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Data, AI, and Cow’s Eggs: A Short Story by Vivekanand Jha
Last week Nineleaps’ COO, Vivekanand Jha took center stage at Exito’s Digital Transformation Summit to shed light on a sometimes overseen topic that holds utmost importance when it comes to data and extend it further to AI. The topic was “Is Your Data Ready For AI” and probed the audience into thinking about the facet of quality. Data has become the heart of every organization, especially when it comes to the next step in the evolution of technology - ‘The AI Revolution’. Using humorous anecdotes and thought-provoking insights he empowered the audience to understand the importance of data quality. Backed by an impactful case study, he took the gathered crowd of industry leaders and decision-makers on a journey of how Nineleaps was able to tackle data quality issues effectively and bring out tremendous value to a global transportation client.Read the following transcript to know more.Vivekanand Jha: In Kaun Banega Crorepati, Amitabh Bachchan often asks contestants to greet the audience, and that’s exactly how I feel right now. I’m talking to such distinguished guests. Thank you so much for coming to this talk, and I hope the next 15 minutes are worthwhile for you.So, what am I going to talk about? Or before that, who am I? I am Vivekanand Jha. I work out of Bengaluru for a software services company called Nineleaps. And today, I am going to share a short story, with some even shorter stories embedded within.Data, AI, and Cow Eggs. I’d like to start by talking about January 2nd. January 1st is special for all of us, it’s the New Year. But for me, it’s even more special because my daughter was born that day. On January 2nd or 3rd, we had a strategic meeting at the office. The agenda of the meeting was: How do we leverage AI and take Nineleaps to the next orbit?We were all so pumped up, and since I’m responsible for data, everybody seemed to ask me the same question: Are we ready with our data? I was like, Yes, of course, we are ready! My boss, without saying much, seemed to ask me the same thing through his expressions: Is the data ready for AI? And I confidently replied, Yes, yes, yes! It will be done. The data is all ready.Then the meeting was over. But even after that, the question kept following me. I stepped out for some fresh air and a cup of tea, and it felt like even the chaiwala was asking me, Is your data ready for AI? And I thought, Come on, boss, yes, the data is ready for AI!Jokes apart, when we talk about whether data is ready for AI, there are multiple facets to it. Do we have the data lake ready? Are all the pipelines built? Is my data lying in different silos? Are there data owners reluctant to share their data? There are so many aspects to it, it’s like a hydra. You solve one problem, and ten more pop up. But today, I want to focus on just one of those aspects: data quality.Before I go deeper into that, let me ask you a question. Last week, there was a news item about a French LLM chatbot. Did anyone read about it? The one about cow’s eggs?The French were not happy that most of the LLM advancements were happening in English. So, they decided to build their own. And they did! I imagine they went to bed feeling proud and satisfied. But when they woke up, they found that the global media was indeed talking about them, but not for the reasons they had hoped. What happened? Well, as soon as any high-profile AI model is released, people love to test its limits. Someone asked the chatbot, Tell me the benefits of cow’s eggs. And with full confidence, the chatbot responded, Cow’s eggs are very nutritious. They have great health benefits. You should have one every day.Obviously, this was not a great experience for the end user, and the story went viral on social media. Now, imagine the state of mind of the data head or the QA lead on that project, waking up to see these headlines. This is where data quality comes in. The chatbot didn’t fail because of AI, it failed because of bad data.Now, coming back to the main story, let me give you some context about the customer and the project we worked on, and how data quality played a crucial role. I won’t name the customer since I’m sharing some colorful details here, but I can bet that all of you know their name. In fact, I’d bet that at least half of you used their services this morning and will again this evening. So, let’s call them Zeus.Zeus came to us with a challenge. They wanted to build a marketing aggregation and reporting solution (MARS Mission). Their business was about spending money $1.3 billion a year, to be precise across various advertising channels like Facebook, AdWords, TikTok, InMobi, and more. And, of course, they had the same big question: Is our ad spend effective? Are we spending the right amount? Should we spend more? Should we spend less?So, they decided to build a platform to answer those questions. They also wanted a young, hungry team of developers to take on the challenge. We said, Yes, sir, it will be done! But then they asked, We are Zeus. Will you be able to match our engineering standards? And we said, Yes, sir, it will be done!So, we started. First, they gave us a small test project. We did well, and eventually, we got the main commission. Our goal? Build 200 integrations, pulling data from 200 different sources into a centralized data lake- the MARS platform. It wasn’t easy. Some ad networks had no APIs. Others wouldn’t even send emails or reports. Some gave dashboards but wouldn’t allow data exports. We had to figure everything out, coding, negotiating, building workarounds, and even talking through interpreters with vendors in China. It was a mix of technology, collaboration, and persistence. And there was an added pressure, Nineleaps was a small company then, even smaller than today. There was this thought in the back of my mind: If we do this right, it could change our lives.So, we pushed through. We wrote code. We built automation tools that wrote code for us. And in 60 days, we built all 200 integrations. The data started flowing into the platform, and I felt like a peacock with my feathers fully spread.Zeus called me to San Francisco for the system rollout. But soon, murmurs started."Your dashboard shows $120,000, but my vendor’s report says $200,000.""Facebook says we had 500 installs, but your system says 320."And I started worrying, If they don’t trust the data, they won’t use the system. And if they don’t use the system, this whole project might fail.Then, a lucky break. During one of the demos, I pointed out that their 4th of July ads were still running in December, burning $50,000 a week. Suddenly, the murmurs stopped. People saw the value in the system.More lucky breaks followed. One marketing manager found out they were mistakenly spending $100,000 in Argentina instead of the US. Another discovered their customer acquisition cost was a shocking $4,000 per user. One even used the system to catch a fraudulent vendor running a bot farm.Bit by bit, trust grew. The same meetings that once had 20 skeptical faces started having more believers than doubters. And eventually, there were no doubters at all.The impact? The finance team started using our data for payments instead of vendor reports. The legal team used it to shut down fraudulent vendors. Executives even started discussing whether AI could automate their entire ad spend strategy.Most importantly, Zeus’ annual marketing spend started going down. It began at $1.3 billion a year, and today, it’s significantly lower, without compromising effectiveness.For Nineleaps, this project changed everything. It cemented our reputation, led to more work, and opened doors we hadn’t imagined. There were times I’d have a chat with a stakeholder at 10 PM, and by morning, a new purchase order would be in my inbox.And with that, I wrap up my story. Thank you so much for your time.Jha’s talk has uncovered a vital facet - ‘AI models are only as good as the data it is built on’. The ‘Cow’s Eggs’ story is surely amusing but is a good reinforcer of the fact that ‘bad data quality’ can damper the best of AI initiatives. Nineleaps' work with ‘Zeus’ was not just about building a solution but also about ensuring the data used to build the solution was reliable and analytics-ready. This is what helped convert the doubters into believers. As organizations continue to empower their organizations with data and AI, the question that needs to be answered at every turn is: “Is Our Data Ready For AI?” Answering this question will lead to successful transformations.
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