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Tag: machine learning

  • Best AI Training Courses In 2025

    A world where machines can think, learn, and make decisions just like humans? It’s not science fiction anymore. It’s the reality of artificial intelligence. As AI continues to shape our future, more and more people are eager to dive into this fascinating field. We at StartupTalky understand the buzz around AI and how it’s changing the game for businesses and individuals alike. So, you’re interested in riding the wave of AI but don’t know where to start? Don’t worry, we’ve got you covered.

    The internet is brimming with artificial intelligence courses that cater to beginners and experts alike. From Google’s AI essentials to Stanford’s healthcare-focused AI program, there’s something for everyone. Whether you’re looking to boost your career or simply satisfy your curiosity, these online AI courses offer a chance to learn from industry leaders and top universities. 

    Google AI Essentials
    IBM AI Developer Professional Certificate
    DeepLearning.AI’s Deep Learning Specialisation
    Udacity’s Artificial Intelligence Nanodegree
    edX’s Artificial Intelligence Professional Certificate
    MIT OpenCourseWare’s Artificial Intelligence
    Google’s Machine Learning Crash Course

    Google AI Essentials

    Course Google AI Essentials
    Price Free
    Course Length Approximately 10 hours
    Google AI Essentials - Best AI Training Courses
    Google AI Essentials – Best AI Training Courses

    Google AI Essentials is a self-paced artificial intelligence course designed to help individuals across various industries boost their productivity using AI tools. In under 10 hours, learners gain practical skills to apply AI in real-world scenarios. The course covers essential topics such as using AI for idea generation, content creation, and effective prompt writing. It also emphasises responsible AI use by teaching learners to identify potential biases and avoid harm. This AI course is accessible to everyone, requiring no prior technical experience, and is structured to fit into busy schedules.

    Course Overview

    The course comprises five modules: Introduction to AI, Maximising Productivity with AI Tools, Discovering the Art of Prompt Engineering, Using AI Responsibly, and Staying Ahead of the AI Curve. Through a mix of videos, readings, and interactive exercises, participants learn to use generative AI tools, create effective prompts, and select appropriate AI tools for various work needs. The hands-on approach allows learners to immediately apply their newly acquired skills to workplace tasks.

    IBM AI Developer Professional Certificate

    Course IBM AI Developer Professional Certificate
    Price $49 per month on Coursera
    Course Length Approximately 6 months
    IBM AI Developer Professional Certificate - Best AI Training Courses
    IBM AI Developer Professional Certificate – Best AI Training Courses

    The IBM AI Developer Professional Certificate is a comprehensive online programme designed to equip learners with practical skills in AI development. This self-paced course, offered through Coursera, can be completed in about six months with a commitment of 4-10 hours per week. It’s suitable for both beginners and experienced professionals looking to enhance their AI skills.

    Overview

    This certificate programme covers essential aspects of AI, including machine learning, deep learning, natural language processing, and computer vision. Learners engage in hands-on projects and labs, gaining real-world experience in building AI models and applications. Upon completion, participants receive a professional certificate from IBM, a globally recognised leader in AI and technology.

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    DeepLearning.AI’s Deep Learning Specialisation

    Course Deep Learning Specialisation
    Price $49 per month on Coursera
    Course Length Approximately 2–6 months
    DeepLearning.AI's Deep Learning Specialisation - Best AI Training Courses
    DeepLearning.AI’s Deep Learning Specialisation – Best AI Training Courses

    Deep Learning Specialisation is a comprehensive artificial intelligence course that equips learners with foundational skills in deep learning. This programme, led by AI pioneer Andrew Ng, offers a blend of theoretical knowledge and practical applications. The specialisation comprises five courses, covering neural networks, optimisation techniques, and advanced topics like convolutional and recurrent neural networks.

    Specialisation Course Structure

    The specialisation is structured into five courses:

    1. Neural Networks and Deep Learning
    2. Improving Deep Neural Networks
    3. Structuring Machine Learning Projects
    4. Convolutional Neural Networks
    5. Sequence Models

    Each course builds upon the previous one, providing a logical progression through the field of deep learning.

    Udacity’s Artificial Intelligence Nanodegree

    Course Udacity’s Artificial Intelligence Nanodegree
    Price $399/month.
    Course Length Approximately 3 months.
    Udacity's Artificial Intelligence Nanodegree - Best AI Training Courses
    Udacity’s Artificial Intelligence Nanodegree – Best AI Training Courses

    This one is designed to equip learners with essential skills in AI development. This programme offers a blend of theoretical knowledge and practical applications, making it an excellent choice for those looking to start or advance their careers in AI.

    Programme Structure

    The nanodegree is structured into two terms, each lasting three months. Students are expected to dedicate approximately 15 hours per week to complete the programme successfully. The curriculum

    covers a wide range of topics, including machine learning, probabilistic reasoning, robotics, computer vision, and natural language processing.

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    edX’s Artificial Intelligence Professional Certificate

    Course edX’s Artificial Intelligence Professional Certificate
    Price $447 total for the certificate program.
    Course Length Approximately 6 months.
    edX's Artificial Intelligence Professional Certificate - Best AI Training Courses
    edX’s Artificial Intelligence Professional Certificate – Best AI Training Courses

    It is a course that equips learners with essential skills in AI development. This professional certificate program covers a wide range of topics, from basic concepts to advanced techniques in machine learning.

    Curriculum

    The curriculum includes courses on supervised machine learning, unsupervised learning, deep learning, and reinforcement learning. Students learn to apply common operations to datasets using Python, explain various learning models, and implement algorithms using Scikit-learn.

    Skills Gained

    Participants gain practical skills in data preprocessing, plotting, and analysing factors that impact algorithm performance. They also learn to optimise machine learning pipelines, implement clustering techniques, and train deep neural networks for classification and regression tasks.

    MIT OpenCourseWare’s Artificial Intelligence

    Course MIT OpenCourseWare’s Artificial Intelligence
    Price Free
    Course Length Self-paced, varies by user
    MIT OpenCourseWare's Artificial Intelligence - Best AI Training Courses
    MIT OpenCourseWare’s Artificial Intelligence – Best AI Training Courses

    This artificial intelligence course, offered by MIT OpenCourseWare, provides a comprehensive introduction to AI fundamentals. Led by Professor Patrick Henry Winston, it covers essential concepts in knowledge representation, problem-solving, and learning methods.

    Course Structure

    The course comprises lectures, recitations, and tutorials, offering a well-rounded learning experience. It delves into three major areas: Search, Machine Learning, and Knowledge Representation and Inference. Students engage with topics such as graph search, neural networks, and natural language processing.

    Key Concepts

    Learners explore AI applications in rule chaining, heuristic search, and constrained search. The course also covers decision trees, SVMs, and other learning paradigms, equipping students with practical skills in developing intelligent systems.

    Google’s Machine Learning Crash Course

    Course Google’s Machine Learning Crash Course
    Price Free
    Course Length Approximately 15 hours
    Google's Machine Learning Crash Course - Best AI Training Courses
    Google’s Machine Learning Crash Course – Best AI Training Courses

    This one is all about a practical introduction to artificial intelligence and machine learning. This free AI course comprises over 30 exercises, 25 lessons, and takes approximately 15 hours to complete. Taught by Google researchers, it provides real-world case studies and interactive visualisations of algorithms in action.

    Crash Course Overview

    The course covers fundamental machine learning concepts, including supervised and unsupervised learning, regression, classification, and neural networks. It’s designed for both beginners and those with some programming experience, making it an excellent choice for anyone looking to enhance their AI skills.

    Key Topics

    Key topics include linear regression, logistic regression, neural networks, and working with numerical and categorical data. The course also delves into advanced subjects like large language models and ML fairness, ensuring learners gain a comprehensive understanding of artificial intelligence and machine learning.

    Practical Exercises

    Practical exercises run directly in the browser using the Collaboratory platform, allowing learners to apply their knowledge in a hands-on manner. These exercises cover various aspects of machine learning, from basic concepts to more advanced topics, helping students build practical skills in AI development.

    End Note

    We at StartupTalky believe that staying ahead in the AI game is crucial for success in the 21st century. No matter who you are – a budding entrepreneur or a seasoned professional, these courses offer a chance to level up your skills and stay competitive. And hey, if you’re a founder looking to partner or work with experts in the startup world, don’t hesitate to reach out to the StartupTalky team at shubham@startuptalky.com for all things ‘startups’. When every day is a beginning, so why not start your journey today?

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    FAQ

    Which is the best course to learn AI?

    Here are the best courses to learn AI:

    • Google AI Essentials
    • IBM AI Developer Professional Certificate
    • DeepLearning.AI’s Deep Learning Specialisation
    • Udacity’s Artificial Intelligence Nanodegree
    • edX’s Artificial Intelligence Professional Certificate
    • MIT OpenCourseWare’s Artificial Intelligence
    • Google’s Machine Learning Crash Course

    Can I learn AI in 3 months?

    Yes, you can gain a basic understanding of AI in three months through focused online courses, especially covering foundational topics like machine learning, data science, and neural networks.

    Is AI hard to study?

    Studying AI can be challenging due to its technical concepts like machine learning, algorithms, and data analysis, but it’s manageable with dedication and consistent practice.

  • RBI will Implement AI-Powered Real-Time Technologies to Detect Cybercrime

    As per the recent media reports, the Reserve Bank of India (RBI) is developing an AI-enabled system that will alert people in real time about financial scams.

    An AI-based warning system would identify suspicious transactions as they are going to be made, and individual banks will access a central bank data repository that has information on different kinds of frauds and their offenders. In order to lower the danger of cyber fraud, the system will employ AI to gather and evaluate data on possible frauds, identify high-risk platforms, and alert users during transactions. 

    Even while cybercrime is still under control, the RBI believes that taking a proactive stance is essential to tackling new issues in the online financial sector. Indeed, MuleHunter AI, an artificial intelligence and machine learning (AI/ML) model, has already been created by the Reserve Bank of India Innovation Hub (RBIH), a division of the RBI, to assist banks and other financial institutions in identifying so-called mule accounts that fraudsters utilise. In contrast, the new system will protect digital transactions and notify users.

    RBI Shaping the Cybercrime Fee Banking Services

    To identify and stop cyber fraud, the RBI has been developing an AI-enabled fraud information system for some time. Implementing AI that can learn from previous frauds to flag high-risk transactions is one of the proposals made by an expert panel on cyber frauds that the RBI established.

    The method seeks to increase banks’ and payment gateways’ readiness to identify possible fraud while making it more difficult to cash out illicit funds. The frequency and average magnitude of cyber frauds are increasing, despite the fact that current fraud rates are still modest at about one every 114,000 transactions. All of them, then, are a component of the strategies to raise public awareness in order to help stop victimisation.

    In 2015, the central bank formed the Cyber Security and IT Examination (CSITE) cell under its Department of Banking Supervision, in addition to establishing several groups to combat cyber fraud. Additionally, it established a Fraud Monitoring Cell that publishes a list of bank and financial institution officials who are in charge of reporting fraud.

    Making Guidelines More Stringent

    Using information from the Indian Cybercrime Coordination Centre, a government agency tasked with combating cyber fraud, the central bank has also revised instructions for banks. The 2024 Deloitte-NASCIO Cybersecurity Study states that as the digital landscape grows and more personal, health, and financial data is available online, along with critical infrastructure like power, water, and transportation systems integrated with online components, cybersecurity is becoming a top priority for governments, regulators, and corporations. This increases vulnerabilities.

    According to the research, which was published on September 30, governments and regulators are realising more and more how important strong information security is to the dependable running of important government services.The area of attack is expanding. Both the Internet of Things and the Internet itself are generating more information. The public’s financial, health, and other personal information is stored on more servers in more locations than ever before. According to the report, online operational components are connected with more important infrastructure.

    It further stated that state officials are realising that information security is fundamental to the effective operation of vital government services and that all of this leads to an increase in the number of sites of risk.

    Online Fraud Cases are Growing

    The number of banking frauds rose by almost 300% in FY24 compared to the previous two years, according to the RBI’s May annual report. According to the data, public sector banks reported 75% of the total fraud amount in FY24, while private sector banks recorded 67% of the fraud incidents. According to the RBI statistics, the total number of online fraud cases rose by 708% to 29,082 in FY 23 and FY 24.

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  • How AI is Revolutionizing Supply Chain Management: A Deep Dive

    This article has been contributed by Atul Kumthekar, Co-Founder, 3-F Software – a Design Thinking Company.

    The use of artificial intelligence in various fields is increasing day by day. In this context, the supply chain does not fall behind. The supply chain is simply a distribution network that connects brands and consumers through distributors. In this article, let’s learn more about what a supply chain is, areas where AI can be used in the supply chain, how AI can be useful in enhancing the supply chain, and more.

    What Is A Supply Chain
    Problem And Approach So Far
    Areas Where Ai Be Used In Supply Chain?
    What Is Ai From Technological Standpoint?
    How Can Ai Be Useful In Supply Chain?

    What Is A Supply Chain

    If you are a living being, you are part of some supply chain! There is no escaping! Essentially it is a distribution network. From Brand to Consumers via Distributors Products and goods move from Brand to Consumer and Money flows in the reverse order. This is the best simplistic definition of supply chain that I have ever come across.

    This brings in a lot of complexities and the challenges thereby. Complexity is at multiple levels and scale because of various challenges:

    • Scale and affordability at various layers mean plethora of different solutions used from start to end. From excel sheet to SAP like expensive ERP
    • International nature of today’s world
    • Prediction of demand to meet supply in time
    • Management of unsold in case of retail chain
    • Inventory management at various levels
    Supply Chain Management
    Supply Chain Management

    Problem And Approach So Far

    Various integrations and APIs helped intercommunication between Tally like software to SAP and Excel both People have addressed various issues using the latest technologies of the day. Blockchain being a recent example especially for global trade. The blockchain technology proved that the documentation time saving is possible and also that assurance of quality and source can be guaranteed beyond doubt.

    But these technologies and solutions did not address more complex problems like demand predicting, analyzing vast data.

    Now Artificial Intelligence is the new kid in town to help ease out these complexities a bit!

    Areas Where Ai Be Used In Supply Chain?

    Now that we know what was possible so far and some clue to unsolved problems, let us look into the problem areas little more closely. I got following response when I enquired on ChatGPT which was pretty accurate! So, I am going to reproduce the same with my elaborations on interesting ones –

    1. Demand Forecasting: AI algorithms can analyze historical data, market trends, weather patterns, and other variables to predict demand more accurately. This helps in optimizing inventory levels and reducing stockouts or overstock situations.
    2. Inventory Management: AI-driven systems can optimize inventory levels by analyzing factors like lead times, demand variability, and supplier performance. This ensures that inventory is maintained at optimal levels, reducing carrying costs while ensuring products are available when needed.
    3. Route Optimization: AI algorithms can optimize delivery routes considering factors like traffic patterns, weather conditions, and delivery windows. This reduces transportation costs, improves delivery times, and minimizes fuel consumption.
    4. Supplier Relationship Management: AI can analyze supplier performance data to identify trends, assess risk, and predict supplier behaviour. This helps in making informed decisions about supplier selection, negotiation, and risk management.
    5. Warehouse Management: AI-powered systems can optimize warehouse operations by automating tasks such as picking, packing, and sorting. This improves efficiency, reduces errors, and lowers operational costs.
    6. Quality Control: AI can analyze sensor data and visual inspections to detect defects in products or packaging. This ensures that only high-quality products are shipped, reducing returns and improving customer satisfaction.
    7. Supply Chain Visibility: AI can provide real-time insights into the entire supply chain, allowing businesses to track inventory levels, monitor supplier performance, and identify potential bottlenecks or disruptions.
    8. Predictive Maintenance: AI can analyze equipment sensor data to predict when maintenance is needed, reducing downtime and preventing costly equipment failures.
    9. Risk Management: AI can analyze various risk factors such as geopolitical events, natural disasters, and supplier financial stability to identify potential risks to the supply chain. This enables businesses to develop contingency plans and mitigate risks proactively.
    10. Sustainability: AI can optimize supply chain processes to minimize environmental impact, such as reducing carbon emissions through optimized transportation routes or minimizing waste through better inventory management.

    But before I add my elaborations let us peek a bit into what are typical AI techniques

    What Is Ai From Technological Standpoint?

    The word artificial intelligence typically deals with large data. I used to mock AI saying this is our Ajji’s (Grandmother’s) wisdom! What a person gathers over years or our ancient culture have already noted down. But that’s more or less about how humans behave, individually Vs in masses. But let’s not divert there!! May be in some other blog

    It is impossible to make ANY sense out of the vast data that is flowing in today. The producing brands (Manufacturers) need to listen to those in a meaningful way, in order to save any costs and time. You can employ men (and women) power in all the countries you deal with. That is one possible solution. But then there is issue of miscreants and malicious feeds. And even any number of these will not match the scale and capability of technology today. So why not technology, if that is one way?

    The AI basically started with machine learning which rests heavily on statistics. Let’s say you have an excel sheet of student’s marks. And you want to know the distribution. Most likely you will get a Gaussian curve. Meaning most students near about average and few below and few above, the curve tapering down both directions. This is understandable.

    Gaussian Curves Example
    Gaussian Curves Example

    But now consider the problem – can I find out if students have copied? I will leave this problem to you and get back to me on any thoughts!

    Ah ha! Welcome to machine learning.

    A typical approach in machine learning is give enough sample data, ask the algorithm to understand the pattern and apply this learning to test data and judge the output and then think about improving output. Yes, you probably have guessed it right, that the output is probabilistic. You need to get comfortable with this! (And we are not going to quantum mechanics as yet)

    The above paragraph is the essence of Artificial Intelligence. The major differential between machine learning and AI started emerging with Large Language Models (LLM) which started putting chatGPT like tools into public hands. How do they work? Are they the advanced versions of Google search? Well, basically they can address much much much more parameters to analyze. From few hundreds to millions! That is amazing. Even our brains may not be doing so. Take a look at Hugging Face and you will get the picture. The company started with chatbot application and with multiple rounds of funding reached at LLM models. And if you care to read “Attention is all you need” mentioned in references at the end, you will need to look at Transformer architecture (see diagram below)

    Transformer ArchitectureTransformer Architecture - Source: http://rpradeepmenon.medium.com/
    Transformer Architecture – Source: http://rpradeepmenon.medium.com/

    With this, let’s get back to Supply chain problems.

    How Can Ai Be Useful In Supply Chain?

    Let us look at some of the problems mention in the earlier section (“Areas where AI be used in supply chain?”).

    Demand Forecasting is definitely the one. This is not humanely possibly and achievable by employing any number of employees in any number of countries. Will LLM be useful here? Definitely. This is because it fits into the criteria of what LLM kind of models can work on. Basically, millions of parameters.

    Inventory Management is related to Demand Forecasting. For a big brand, it is not just about warehouse but localized inventory to supply quickly to local market. You may have seen Duzo like B2C supplier having small storage locations nearby your area with many biker riders picking things from there and delivering in 10 minutes! What inventory to maintain at such local locations can be better judged only by AI models.

    Route Optimization a well-known problem in industrial engineering and operations research (Traveling salesman problem) The solution to this was once modelled as Simplex algorithm (Narendra Karmarkar) The large parametrization that is possible with LLM models will throw and interesting solutioning scenarios of solving this problem.

    Supplier Relationship Management also known as CRM (Customer Relationship Management) is an important area where sales and support are managed. Salesforce the world leader in this domain is funding good amounts for the growth in Artificial Intelligence and LLM (Large Language Models). Pretty soon we will see a day to day usable output from these efforts.

    Quality Control is an important area. During Covid days, this gained importance and quality people could not reach work location. The usual technique was to deploy cameras, send images which could be checked using AI with possible false positives but reducing the overall load. Radiology is one good example where this is used today.

    Predictive Maintenance is also undergoing big impact due to AI. More the historical data, better is the prediction and preventive measures, which save bigtime in repairs cost. You can send your support technicians for preventive maintenance in time than to fix things! I had personally developed and delivered an IOT solution for X-ray manufacturer to send technician based on number of X-rays taken instead of earlier method of end of year. No AI but you can sense where the next step could be!

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    Important!

    Having seen all this, you must have realized that AI is not a magic wand. Also, it is not an overnight solution. One needs to select an existing model, if not develop a new one, then customize it, play with it and most importantly mature it to predict better and better. All this happens asymptotically. Meaning the gap between actual solution and reality will keep reducing slowly but surely. Keep in mind the dynamics of reality is also dynamic! So, something that worked well may not work equally well in future.

    More importantly…

    All AI solution providers stress importance of human touch. The solution needs to be looked at from human perspective. This means that the output is not harmful to certain section of community, or simply very negative etc. And even in this space there are tools and datasets evolving to improve solution from this perspective.

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  • Indian FMCG Consumer Engagement Revolutionised by AI

    The analysis by the global market research firm EY states that consumer goods companies have enormous opportunities to use AI to improve asset tracking, supply chain management, and consumer experience and engagement. The use of artificial intelligence (AI) technologies to improve customer centricity and operational efficiency is on the rise among Indian merchants. An overwhelming 82% of Indians surveyed by the EY consumer index are optimistic that artificial intelligence will one day make shopping much easier.

    Several industries have jumped on the bandwagon of innovation and adaptation in response to recent tech developments and the expansion of the internet. This trend has been especially accelerated by the fast-moving consumer goods (FMCG) industry’s collaboration with the e-commerce sector. According to McKinsey, E-commerce sales for consumer goods will quadruple from 2016 to 2025, reaching $1.8 trillion. With so much competition and so many brands entering the industry, AI is becoming an important differentiator for brands looking to remain ahead of the curve.

    AI is crucial to draw customers closer to the company. Finding reliable customer insights to improve data-backed decision-making is a persistent problem in the fast-moving consumer goods (FMCG) sector. Insights AI ensures organisations acquire in-depth customer behaviour data by combining powerful AI technologies like Emotion AI, Behaviour AI, and Generative AI. These innovations provide precise data for effective decision-making and aid brands in comprehending the wants and needs of target consumers.

    Customer wants and demands in the fast-moving consumer goods industry (FMCG) are dynamic, just like in any other industry. Thanks to AI’s data-processing prowess, organisations can swiftly and accurately adjust their marketing plans to meet the needs of their target audience. Insights AI has the potential to greatly enhance the quality and affordability of products and services in the Indian fast-moving consumer goods market.

    What are FMCG Products?

    Providing Forecasts to FMCG Companies
    Customised Suggestions

    Providing Forecasts to FMCG Companies

    Through AI, Fast-Moving Consumer Goods (FMCG) companies may better understand their customers’ habits and preferences, which in turn allows them to provide better service and encourages more participation. Artificial Intelligence also allows for substantial process automation, which saves time and money. According to an IBM poll, retail and brand executives expect cognitive automation capabilities to slash operational expenses by seven per cent on average.

    Adapting to the latest developments in data-driven technologies like deep learning, artificial intelligence, and machine learning can greatly benefit FMCG companies. There have been revolutionary shifts in the fast-moving consumer goods (FMCG) industry as a result of Machine learning and deep learning. With the massive amounts of data produced by the FMCG industry, Machine learning approaches help businesses identify and segment their target markets by analysing customer behaviour, preferences, and buying habits. Companies in the fast-moving consumer goods industry can use this information to improve their demand forecasting, personalise their marketing campaigns, and optimise product positioning and pricing tactics.

    Leading FMCG Companies in India by Market Capitalization
    Leading FMCG Companies in India by Market Capitalization

    Customised Suggestions

    AI is having a major impact on consumer choices by providing tailored suggestions. In contrast to 23% worldwide, 48% of Indians trust AI for personalised promotions and sales, according to the EY report.

    Online retailers, media streaming sites, and social media sites all use AI algorithms to sift through customers’ tastes, habits, and online activity. Customers have better shopping experiences, are more satisfied overall, and are likelier to a brand because of AI’s ability to provide personalised product recommendations based on their interests and preferences.

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    Using AI-based technologies to make buying decisions is becoming more and more acceptable to Indian shoppers. While just 58% of people worldwide are receptive to the idea of AI helping them make better purchasing decisions, 82% of Indians are. Indian customers have more faith in AI-generated personalised suggestions and AI-powered targeted marketing and sales. When asked for assistance, 82% of Indians would be willing to use a chatbot.

    Variations in consumer demand contain useful information for fast-moving consumer goods (FMCG) companies due to the many connections and patterns they contain. Discovering these insights is essential for getting ahead in the industry. FMCG companies need to process this data to make informed decisions about product placement, product prioritisation, workflow optimisation, marketing segmentation, pricing and offer launch timings. Applying state-of-the-art tools and algorithms allows for thorough planning and optimisation in the FMCG landscape. As a result, more and more fast-moving consumer goods (FMCG) companies are using AI-powered automation to reimagine the customer service they provide and boost engagement with their brands.

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    FAQs

    What is the future of AI in the FMCG industry in India?

    • AI is expected to play an increasingly important role in all aspects of the FMCG industry, from product development to marketing and sales.
    • As AI technology continues to evolve, we can expect to see even more innovative applications in the FMCG space.

    What are some of the benefits of using AI for FMCG companies in India?

    Some of the benefits of using AI for FMCG Companies are increased customer satisfaction and loyalty, improved operational efficiency, reduced costs and better decision-making.

    How can FMCG companies in India use AI to improve customer experience?

    • AI can provide personalized recommendations based on customer behaviour and preferences.
    • AI-powered chatbots can offer 24/7 customer support.
    • AI can help companies understand customer needs through sentiment analysis.

  • Are AI and ML Potent Enough to Take Human Jobs?

    The question of whether AI and ML are powerful enough to displace humans from the workforce is hotly contested. Some in society worry that this shift will eventually put people out of work, while others see it as a way to help people out and make their careers better. One recent study that sheds insight on the possible effects of AI on the US employment market is “Generative AI and the Future of Work in America” done by McKinsey Global Institute. The research claims that people will have to seek out new career prospects across a variety of sectors as a consequence of artificial intelligence and shifting consumer behaviors. According to the research, artificial intelligence (AI) might account for 30 percent of US labor hours by 2030, significantly speeding up economic automation.

    To increase productivity, the paper claims that AI will eventually replace humans in all occupations that necessitate automation, including data collecting and repetitive operations. The office support, customer service, and food service industries stand to be the most affected by the AI transformation. By 2030, the research predicts that 12 million more people may need to change careers in the United States alone.

    In addition to a potential loss of 830,000 employment for retail salespeople, 710,000 for administrative assistants, and 630,000 for cashiers, the report predicts that demand for clerks might fall by 1.6 million jobs. Automated systems can efficiently handle the high share of repetitive tasks, data gathering, and rudimentary data processing that these positions involve, according to the paper.

    Altering the Character of Employment
    A World Driven by AI that Empowers All
    Upcoming Employment Opportunities Brought Forth by AI

    Altering the Character of Employment

    Contradicting the above information, the International Monetary Fund’s report states that a significant portion of the world’s workforce is involved with AI. While IT and automation have mostly affected low-skilled occupations in the past, AI stands out due to its capacity to affect high-skilled occupations as well. Therefore, compared to emerging markets and developing countries, advanced economies have more hazards from AI and more potential to gain from it.

    Artificial intelligence has the potential to affect 60% of jobs in developed economies. The potential for artificial intelligence to increase productivity in almost half of the exposed jobs is substantial. The other side of the coin is that AI apps might conduct crucial human-only jobs, which would diminish the demand for labor, which in turn could cause wages to fall and hiring to slow down. Some of these positions might go away entirely in the worst-case scenario.

    Nevertheless, many specialists believe that the recent surge in artificial intelligence occurred following the global pandemic. As a result of people realizing they could get their work done without physically visiting an office, the new work-from-home culture emerged and a plethora of advancements occurred in the artificial intelligence field.

    The Number of Jobs Created and Eliminated Due to Artificial Intelligence Worldwide in 2022
    The Number of Jobs Created and Eliminated Due to Artificial Intelligence Worldwide in 2022

    A World Driven by AI that Empowers All

    The urgency for lawmakers to take action is heightened by the rapid integration of AI into enterprises globally.

    Digital infrastructure, human capital and labor-market strategies, innovation and economic integration, regulation and ethics, and the AI Preparedness Index were developed by the International Monetary Fund to assist nations in formulating appropriate policies.

    For instance, factors like the percentage of the population covered by social safety nets, the number of years of schooling, and job-market mobility are assessed within the human capital and labor-market policy component. How well a country’s legal system accommodates digital business models and whether there is robust governance to ensure effective enforcement are evaluated in the regulation and ethics component.

    A total of 125 nations were evaluated by the International Monetary Fund personnel using the index. The results show that low-income countries are less prepared to adopt AI than wealthier economies, including advanced and even emerging market economies. However, there is a lot of variety across countries. Based on their exceptional performance in all measured categories, Singapore, the United States, and Denmark achieved the top ratings on the index.

    Upcoming Employment Opportunities Brought Forth by AI

    Artificial intelligence is not going to supplant humans in employment any time soon. Automating routine, predictable, and rule-based tasks is where AI shines. But jobs requiring imagination, compassion, and social skills are still better suited to people.

    However, artificial intelligence will most certainly change the way people work. Some occupations, including those in manufacturing and customer service, are already being supplanted by AI-driven automation. An increasing number of employment will probably be automated as AI technology advances.

    Unemployment rates could rise as a result, particularly for people lacking expertise in the fields where AI is most developed. Keep in mind, though, that AI also generates employment opportunities. Workers with expertise in artificial intelligence system development and maintenance, for instance, are in high demand.

    In general, AI is expected to have a mixed effect on the workforce. There will be some employment losses, but there will also be some job gains. To prepare employees for the AI-driven economy, it is critical to spend on their education and training. To thrive in the AI-driven economy, workers must have the necessary skills. Skills like creativity, problem-solving, and teamwork are part of this, as are STEM (science, technology, engineering, and math) subjects.

    To help those who lose their employment because of AI, governments should create new social safety nets. Assistance with finding work, retraining, and unemployment compensation are all examples of what may fall under this category.

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  • AI Firms Say Analysing Use Cases and Robust Data Strategies Key for AI

    One phenomenon that knocked the wind out of everyone’s lungs in 2023 was artificial intelligence! Right from chatbots to AI tools for video and content creation, it has caught everyone’s fancy.

    India wasn’t far behind, as several companies and apps mushroomed, reflecting the global scenario. A NASSCOM report shows that more than 60 Indian startups started shop in April and June 2023. The optimism surrounding AI has rubbed off on investors, too.

    NASSCOM’s India Data Science & AI Skills Report shows spending on AI in India topped $3 billion in 2022 and is expected to jump to $4.2 billion by 2024.

    In fact, Goldman Sachs sees investments in the AI sector topping $160 billion globally by 2025, with India having an advantage of “resilient growth and strong demographics,” which could attract investments from global investors and corporations.

    “The intricate dance of AI and ML capabilities, coupled with multi-channel integration, has propelled businesses toward a future where agility and scalability are paramount,” said Abhijit Dutta, Chief Strategy Officer of Hostbook, a cloud-based accounting services firm that also offers automated business solutions.

    Despite the rapid growth of AI companies, challenges remain when it comes to integrating AI into traditional corporate processes.

    In this article, StartupTalky speaks to a few AI consulting companies that shed light on AI integration in India.

    AI Awareness
    Data Mining Strategy
    Identifying Use Cases
    AI Training

    AI Awareness

    The top challenge faced by AI companies is to dispel myths surrounding AI integration, said Agam Chaudhary, founder and CEO of Two99, a collective of agencies with a focus on advanced e-commerce, technology, and marketing.

    Explaining how they tackle the problem, Chaudhry said, “We show them it’s not a sci-fi flick but a practical tool that can make their lives easier. We bring out the success stories custom-made for their industry. We lay it all out on the table—the good, the bad, and the ethical considerations. Building trust is crucial. We’re like AI consultants, working hand-in-hand with them, understanding their worries, and customizing solutions that fit like a glove,” Chaudhary said.

    Based on client experiences, PwC had listed some myths that clients seemed to express about AI: ‘ businesses don’t need AI, and they are ‘too risky, to name a few.’

    Over time, there seems to be a gradual attitudinal shift towards AI. A survey of 54,000 workers conducted by PwC in September showed that a third of respondents believe AI will help increase productivity and efficiency. More than a quarter said it would help them learn valuable new skills.

    An AI survey by Uplekha found that 61% of Indian companies feel AI will make work more efficient.

    Employee Attitudes on AI by PwC
    Employee Attitudes on AI by PwC

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    Data Mining Strategy

    Data mining is the cornerstone of AI and ML. AI heavily leans on vast sets of existing data and statistics to come up with a near-perfect AI model of content or analysis, which can then be applied to the problems in question.

    According to E&Y India Chairman and CEO Rajiv Memani, India is the second largest generator of data after China, which is an added advantage for training AI models.

    Yet, the availability of clean data sets has been an issue.

    “All three aspects, i.e., clean data, relevant data, and a sufficient amount of data, are important. The models need sufficient data, and for financial risk use cases, they should cover historical data from at least 1-2 economic cycles. In the absence of such data, these AI and ML models produce suboptimal results and end up losing user confidence in using these models,” said Abhinava Bajpai, Co-founder and Head, Acies TechWorks.

    The government is in the process of developing India’s comprehensive AI strategy, which involves building an India Dataset Platform and an AI Compute Platform. 

    Information and Technology Minister Rajeev Chandrasekhar recently elaborated on these, saying that the Indian dataset platform will be one of the largest and most diverse collections of anonymized datasets to train multi-parameter AI models. Meanwhile, the India Compute Platform will create a substantial graphic processing unit (GPU) capacity for enterprises to train AI models under a public-private partnership.

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    Identifying Use Cases

    In a bid to jump onto the AI bandwagon, companies are struggling to adopt specific use cases, AI experts said.

    “I would say that you know, if organizations are looking at adopting AI, look at some of those achievable use cases, which they can then take right and partner with companies to achieve those,” said Rohit Yadava, Chief Operating Officer, MSys Technologies, which offers digitalization services to companies.

    Echoing this view, Sairam Vedam, Chief Marketing Officer of Cigniti Technologies, says, “Our approach has always been to understand the existing data strategy of the company. Also, what is the existing automation strategy of the company because we are a born-testing quality engineering company? So, we look at AI applications through those two lenses. And then, as I said, educate, experiment, experiment in the sense of experiment on use cases.”

    Another report by Deloitte outlined the use cases of AI across six major industries: consumer, energy, resources, and industrial; financial services; government and public services; life sciences and health care; technology; media; and telecommunications.

    For instance, use cases within the consumer goods segment could include aiding content generation, trade promotions, creating new product prototypes, creating an immersive marketing experience, market intelligence through data access, on-demand customer support, and shopping assistants.

    “Performing cost-benefit analysis of AI and ML models before implementing them is important for the continuous and persistent use of such models… The size of the business and revenue and cost impacts need to be considered before implementing AI and ML models,” said Bajpai from Acies Consulting.

    Global Data Science and AI Installed Talent
    Global Data Science and AI Installed Talent

    AI Training

    Training staff with AI know-how has now become imperative. This is apparent from the rise in demand for AI training and AI-related upskilling courses.

    “It has become essential for executives to learn AI. Data science training is specifically helpful to train aspirants in AI, and by ensuring the holistic development of executives, these programs can become a game-changer in helping industries realise the true potential of AI,” said Piyush Arora, senior director of business strategy at AI-based learning platform Edvancer.

    Edvancer has seen a 4x rise in applications for AI courses and a 100% increase in interview opportunities for students with data science and AI qualifications.

    A NASSCOM report shows India is currently ranking 2nd in training and hiring AI talent in the world.

    “A major portion of the future talent demand will come from the existing tech workforce through upskilling; learning curves are becoming shorter, and skills are becoming redundant in 18 months,” NASSCOM said, adding that “design thinking” is a key skill to implement AI and not merely the ability to build and run complex algorithms.

    However, all this training comes at a heavy cost.

    A study conducted by the Boston Consulting Group and the Indian Institute of Management-Ahmedabad estimated that just the top 500 Indian companies would require “at least one million hours of training.”

    “Organizations must invest in significant upskilling of mid- and senior-level management on the business aspects of AI, digital transformation, ‘agile’ ways of working, and more. Companies have a choice to prioritize AI and adopt it or perish—and the nature of this technology is such that either scenario would come about very quickly,” the report said.

    The top 500 listed companies would need at least 25,000–30,000 advanced practitioners of AI and ML in the next 3–5 years, including AI professionals, data scientists, data engineers, and enterprise architects, the report said.

    Conclusion

    AI maturity has been a buzzword in 2023, given the boom in AI and its peripheries. According to AI watchers and experts, this maturity will accrue over a period of time with enough use cases to innovate, experiment, and smartly apply collated data. However, the large boom in AI within the country has laid bare the talent and skills gap. Hence, training and upskilling pertinent AI skills must become a priority for companies going forward.

  • BigID: How It Manages and Protects Business Data?

    Company Profile is an initiative by StartupTalky to publish verified information on different startups and organizations. The content in this post has been approved by BigID.

    Many companies in this technologically advanced age struggle to manage their sprawling data estates and minimize data risks. Vendors are furiously developing tools to help manage data while addressing privacy concerns. However, the disconnected nature of point-tool development is hurting efficiency with data becoming more distributed.

    Now the question is, can a single platform approach address the modern challenges associated with data management? It’s when BigID came forward as the most appropriate answer. BigID is an America-based company offering cloud-native solutions to help organizations organize and secure data while ensuring compliance with protection regulations.

    Let’s dive in to learn about BigID in detail.

    BigID – Company Highlights

    Company Name BigID
    Headquarters New York City, New York, United States
    Sector Enterprise Data Management
    Founders Dimitri Sirota and Nimrod Vax
    Founded 2016
    Valuation $1.25 billion (2021)
    Website Bigid.com

    About BigID
    BigID – Industry
    BigID – Founders and Team
    BigID – Startup Story
    BigID – Mission and Vision
    BigID – Business Model
    BigID – Revenue Model
    BigID – Products and Services
    BigID – Funding and Investors
    BigID – Growth
    BigID – Partners
    BigID – Awards and Achievements
    BigID – Competitors

    About BigID

    BigID is a data intelligence company developing software offering data security, compliance, privacy, and governance. The platform leverages advanced machine learning and data intelligence to enable enterprises to proactively discover, manage, secure, and get value for their business data for better visibility and control.

    Customers deploy BigID to minimize their data risk, automate security and privacy control, achieve compliance, and gain valuable insights from business data across their entire data landscape, including hybrid cloud, multi-cloud, SaaS, IaaS, PaaS, and on-prem data sources.

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    BigID – Industry

    BigID operates in the enterprise data management industry, the global market size of which is estimated to reach $122.9 billion by 2025 from $77.9 billion in 2020, at a remarkable CAGR of 9.5%. The increasing need for effectively managing the hierarchical master data generated across different departments, adoption of IoT devices, and digitalization are key market growth driving factors. Even the global industry benefited from the Covid-19 pandemic.

    The global enterprise data management market comprises prominent players, such as IBM Corporation, SAP SE, Oracle Corporation, Talent, Symantec, and Informatica.

    BigID – Founders and Team

    Dimitri Sirota and Nimrod Vax are the co-founders of BigID.

    Dimitri Sirota

    Dimitri Sirota - Co-founder and CEO, BigID
    Dimitri Sirota – Co-founder and CEO, BigID

    Dimitri Sirota completed his bachelor’s in Physics with Honors from McGill University and master’s in Engineering Physics from The University of British Columbia. Currently, he is the co-founder and CEO of BigID. Moreover, he is the Investor at Pomelo Pay, Subscribe, Zuplo, Sifflet, Kili Technology, TopCoat Data, AtomicJar, Inc., and many other leading companies. In addition, Sirota co-founded eTunnels and Layer7.

    Nimrod Vax

    Nimrod Vax - Co-founder and CPO, BigID
    Nimrod Vax – Co-founder and CPO, BigID

    Nimrod Vax completed B.Sc in Computer for Bar-Ilan University and MBA in Marketing from Tel Aviv University. He is the co-founder and CPO of BigID. Additionally, he is the Seed Investor at Jit.io and Slim.ai. Vax worked as a Development Manager at Business Layers and Netegrity and as VP of Product Management at CA Technologies.

    BigID Team

    • Scott Casey – Chief Operating Officer
    • Avi Aronovitz – Chief Financial Officer
    • Marc DeGaetano – Chief Revenue Officer
    • Sarah Hospelhorn – Chief Marketing Officer

    BigID currently employs 400+ employees.

    BigID – Startup Story

    Dimitri Sirota and Nimrod Vax co-founded BigID in 2016. When Sirota was running a security strategy for the security businesses in California, he saw many companies focused on the problem of authentication, authorization, single sign, and all traditional IAM capabilities. He lived in West Chester, and when commuting to work, he read about the data being stolen, lost, or misused in the Wall Street Journal and New York Times.

    When he looked for the solution, he didn’t see anything focused on the problem of identity security but identity management. He then thought of commencing his new venture. So, for about one-and-a-half years, he thought about what he wanted to do and started networking in New York. As a part of that, he thought about the problem space and realized there is a gap, white space, between the protection of personal information and available technologies. Moreover, he was aware that a new regulation named GDPR would come with fines for not doing anything to protect personal information.

    When he left California for his second anniversary, he reached out to Nimrod Vax, whom he worked with, who ran identity products. He talked to him and got this opinion around this venture idea, and then they workshopped it. They worked on the idea for a few months to give it a final shape so that they could take it to the investors. Within six months, they raised money and started building BigID in 2016.

    BigID launched its first GDPR & Privacy Compliance Product, “BigID BigOps,” in October 2017. Furthermore, the company introduced Data Exchange App for ServiceNow in 2020. A year later, in 2021, it expanded Access Intelligence for Cloud and Data Centers. The company introduced Cookie Consent Management in 2022 and Secrets Detection Capabilities in 2023.

    BigID – Mission and Vision

    BigID aims to revolutionize data privacy and protection for companies in the digital age.

    BigID – Business Model

    BigID is a modern, extensible data privacy, protection, and perspective platform. The platform combines next-generation ML cataloging, classification, cluster, analysis, and correlation across each data type to help customers find sensitive and critical data anywhere with transformative data discovery. The modular apps are built on the first-purpose build app framework to let enterprises take action for data privacy and protection.

    Furthermore, the BigID platform lets its customers unleash the value of their business data by automating data-driven initiatives. They can accelerate data solutions by leveraging an active metadata hub to enable the data fabric.

    BigID – Revenue Model

    BigID pricing model depends on a few factors specific to the organization’s team and data. The client organizations are asked to choose a pricing bundle from multiple options, including Data Discovery, Classification & Inventory, Zero Trust, Insider Threat, DSPM, Data Minimization, Data Lifecycle Management, Data Rights, Preferences, Data Mapping, and other bundles. In addition, enterprises need to choose where their data live- “Cloud, Hybrid, or On-Prem,” to get the pricing plan.

    BigID – Products and Services

    BigID offers a wide range of products, including:

    • Data Discovery & Classification
    • Automated Labeling
    • Data Retention
    • Metadata Exchange & Enrichment
    • Consent Governance
    • Breach Data Investigation
    • PIA Automation
    • Data Deletion
    • Data Risk Scoring
    • Privacy Portal
    • Access Intelligence
    • Data Quality
    • Data Remediation
    • Data Stewardship
    • Data Rights Automation
    • Cookie Consent

    Get Full Visibility On All Your Data, Everywhere.

    BigID – Funding and Investors

    BigID has undertaken 12 funding rounds to raise $246.1 million. Its latest funding round – Venture Series Unknown Round, was conducted on October 21, 2022. Bessemer Venture Partners, Boldstart Ventures, ClearSky, Comcast Ventures, Genacast Ventures, Information Venture Partners, Salesforce Ventures, and Tiger Global Management are some investors of BigID.

    Date Round Number of Investors Money Raised Lead Investor
    October 21, 2022 Venture Round 1 – –
    February 3, 2022 Corporate Round 2 – ServiceNow
    January 25, 2022 Venture Round 1 – Splunk Ventures
    April 22, 2021 Series D 2 $30 million Advent International
    December 16, 2020 Series D 6 $70 million Salesforce Ventures, Tiger Global Management
    October 1, 2020 Venture Round 2 – –
    January 6, 2020 Series C 1 $50 million Tiger Global Management
    September 5, 2019 Series C 12 $50 million Bessemer Venture Partners
    June 25, 2018 Series B 6 $30 million Scale Venture Partners
    January 29, 2018 Series A 5 $14 million ClearSky

    BigID – Growth

    BigID grew by 7,242% in 2021, and its valuation accelerated from $1 billion at the end of 2020 to $1.25 billion in 2021. Moreover, the company’s employee count increased from 300 in 2021 to 400 in 2022.

    BigID – Partners

    BigID has partnered with various service, technology, marketplace, and resell partners. Some of these are as follows:

    BigID – Awards and Achievements

    The industry has recognized BigID for its data intelligence innovation as it was:

    • 2023 Globe Awards Gold Winner by Disruptors
    • 2023 The Global Infosec Awards Winner by Cyber Defense Magazine
    • Cybersecurity Speak Through Award in 2022
    • Named as one of the Top 100 Cloud Computing Companies by Forbes in 2021
    • Listed as the 19th Fastest Growing Private Company in America and ranked first in Security by Inc 5000 in 2021
    • Recognized as AI Startup to Watch by Business Insider in 2020
    • RSA Innovation Sandbox Winner in 2020

    BigID – Competitors

    The top competitors of BigID are as follows:

    • Egnyte
    • Segment
    • Smartsheet
    • Demandbase One
    • Planhat
    • BetterCloud

    FAQs

    What does BigID do?

    BigID is a data intelligence company developing software offering data security, compliance, privacy, and governance. The platform leverages advanced machine learning and data intelligence to enable enterprises to proactively discover, manage, secure, and get value for their business data for better visibility and control.

    Who are the founders of BigID?

    Dimitri Sirota and Nimrod Vax are the co-founders of BigID.

    When was BigID founded?

    BigID was founded in the year 2016.

    Who are the main competitors of BigID?

    The main competitors of BigID are Egnyte, Segment, Smartsheet, Demandbase One, Planhat, and BetterCloud.

  • Alation: The Leader in Data Intelligence

    Company Profile is an initiative by StartupTalky to publish verified information on different startups and organizations. The content in this post has been approved by Alation.

    In this data-driven corporate world, data governance is vital for business users to leverage data assets. Organizations look for innovative ways to turn big data into actionable insights and gain competitive advantage. However, several companies find it challenging to collect, store, organize, share, and manage data at today’s rapid pace.

    It is when data catalog software comes in handy as it helps enterprises organize and use data. Moreover, organizations need software to collect, manage, and analyze data assets. Alation is a venture-based, B2B software organization that offers data catalog, analytics, and management solutions.

    This article constitutes Alation’s details, including the startup story, founders, products, acquisitions, funding, growth, and more.

    Alation – Company Highlights

    Company Name Alation
    Headquarters Redwood City, California, United States
    Sector Software Development
    Founders Satyen Sangani, Aaron Kalb, Venky Ganti
    Founded In 2012
    Revenue $1.7B (2022)
    Website Alation.com

    Alation – About
    Alation – Founders and Team
    Alation – Startup Story
    Alation – Mission and Vision
    Alation – Business Model
    Alation – Revenue Model
    Alation – Products and Services
    Alation – Funding and Investors
    Alation – Mergers and Acquisitions
    Alation – Patents and Trademarks
    Alation – Growth
    Alation – Partners
    Alation – Awards and Achievements
    Alation – Competitors

    Alation – About

    Alation is a leading enterprise that offers several data intelligence solutions. Moreover, the company’s initial offering dominated the data catalog market. Companies can identify, understand, and manage their data assets with this data catalog tool.

    Alataion is headquartered in the U.S. with offices in the U.K., London, and India and has a presence in more than 25 countries. With over 450 enterprise customers and 300,000+ subscribers worldwide, the company serves around 34 industries.

    Alation – Founders and Team

    Satyen Sangani, Aaron Kalb, Feng Niu, and Venky Ganti are the co-founders of Alation.

    Satyen Sangani

    Satyen Sangani completed his A.B., Economics from Columbia University and his M.Sc., Economics for Development from the University of Oxford. Currently, he is the co-founder and CEO of Alation. Sangani has diverse work experience.

    He worked as an Analysts, Mergers, Acquisitions & Restructurings professional at Morgan Stanley & Co., Associate at Texas Pacific Group, Senior Director at Andale, Inc., and Vice President at Oracle Corporation.

    Satyen Sangani - Co-founder and CEO, Alation
    Satyen Sangani – Co-founder and CEO, Alation

    Aaron Kalb

    Completed his Bachelor’s and Master of Science in Symbolic Systems from Stanford University, Aaron Kalb is the co-founder and Chief Innovation Officer at Alation. Moreover, he is a part-time Partner in Alchemist Accelerator. He worked as a Software Engineer, Designer, and Researcher at Apple.

    Aaron Kalb - Co-founder and Chief Innovation Officer, Alation
    Aaron Kalb – Co-founder and Chief Innovation Officer, Alation

    Venky Ganti

    Completed graduation from the Indian Institute of Technology, Madras, and the University of Wisconsin-Madison, Venky Ganti is Alation’s co-founder and board member. He also worked as co-founder and CEO at Mesh Dynamics. Moreover, he was a Senior Researcher at Microsoft and a Member of the Technical Staff at Google, Inc.

    Venky Ganti - Co-founder, Alation
    Venky Ganti – Co-founder, Alation

    Talking about its team size, the company employs over 700 global employees.

    Alation – Startup Story

    Alation came into the picture with different ideas to solve the same question: How can you connect workers with questions to colleagues having answers?

    Co-founders of Alation, Satyen Sangani, and Aaron Kalb, determine the solution from two perspectives. Sangani theorized that machine learning could help, and Kalb theorized crowdsourcing could help. In 2021, they both combined these two ideas and created Alation.

    Over time, the company improved its solutions, enabled data catalog efforts, and integrated with different data and related technology products. In early 2021, the company acquired over 250 customers. It launched the ‘Data Radicals’ podcast in January 2022 to help business, data, and technology enthusiasts use data powerfully.

    In September 2022, the company announced it achieved $100 million of ARR (annual recurring revenue).

    What is Alation?

    Alation – Mission and Vision

    Alation aims to help enterprises create thriving data cultures where everyone can find, understand, and trust data. The firm pioneered the modern data catalog and now evolving into a platform for data intelligence.

    Alation – Business Model

    Alation leverages its powerful Behavioral Analysis Engine, open interfaces, and inbuilt collaboration capabilities to combine machine learning with human insight to tackle data and metadata management challenges. This tool has four major functional areas- data asset capture, data catalog and collaboration, data discovery, and data governance and stewardship.

    The platform enables enterprises to catalog their data assets, capture technical and business-level information about them, and steward and govern their assets. With Alation, stakeholders can understand what data assets exist, what they are made of, and how they are being used, and manage data privacy, risk, and compliance.

    Alation – Revenue Model

    The company is a subscription-based model. The Alation Data Catalog offers a 12-month plan for $198,000, a 24-month plan for $396,000, and a 36-month plan for $594,000.

    Alation – Products and Services

    Products Offered by Alation
    Products Offered by Alation

    Alation offers multiple products, such as a Data Catalog, Connectors, Platform, Data Governance App, and Cloud Service. Moreover, it delivers a wide line of solutions, including Data Governance, Self-Service Business Intelligence, Cloud Transformation, Privacy, Risk & Compliance, Metadata Management, Digital Transformation, CDMC, and DataOps.

    Alation – Funding and Investors

    Alation has undertaken 6 funding rounds and succeeded in raising a total of $315 million. Its latest funding round – Series E Round, was conducted on November 2, 2022, and raised $300 million. 22 investors fund the company, and some main investors are Blackstone, Databricks Ventures, Union Grove Venture Partners, Queensland Investment Corporation, Thoma Bravo, Sanabil, and Riverwood Capital.

    Date Round Number of Investors Money Raised Lead Investor
    November 2, 2022 Series E 12 $123 million Costanoa Ventures, Thoma Bravo, Sanabil
    June 3, 2021 Series D 9 $110 million Riverwood Capital
    January 17, 2019 Series C 8 $50 million Salesforce Ventures, Sapphire Ventures
    July 18, 2017 Series B 5 $23 million Icon Ventures
    March 4, 2015 Series A 6 $9 million Costanoa Ventures, DCVC
    March 1, 2011 Seed Round 1 – –

    Alation – Mergers and Acquisitions

    Alation acquired 2 companies- Kloud.io on November 28, 2022, and Lyngo Analytics on October 14, 2021.

    Alation – Patents and Trademarks

    The company has 6 registered patents, mainly in the category of ‘Computing; Calculating.’ In addition, it has 5 registered trademarks, and the most popular class is ‘Advertising; Business.’

    Alation – Growth

    The estimated annual revenue of the company in 2022 is $100 million ($134,409 per employee). The valuation of Alation is $1.7 billion as of November 2022. With monthly visits to the website over 51,000, the monthly site visit growth is 59.27%. Moreover, the employee count elevated by 18% last year.

    Alation – Partners

    The company partners with:

    • Snowflake
    • AWS Partner Network
    • Databricks
    • Tableau
    • Fivetran
    • 3Cloud
    • Acceldata
    • Ahead
    • Analytics Hub
    • Anomaly
    • Datactics

    Alation – Awards and Achievements

    Alation garnered many awards and achievements in its lifetime. Some of these are:

    • Recognized as the leading platform in Gartner Magic Quadrant for Metadata Management Solutions for 4 years
    • Winner of the Gartner Peer Insights Customers’ Choice Award in 2020
    • Leader in the Forrester Wave: Machine Learning Data Catalogs report in 2021
    • The U.K.-based Great Places To Work Institute certified Alation as a ‘great place to work in 2022
    • Leader in the BARC Score Data Intelligence Platforms report by BARC in 2022

    Alation – Competitors

    Some main competitors of the company are:

    • Collibra
    • Informatica
    • DataHub
    • data.world
    • Denodo
    • Databricks
    • Aginity
    • Unifi Software
    • binah.ai
    • Waterline Data

    FAQs

    Who are the founders of Alation?

    Satyen Sangani, Aaron Kalb, Feng Niu, and Venky Ganti are the co-founders of Alation.

    What is Alation and what does it do?

    Alation is a venture-based, B2B software organization that offers data catalog, analytics, and management solutions.

    What pricing plans does Alation offer?

    The company is a subscription-based model. The Alation Data Catalog offers a 12-month plan for $198,000, a 24-month plan for $396,000, and a 36-month plan for $594,000.

  • Aerobotics: Optimizing Crop Performance with Data Analytics

    Company Profile is an initiative by StartupTalky to publish verified information on different startups and organizations. The content in this post has been approved by Aerobotics.

    As the global population reaches 7.9 billion in November 2022 and is projected to reach 9.8 billion by 2050, food security has become one of the major concerns across the world. With the scarcity of resources, there is a need to leverage advanced technology in agricultural practices to expand agricultural outputs.

    It has been reported that with emerging agritech startups, the global agrotechnology market is estimated to grow at a CAGR of 12.1% during 2020-2027. Since agriculture remains the main source of livelihood for a huge proportion of people, its crucial to address agricultural challenges.

    Aerobotics is an agritech company set up for helping farmers improve their crop performance and production with AI-driven data analytics. Going through this article, you will know important details of the company- its founders, startup story, services, funding, growth, and more.

    Aerobotics – Company Highlights

    Company Name Aerobotics
    Headquarters Cape Town, Western Cape, Africa
    Primary Industry Agrotechnology
    Founders James Paterson and Benji Meltzer
    Founded In 2014
    Website Aerobotics.com

    Aerobotics – About
    Aerobotics – Founders and Team
    Aerobotics – Startup Story
    Aerobotics – Mission and Vision
    Aerobotics – Products and Services
    Aerobotics – Business Model
    Aerobotics – Funding and Investors
    Aerobotics – Patents and Trademarks
    Aerobotics – Growth
    Aerobotics – Awards and Achievements
    Aerobotics – Competitors
    Aerobotics – Future Plans

    Aerobotics – About

    Aerobotics is a data analytics South Africa-based company that uses aerial imagery and machine learning algorithms for detecting pests and diseases in tree crops and optimizing crop performance for farmers worldwide. The company makes it possible for farmers to interact with this valuable data through its web and mobile applications.

    It has two main offices in Africa and the United States along with Sales Managers working remotely and spending their day on the ground with its growers across the world. Moreover, the company has been able to help farmers manage more than 65 million trees with clients in 18 countries, including America, Africa, Australia, Spain, and Europe.

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    Aerobotics – Founders and Team

    James Paterson and Benji Meltzer are co-founders of the company.

    James Paterson and Benji Meltzer - Co-founders, Aerobotics
    James Paterson and Benji Meltzer – Co-founders, Aerobotics

    James Paterson

    Along with the co-founder, James Paterson is the CEO of Aerobotics. He completed his Master’s in MSc, Aerospace, Aeronautical, and Astronautical/Space Engineering from the Massachusetts Institute of Technology.

    Benji Meltzer

    Benji Meltzer is a co-founder and CTO of Aerobotics. He graduated from Imperial College London with a degree in MSc, Biomedical/Medical Engineering – Neurotechnology Specialization. He has also held the role of Business Analyst at The Cyest Corporation and Operations and Logistics Manager at Uber.

    The size of the company’s team today is over 80 people, ranging from agronomists, engineers, creatives, product developers, and customer service experts.

    Aerobotics – Startup Story

    James Paterson grew up on a farm located outside of Cape Town and there he learned the challenges faced by his family and community of growers. After years, he met Benji Meltzer and they both decided to see how their combined skills can be used in aeronautics, machine learning, and aerial imagery to solve crop-related problems. They took drones and flew them on James’ family farm.

    Soon, they were able to validate that artificial intelligence could be used to process aerial imagery and identify problems invisible to the farmers. Aerobotics was established as an agritech startup in 2014. They generated insights for more than 100 million trees to help growers, insurers, and investors improve their production and profitability.

    Aerobotics – Mission and Vision

    The vision and mission of Aerobotics are to provide intelligent tools for feeding the world.

    Aerobotics – Products and Services

    The company is known for offering two primary solutions – Farm and Insure. It offers a Tree Insights service that supports citrus, pome, stone, berries, grapes, nuts, olives, subtropical fruit, and pomegranates.

    Moreover, it offers traditional crop insurance, precision crop insurance, detailed acre analysis, and optimal premiums through Insure solution. Aerobotics has developed two apps named Aeroview Scout and Aeroview InField.

    Welcome to Aeroview: Getting Started

    Aerobotics – Business Model

    Aerobotics uses a combination of drone and satellite imagery for providing its customers with valuable insights into their farms. The imagery is further uploaded onto servers and then data analysis is conducted. With the help of data procured by the company, farmers can identify underperforming areas, including nutrient deficiencies, pest infestations, and irrigation problems, and also monitor the progression of the season.

    Moreover, its advanced tree-counting technology aids farmers to know the plan count per block, per crop, and per cultivator. The company delivers customers’ Tree Insights within 4 to 7 days for serviced flights after their data has been flown and 3 to 7 days for self-serviced flights when the upload is completed.

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    Aerobotics – Funding and Investors

    Aerobotics has undertaken 7 funding rounds. The latest funding round- ‘Series B’ took place on December 11, 2020. Currently, it’s supported by 17 investors with 6 as lead investors, including FMO, Cathay Innovation, Naspers, BossaNova Investimentos, and Endeavor.

    Date Round Number of Investors Money Raised Lead Investor
    December 11, 2020 Series B 7 $13.5 million Naspers Foundry
    May 20, 2020 Venture Round 1 $5.5 million Naspers Foundry
    February 27, 2019 Series A 1 $1.5 million Paper Plane Ventures
    July 18, 2018 Series A 4 $2 million Nedbank
    November 29, 2017 Non-Equity, Assistance 1 $50K Google Launchpad Accelerator
    August 1, 2017 Seed Round 3 $42K 4Di Capital, Savannah Fund
    April 26, 2017 Pre Seed Round 2 $15K –

    Aerobotics – Patents and Trademarks

    The intellectual property of Aerobotics comprises 4 registered patents primarily in the category of ‘Computing’ and ‘Calculating.’

    Aerobotics – Growth

    The estimated annual revenue of the company in 2022 is $12.9 million per year ($145,000 per employee). Moreover, the employee count increased by 3%, and monthly web visits growth grew by 18.48%.

    Aerobotics – Awards and Achievements

    In the last 2 years, Aerobotics successfully captured 20% of South Africa’s citrus market and 40% of its macadamia market in the last six months. In addition, the company has received multiple industry-related prestigious awards. A few major achievements are:

    • Selected as one of 24 startups to be part of Google’s Launchpad Accelerator in 2018
    • Won AfricArea VivaTech Challenge
    • President Macron Tech Award
    • Innovator of the Year Award at the All Africa Business Leader Awards

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    Aerobotics – Competitors

    The company ranks 1st among its 364 competitors and some of these include:

    • AUS
    • SeeTree
    • Marut Drones
    • Sentera
    • StructureIt
    • Realm Digital

    Aerobotics – Future Plans

    The main plan of the company is to develop diagnostic functions.

    FAQs

    Who is the CEO of Aerobotics?

    James Paterson is the Co-Founder and CEO at Aerobotics.

    What does Aerobotics do?

    Aerobotics is an agritech company set up for helping farmers improve their crop performance and production with AI-driven data analytics.

    Who are the competitors of Aerobotics?

    Competitors of Aerobotics include:

    • AUS
    • SeeTree
    • Marut Drones
    • Sentera
    • StructureIt
    • Realm Digital

  • 6sense: One-Stop Solution for Revenue Generation

    Company Profile is an initiative by StartupTalky to publish verified information on different startups and organizations. The content in this post has been approved by 6sense.

    Both sales and marketing are significant pillars of every business as they act as a catalyst for generating revenue. While marketing involves building brand awareness among audiences, sales turn that viewership into profits by converting leads into actual customers.

    However, the marketing and sales team needs to understand consumer behavior through appropriate data and visibility. It is when 6sense comes into the picture. This company helps the revenue team to access relevant statistics with the help of advanced technology.

    In this article, know about 6sense, its business, founders, products, startup story, funding, and more.

    6sense – Company Highlights

    Company Name 6sense
    Headquarters San Francisco, California, United States
    Primary Industry SalesTech
    Founders Amanda Kahlow, Dustin Chang, Premal Shah, Shane Moriah, and Viral Bajaria
    Founded In 2013
    Website 6sense.com

    6sense – About
    6sense – Founders and Team
    6sense – Startup Story
    6sense – Mission and Vision
    6sense – Products and Services
    6sense – Business Model
    6sense – Funding and Investors
    6sense – Partners
    6sense – Merger and Acquisitions
    6sense – Growth
    6sense – Awards and Achievements
    6sense – Competitors

    6sense – About

    6sense is a San Francisco-based company that reinvents the way B2B organizations create, manage, and convert their pipeline to revenue. Its revenue AI eliminates guesswork and arms the organization’s revenue team with important data and visibility that is required to create and convert high-quality pipelines into revenue.

    Currently, Jason Zintak is the CEO and Viral Bajraj is the CTO of 6sense. The company is growing at a faster pace with 6 branches in the United States, the United Kingdom, and India. Moreover, it is ranked the 130 fastest-growing companies in North America on the Deloitte Technology Fast 500TM.

    6sense – Founders and Team

    6sense, Founders - Amanda Kahlow, Dustin Chang, Premal Shah, Shane Moriah, and Viral Bajaria
    6sense, Founders – Amanda Kahlow, Dustin Chang, Premal Shah, Shane Moriah, and Viral Bajaria

    Set up in 2013, 6sense was founded by Amanda Kahlow, Dustin Chang, Premal Shah, Shane Moriah, and Viral Bajaria.

    Amanda Kahlow

    Amanda Kahlov is a graduate of the University of Colorado Boulder and is the founder and ex-CEO of 6sense. Moreover, she founded CI Insight Inc. and held the role of CEO. Presently, she is working as the director at MAHA global and an advisor at Brit + Co.

    Dustin Chang

    Co-founder of 6sense, Dustin Change has graduated from the University of Washington. He has co-founded Grepdata and is working as CTO of Stealth Startup.

    Premal Shah

    Earned a degree in Computer Science from the University of Southern California, Premal Shah is co-founder of 6sense. Moreover, he has also co-founded GrepData and worked in Livingly Media for almost 7 years.

    Shane Moriah

    Co-founder of 6sense, Shane Moriah has graduated from Stanford University and has a degree in BS, Computer Science. He has co-founded 6sense and GrepData and is now, working as Chief Technology Officer in Settle.

    Viral Bajaria

    Viral Bajaria graduated from the University of Southern California with a degree in Computer Science before co-founding and working as CEO of 6sense.

    Talking about the company size, currently, it employs 1200 employees, ranging from data scientists and business leaders to engineers and mathematicians.

    6sense – Startup Story

    6sense was developed by Amanda Kahlow along with other co-founders in 2013 after consultation with Cisco- the technology company. It was launched as a tool for organizations running marketing and sales businesses by predicting the inclination of potential customers.

    In 2014, the company launched the world’s first B2B sales and marketing prediction intelligence platform. It achieved another milestone by receiving a patent for a machine learning method to predict the future of B2B sales and marketing in 2016.

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    Since then, there is no stopping as 6sense has been recognized as the leader by several renowned organizations.

    6sense – Mission and Vision

    The common goal of 6sense is to change B2B sales and marketing with predictive intelligence. It focuses on offering the best solutions to help marketers and sellers succeed.

    6sense – Products and Services

    The core platform comprises foundational features that the sales and marketing team needs for scaling their ABM programs. The solutions offered by the company are:

    • Account Identification
    • Data Enrichment and Management
    • Intent Data
    • Audience Building
    • Advertising
    • Predictive Analysis
    • Orchestration and Workflows
    • Pipeline Intelligence
    • Conversational E-mail
    • Sales Intelligence

    6sense – Business Model

    The account engagement platform of 6sense helps B2B organizations achieve predictable revenue growth by providing the access to AI, big data, and machine learning to every member of the revenue team. The company’s Revenue AI platform uncovers anonymous buying behavior, predicts the right accounts to target at the right time, and enables the sales and marketing team to engage buyers with multi-channel and multi-touch campaigns.

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    Its business model revolves around helping revenue teams know every necessary detail about their buyers so that they can generate more opportunities, increase deal size, and win over the competition.

    6sense – Funding and Investors

    6sense raised total funding of $426 million over 8 rounds. On 20 January 2020, the latest funding round was held and the company received $200 million in Series E Round. It is funded by 19 investors and some of these are SoftBank Vision Fund, B Capital Group, Harmony Partners, Industry Ventures, Venrock, Insight Partners, and Blue Owl.

    Date Round Number of Investors Money Raised Lead Investor
    January 20, 2020 Series E 10 $200 million Blue Owl, MSD Partners
    March 30, 2021 Series D 4 $125 million D1 Capital Partners
    January 15, 2020 Series C 1 $40 million Insight Partners
    April 16, 2019 Venture Round 6 $27 million Industry Ventures
    February 1, 2019 Seed Round 1 – –
    July 1, 2015 Series B 1 $2 million Salesforce Ventures
    February 19, 2015 Series B 3 $20 million Bain Capital Ventures
    May 20, 2014 Series A 4 $12 million Battery Ventures, Venrock

    6sense – Partners

    6sense believes in partnering with renowned firms to expand its horizons. It has collaborated with around 24 partners and some of these are:

    • Chatfunnels
    • Drift
    • Mediafy
    • Hero Digital
    • Ignitim
    • ROI-DNA
    • Adobe Target
    • Bombora
    • Hushly
    • Reactful

    6sense – Merger and Acquisitions

    Over years, the company has acquired a total of 4 companies. The list of companies acquired is:

    Company Year of Acquisition
    Granite Media Group Inc. 2022
    Slintel 2021
    Fortella 2021
    ZenIQ 2018

    6sense – Growth

    The estimated annual revenue of 6sense in 2022 is $245 million per year. And its current valuation stands at $5.2 billion (January 2022). In addition, the employee count has increased by 127% and the monthly rank growth of the website is 13.63%.

    6Sense in 6 Minutes

    6sense – Awards and Achievements

    6sense, the global platform is rewarded with several awards and achievements. It closed out 2022 with some of these awards and achievements:

    • Best Workplaces for Women
    • Best Company for Diversity
    • Best Company Culture
    • Best CEO
    • Best Company Compensation
    • Best Company Outlook
    • Recognized as a Leader in the December 2022 Gartner Magic Quadrant for ABM Platforms
    • Tech Cares Award by TrustRadius

    6sense – Competitors

    With 6sense offering solutions across different parts of the world, it faces cut-throat competition worldwide. Some of its competitors are:

    • Demandbase
    • ZoomInfo
    • HubSpot
    • Terminus
    • RollWorks
    • Marketo
    • Clearbit
    • Radius

    FAQs

    What is 6sense and how does it help sales teams?

    6sense is an AI-driven revenue intelligence platform that helps sales and marketing teams to identify and engage with high-value accounts more effectively.

    How does 6sense use AI and machine learning to improve revenue generation?

    6sense uses AI and machine learning to identify buying intent signals, providing real-time insights to sales teams for more effective engagement and improved revenue generation.

    What kind of businesses and industries can benefit from using 6sense?

    B2B businesses with longer sales cycles, high-value products/services, and complex sales teams can benefit from using 6sense’s revenue intelligence platform.