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Unlocking Success with AI in 2025: The Future isNow By now, weve all seen the headlines. AI is going to revolutionize everything, automate the world,etc But most AI initiatives in businesses fail. And not because AI itself is broken, but because companies keep treating it like a science project instead of a tool that actually needs to solve problems.
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Stop trying to look like an AI startup and start to be one instead. The market doesnt need another AI gimmick. It needs businesses that deliverresults. It seems like every startup these days is slapping .ai onto its name, like its a shiny golden sticker that will guarantee funding, customers, and success. AI is the new gold rush, and everyones hoping to strike itrich.
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Brikker (anonymized), a children’s toy manufacturing startup, was like a hamster on a wheel. Going nowhere fast. Their primary product, these adorable little building blocks, had gained some traction but had yet to achieve the explosive growth they’d anticipated. Customer acquisition costs were soaring… Engagement was waning… Investors were worrying… The team knew they needed a strategic pivot and fast, as they estimated they had 6 months of funding left to work with.
How to Leverage Data Science in Fintech for Maximum Impact Data science has been a game-changer in the financial industry, and fintech is one of the sectors that has successfully leveraged its power to create innovative solutions for consumers. Fintech companies use data science to enhance their products, improve marketing strategies, streamline operations, and manage risk.
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2022 Readiness?—?Investing in Data Infrastructure [link] In 2022, Big data is transforming businesses in unprecedented ways. Today the question is not just about scalability, it is whether or not you are enabling maximum scalability with the power of data. In the above video, Piyanka Jain talks about how and when to plan and invest in data infrastructure to drive growth. 2022 Readiness?
Feature Engineering?—?Hypothesis-driven vs. ML-driven I was talking to a CTO of a Fortune 100 bank this morning and we got talking about feature engineering in AI/ML models. With the advent of ML and AI, many believe that the statistical methods of feature engineering are redundant. For example, in the case of a supervised classification 0/1 problem, many use LASSO now to identify features of importance instead of using the correlation matrix (stat approach).
Data can be a powerful tool if you know how to put it to work! Often I start my analytics conference keynote addresses by asking the audience to share the issues they face in their organizations. For the past decade, in nearly every conference, the #1 problem cited by analysts and their managers has been the same: their team built the best possible model (read: analysis, dashboard, report, predictive model) but people are not using it.
Pretty much all AI/ML customer and transactional models have been on pause for the last few months. As we speak, history is being written… Continue reading on Becoming Human: Artificial Intelligence Magazine ».
Pretty much all AI/ML customer and transactional models have been on pause for the last few months. As we speak, history is being written. Thus new models can’t be created either as these models rely on the fundamental principle that the past predicts future. So statistical models are not being built unless specifically for COVID impact analysis. What does all this mean for AI and ML models in the future, post-COVID?
Pretty much all AI/ML customer and transactional models have been on pause for the last few months. As we speak, history is being written. Thus new models can’t be created either as these models rely on the fundamental principle that the past predicts future. So statistical models are not being built unless specifically for COVID impact analysis. What does all this mean for AI and ML models in the future, post-COVID?
Customer Segmentation for Growth Hacking (Aryng?—?Unsplash?—?William Iven) Marketers often equate growth hacking with driving user acquisition. They invest all their effort and money to acquire maximum users with a minimum amount of spend. However, not all users are equal. Some will come on your website, create a profile, and never buy a thing from you.
A lesson in behavioral economics will tell you that consumers don’t always practice what they preach. So what better reason is there for product marketers to make testing a key phase in their product lifecycle? Unpeel the Facts Consider the unique predicament of KitchenApplianceCowboy and his Super Peeler. Q : Dear SuperAnalyticsMan , Our new and unique peeler was meant to revolutionize the world!
Feature Engineering?—?Hypothesis-driven vs. ML-driven I was talking to a CTO of a Fortune 100 bank this morning and we got talking about feature engineering in AI/ML models. With the advent of ML and AI, many believe that the statistical methods of feature engineering are redundant. For example, in the case of a supervised classification 0/1 problem, many use LASSO now to identify features of importance instead of using the correlation matrix (stat approach).
BADIR?—?The Antidote to Low ROI from Data Science Projects [link] Data Science, Machine Learning, Robotics, and AI?—?these are some of the hottest keywords in the business right now. Trillions of dollars are being invested in these fields by organizations. One would think with such large investment, these organizations must be reaping a lot of value out of data science.
A few months into his new role as Data Analytics leader, Alan and his team clearly saw the lack of data-driven thinking across the… Continue reading on Towards Data Science ».
What Google’s and Salesforce’s respective acquisition of Looker and Tableau Software means for CIO’s The BI analytics tool space is consolidating to compete against Microsoft’s ensemble of Business Analytics(BA) products which promises to solve for the entire workflow?—?data generation, data capture, data storage, data access stratified by persona and data visualization.
[link] A few days ago I met an old acquaintance of mine, who had been working in the marketing department of an IT giant. As we got to talking, I could see that he was a data skeptic?—?someone who did not believe in the power of data. Data Science is not for those who fall on the business side of the company, he said. Quite surprisingly, it is not just him who considers the myth that only techies and analysts need Data Science skills for their work.
I run a Data Science Consulting company, and I say analytics consulting is a SCAM! Why? [link] I can say so because I have been on both sides of the table, having hired analytics consulting companies for big organizations and also providing analytics consulting services to top companies. I have seen projects worth hundreds of thousands of dollars being wasted, and I have seen them becoming successful too.
[link] A few days ago I met an old acquaintance of mine, who had been working in the marketing department of an IT giant. As we got to talking, I could see that he was a data skeptic?—?someone who did not believe in the power of data. Data Science is not for those who fall on the business side of the company, he said. Quite surprisingly, it is not just him who considers the myth that only techies and analysts need Data Science skills for their work.
As I was wrapping up my work last evening, darkness started to loom outside. I decided to go for a short run on a neighboring track… Continue reading on Towards Data Science ».
Recently I have been mentoring fresh graduates of the Masters in Business Analytics program from a historic Virginia school. Ironically… Continue reading on Towards Data Science ».
Whether you are a seasoned data scientist or a business executive with a significant investment in analytics, chances are you’ve seen the… Continue reading on DataSeries ».
Joel is the newly-appointed CEO of a 100-year-old consumer company. He has been brought in with a vision to make the company customer-centric and tasked with leading digital transformations so that it is at par with the new information era. After taking the reins of the company, he spends his first six weeks getting the lay of the land, understanding its culture, products, and customers.
Unsplash Neha has an engineering and MBA background. She was looking to rejoin the workforce after several years on a personal sabbatical. Her last role had involved business process analysis, planning, and market research. It wasn’t something she wanted to go back to. When she started exploring options for a more satisfying career, analytics emerged as a strong contender.
Are you a Data Science hero?—?aka BADIRist? One of my colleagues is a big fan of detective stories and Sherlock Holmes. One day we started talking about Sherlock and what it would look like if Sherlock transitioned his career to become an analyst or data scientist? He wondered if Sherlock would be a BADIRist? I definitely think so. Sherlock used something similar to BADIR™ to solve crimes, though Sir Arthur Conan Doyle didn’t know of the term back then.
Do you need to be able to code in R, Python, or any other programming language to put Data Science to work for you? The answer is NO! Continue reading on DataSeries ».
I run a Data Science Consulting company, and I say analytics consulting is a SCAM! Why? [link] I can say so because I have been on both sides of the table, having hired analytics consulting companies for big organizations and also providing analytics consulting services to top companies. I have seen projects worth hundreds of thousands of dollars being wasted, and I have seen them becoming successful too.
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