To become a data-driven organization, you need to answer the question “What’s in it for me?”
Jack Lampka is an AI and data strategist who helps organizations transform complex data into clear, actionable business decisions. With his experience in senior leadership roles across global companies, including as Head of Data Science at MSD, he is widely known for his ability to explain how data and analytics can be applied in ways that deliver measurable value.
In this exclusive interview with London Keynote Speaker Agency, Jack Lampka shares insights into why AI initiatives fail, how companies can build stronger data cultures, and what it really takes to scale data-driven decision-making.
It states that approximately 70% of AI projects fail. From your experience, what are the most common factors behind those failures?
they. people. The people who develop AI solutions and the people who use them. When speaking on this topic, I always ask my audience what they think are the main reasons why AI projects fail.
And success has three fundamental ingredients: people, data, and technology. If you ask the audience, most of the time, actually all the time, most people will say it’s the people. People are the biggest factor in success, but they are also the cause of failure in AI projects, and that includes the people using and developing AI solutions.
How can we break through the hype and demystify AI so we can focus on what actually matters?
AI is not as complex as most people believe. At the end of the day, AI analytics is just math. These are mathematical models that attempt to replicate and model reality as closely as possible. So, it’s mathematics. Mathematics may be complex, but ultimately it’s just numbers, just data.
The second factor when thinking about AI technology is that you shouldn’t think about it as a business user. It only happens at the end. Start with your business needs. Identify what data is likely to be needed to address these business needs, and only then can technology come into play.
In some cases, AI is also used to address business needs. In some cases, a simple dashboard may be sufficient to meet your business requirements. So don’t start with technology. Also, don’t think that AI is as complex as most people want it to be.
The concept of “data village” is often used. What does this actually mean and why is it important for organizations looking to extract real value from their data?
I use the term data village to describe the different skill sets needed to succeed with data and AI. And to develop these great solutions, you don’t just need data scientists; you also need data analysts, data engineers, visualization experts, and data stewards.
So this is basically everything along the data analytics value chain, and I call it the data village. Another element of that skillset is data product marketing. In other words, it is part of the data village for any organization to succeed with data.
What are the most persistent misconceptions that companies have about big data, and how do these misconceptions hinder data-driven decision-making?
Therefore, most companies believe that they need big data to become a data-driven organization. But in the end, it always starts with business needs. You need to identify business hypotheses that can be answered with data.
The next important thing is whether the data is large or small. First, identify your business needs. And sometimes small data can be beautiful. Sometimes a small amount of data is enough to address a business problem. So start with your business needs, no matter how big or small.
How can companies develop a data mindset in non-technical employees?
In any organization, people are the biggest factor in AI success or failure. This includes people using data and AI solutions. And to use them, you need to have the right data mindset level.
If not, you need to implement a data literacy program that brings you to that level of thinking. You need to identify your current situation and future goals, and identify the bottom-up data literacy program you should acquire.
You frequently emphasize the question, “What’s in it for me?” Why is this idea central to successful data and AI adoption?
When I think about myself, when I buy a product, when I buy a smartphone, when I choose a place for vacation, when I decide on a job offer, I think about what it will be for me.
We don’t ask it explicitly, but we think about why we need to buy this product, why we need to go on that vacation, why we should take this job offer. So, at the end, I think about every product I buy or choose, and I think about the question: What is it going to do for me?
Why should data products be different? Every user of data and AI has questions about what it means for them. They won’t ask it explicitly, but if you don’t answer that question, your business colleagues will at best not use these data and AI solutions, and at worst they’ll just pretend they are.
Therefore, in order to become a data-driven organization, we need to answer the question: What is it in for me?
About Jack Lumpka
Known as a hype-busting big data speaker, Jack focuses on the people, cultural, and strategic foundations of successful data-driven organizations. His work focuses on data literacy, decision making, and ensuring technology supports, rather than leads, actual business needs.
This exclusive interview with Jack Rumpka was conducted by Tabish Ali of Motivational Speakers Agency.
