Vin Vashishta

V Squared

Go with experience and a track record of success. V Squared is one of the oldest data and AI consulting companies in the world. Since 2012, that singular focus has built deep expertise in monetizing data, analytics, and AI.

V Squared's AI advisory services give clients clarity into the complete data and AI monetization journey. They trust us to implement frameworks that facilitate and streamline the journey. We provide expertise to remove roadblocks and avoid setbacks.

Our past and present clients include Airbus, Siemens, Walmart, JPMC, and over 20 SMEs.

Reach out for help with:
AI Product Strategy and Delivery
AI Strategy
Data & AI Organization Building and Scaling
Product Evangelization and Positioning

email: info@v2ds.com or DM me for more information.

Founder & AI Advisor

HROI Certification Training

Why HROI Certifications?

Well Recognized and Respected: Taught by an award-winning industry expert on AI, Analytics, Leadership, and Strategy. HROI certifications prove capabilities.

Customized For Data Professionals: Lessons are connected to the field and relevant to data professionals’ unique needs.

Curriculum Built From A Rich Set Of Sources: Use cases, research, industry surveys, and dozens of authors.

Course Design Best Practices: Courses are distraction-free and built on adult education principles. A combination of engaging methods delivers key learning objects for multiple learning styles.

Founder & Principal Course Instructor

WMS Gaming

Senior Principal

Innotrac

Sr Engineer

GameTech International

Team Lead

Endgame Engineering

Owner, Consultant

AI Advisor | Author “From Data To Profit” | Course Instructor (Data & AI Strategy, Product Management, Leadership)

In the last decade, I have built 2 thriving companies by helping clients generate $8 billion with data and AI and teaching the frameworks I used to do it to 3,500 students.

I built a community of 200K decision-makers and problem solvers. Since 2014, I have influenced the influencers by defining categories and being at the front of data and AI waves.

I was one of the first LinkedIn AI Top Voices in 2018 and an early Gartner Ambassador.

I am the author of ‘From Data to Profit’ (Wiley 2023). It’s the playbook for monetizing data and AI and an excellent entry point into technical strategy.

Courses: www.datascience.vin
Private advisory sessions: linktr.ee/VinVashishta
Brand partnerships, paid speaking engagements, analyst events, and other inquiries: info@v2ds.com

If “we need people who can hit the ground running” worked, companies would be drowning in AI billions. The last 8 years have shown that companies need people who continuously learn.

In 2012, companies needed people to hit the ground running with Big Data.
In 2014, they needed people with data science and deep learning skills.
In 2016, they needed ML engineering capabilities and NLP or CV experience.
In 2018, data engineering and MLOps were the significant skills gaps.
In 2022, it was Generative AI.
Today, they need people who can create more business value and reliable products.

For 12 years, companies have argued they don’t have time to train or upskill, but for 12 years, companies have really needed people who can upskill every 2 years.

How’s all that “hit the ground running” working for you? Let me guess, you’re still chasing data talent, trying to catch up.

#DataScience #DataEngineering

Data quality is an AI power practice and revenue multiplier. I couldn’t get buy-in for data quality and infrastructure until I reframed the conversation with senior leaders.

Quality, curated datasets make more AI initiatives feasible by lowering delivery times and total costs.

A quality, curated dataset can be monetized multiple times. $1 invested in data quality generates returns that compound for every dataset and use case.

Someone convinced your CEO that Generative AI was the path to riches. Data quality is ignored mainly because we don’t explain it the same way.

Curated datasets make the business more valuable and create more opportunities. Data is like a diamond. The higher the quality, the greater the value.

I have built machine learning and AI platforms since 2015. Quality data is the foundation of every successful AI app and platform.

Building AI first results in backtracking after everything has been built the wrong way. Data curation and infrastructure cannot be skipped.

I explain how to quantify the value of a dataset, justify data quality, and build AI platforms in my book and courses. Follow me here and click the link under my name to learn more.

#DataQuality #DataEngineering #DataStrategy #GenerativeAI

A data leader must understand the technology to lead and advocate for the team. Why is that a controversial statement? Would you put an engineering leader in charge of HR or sales?

Data leaders must understand how data and AI products create value so they can quantify the data team’s impact and justify its budget.

Leaders must know the capabilities required to deliver data and AI products to ensure the team is staffed with the right capabilities.

They must understand the data science lifecycle and research processes to keep the team’s work on track.

Assessing individual performance requires understanding what defines a high-quality work product and efficient process.

Without hands-on experience or an educational background, data leaders don’t have answers when asked tough questions.

The default solution is to give the leader someone with technical knowledge to support them. Why not develop that person into the data team’s leadership role instead of playing the telephone game?

We must create a career path from technical IC to leadership. It must be a secure path where people are trained and mentored at each level.

Technical and engineering ICs develop into excellent strategic thinkers and leaders. I have built training programs and mentored leaders for over a decade.

The talent and aptitude are just as prevalent in tech as in other teams. It must be nurtured, and that’s the missing link. Hire mentors. Create career paths. Support them with structured training.

Help leaders from technology backgrounds thrive and reap the growth, innovation, and efficiency their organizations create.

Follow me here or click the link under my name for more resources. I offer certifications, seminars, and coaching.

#Data #Leadership #Career

RAG with vector similarity doesn't work. GraphRAG does.

Vectors lose meaning. Knowledge graphs preserve it.

Vectors don't scale efficiently. Knowledge graphs do.

Don't believe me? Ask people building LLM products, review the research, or just read this.

https://lnkd.in/gK_sdjqb

Microsoft partnered with Neo4j to support knowledge graphs on Azure (thanks Scott DeGeest, for pointing this out), and it isn’t alone. Palantir, SAP, Naas AI, and NVIDIA also provide platform-level support.

Knowledge graphs aren’t just a Generative AI thing. GraphRAG is a jumping-off point for Digital Twins and advanced simulations. They have connections to structural causal models and underpin simulations.

Simulations are powerful tools. Think of what we do with weather models that power hurricane forecasts. We know the storm’s coming days before it arrives and take action.

Knowing what will happen in the future optimizes our response today, so people with a weather forecast have better outcomes than people without them. In business, simulations are competitive advantages.

I discuss simulations in more detail in my book and write about how to build knowledge graphs in my newsletter.

I also cover building AI products with knowledge graphs in my AI Product Management Certification. Click the link under my name to learn more.

#GenerativeAI #DataEngineering

microsoft.com • 7 min read

GraphRAG: A new approach for discovery using complex information

I have hired AI Strategists and Product Managers for 5 years. Most candidates fail at the same interview phase because they lack the one skill that makes them so valuable.

In both roles, candidates talk up their experience delivering successful data and AI strategies or high-value products. But that’s not what I and other C-level leaders hire for.

Businesses need people who understand why they were successful enough to repeat their success in a different business environment.

To get hired, candidates must spend less time discussing what they did. Answers to questions must contain 2 new elements:

Why their approach was successful. This is a first-principles breakdown of what caused them to deliver results.

The frameworks they use to duplicate that success under new conditions. Frameworks are flexible strategy-level progressions that reveal multiple paths to success.

It’s not enough to have done it, been a part of it, or have a plan to do it. C-level leaders seek people who can adapt their knowledge to novel opportunities, challenges, and constraints.

To get paid the big bucks, candidates must have systems, models, and frameworks to duplicate past results in a new setting and when everything doesn’t go according to plan A.

I built my AI Strategist and AI Product Management Certifications around the systems, models, and frameworks that I have used for 9 years. Use the link under my name to reserve your spot and advance.

#ProductManagement #Career

Generative AI does 3 things.

👍 Interface: GenAI interfaces detect intent and break it down into tasks.
👍 Access: GenAI can provide access to data, models, or other apps based on an intent or well-defined outcome.
👍 Orchestration: GenAI can orchestrate interactions between people and multiple agents, apps, and models to facilitate a complex workflow.

👎 If you hear promises of a single LLM automating a person’s job alone, it will never get beyond the demo stage. It takes multiple agents and apps to handle complex workflows.

👎 If the LLM isn’t supported by a knowledge graph, the product won’t be reliable enough for real-world applications. RAG over unstructured data doesn't work. RAG over a knowledge graph or ontology does.

Companies wouldn’t be spending billions on chips and infrastructure if Joe Bob’s 12-month-old startup could build a super-capable LLM with a few thousand GPUs.

#GenerativeAI #Data #AI

I would have made CTO by 30 if I understood the importance of strategy earlier in my career. A 3-step progression can get you there.

Take the initiative and build from the ground up. Most people don’t realize they can do this and how simple it is.

👁️‍🗨️ Step 1 is switching from anonymous delivery (dropping off code) to managed delivery (iterating on solutions with customers and users).

Where’s the strategy in that? By working with stakeholders and tracking projects’ impacts, you’ll realize that technology is an instrument of change.

Managed delivery also allows you to build coalitions. Each team you deliver value to joins your coalition. They are social proof that you deliver outcomes and can manage change.

If you build a big enough coalition, you’ll attract C-level attention. That brings a formal title and the chance to develop an AI strategy.

👁️‍🗨️ Step 2 is where we move from managing change one project and team at a time to business-wide. It starts with an internal advancement, but the big promotion is often at another company.

Once you’re in the role (typically VP level), the transformation you manage becomes your value proposition. Executive and C-level leaders are hired to manage a specific business transformation.

In this case, that means creating an AI strategy and transforming the business from an early maturity level to generating the majority of revenue and cost savings from the technology.

👁️‍🗨️ Successfully managing the transformation and delivering or exceeding expectations 2 or 3 times will attract your first C-level offer or make you an effective Founder.

AI strategy is the gateway to Founder, CDO, and CAIO roles. Follow me for more, or click the link under my name to upskill for career advancement.

#AIStrategy #Leadership #Career

One thing I’ve learned after a decade of building data and AI products is that businesses must crawl-walk-run with AI.

Crawling is data. Businesses can’t walk until they have high-quality data with context about the process that generated it.

Walking is descriptive models. Businesses can’t run without the right metrics, transparency, and baselines.

Running is predictive, prescriptive, diagnostic, and foundational models.

Crawling and walking make running less expensive. It sounds slower, but maturity advances faster in phases. Each phase delivers immediate value while laying the foundation for the next one.

Maturity through progressive iterations works while trying to do everything in a single leap fails.

Walk and run handle about 90% of use cases, reducing time to value.

By eliminating use cases that can be served by lower-cost solutions, the team can focus advanced methods on the 10% of use cases with the highest value.

Crawl-walk-run is a foundational AI Product Management framework. Follow me here or click the link under my name for more.

#AI #ProductManagement #DataScience

I didn’t realize how valuable Product Managers are until I saw one perform a miracle. She ended our fire drills, constant initiative pitching, arguing over requirements, and begging for resources.

She got buy-in for projects upfront and headed off most of our problems with the same simple statements:

“We’d miss our annual revenue targets.”
“That touches our margins.”

Most data and AI initiatives lack a connection to business value. Fix that, and priorities can be defended against fire drills. Resources and infrastructure can be justified by returns and long-term impacts.

Most business leaders and external teams don’t understand what data and AI can do for them. Fix that, and you’ll never have to hear, “We need the data team to find more ways to create value” again.

The data team gets an initiative pipeline instead of looking like they don’t have enough to do.

AI Product Managers help stakeholders, customers, and leaders articulate their needs. They define initiatives so data teams don’t need to guess about what could create more value.

That’s why demand for the role is rising so fast. When you’re ready to advance in your career, my AI Product Management Certification can help. Don’t miss out on early bird pricing for the next cohort.

Take a look at the course outline and enroll by clicking the link under my name.

The Internet and social media have created a lot of space for mediocrity to thrive. With so much digital space to fill, standards have plummeted. AI unchecked will amplify the problem.

When space was limited, only original thoughts, research, and content were published. Social media content walls need millions of creators to fill the endless space.

The problems start when there’s more content from AI and bots than people. Bots broke Amazon’s reviews and product recommendations. They broke X’s trending content recommendations.

You saw signs of bot takeovers when you clicked on a trend and got a wall of the same spam post from thousands of accounts or searched for a product, and the top five results were trash with great reviews.

People leave platforms because they don’t trust product reviews and recommendations or because bots overtake their social media feeds.

Amazon and X (just this week) were forced to address the problem. The cycle of more bots and fewer people would eventually crash their platforms.

Bot and generative AI-created content mitigation will soon be essential for platform survival.

Hold on. Search results, product recommendations, news aggregators, and content walls are about to get even worse.

#GenerativeAI #AI #Data

One of the most influential business leaders in the world just set the bar for data team performance. No matter your role, you need to understand this to advance.

Jamie Dimon (CEO of JPMC) says they use AI for over 400 use cases in production, and most initiatives pay for themselves. When he talks, CEOs listen, and now they expect more from us.

In his annual letter, Dimon created an expectation that data teams should be value generators, not cost centers. Data teams must deliver to production and measure impact.

JPMC is all in on AI and infrastructure because it will “unlock our ability to use our data in ways that are hard to contemplate today.”

CEOs will expect data engineering and infrastructure purchases to be growth drivers. Data teams must quantify the value both create.

Dimon spent an entire section explaining how critical iterative and continuously improving strategy and decision-making are to the business.

He believes business is prediction, and those with the best data, models, and people will win. He sees every role being impacted by data and AI tools, and everyone must reskill.

He makes the case for data and AI as forward-looking, prescriptive decision-support tools and explains the need for culture change to make that work.

“Use your brains to figure out the truth — not to justify what you already think... It's hard to see certain long-term trends, but you must try.”

CEOs are looking to their data teams for solutions and value. Dimon’s letter is the latest call to action, clearly defining the data team’s opportunity.

Data teams also need to reskill and adapt. Data and AI Strategists and Product Managers are critical roles. They connect the data team with value and help it exceed CEOs’ expectations.

I offer certifications for both roles and leadership training. Click the link under my name to get ahead of the opportunity.

#DataScience #DataEngineering #ProductManagement

Google doesn’t want you to read this. Gmail flagged my article as “dangerous” for several people, which is odd since other corporate and public email filters allowed it through.

Google is changing its terms and conditions (ToS) for all its services to give it rights to your intellectual property. These rights allow Google to analyze your data and use it to train AI models.

It applies to Google Drive, Gmail, Meet, Nest, Pixel, Android, Sheets, Fitbit, and other services. The terms impact businesses, nonprofits, schools, and individuals who use these services.

These terms appear to cover both free and paid tiers, although I couldn’t find anything specifically mentioning Google Workspace.

I think... maybe… I’m not a lawyer, and interpreting the ToS makes me worry about how Google uses my data. Google leaves customers with trust issues that data and AI products shouldn’t.

IP protection concerns have already led to the ban of Generative AI apps in most enterprises. Perceptions about a business’s data handling policies extend to every product it offers.

Google must clarify these terms because, as written, businesses will be forced to restrict access to common services like Chrome and can’t use anything with Android OS.

Google provides a lot of value in its open source and open app ecosystems. However, this change to its ToS is worrying, and the perception will reduce Google Cloud adoption in the long run.

I explain it all in a hazardous article. Click at your own risk.

https://lnkd.in/gnxM5jrX

#DataPrivacy #GenerativeAI #Google

vinvashishta.substack.com • 6 min read

What Is Google Thinking With It’s New Terms Of Service?

Step outside the hype bubble, and people don’t obsess over AI. Most C-level leaders don’t follow the latest LLM releases, and customers don’t care if their mouse uses AI.

The last time I talked with Scott Luton, we had an outside-the-bubble conversation about data. We talked about what works and what doesn’t in Generative AI.

My first data science project in 2012 was a supply chain use case. Most people don’t realize that supply chain managers have been using data and models for longer than most.

They deal with risk, uncertainty, and complexity. All 3 are use case categories that data and AI can serve better than any other technology.

Supply chain data must also be interoperable across businesses. It’s one of the early adopters of structured vocabularies and ontologies.

On Monday morning, I’ll be on The Buzz for the second time. We’re planning another pragmatic AI discussion. It won’t go viral but will deliver a lot of value.

I’ll be fielding questions, too. I appreciate Supply Chain Now putting this together.

#Data #Analytics #DataScience

Most companies get a failing grade in developing career paths for data professionals. To create your own career path, follow successful people in our field.

✅ Yann LeCun: Advanced Researcher -> Research Organizational Leadership.
✅ Andrew Ng: Advanced Researcher -> Educator -> Leadership -> Founder -> Technical Strategy/Advisor.
✅ Allie K Miller: Product -> Strategy -> Founder -> Investor.
✅ Cassie Kazyrkov: Advanced Researcher -> Leadership -> Founder.
✅ Satya Nadella: Product -> Strategy -> C-level Leadership -> Google’s Dance Coach.
✅ Mark Zuckerberg: Dorm Room -> Founder of a Massive Social Media Company. (It’s good to remember that nontraditional career paths sometimes work.)

My career path was inefficient but a lot of fun: College For AI -> Failed Founder -> Sales -> Software Engineer -> Leader -> Product -> Successful Founder -> Applied Researcher -> AI Strategist -> Author -> Educator.

We eventually get bored or are pushed out of technical IC roles. While they’re a great place to start, we can’t spend our entire careers as ICs.

The more capable we become, the more impact we want to have. An IC can only work so many hours and doesn’t have the authority or ownership to do bigger things.

In our 4 most common tracks (Founder, Leadership, Product, Strategy), we become multipliers, and everything we do is amplified by how it enables others to deliver value. 

Your company SHOULD provide structured career paths, a learning path to prepare for your next track, and mentorship to help you succeed after the transition.

Each career transition requires upskilling and a roadmap, but few companies provide one. I made my latest career transition (educator) in 2019 to fill that gap.

I developed my certification courses to upskill technical data ICs in all four tracks. I also provide coaching. The link below my name is the starting point for your next big step.

#DataScience #DataEngineering #Career

The hardest advice I have ever gotten made me angrier than I have ever been, but I owe everything I have built over the last 12 years to the person who gave it to me.

“People in a position of strength, with leverage, do what they want. People who are out of position do what they are allowed to.

You’re smart, work harder, and have better technical skills, but you don’t know how to leverage them as advantages. That’s why you’re down there, and I’m up here.”

People with multiple job offers set their salaries and choose the best workplace. We must position ourselves with capabilities, expertise, and timing to get there.

Companies with unique, high-value products have pricing power and customers who line up to buy.

Leaders and founders must understand something about the market others have missed and deliver a product that fills the gap.

I compete successfully against companies like Bain and BCG. Tiny V-Squared is still here 12 years later because I learned how to develop levers and use them to create advantages.

Learn to ask a new question, and you’ll understand how to put yourself, your team, or your business in positions of strength.

What capabilities and expertise should we acquire? The ones that deliver the greatest leverage and create an advantage to achieve our career goals.

What features and products should we build? The one that delivers the most powerful lever to attract customers and creates the greatest advantage over competitors.

Strategic thinking reveals advantages and how we can use them to put ourselves into positions of strength. Tactical thinking makes us easy to take advantage of.

I wrote my book and built my courses to teach strategic thinking. Click the link under my name to start using leverage to your advantage.

#Leadership #Career

Most data and AI products are unusable because data teams and their leaders can’t get buy-in to build products the right way. “It takes too long.”

Trash data trains trash models, but the business needs tangible returns in months, not years. Fixing the data doesn’t deliver them unless data teams and leaders take a product-first approach.

High-quality data can deliver immediate returns, and descriptive models trained on it deliver quarterly returns. These initiatives build the basis for AI products and much bigger returns.

Big opportunities justify AI products, and buy-in is maintained by incremental delivery. The roadmap keeps them all aligned.

It sounds slower, but AI products are delivered faster. It sounds like waterfall, but iterative delivery allows the data team to be more agile and responsive to business and customer needs.

Data teams and their leaders need someone with frameworks and capabilities who can build the data and AI product roadmap this way.

AI Product Managers own the connection between strategy and execution or opportunity discovery to go to market. That’s why demand and salaries are so high.

My Data and AI Product Management course certifies people on the frameworks I use to deliver 8 and 9-figure data and AI products. The curriculum is built for technical ICs and product managers.

Time is running out to enroll in the next cohort. Click the link under my name to get started.

#ProductManagement #Data #AI

What is certification worth? I spent a year researching this before creating my certification programs, and here’s what I found.

👁️‍🗨️ A Python certification isn’t very valuable, but Google’s Python certification is. The differences are the reputation and evaluation criteria.

Google certifies people on its approach to Python development. It’s not enough just to call someone a Certified Python Developer or, in my case, a Certified AI Product Manager.

I had to explain what they learned and why that made them capable of doing the job. My curriculum certifies people on my frameworks and approaches, and most certifications make a similar justification.

👁️‍🗨️ I talked with hiring managers to understand how certifications impact their perceptions of candidates. Two types of certifications make an impression: differentiators and prerequisites.

A differentiator makes the holder stand out because few others have it, or it is superior to similar certs. A prerequisite doesn’t make the holder stand out, but not having it puts people at a disadvantage.

👁️‍🗨️ What makes a certification a differentiator? It’s all about credibility. That can come from a company, academic institution, or the instructor. The first two are simple, but the last one needs examples.

Zach Wilson worked as a data engineer at 3 Big Tech companies and has solid social proof (he’s followed by over 300K people and called an expert by other experts).

Rita McGrath is a business professor at Columbia and authored multiple best-selling books on business strategy and innovation.

Joe Reis is a best-selling author and well-recognized speaker, ran a data consulting company for years (still does), and has strong social proof.

👁️‍🗨️ Will certifications get you a job? Not by themselves. Differentiator certifications can support promotions, a career transition, or getting called for an interview. It won’t be THE deciding factor for a job offer.

What you’re certified on should make your answers to interview questions stand out. The frameworks and approaches taught will structure your answers like an experienced practitioner would.

Let me know if this breakdown was helpful, and follow me here for more.

#DataScience #DataEngineering #Analytics

According to Glassdoor, the average compensation for AI Product Managers is higher than for AI Engineers, Data Engineers, and Data Scientists.

Digital product capabilities don’t translate to the new role, and businesses are raising salaries to attract one of the few people with these critical skills and frameworks:

👁️‍🗨️ Opportunity Discovery and Use Case Selection: Data and AI create new opportunities and support use cases that weren’t feasible with digital. AI PMs help the business monetize the technology.

👁️‍🗨️ Opportunity Estimation: Data and AI are monetized differently, so costs and ROI must be estimated differently. AI PMs ensure accurate estimates so business leaders can prioritize the highest-value initiatives.

👁️‍🗨️ Initiative Breakdown and Roadmap Development: Data and AI products are built differently, and many businesses must mature as they deliver.
AI PMs build the roadmap to support both while creating an immediate impact with each delivery.

👁️‍🗨️ AI Product Design: Users interact with AI products differently. The AI PM must gather a new type of requirement and design the product for usability, interpretability, and integration into workflows.

👁️‍🗨️ AI Product GTM: Pricing, marketing, release management, and most other GTM elements are entirely different for AI products. AI PMs ensure products generate value for customers and return value to the business.

🔜 My instructor-led AI Product Management Certification cohort starts soon. Only a few days remain to take advantage of the early-bird discount.

Use the link under my name to learn more and reserve your spot.

Companies hire advanced data talent and put them to work on spreadsheets and BI. In business leaders’ minds, data = reporting, AI = chatbots, and data team = cost center.

Getting buy-in for advanced projects is challenging, especially in small and medium-sized enterprises (SMEs). Many business leaders simply don’t believe that data and AI can deliver higher returns.

The same business leaders need a new growth engine because the economy isn’t growing the pie for them anymore.

They understand that data and AI drive growth for some businesses, but no one’s connecting the dots. That’s the data team’s opportunity, but it’s a race against the clock.

Once the CEO abandons growth for cost-cutting, the window of opportunity closes. I explain how to build trust and get buy-in for more advanced initiatives in this article.

https://lnkd.in/gw9ExZRd

#Data #Analytics #AI

vinvashishta.substack.com • 8 min read

It’s The Data Team’s Moment To Shine Or Halt And Catch Fire

Who owns data in a business? Data is a novel asset that is monetized multiple times, serves multiple functions, and requires multiple owners. Here’s how to break it down.

Finance should own the monetization and valuation of the business’s data assets. This allows it to estimate the near- and long-term AI opportunity size to budget appropriately.

Individual business units have functional objectives for data and must have ownership that allows them to leverage data to improve outcomes. That includes self-service tools and upskilling.

The data team owns engineering new data-generating processes, curating datasets, and transforming the asset into artifacts that can be productized to deliver value.

AI strategy owns opportunity discovery and defining the highest value datasets and artifacts to be built. AI product owns the initiative breakdown, productization, and taking data and AI products to market. 

The security team owns validating and safeguarding the asset. The quality and governance team audits and enforces standards.

Other technical teams own integrating data artifacts into existing systems and products. IT may own the infrastructure and architecture.

Every part of the business generates data, can leverage it to create more value, and needs ownership.

#AI #Data

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