As enterprises try to experiment, build, and scale for/with AI (including and especially with Gen AI), there are four clear areas where the impact is going to be:

1. People: While is it universally accepted that your workforce will “on average” become smarter using technology”, at the same time, two other things can be true as well. 1/ From an HBR article, “If one universal law regarding the adoption of new technologies existed, it would be this: People will use digital tools in ways you can’t fully anticipate or control.” And 2/ getting inspired from a famous source, Uncertainty principle, I extrapolate that “enterprises can’t determine with precision their velocity and momentum of AI adoption“. By enterprises I mean large companies with many functions or departments, and at least seven levels of hierarchy. At those levels of scale and complexity, it is hard to get precise information and metrics about AI usage, while simultaneously knowing whether it is useful or not.
   
   How do I use this information? Think of employees as users or customers. At every stage of AI enablement, talk to them. Ask questions. Understand what they can, should, and would do. Compare expected behaviors against motivations and incentives to anticipate true behaviors.
   
   Next up… Processes

Ever Felt Overwhelmed by Information? tisit Organizes Your Knowledge and Suggests What You Might Want to Know Next

Hello, dear readers!

In today’s digital age, information is abundant, but organizing it meaningfully is a challenge. How many times have you encountered a fascinating fact or pondered a question, only to lose track of it later? This dilemma is precisely what tisit aims to solve.

What is tisit?

tisit—short for “what is it?”—is not just another tool; it’s your personal learning machine. Think of it as a treasure chest for your thoughts, questions, and ideas that not only stores them but actively helps you make sense of them.

The Problem We All Face

We often come across exciting ideas or questions that tickle our curiosity. But storing and revisiting this information in a way that adds value to our lives is not easy. Traditional methods like bookmarking or note-taking don’t capture the dynamic nature of learning.

How tisit Provides a Solution

Here’s where tisit’s true power comes into play. It uses state-of-the-art artificial intelligence to not only capture your questions and curiosities but to provide well-rounded answers to them. tisit organizes this information neatly, serving as your “second brain” where you can save, nurture, and revisit your intellectual pursuits.

Taking it a Step Further: Intelligent Recommendations

But tisit doesn’t stop there. What sets it apart is its ability to make intelligent connections between what you already know and what you might find interesting. It offers recommendations that help you dive deeper into subjects you love, and even suggests new areas that could broaden your horizons. Imagine a system that evolves with you, continuously enriching your knowledge base.

Want to be Part of Something Big?

As we put the final touches on tisit, we’re looking for alpha and beta testers to help us make it the best it can be. If you’re passionate about learning and personal growth, we invite you to be a part of this transformative experience.

Stay Tuned

For more updates on tisit and how you can be involved in its exciting journey, subscribe to this blog. We promise to keep you informed every step of the way.

Jeff Bezos is famous for Amazon’s focus on things that aren’t going to change in the next 10 years.

“I very frequently get the question: ‘What’s going to change in the next 10 years?’ And that is a very interesting question; it’s a very common one. I almost never get the question: ‘What’s not going to change in the next 10 years?’

And I submit to you that that second question is actually the more important of the two — because you can build a business strategy around the things that are stable in time … In our retail business, we know that customers want low prices, and I know that’s going to be true 10 years from now. They want fast delivery; they want vast selection.

It’s impossible to imagine a future 10 years from now where a customer comes up and says, ‘Jeff I love Amazon; I just wish the prices were a little higher,’ [or] ‘I love Amazon; I just wish you’d deliver a little more slowly.’ Impossible. […] When you have something that you know is true, even over the long term, you can afford to put a lot of energy into it.”

From the article: https://www.ideatovalue.com/lead/nickskillicorn/2021/02/jeff-bezos-rule-what-will-not-change/

Original video can be found here: https://youtu.be/O4MtQGRIIuA?t=267

I have been analyzing data since 2004 – basically all my professional life. I have seen the tools people use change (from Spreadsheets and calculators to reporting tools and infographics, to even more fancy BI tools and slick dashboards. At the same time, the methods we use to analyze data have changed from basic statistics (total, average, min, and max), to advanced statistics (distributions and variance), to advanced statistics, mathematics, and computer science (forecasting, predictions, and anomaly/pattern detection).

While the methods and tools to process and present that data have changed significantly over the years, there is one core skill that is increasingly becoming more important in terms of our ability to make meaning out of data.

That skill is the ability to do analysis, and the role is of a business or quantitative analyst. I know it sounds basic – almost too simple to write about. And therein lies the key reason why many enterprises fail to use data properly within their organizations. Yes, I admit that there are other roadblocks in a firm’s ability to use data – such as access to data, understanding the meaning and acceptable use of data, having the tools and methods to process the data, and finally, the ability (and time) to interpret the meaning of data. I contest that many of these challenges are symptoms of a bigger problem – the lack of a full-time role to analyze and make meaning out of data.

As we shifted to “Big Data” around 2010s, companies quickly pivoted to creating new roles to follow the trends. Hiring data scientists was a top-5 priority for almost any organization. Then came the realization that good data science requires data that is both broad in its sources, and deep in its volume. This followed the move to data lakes to bring all the data into one place. Many companies invested in large Hadoop-based infrastructures, only to find that once data is in one place, it’s almost too much to make sense of, and very expensive to maintain. Cataloging, searching, and distributing data proved to be a big IT overhead. With the advent of the cloud, suddenly there was a new way to solve the problem – migrate to the cloud, uncouple storage and processing, and save a ton on your overheads. The move to the cloud has proven moderately challenging for many customers, and frustratingly complicated for others. The skills to build, maintain, and evolve complex architecture is a full-time job that modern technology organizations should focus on 100%.

What has not changed during this time? It is still a person’s ability to ask business questions, identify the data needed to answer those questions, apply the stat/math/cs methods to process the data, and present the information in a consumable manner. That’s the job of a data analyst, and I argue we need more of them – that is a skill that does not scale non-linearly. At least, not yet.

A person can spend years, and make a career out of designing and building Data Models. There are generic data models, industry-specific ones, and then there are functional data models. Each with its own sets of signs and symbols, sometimes its own language, and certainly, its own learning curve.

Here is an example from IBM, ServiceNow, and Teradata. SaaS companies publish their data model blueprints so that enterprises can map their internal systems to a “standard”, which makes migration/integration easy. Industry-focused institutions (such as TM Forum) publish their version to focus on industry-specific nuances and use a bunch of jargon. And finally, there are ontologies.

The simplest way to understand data model vs. ontologies is to think specific vs. generic. Data models are often application-specific (industry or software applications; hence TM Forum and SaaS providers publish their data models). IMO, Ontologies are meant to help understand what exists and how it is related, without drilling into every specific detail (although many have gone that route too). They allow for a scalable understanding of a concept, without explaining the concept in detail.

Here, I attempt to represent the simplest data model one can draw for an enterprise. It probably applies to 80% of situations and to B2B, B2C, B2B2C, or D2C business models.

I think that any enterprise process can be represented as a combination of one of the more basic entities (asset, product, customer, workforce, and systems). Take customer service for example. A customer tries to pay their bill online (customer -> systems). The transaction fails. The customer then calls into a contact center, talks to an employee, complains about the issue, and eventually resolves the issue. (customer -> workforce -> system).

How would this picture work for a multi-sided platform company (because everyone is a platform company, just like everyone was a technology company). In a multi-sided platform, your producers/suppliers are your consumers too. Unless you treat them like customers, serve them as customers, and retain them as customers, you won’t have a thing to sell on your platform.

Now the trick is to expand this picture as needed. But let us save that for another post.

One of my favorite writers is Matt Levin. He writes for Bloomberg, and has this natural (and funny) ability to explain complex financial concepts to lay-people like me. Check out his (mostly) daily emails and various podcast appearances (here and here) where he explains is writing process. Matt, very famously, exclaimed(?) in his blog post:

The essence of finance is time travel. Saving is about moving resources from the present into the future; financing is about moving resources from the future back into the present. Stock prices reflect cash flows into an infinite future; a long-term interest rate contains within it predictions about a whole series of future short-term interest rates.

The essence of AI (and any data-driven insight or decision) is time travel.

In a sense we time travel when we use data to understand what happened (describe), to explain why it happened (diagnose), to predict what will happen (predict), and to plan against that prediction (prescribe).

The size of data does not matter – it could be very small samples (i.e. from personal experiences) or “Big” data (i.e. a million user click-throughs). As long as we used the data that exists, to inform our knowledge, we’re doing time travel. Isn’t that exciting?

When presenting last quarters sales figures, we often describe the circumstances and results, as if they were unfolding in that very instance. Similarly, when we predict a sales forecast (using a simple regression technique, or an advanced neural net model, we’re using historical data and projecting it out in the future (with some assumptions that are expected to remain valid in the future).

So just to recap 2021, it was a breakthrough year for Tesla and for electric vehicles in general. And while we battled, and everyone did, with supply chain challenges through the year, we managed to grow our volumes by nearly 90% last year. This level of growth didn’t happen by coincidence. It was a result of ingenuity and hard work across multiple teams throughout the company. Additionally, we reached the highest operating margin in the industry in the last widely reported quarter at over 14% GAAP operating margin. Lastly, thanks to $5.5 billion of [Inaudible] small finger by now — $5.5 billion of GAAP net income in 2021, our accumulated profitability since the inception of the company became positive, which I think makes us a real company at this point. This is a critical milestone for the company.

Elon Musk — Tesla CEO from Q4’21 Earnings Call Transcript

As it would be true with time travel, we must remember that moving back and forth in time is not only a little difficult, but we must accommodate for external situations (macro variables) which may have not been captured in the data. See, in the AI modeling world, we like to emphasize on data cleanliness, completeness, and consistency. A model in a computer is much cleaner and simple than the real world because our focus is on building a highly accurate model. But in that endeavor we often lose the details which we believe, at the time, to be less useful. Such as anomalous (very high or low values which seem to skew our results or exaggerate certain results) or missing values. It is very convenient to filter out rows and columns than to dive into another rabbit hole (of why the data is missing… what might have happened to the data – which is another time travel dimension).

In fact, this may be the reason why most data scientists spend less time doing EDA, or exploratory data analysis, than they should. Everyone’s looking for what the model says, so let’s give them an answer… how we arrived at the answer is a conversation for later (which we often don’t get to… until it’s too late).

Welcome to my blog about all things data. This blog is really meant to be a place where I think clearly about the topic, by writing about it.

If you happen to visit this blog, appreciate any feedback, questions, or inputs you have on the topic.

Some things to make clear before I start writing:

1. About the blog title: I’m shamelessly inspired from one of my absolute favorite books/writer The Psychology of Money by Morgan Housel. So I apologize in advance if it hurts his or others’ feelings (that I did that). Like the book (and the original blog), it is less about sharing new insights or silver bullets. To me, this blog is more about sharing what has not changed, and remains relevant to people who want to use data in decision making.
2. About the content: I’ll mostly be synthesizing opinions and experiences from my career, as well as books/blogs/content from others who know and have done better. It’s about connecting the dots as I see it.
3. About the format: it will be mostly open ended, long form, and without a specific flow. I’ll write about data-related topics as they develop in my mind, but this is not a book with a sequence of chapters to read from.

See you next time!