Study the Data, But Eat the Cake—Put the Human Factor Forward

Are you guilty of cognitive bias? The answer is yes, probably on a daily basis. It’s a fancy way of describing how humans typically make decisions. Researchers have identified over 180 types of cognitive bias. You’ll recognize these examples.

• Confirmation bias is the tendency to place the greatest value on information that supports your pre-existing beliefs. You heard dark chocolate was a healthy option and ate that second slice of cake.
• Anchoring bias makes people rely too heavily on the first bit of information they receive. You saw the word SALE and bought the overpriced widget.
• Sunk cost fallacy is that soul-sucking tendency to continue investing time, money, or effort into activities that are clearly failing. You kept your gym membership even though you can’t remember your last workout.

Cognitive bias can be helpful when you need to make decisions quickly. But when the stakes are high, data is the knife that cuts through preconceptions to uncover the objective truth. AI turns that knife into a laser taking data-driven decision-making to unprecedented levels of accuracy, efficiency, and ease.  

Industry giants are investing millions to tap the large data sets that give them competitive insight into their customers’ hearts and minds. Machine Learning, predictive modeling, and natural language processing are a few of the ways AI makes data more meaningful. These functions put facts in the hands of CEOs, boards of directors, and any other leader who, in an increasingly competitive environment, must take off their rose-colored glasses and see that their pet pony is actually a nag. 

Dig Deeper and See Farther

You may wonder how AI-driven analytics differ from the queries, dashboards, and statistical packages that have been around for some time now. Traditional analytics is like using a microscope to explore a limited area of information; whereas adding AI provides a satellite image of an entire data landscape. These are some enhancements gained with AI.

Deeper insights

• AI algorithms identify subtle patterns and relationships within complex datasets that humans might miss, allowing for a more comprehensive understanding.
• Predictive modeling reveals future outcomes and trends with greater accuracy than traditional methods, enabling proactive decision-making and change management.
• Automating data exploration frees time to focus on higher-level analysis and interpretation of results.

Increased efficiency and scalability

• Automation also performs tasks such as data cleaning, preparation, and transformation that reduce the time and effort needed for data analysis.
• AI-powered analytics handle large volumes of data efficiently.
• AI can analyze data in real-time, enabling businesses to make immediate decisions based on the latest information.

Improved decision-making

• AI can provide data-driven recommendations.
• Algorithms can be designed to be objective and unbiased, reducing the risk of human error and judgment in decision-making. Caveat here—that depends on the human’s perspective.
• Analytics can be personalized for individual customers or users.

Take a Test Drive

You might think the combination of AI and data is a superpower reserved for corporate behemoths. Not necessarily so. These are examples of AI analytic tools that are available at multiple price points and levels of complexity. Several providers offer open source or limited free access, which is a great way to test options.

Free and Open-Source

Apache Spark provides a powerful platform for large-scale data processing and machine learning tasks. While requiring some technical expertise, it offers a free and flexible solution for building AI-powered analytics workflows.

RapidMiner is another open-source platform with a drag-and-drop interface. The software offers pre-built modules for common data analysis tasks and integrates with various AI libraries for model building and deployment.

Cloud-based Solutions

Amazon SageMaker offers a comprehensive suite of tools for building, training, and deploying machine learning models. It’s free with limited usage.

Microsoft Azure Machine Learning is an Azure-based environment for building and deploying AI models. It offers various pre-built tools and connectors for easy data integration and analysis.

Subscription-based Tools

MonkeyLearn’s  AI-powered text analysis capabilities include sentiment analysis, topic modeling, and keyword extraction. It provides affordable plans for individual users and small businesses.

Polymer Search utilizes AI to analyze and visualize large datasets. It offers a free tier with limited features and subscription plans for scaling up your analysis needs.

It’s exciting that there are so many possibilities for experimentation. I encourage you to check these out as well as options from our partner providers on .orgCommuity’s Solutions Center.

Use Data to Reach People

If you’re looking for a real-life example of how data can improve decision-making and by extension the bottom line, I’ll share one group’s impactful experience. I’ve highlighted this story before, but it’s a perfect reminder of the best reason to explore data. And what might that be? There is only one right answer, to better understand people.

Even associations that boast celebrity members sometimes struggle. Tiffany Kerns is the Executive Director of the Country Music Association Foundation and CMA’s Vice President of Industry Relations and Philanthropy. In her keynote presentation at .orgCommunity’s 2022 Solutions Day, she outlined how CMA used data to turn around struggling membership initiatives. You heard that right, one of the country’s most famous associations was having difficulty retaining members.

When Tiffany became vice president of industry relations in March 2020, she sensed that the association’s approach to its members needed realignment. The organization’s outreach was focused on the glitter and the brand but missing the people who are at the heart of its mission.  

The pandemic added to an already precarious position. CMA experienced multi-million-dollar contractual losses. The group was dealing with a triple threat—uncertainty as people, as an organization, and as an industry. The experience was traumatic, but it was also an opportunity for a reset.

CMA started their transformation where I advise our clients to begin. They studied the member experience and evaluated how to engage and communicate on a personal level.

The group didn’t want to be trapped by traditional assumptions or by imagining that members simply didn’t understand the organization. The tables were turned. It was CMA that had to learn more about its community.

“If you’re a member organization, and you’re not asking about both personal and professional needs, then you are not relevant,” Tiffany observed. “Those two aspects of life are intertwined. And, when you work with people you need to lean on data. We wanted to understand all the human behavior occurring throughout our organization.” Including—

• What members are looking for as professionals and people.
• Whether CMA has resources to meet those needs.
• How members interact with each other, the organization, and the profession.
• Why some country music professionals are not members.
• How relevant the association is to its constituents.
• How the music business is evolving.
• Where opportunities lie and whether CMA is agile enough to capture them.
• How the organization’s value proposition needed to change.

“When we examined the data, we saw patterns of behavior that we would not have imagined,” Tiffany recalls. “We’re still processing the information we learned about the purchasing habits of younger people. We also discovered the answer to a mysterious phenomenon. Artists were experiencing rows of empty seats at ‘sold out’ concerts. Research revealed that when younger people purchase their tickets months in advance, the money’s been spent for so long that they don’t feel obligated to attend the event. When you dig into the data, you uncover significant surprises.”

“It took two years to reshape and redefine our membership,” Tiffany advised. “We updated criteria and added new tiers. Our statistics indicate that 40 percent of our business comes from individuals outside of country music. So, we opened the membership to anyone in the music industry. This was a controversial move. But we gained direct sightlines to shifts in our broader environment.

“Students are now eligible to join at age 15, which is a new pipeline to the next generation of talent. Our diversity strategy puts inclusion at the center of all our activities.”

The effort was successful because CMA didn’t make assumptions about what was hurting their membership numbers. They side-stepped cognitive bias and went straight to the data. I can only imagine how AI might have supercharged this effort.

In response to what they learned, CMA launched an array of programs to promote engagement and collaboration among their community. One of their most impressive strategies is old school. The team watches data and member behavior. But they also pick up the phone and call people to find out why they aren’t participating.

The intersection of technology with the human touch is an area I’m going to explore in future posts. As we become more steeped in artificial intelligence, it’s critical to balance what technology reveals with an emotional perspective.

Cognitive bias isn’t always a bad thing. Our individual experiences are the source of joy, meaning, and purpose. Sometimes that second piece of cake is the spice that makes life truly worthwhile.