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Thanks to AI and ML, We Finally Have Answers to Age-old Data Questions

AI and ML have the power to quickly scan data for patterns, process information, and provide on-the-spot recommendations.

Thanks to AI and ML, We Finally Have Answers to Age-old Data QuestionsIn the 40+ years since the term “big data” was first coined, companies have asked the same questions. How do we manage the vast amount of data we have? Where is the data coming from? How can we take advantage of it?

You’d think we would have learned the answers by now. And yet, in 2023, we are still asking the same questions.

Artificial intelligence (AI) and machine learning (ML) provide some answers. AI and ML have the power to quickly scan data for patterns, process information, and provide on-the-spot recommendations. The more data that are ingested, the smarter the system becomes, and the more accurate the recommendations.

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But lingering questions remain. How does one piece of information relate to another? What does that correlation mean in the larger context of an organization’s goals? Understanding the ontology of data types–what they mean to each other, and how they can, collectively, impact the business–is key to unlocking the full potential of seemingly disparate data points.

AI and ML have some answers here, too. AI and ML models can be built to automate the necessary but time-consuming and repetitive tasks required to comb through different data streams to identify commonalities, patterns, and relationships between separate data sets.

Automating data ontology

Finding and parsing a particular piece of data to understand how it fits contextually with an organization’s many other data sets can be like putting together a puzzle with millions of pieces. Data scientists can do it, but it might take hundreds of hours of combing through similar data sets–time that could otherwise be spent building and perfecting models that could add value to their organization.

Automation technology can be applied to the collection and parsing of data from multiple sources, while AI and ML frameworks can be used to identify key aspects and patterns within the data that are related to other data sets. These tools can give data scientists a more holistic view of how different data streams relate to one another. Instead of searching through terabytes of data, they can focus on putting previously interdependent data sets together to create more accurate and useful intelligence for better business decisions.

For example, AI and ML tools can analyze multimodal data distribution.

With multimodal, data can come from many different sources–unstructured log files, troubleshooting reports, website journeys, and more. Common features and subtext within these disparate data sets can be discovered to provide a better perspective on customer purchasing habits and intentions.

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Discovering this ontology, this link between data sets can help create a more rounded and useful picture of a company’s customers, including their preferences, likelihood to purchase, sentiment towards the company, and other factors that could influence a sale. 

Imagine doing this for every customer, and the impact that could have on revenue generation. Then, imagine having to spend only a few human resources to achieve these insights. It’s AI and ML put to very practical–and potentially profitable–use.

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Opening the toolbox

A recent large-scale example of the practice of intelligent data ontology is the work being done by Los Alamos National Laboratory, which is using AI and ML to find patterns in vast, separate data streams. Organizations can achieve similar results on a smaller scale by creating their own models and training them to look for ontological patterns if they have the right tools.

Increasingly, innovation in AI and ML capabilities is being driven by open source communities. Ask any data scientist about their go-to solutions, and they’ll likely name Jupyter, TensorFlow, Pytorch and others. These are all powerful, flexible, and readily available technologies–and they all stem from the open source community.

Each has parts that are beneficial to the creation of AI and ML models for interpreting information from different sources, but only some data scientists use them all, or for the same projects. They might choose one over the other, depending on personal preferences or the project they’re working on.

That’s why it’s important to provide data scientists with the flexibility and choice to use whatever tool works best for them. Give them a platform that supports multiple tools and allows them to use everything available, a single solution, or a combination of technologies depending on their needs. Offer them the toolbox and let them choose the screwdriver, wrench, hammer, or all three.

Moving forward

While many viable proprietary AI and ML options are available, there can be risks associated with investing in them. Innovations in AI and ML are moving very quickly. Often, proprietary solutions require being locked into multi-year contracts. While organizations may receive upgrades during the course of those contracts, they’ll continue to be relegated to the solution they first purchased, thereby risking falling behind as new innovations are unveiled.

If recent history is any indication, those innovations will likely come from the open source community. That’s the place that is actively changing how we manage and take advantage of vast amounts of data. Organizations that do not embrace open source may find themselves continuing to wrestle with the same questions they’ve been asking for four decades.

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