We hear about artificial intelligence (AI) and machine learning (ML) everywhere. As the computing power to process massive amounts of data becomes technically realistic and affordable, AI and ML are powering everything from our Alexa and Siri digital assistants and recommendations on Netflix, to decision-making processes for autonomous vehicles and precision agriculture.
The promise of AI glitters for so many industries and organizational leaders as they look to harness the power of their data, gain valuable insights from it, and inform new ways to deliver value. But to take advantage of AI’s promise, you need intelligent data management—including clean and robust data sets—in order to derive information that is accurate and valuable.
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An important question posed by Forrester principal analyst Seth Marrs, is “What if the world depended on the accuracy of your org’s data?”
He comments that data accuracy is still a struggle for even the most important tasks and analyses, and emphasizes that AI requires accurate, real-time data. He looked at available data for the number of positive COVID-19 cases in the U.S.—some of today’s most visible and critical data – and found that numbers from three reputable U.S. government agencies varied by close to 10%. That is quite a wide margin of error when you are counting on that data to guide decision-making.
How to Carefully Prepare Your Data for AI/ML
Data is growing exponentially, and you must have a strategy for your data to manage it, protect it and use it. The old adage “garbage in, garbage out” proves even more true in the world of AI.
Here are some basic steps you can take to prepare your data for AI and machine learning platforms – even if you’re not ready to take the plunge just yet:
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- Capture the data required. This could be physical data, such as paper files, backup tapes or hard drives, or digital data, that is either application generated or human-generated.
- Classify the data by type and organize it with metadata.
- Index the data to create a baseline library, which can later be searched for patterns and trends.
- Enrich the data with context and metadata as needed to make your library more useable.
- Visualize your data, regardless of format, to gain actionable business insights and predictive analytics.
Of course, these steps only scratch the surface. Data preparation is a detailed and extensive process. One must understand how these work for machine learning projects.
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Clearly, data preparation is not a simple task. It is advised that a data scientist or data engineer is involved if you need to automate data collection mechanisms, set the infrastructure, and scale for difficult machine learning tasks.
Solving Complex Business Problems with AI/ML
How are companies extracting meaning and value from their data? Here are a few examples in various industries:
- Mortgage industry – Companies in the mortgage industry face the business challenge of increasing loan file accuracy and completion. By applying AI/ML to online applications, they can speed up loan processing by automating and validating loan completeness. In addition, they can analyze data to mitigate losses, identify new opportunities, and develop new programs.
- Medical research industry – In the field of healthcare and medical research, uncovering existing research and classifying data is a challenge. AI/ML platforms can help find data enriched through document classification and data extraction, which dramatically improves search results.
- Media and entertainment industries – The media and entertainment industries face the business challenge of protecting IP against copyright violations, distribution rights infringement, and contract breaches. With AI/ML, studios can identify relevant information in video and film within minutes. Virtual video clips can be defined by camera cuts, dialogue, or time intervals.
- Transportation industry – The transportation and logistics industries face the business challenge of increasing fleet efficiency, especially as ecommerce booms. With AI/ML, automated dispatch and routing systems can collect driver and delivery data to create optimized routes.
- Oil and gas industries – Companies in the oil and gas industries must be able to intelligently search and manage geographical information about wells and land use. AI/ML platforms can help to access relevant information for a given geographical region or project to determine its viability for future use or sale.
How Do You Want to Drive Innovation in the Future?
As you evaluate future opportunities for your business and industry, you realize that your company’s data contains valuable information—if you are able to unlock its value. In the abstract, AI seems like the answer to so many business challenges, but you can’t implement it by just waving a magic wand. Like most things, there is a lot of work behind the curtain to make the magic.
The power of AI/ML lets you identify previously unseen relationships in your data to identify opportunities, reduce costs, and minimize risk. But first, you must make sure your data is clean, robust, and properly prepared to realize AI’s promise.
[To share your insights with us, please write to sghosh@martechseries.com]
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