracy of any assumptions and establish clear governance and oversight structures to ensure that the data model is developed and deployed responsibly. This includes assembling a cross-disciplinary team with expertise in machine learning, ethics, and domain expertise and ensuring that the team has the resources and authority to make critical decisions about the model throughout the development process.
If you have more questions about metadata or wish to learn more about the advantages of applying metadata within your data models or as part of your overall data strategy, you can get in touch with our team.