In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the limitations of generative analytics, particularly for casual users. Discover why subject matter expertise is still essential for accurate data analysis, even with these powerful tools. Learn practical strategies for determining if a task is suitable for generative AI and how to incorporate it effectively into your analytics workflow. Finally, understand the importance of process mapping in maximizing the benefits and mitigating the potential challenges of using generative AI for analytics.
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Machine-Generated Transcript
What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.
Christopher S. Penn – 00:00
In this week’s In Ear Insights, this is part two, the cliffhanger resolution from last week’s episode on generative analytics. So, we talked last week about generative analytics, the use of generative AI tools to enable your analytics data. We’ve been sitting around for decades with data, and it just sits there. We do nothing with it. Generative AI tools give us the ability to give this data to tools and then have them tell us “so what?” and “now what?”. But, Katie, you said at the end of last week’s episode, therein lies the challenge: thinking to ask a question, knowing what to ask the machine so you’re getting the right information. I think a lot of us have already made the assumption, “Well, generative AI must have the right answer.” How do we find out before we give them all of our data how do we find out whether or not it even has the right information or the right knowledge so that we can use it to do insightful analysis?
Katie Robbert – 00:53
Yeah, and I stand by those questions. It’s interesting. It’s not just for generative analytics; it’s really for anything. But, if we focus in on generative analytics, my concern is — or my question is — if you’re going to start using large language models to help you with your analysis and insights, how do you know it’s right? This goes back to something we always talk about: you have to have some kind of subject matter expertise to validate that the machines are giving you the right information. That’s not something you can just, like, wake up and be like, “Oh, and I’m a subject matter expert.” Like, those are things that take a long time, a lot of practice, a lot of making mistakes, a lot of trial and error, and just a lot of experience. I think what I’m concerned is going to happen is that companies maybe don’t have the budget or aren’t willing to spend the budget on an analytics team, on a data analyst, on data governance — whatever the thing is — and they’re just going to feed their data into a large language model and say, “What is this telling me?” They’re not going to have a way to say, “That doesn’t seem right.” Or “How do I know that this is correct?” They’re not going to do those validation checks. So I guess, what can we do?
Let’s say we don’t have an analyst on our team, and we’re going to rely on generative AI to help us with our analytics; we’re going to do generative analytics. What can we do to prime the model, to validate that it knows what it’s talking about if I’m not an analytics expert? I think that’s the challenge.
Christopher S. Penn – 02:48
Okay, so here’s the big challenge: today’s language models — even the very best ones — by, if you use just the model itself, they can’t do math at all. If you want to try this, something I saw Rebecca over on LinkedIn share — ask any language model how many R’s are in the word “strawberry”. They will rarely, if ever, get the answer correct, which is three.
Katie Robbert – 03:14
Yeah, I had. I was, like, mentally; I was like, “One, two, three.”
Christopher S. Penn – 03:22
They will say, like, two, four, five for a very simple task like that. The reason that’s happening is because the models — they can’t see letters; they can only see tokens. This is relevant when it comes to analytics and generative analytics because when you load data into a model, it is seeing numbers as essentially pieces of words. It’s looking at the massive statistical correlations to see what things had seen like that in the past. Now, today’s generation of models is much smarter than it was two years ago when ChatGPT first came out, for example, but they still can’t fundamentally count. The way model makers have gotten around that is by adding in hidden pieces — hidden? Well, sometimes hidden, sometimes not — pieces of code.
So, if you use Gemini and turn on code execution, if you use Lama 3.1, if you use ChatGPT, you will see them start writing Python code in the background. They say, “Hey, I see you’ve loaded up a spreadsheet. I’m going to write Python code to interpret this spreadsheet and then process the data that way.” That is how they get around not being able to count. But, that approach, too, is not necessarily the right one all the time because they’re trying to guess at the analysis you want to run and then write the appropriate code for it. So, it’s a very long way of answering the question that these tools do need subject matter expertise. There is no getting around that.
If you don’t have someone who’s got data governance skills, you are flying blind and relying and hoping the tool is sophisticated enough to invoke the right tool usage within itself to come up with the answer. One thing you should do anytime you’re doing these analytics is do a little canary trap, which is you give it a problem that you know the answer to, and you see how well it interprets it. So, if you were to export, Katie, our Google Analytics data and you know what happened in our company, you would give it a table to analyze, you would see what conclusion it comes up with, and you would validate, “Well, yeah, you did not read that table. Like, you don’t understand what the actual task I’m trying to have you do.” Or it does read the table.
Go, “Okay, I can see that generally speaking, they can do addition, subtraction, multiplication, and division.” More complex things than that, they have to invoke Python. And, then you’re into a territory of, “Do you know how to solve that problem without AI?”
Katie Robbert – 06:04
Well, so I feel like that answers my question of “Can you use these large language models to do analytics?” And, the answer is not if there — you don’t — not if you don’t have a subject matter expert on your team. Because I think that’s one of the misconceptions about generative AI, is that it can do math. I think people — I’m generalizing now — I think there’s an association with computing because it’s computers, it’s technology. The word “compute” and “generative AI,” I think they’re lumped into the same category of, “Well, it’s generative AI, it’s technology, it computes; therefore, it must be able to handle statistics and analysis and all these other things.” It sounds like what you’re saying is that is absolutely not the case. That is a misconception.
But, what generative AI can do is write code that would then do the computing. But, then you need to be able to execute the code. I think that’s the other step that perhaps is missing, is it’ll write code, but then the code just sits there unless you actually know how to execute the code. So, it’s like, all right, so ChatGPT, “I have two spreadsheets, and I need to find the statistical significance between these things.” It’s going to start to write code, and it might give you a response, but you don’t know if that response is correct until you can check the code. You probably want to take the code out of that system just to check it, to run it.
I can see you’re about to disagree with me — and that’s fine — but basically, it sounds like the code is the key to generative analytics, and what you then do with that code is going to make the difference in whether or not it’s a workable system to, quote-unquote, “replace a true analyst” on your team.
Christopher S. Penn – 08:11
You are mostly correct. The tools can execute the code as well. So, ChatGPT does this; it will run Python code. Gemini does this; Anthropic Claude does this. However, to your point, just because they can run the code does not mean it’s correct. You will actually see ChatGPT sometimes screw up six or seven times in a row, even just trying to load Python packages that don’t either don’t exist — because it hallucinated them — or can’t run in the environment that OpenAI has set up for them. So that’s an issue. But, you also said something really important, which is that generative AI is the language branch. Well, the language and image branch of — there’s an outgrowth of traditional AI, classical AI, stuff like regression analysis and classification. Those branches can do math because they are entirely math.
Generative AI under the hood is also entirely math. But, that’s a topic for another conversation. As a result, even if you have experience in traditional machine learning, you can get your — you can cross the streams, if you will — and believe that what the tool is creating is mathematically sound. And, it may not be. Something, for example, as predictive analytics, for example, saying, “Here’s some trend data, forecast this trend.” If you do that in ChatGPT, it will write forecasting code, whether or not it runs. But, if you don’t know forecasting methods like ETS and ARIMA and stuff, it may produce a forecast, and you’re like, “Well, it forecasted it. I have no idea if it made the right choice or not based on the code it wrote.”
Katie Robbert – 09:59
So, I see a lot of people have been talking about predictive analytics as one of the use cases for generative AI. But, is that then not really a great use case of generative AI for the casual user, like myself?
Christopher S. Penn – 10:17
That’s an important clarification: Generative AI tools — the transformers model — is fantastic at predictive analytics, but not for the casual user. The reason for that is there are special models that are trained only to do time series forecasting. Lag Llama is an example of one of those. They are not for the traditional user, typical user because you have to download the model, you have to put it in a Docker container, and then you have to execute the Python code around it to do the forecasting. It’s very accurate forecasting; it’s very good forecasting, but it is not for the casual user, unless the casual user happens to be an IT person.
Katie Robbert – 10:58
So, let’s assume the casual person is not. I like to talk about the casual user because I feel like that represents the majority of the population. You have, if you think about athletics, you have elite athletes, you have people who play professional sports. That is such a small percentage of the people overall who work out, who run, who play sports. It’s not a fair assumption to assume that, when we talk about generative AI and generative analytics, that we’re all going to be power users. The majority of us are going to be casual users. We’re going to be people who essentially scratch the surface, get some things done, maybe can do some advanced technical stuff. But, for the most part, we’re going to leave that to the experts. We’re going to leave that to the people who are in that elite group who really understand how the machines work. So, when I think about generative analytics and the casual, everyday user, what questions do we need to be asking to get the systems to work for us?
Christopher S. Penn – 12:07
“Should you even be doing this with generative AI?” is question number one. Is the task itself suited for generative AI? I was having a chat with a friend over the weekend. She wanted to use Google’s Gemini to help her build a calendar, build a schedule, based on a bunch of inputs. It’s really bad at that because calendaring and scheduling is not a language task; it is a symbolic logic task. Does this task fit within this timeframe? Does the task overlap with another task? Things — it’s that the tools are not designed to do symbolic logic; they’ve designed to do language. So, the first thing you should be looking at with generative analytics is, “Am I approaching this as a language task, or is this a mathematics or statistics task?” A good chunk of generative analytics is a language task.
Like, “Here’s the results. Write a report. Draw some conclusions. Infer what the strategy might be from this information.” Those are language tasks, and they are well-suited for that. But, “Perform an ARIMA analysis. Do a chi-squared test.” That is not a language task. So, the first thing you have to do is, unsurprisingly, pull out the 5 Ps. And, part of the 5 Ps is in the purpose: “Is the task itself suited for generative AI?”
Katie Robbert – 13:33
Well, if you’re talking about using the 5 Ps, the first thing you want to start with is — because you’re talking about a platform, generative AI being the platform — you want to clearly define the problem you’re trying to solve. Forget the technology, forget the platform for a minute. So, the problem I’m trying to solve — the purpose of me doing this — is to expedite report writing, to use your example. So, I have data, I need to write a report; I have no time. So, the purpose is to write reports faster, period. Forget about the technology because, as soon as you include the technology in the purpose, you’re already biased to that solution and you do not explore other solutions.
I think that’s where, as you go through the 5 P exercise, you’ll figure out — this is where I can — when you get to process and platform, you’ll figure out, “Can I use generative AI? Should I use generative AI? Or, do I just need to hire someone super part-time to just write this stuff up? Or, do I just need to suck it up and do it myself because it is the best option and I need to prioritize my time better?” I would say the slight alteration, Chris, to your statement is, “What is the problem you’re trying to solve? Period.” Leave the technology out of your purpose statement because that’s just going to set you up for failure.
Christopher S. Penn – 14:58
Okay, so let’s say I have — I will just use a public example — let’s say I’m doing this month’s reporting on the “Safe Warrior Nun” campaign, and I have a big pile of data. My purpose is I need to crank out this report as quickly as possible because I ain’t getting paid to do this stuff. So, that’s the purpose. The people is me. How do you — how would you think about this? Like, I’ve got all this social data and news coverage data and all these spreadsheets. How do you — how are you going to tackle this as the typical user?
Katie Robbert – 15:33
So, the first thing I would — well, the first questions I would start to ask is — your purpose: it’s to expedite the reporting. The people sounds like it’s you, but it’s also, “Who’s the end-user? Who’s getting the report? What do they need from the data?” That’s where you do your user story. “As the recipient of this information, I need to understand how we’re doing on social so that.” And, if they don’t have a reason for looking at this data other than, “It’s nice to know,” I immediately throw that off the table because it’s like, all right, well, now it’s just a waste of time because you can go look at it yourself. I don’t need to be generating reports for you so that you can just — okay, cool.
Like, that’s not a good use of anybody’s time. But, if they say, “So that I can change what we’re doing, I can make decisions, I can take an action,” then you can say, “All right, well, this is important for the following reasons.” So, that sort of — and that becomes like your KPI map of like, “I need to know the following things because it leads to my goal.” Anyway, so you have your purpose: I need to expedite these reports. You have your people: you, being the person executing it, and the feedback from the people receiving the reports. So, the next step is you want to outline your process. So, what are you doing week over week to complete these reports?
So, you’re pulling data from social media, you’re pulling data from email newsletters, you’re pulling conversation data from a community platform, and you’re compiling all of it. How long does that take, and how many different tools in the stack are you using? Then you look at — so you have your process and platform is sort of combined in that sense — and that’s where you can take a step back and go, “Does what I’m doing fall under one of the categories of use cases of generative AI? Is that an option?” The answer, in this case, is yes. Summarization is a really great tool — rewriting, classification. Those are all things that you’re likely doing yourself with the data. So, you’re not saying, “Analyze this data to be statistically significant. Find the chi-square. Do the ARIMA.”
You’re basically compiling everything and making one general report to say, “This is everything, and this is the output.” Then, you can say, “Okay, now that I know my process, I know where I can insert generative AI into it.” The performance is then, “Is using generative AI making it faster? Are the solutions that I’m choosing expediting the reporting, which was my original purpose? Is, ‘I need to expedite reporting’.” So, that’s sort of how you go through it. So, you don’t say, “I need to expedite my report with generative AI.” It’s, “I need to expedite my report, period.” Then, you go through the other pieces to figure out what are my options and solutions. Once you start trying those solutions, you can measure the performance to say, “Did I solve the problem? Was this the right solution?”
Christopher S. Penn – 18:53
Yep. And, in this particular example, there is no generative AI “hit them” answer for the actual processing of the data. There is not because it’s so unwieldy and requires so much mathematical transformation first. What can be done is — you, and this is what we do this for all of our clients — we build the code to process the data first, transform into a format that generative AI can then understand easily, and then use generative to actually generate the report. It’s nothing. You can’t just hand it off to a language model. You have to do the processing first in classical AI, and then generative AI can take over. That kind of becomes the umbrella of generative analytics. You have statistics, you have classical machine learning, and then you have generative AI in sort of almost like a supply chain of data.
It goes from your data source and extraction to statistical processing to classical machine learning to generative AI, as like an assembly line almost. That’s that chain — value chain — is generative analytics.
Katie Robbert – 20:05
So, I asked you the question of, “What does the casual user need to know?” I think what they need to know is what their process is. Honestly, that’s the — I don’t want to say it’s the key to success, but it’s the key to success with using generative AI, with using classical AI. It’s understanding and clearly defining your process because then you can figure out where these technologies fit in, where they can replace. You want to find those spots in your process that are highly repeatable and consistent, where the output is 100% clear and defined. Let’s say that you’ve used classical AI or code, or even just a spreadsheet to clean up your data, so now it’s in a readable format. I would venture a guess to say — then, once the data is clean — your process is to take the data, draw some conclusions, write the report, summarize it. That is the consistent part. You know what’s supposed to happen. That’s where you can say, “Okay, maybe this is where I can insert generative AI into my process to expedite my reports.”
Christopher S. Penn – 21:27
Yes, in a lot of ways, it’s almost like supply chain mapping. If you do supply chain mapping, you chart out every step of your supply chain, from where you source your raw materials from to the trucking companies and logistics companies that move it from the raw materials to a processor. Processor turns into precursors, the precursors turn into product. The product then gets trucked to distribution centers, and that goes to retail stores, and you have that whole long supply chain. Every step along the way has to be documented out because you need to know what — A — you need to know what’s going on. You should. And, B, for at least enterprise companies, you need business continuity plans for every step of that process.
Because, if — I don’t know — a ship gets stuck in the Suez Canal for two weeks, a part of your supply chain is broken, and you need to have alternate ways to deal with that problem to keep the rest of your supply chain from grinding to a halt. The same thing is true with generative analytics, exactly what you’re saying, Katie. You diagram out the process from beginning to end, your generative analytics value chain, and you say, “Okay, here’s where my data starts. It starts in Google Analytics or Talkwalker or Brand24. That data moves into here. It gets processed by a piece of code that — the piece of code that hands it off to another piece of code to be run through statistical analysis or regression analysis that then gets transformed into markdown tables that get handed to generative AI. Generative AI writes the reports, then the reports get handed to a client.” That’s your generative analytics value chain, and you’ve got to map that out. If you don’t map that process out — A, to your point — you don’t know where generative AI fits and doesn’t fit because you don’t know what’s a language task and not — what’s not — but, B, you don’t know where your data is going to and where it’s from. That’s a business risk above and beyond just whether or not you should use generative AI. That’s a, “My business might actually grind your halt because I have no idea what’s going on.”
Katie Robbert – 23:24
Well, spoiler: we can help with that. But, that’s — I mean — that’s a lot of what I’m doing: documenting what the heck is happening. It’s not just, “Here’s the thing.” People, in my experience, people don’t like to document process because they feel like it slows things down. They’re like, “Oh, process for process’ sake. And, why do I have to document it? I just need to do it.” Well, that’s all well and good until it stops working, and then you don’t know where in the process things broke down. So, if you yourself are not willing to do it, bring on a company like ours to do it for you so that when you say, “I need to be smarter, faster, better, cheaper,” — whatever the thing is — “I need to increase my profit margins. I need to pay my people more money because we’re producing more work. I need to have AI services.” Whatever the problem statement is, we’re always going to start with, “All right, so what are you currently doing? What does that look like? What is your process?” Because, introducing new things without evaluating what’s currently happening is just going to create more problems. You might get those quick wins, you might have that short-term success, but it’s not sustainable. The same is true with using something like generative AI for your analytics. You can throw all of your data into the system, but you don’t know if what’s coming back out is useful, valuable, correct, repeatable, scalable, more efficient. You have to do that process documentation first. I will die on that hill.
Christopher S. Penn – 25:04
And, if you do the process mapping, you’re also going to find out the consequences of generative AI. So, in the generative analytics value chain, if part of your chain says, “Hey, we review all of our client reports and provide feedback, and then we hand it off to the client.” That’s a perfectly sensible thing to do. If you swap in generative AI to one of those stages — normally you have a workflow that says, “The report is done on this day. It’s handed off to the manager for review on this day, and then it goes to the client on this day.” If generative AI suddenly says, “Hey, here’s 28 client reports,” all the same day, now you have a problem. You have a clog in your value chain.
In your generative analytics value chain, you have this big bulge of stuff that now — like — “Okay, well, our manager now has to be out of commission for three days to review all 28 reports that happened at the exact same time.” So, even if the workload remains the same, the timing of the workload matters. You have to have that documented because, if you don’t, you’ll be very surprised. I see this happen with content creation. People are like, “Oh, I can use generative I write 10,000 blog posts.” Yes, but your approvals process hasn’t changed. Therefore, you now need to hire 48 new editors because you’ve created 10,000 blog posts for them to review.
Katie Robbert – 26:18
Let me tell you what would not make me happy: getting surprised by all of this work that generative AI created. That’s exactly it. You have to think about, if you’re putting generative AI in your process, you need to think about what’s book-ending it. That’s usually the human-related pieces. What does that do? How does that change what the human has to do? Chris, like, you’ll create generative AI outputs of scopes of work, of content. I still have to go through and read it. How many times do I go back and ask you, “Did you write this or did generative AI write this?” The reason I ask you that is not to be like, “Don’t do that,” like not to be a scold, but it helps me think through how much more cognitive load I need to attach to this thing to review it. Because, if you wrote it, you the human, then I know how you write; I know how you think about things. If generative AI wrote it, I need to take a different lens because there’s going to be more nuance to how to make corrections. For me as a human, it changes my ask; it changes my workload if you wrote it or generative AI wrote it. Those — it might be a matter of five minutes, but those five minutes add up over time.
Christopher S. Penn – 27:48
Exactly. If you’ve got some thoughts about generative analytics and how you are incorporating generative AI into your analytics process, or, frankly, any of your value chains, and you want to share those stories, pop on over to our free Slack group. Go to TrustInsights.ai/analyticsformarketers, where you and over 5,350 other marketers are asking and answering each other’s questions every single day. Wherever it is that you watch or listen to the show, if there’s a channel you’d rather have it on, you can find it at TrustInsights.ai/tipodcast, where you can see our podcast is in places that mostly serve podcasts. Thanks for tuning in, and we’ll talk to you next time.
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Trust Insights (trustinsights.ai) is one of the world's leading management consulting firms in artificial intelligence/AI, especially in the use of generative AI and AI in marketing. Trust Insights provides custom AI consultation, training, education, implementation, and deployment of classical regression AI, classification AI, and generative AI, especially large language models such as ChatGPT's GPT-4-omni, Google Gemini, and Anthropic Claude. Trust Insights provides analytics consulting, data science consulting, and AI consulting.
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