How To Manage AI Data Sources
Artificial intelligence has played pivotal roles in various industries, with data management as one of the biggest examples. However, it’s interesting to know that the development of AI itself requires careful management of data sources as well. This task often falls on the shoulders of AI developers and data scientists.
Most organizations would relegate the task of data sources management to third-party companies to save time and resources. Before they can do that, they need to have an idea of how it works. If you’re in that position, you would want to go through this simple guide.
Identifying data
Most instances of AI development use information sourced from many data feeds on a global scale. This can pose a challenge to developers because it means you’ll have to filter irrelevant information later on. To identify different kinds of data, you need effective data labelling.
Data labelling or data annotation is a process usually done before an AI starts machine learning (ML). These labels can provide an AI with some context on information coming from various data sources. Effective data labeling can also be used to shape the AI’s predictions and priorities later on. (1)
Of course, it’s up to the developer or scientist if they need a specific system of labelling, depending on if they want their AI to have supervised or unsupervised learning. Each mode of learning has its own pros and cons, but for projects that require better data source management, data labelling is so useful that it’s borderline mandatory. (1)
Knowing the type of data sources you have
Data can be classified in three ways. Depending on the source of the info, you can classify data based on its form, whether it’s structured, semi-structured, or unstructured. You can also classify data based on when or how they are created, whether they are real-time, or historical. Last but not least, you can categorize data based on their source as either internal (data from within your organization) or external (from a third-party source). It’s up to you or the AI to give those sources their appropriate weight and how they can affect predictions later on. (3)
Keeping relevant labeled data
Even if you know what data labeling is, it will be useless if you don’t know which are the relevant data sources you need to keep. After all, your artificial intelligence won’t have a clue about it, especially if machine learning hasn’t started yet.
You should focus on identifying the ones relevant to the purpose of AI. For example, if the AI will be designed for use in the health industry, you should keep all labelled data related to health care and management.
Filtering out data seems straightforward; the tricky part is identifying information that seems unrelated but is actually relevant. For example, it would seem fine to remove traffic data from a city for an AI built for the healthcare industry. While some may prematurely think that traffic has nothing to do with health, it has much to do with hospitals, particularly with ambulance routes.
Setting data source priorities
Continuing with the previous example, even if traffic data can be helpful for hospitals, it doesn’t mean that a healthcare AI needs it now. While it’s wise to keep that traffic data, it probably has to be “set aside” for improving the AI at a later time, especially since other healthcare-related info may be more useful at the moment.
Because of that, you should ensure that you also consider the priorities of the data sources you have for your AI when annotating them. This greatly helps with exploratory data analysis (EDA). It gives the AI the ability to quickly analyze data, learn what they are, and determine their importance. This, in turn, allows the AI to generate critical insights and proceed with advanced analytics. (2)
Setting a bar for your AI
Most AI developers set a threshold of sorts for their AI accuracy. Typically, the magic number is 95%—derived from the “95% rule” in statistics. This means an AI should achieve a 95% passing mark when it comes to fulfilling its purpose. To measure this accuracy, constant revisions and repeated testing of the algorithm are required.
If the AI can’t reach the standard score, its algorithms are fine-tuned to achieve better consistency. However, if continuous fine-tuning is no longer yielding significant improvements, it could mean that there’s a problem with its data sources. Data labeling should help in this area, but there are cases when even that is not enough, and manual data filtering has to be done.
Conclusion
That’s the basic rundown on the essentials of managing your AI’s data sources. While the process is long and involves a lot of trial and error, the rewards of being able to fine tune your AI can help save a lot of time and resources in the long run, just by being able to effectively manage business-critical information that you can use to keep your business going.
References:
- “What is data labeling?”, Source: https://www.ibm.com/cloud/learn/data-labeling
- “What is data exploration in artificial intelligence (AI)?”, Source: https://sisudata.com/blog/what-is-data-exploration-in-ai
- “What are the Sources of Data?”, Source: https://byjus.com/commerce/what-are-the-sources-of-data/
