Data-centric AI/ML: Learn how to ensure data-centricity to support AI and ML and how to operationalize your data to support your AI/ML goals.Whether it’s generative AI or machine learning (ML), artificial intelligence is on everyone’s mind, and they want to put it to work right away.
While generative AI tools like ChatGPT are great at creating content, it’s not the same as implementing AI and ML tools, as generative AI tools can’t learn based on your data. In fact, your AI/ML tools are only as effective as your data – which should be centralized and secure.
Often, businesses don’t pay much attention to operationalization, especially if they are not building massive projects like self-driving cars or intuitive virtual assistants. But data operationalization and a data-centric framework are critical to effective AI/ML.
Data operationalization is something many organizations implement already, but it is not a term we often recognize outside of data science. Generally speaking, it’s putting data to practical use in an organization’s day-to-day operations and decision-making processes.
Operationalizing data involves integrating data-driven insights and analytics into the core business processes to drive efficiency, improve performance, and enhance overall decision-making. Those insights can be used to inform AI models to perform predictive modeling.
Below you’ll learn the steps to start operationalizing your data to create a data-centric AI ecosystem.
The operationalization of data can be divided into four steps:
As we mentioned earlier in the article, your data strategy should align with your business objectives to ensure the right data is effectively managed and utilized.
Data acquisition and management are central to any operationalization scheme. Data-centric AI models are predictive tools that learn how to solve new problems by analyzing large datasets.
You must carefully consider your company’s objectives while picking data sources and what data to collect. Instead of mindlessly retrieving data, strategically fishing out datasets that coincide with your goals saves time and resources.
One thing to remember is that when it comes to your data is that quality is always better than quantity. In fact, too much (bad) data can often be worse than no data at all. In fact, too much (bad) data can often be worse than no data at all. According to a survey from Gartner, organizations believe poor data quality to be responsible for an average of $15 million per year in losses.
While the upfront assessment and strategy work is the heaviest lift, your team needs to continue to evaluate your data strategy to ensure it aligns with your organizational goals.
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Data architecture is a set of standards that define what data an organization collects and how it is stored and integrated into its data management system. In operationalization, your data architecture can be optimized to utilize machine learning and artificial intelligence models to solve new problems or improve existing processes.
In the case of data-centric architecture, data is a standalone asset. Instead of manipulating data to work with the digital products and programs that use it, a data-centric architecture requires products and programs to be built around the data.
It’s important to note that data-centric architecture doesn’t mean your data can’t change. It can and should be evaluated often. Under a data-centric architecture, data is independent of apps and products that use data.
Once you’ve established your data-centric architecture, you are ready for AI/ML modeling.
We can’t reiterate enough that your modeling work is only as effective as the quality of your datasets. Data modeling based on data-centric AI relies on quality, well-maintained and accurately named data to produce and predict accurate information.
There is often an argument for data-centric vs. model-centric modeling. You may run into situations where information is subjective based on whoever is inputting or interpreting it. When that happens, the response is to change the model, not the data.
When taking a data-centric approach to AI modeling – as we discussed earlier – there is clear alignment on data governance and conventions that make it clear what the model is supposed to learn. The data is frequently evaluated to ensure accuracy and quality, and your AI modeling is up to snuff.
At some point after deployment, you’ll get an unexpected output from your AI/ML model as a result of poor data quality. But it’s not the end of the world. Data should be regularly monitored and maintained to ensure quality. Learn to let irrelevant data go once it’s no longer useful (or accurate) and use more appropriate data.
So how often should organizations review their data strategy and make changes? It all depends on what you need from your data and how it aligns with your larger business goals.
Artificial intelligence and machine learning are taking the industry by storm but in order to take advantage of its benefits, the first step is to work toward properly organizing your data and working toward a data-centric framework to support AI/ML.
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