In the rapidly evolving digital landscape, business leaders are often captivated by the allure of cutting-edge technology—AI, machine learning, and neural networks—as the silver bullet for their analytics needs. However, the real power lies not in technology alone but in marrying it with a deep understanding of analytics fundamentals. You can unleash immense power by combining tech and training in analytics.
Misconceptions Around Advanced Methods
Analytic tools like AI, machine learning, and neural networks can do some amazing things. You’ll see the incredible potential if you spend ten minutes working with Open AI’s ChatGPT.
In contrast to recent technological advancements, there are ongoing attempts to rebrand traditional analytic methods in terms of advanced analytics. By referring to linear regression models as “unsupervised learning,” young analysts have figured out how to pad their resumes. Suddenly, every new analyst is “experienced” in “machine learning.”
However, this repackaging of traditional tools overlooks an essential truth: fundamentals are critical. Without a solid grasp of analytic fundamentals, the application of advanced technologies will likely fall short of its potential.
I once worked on a contract for which the client required us to create interactive dashboards for all analytic results. The only problem was nearly all of our analyses were best presented in forms other than dashboards. In this case, the client’s infatuation with new technology prevented us from providing better reporting.
The Limitations of Advanced Analytics Alone
While AI and advanced analytic techniques hold transformative potential, they are not silver bullets. In fact, while many advanced tools hold tons of promise, they also have important limitations.
The primary limitation of advanced analytics tools, like AI and machine learning, is that they lack human cognition. They cannot innovate or provide strategic thinking.
They don’t understand context beyond what we tell them. Even with our input, they don’t truly “understand” the context. Furthermore, AI and machine learning models lack emotional attributes like care, empathy, concern, and trust.
The truth is that the backbone of advanced analytic tools remains rooted in basic mathematical and statistical methods. They leverage means, medians, modes, standard deviations, correlation, and regression in highly complex workflows to achieve their end results. These foundational elements are the unsung heroes, often overshadowed by the buzz around ‘sexy’ new analytic technologies.
In another example, I had a client with a large amount (i.e., millions of records)of unstructured text data. The client was notorious for asking us to find interesting insights for improving organizational outcomes, but without concrete direction.
My team started developing natural language processing (NLP) tools for classification and text extraction. After two months of demonstrating proof of concept, the client didn’t understand why it was taking so long.
They didn’t understand the extent of human involvement needed to properly train the models. Unless they were willing to invest significant resources in the project, the timeline would be necessarily long.
The Critical Role of Training
Success in analytics is not just about having the latest tools; it’s about knowing how to use them effectively. This includes being able to troubleshoot when you run into problems. Therefore, emphasis should be placed on training your teams on the fundamentals of analytics.
Training existing staff on analytic fundamentals gives them the knowledge and skills to successfully adapt to emerging technologies. Investing in training to do the basics correctly can yield incredible dividends across the organization.
The vast majority of all analytics performed worldwide continue to be simple descriptive analyses. Nearly as common are analyses that focus on the relationships between small groups of factors.
In comparison, the advanced analytics applications that receive the most media attention are the exception rather than the rule. For example, only 4.4 percent of U.S. businesses indicate using AI to produce goods or services recently.[1] And only 6.9 percent said they might use AI in the next six months.
A well-trained team can thoughtfully prepare data, understand system limits, and apply appropriate analytic principles to gain actionable insights. More importantly, they recognize that the most valuable insights often come from basic, not cutting-edge, analytics.
Embracing Fundamentals for Future Success
Given the evidence, it’s clear there is a lot of hype around advanced analytic technologies. There’s nothing wrong with being excited about the possibilities, and the unprecedented access to emerging tech is tantalizing.
However, as the old saying goes, you have to learn to walk before you can run. So, unless you are willing to spend huge amounts of money, you’re probably walking. In fact, only 11 percent of global employees feel fully confident in their data literacy skills.[2]
So, there is a lot of walking going on. That’s okay. By emphasizing education and solid comprehension of analytic principles, organizations can enhance their ability to use analytic technologies wisely.
By embracing organization-wide analytics training across your workforce, you’ll be able to reap both immediate gains and long-term dividends. Additionally, your team will have the foundation to leverage more sophisticated tools while recognizing their limitations.
Conclusion
Advanced analytics technology is a shiny new object for business. However, the key to unleashing immense power lies in combining tech and training in analytics.
The latest analytic tools remain rooted in the timeless principles of statistical analysis. Yet nearly 90 percent of employees do not feel confident in their data skills.
For business leaders, the path forward is clear – invest in training your people with organization-wide analytics. As advanced technologies become more accessible and affordable, they will unlock its true potential to drive your business forward.
[1] NBC News. December 4, 2023. There’s a gap between AI talk and businesses actually using it. Available at https://www.nbcnews.com/data-graphics/wide-gap-ais-hype-use-business-rcna127210; accessed on March 7, 2024. Analysis of data obtained from the U.S. Census Bureau’s Business Trends and Outlook Survey (https://www.census.gov/hfp/btos/about).
[2] Qlik.com. March 22, 2022. Data Literacy to be Most In-Demand Skill by 2030 as AI Transforms Global Workplaces. Available at https://www.qlik.com/us/company/press-room/press-releases/data-literacy-to-be-most-in-demand-skill-by-2030; accessed on March 7, 2024.
