There is a revolution in how marketers are using artificial intelligence (AI) and machine learning (ML) to help them execute intelligent strategies and campaigns at scale. One important area where AI and ML can be put to good use is market data management.
"It's basically turning AI and ML into a useful tool for marketing," said Theresa Kushner, head of the North American Innovation Center, NTT Data Services, at The MarTech Conference.
This way, businesses can better understand all the data streaming in that relates to what is happening in the marketplace, including who is buying the products and other important buying trends.
“AI and ML can help you sort, organize, and present that information to you in a way that makes it more digestible within your marketing program,” Kushner said.
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Here are three key steps for how to work AI and ML into your market data management.
(Web scraping is one of many ways to collect market data, discussed in depth here.)
Connect data across Teams
Data is growing rapidly. And it doesn't just sit idly by in your company's database and data management platform. It gets piped into the streams, Kushner said.
"And many times that data is just as important to marketing as it is to the product divisions that use it," he added. "So using AI and ML can help you identify where data goes for marketing, where data goes for product design, where data is most important for finance, etc."
Therefore, AI and ML can help create rules for where the data goes. And it helps if this constantly updated data appears on dynamic dashboards, as opposed to clunky spreadsheets.
But to get started with making all this market data more manageable, the marketers who own the data need to connect with the other departments that will benefit from it. Marketers also need to be in close contact with data engineers.
"[Data engineers] understand where the data is coming from and how it can be transformed from one system to another, where the data is being stored or where it is not being stored," explained Kushner.
Because they are aware of all sources of data, data engineers are the first to investigate any data quality issues.
Evaluate where AI and ML can solve problems
With all this market data being piped in from various sources, it is a constant challenge for marketers to connect the dots. Often, data engineers are manually going through and ensuring that important financial and product data are being compared on an equal basis.
Therefore, these labor-intensive tasks can be identified as areas where AI and ML tools can help make market data management more efficient.
"AI and ML can detect those patterns of defects, so to speak, and fix them," Kushner said.
Implement key programs supported by reports to show progress
Once these areas are identified, put in place a program where AI and ML can be used so that data people don't have to go through each data point to inspect it themselves.
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A simple example would be where service information is stored in multiple locations within the organization. In some places, the data may be tagged as services, but elsewhere this data may be labeled as product data. Using an algorithm to identify and bring together these seemingly disparate data sets could be a very important business problem that AI can solve.
For that matter, or any other market data management program that uses AI, make sure the problem is covered in the report. This way, leadership will be able to understand, from the report, the problem that exists and how AI and ML are being used to solve it.
"You need the report to make sure that you point out the issue most important to the business...so that the business understands that this is of great value to them," Kushner said.
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