Analytics to Fuel the Next Big Bang in the Legal Industry

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People might associate Big Data with the future of multiple industries but the exponential advancements in Artificial Intelligence, Data Science and the Internet of things have ensured present is equally affected by it.

Big Data is no longer the promising buzzword reminiscing of a distant future. The future’s already happening before our very eyes.

  • Every person in the world generates 1.7 megabytes data in but a second
  • The market is expected to grow by 20% in 2019, and by 2023 the BD analytics market is expected to reach $103 billion
  • 2% of organisations are investing in Big Data, and cases such as Netflix saving $1 billion per year on customer retention alone illustrate the trend’s capability of showing an earth shattering ROI

The legal industry is not an exception as it already started to adopt and master Big Data analytics to cut a few corners, get more cases, price them properly, and of course, to win them!

Legal Analytics

While the term Legal analytics originated back in 2013, the full extent of its definition is unclear even today. The biggest issue is that businesses, law firms, and private practitioners use different tools and solutions.

All of these tools share common roots as all of them are powered by AI and Big Data analysis, but they may drastically differ one from another when it comes to the delivery of viable results.

If there’s one thing that can be said for certain, it is that Legal analytics work regardless of how or why one uses them. According to an ALM survey that questioned 300 librarians, trial attorneys and litigators from Am Law 200 firms, a whopping 90% see real value in Legal Analytics.

By that definition, we are safe to deduce that Legal Analytics are an amalgamation of AI-powered tools used to gather, store, protect, and analyze colossal volumes of legal, sensitive, and personal data.

As for the various use cases:

Legal Analytics in saving costs

From having all of the most valuable data at one’s fingertips instead of gathering dust in a file folder among the endless shelves of a library to automating every step of the client management/nurturing process, Legal Analytics shines brightest when it saves time -the very same billable time Senior Partners use to work with clients instead of diving heads-deep into the administrative routine.

Legal Analytics in winning new clients

82% of responders from the ALM survey state that Legal Analytics offers great value in attracting new clients. More on the matter, they are planning to use LA more frequently in their work.

Frankly, why wouldn’t they when a smart use of data can:

  • Quantify one’s relevant experience
  • Compare said experience to the ones from the biggest competitors
  • Assist in designing a killer pitch deck
  • Demonstrate proven, relevant experience instead of treating the clients with the so-called “overall approach”


Legal Analytics in winning big!

Winning a case in the court of law is almost always based on knowing the precedent. With legal Analytics, one can process an entire history of relevant cases based on certain keywords or precedents in a matter of moments.

Yes, AI is quite skilled at finding peculiar patterns that can be related to prior litigations. This knowledge helps building up a couple of facts lawyers can use as an ace in the sleeve. Knowledge about prior similar cases grants understanding of other factors, such as the approximate duration of the litigation, time to trial, and time to termination per judge!

Projects like DISCO that are currently utilised by more than a third of Am Law 200 companies simplify (and even trivialise) e-discovery.

Brainspace harnesses advanced Machine Learning power and continuously trains itself in winning cases based on the firm’s previous track record and available data.

Ravel Law can identify patterns and pinpoint data related to everything from state case outcomes to judge’s typical rulings.

All that – at a tip of one’s finger.

So yes, saying that Big data analytics is fuelling the next Big Bang in the legal industry is not an overstatement. If anything, it is probably going to surpass our expectations.

This is How AI will disrupt Fintech

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Artificial Intelligence (AI) has been slowly asserting its influence in almost every industry in the 21st century. With advanced technological advancements over the years, AI has become a supreme force that when implemented can do wonders for any business.

The financial industry has its own share of technologies called “Fintech” (a combination of finance and technology) that are used to enhance and automate various financial processes. With the arrival of AI, financial institutions are leveraging AI-based models to work in tandem with their Fintech applications to boost operations and revenue.

Earlier, the financial industry was dominated by people who were experts in areas such as investing, lending, and trading. Today, AI has proven that it can perform the roles of these experts with much better speed and accuracy. By analyzing large amounts of financial data, advanced AI algorithms are capable of learning on their own and make optimal financial decisions without human bias.

According to a recent research report, the AI in Fintech market is expected to grow up to $26.92 billion by 2024. Owing to all the above factors, let’s take a look at how AI will disrupt Fintech and the financial industry:

  1. Customer Engagement – This is one of the primary areas where AI will play a major role. Every company in the financial industry is looking to increase user engagement on its website and other digital touch points. There are a lot of digital technologies in the market which can assist in increasing customer engagement. However, these technologies don’t have the means to operate at scale as well as target each individual customer. Coupling AI with these Fintech tools can help increase engagement with millions of customers.
  2. Financial Advisory – Digital financial advisors can be integrated with AI-powered platforms to offer users advising services related to investments, banking, asset management. These advisors have completely eliminated the need to have human financial advisors and relationship managers.

For example, an AI-based digital advisor can collect the details about an investor’s financial goals and the level of risk that they are willing to incur. Using advanced algorithms, it can analyze the data and come up with investment suggestions to the investor. It also has the capability to fully automate the purchase and management of the person’s investments.

  1. Underwriting, Pricing and Credit Risk Assessment – For underwriting services, AI can help in speeding up the whole process and reducing the turnaround time. It, therefore, increases the efficiency of client proposals and improves the overall customer experience. For assessing a client’s credit risk, an AI-powered model can provide an immediate assessment, which then helps advisors to deliver the right offer to the client.
  2. Regulatory Compliance – The regulatory landscape is a constant challenge for most financial companies as it keeps changing all the time. AI’s biggest advantage is that it can remember, learn, and comply with all applicable financial laws. It can help financial institutions in meeting regulatory standards on a regular basis. Moreover, it eliminates human errors from compliance and detects patterns to spot fraudulent activities.
  3. Risk Management and Fraud Detection – AI has many applications when it comes to risk management and fraud detection. It helps banks and other financial firms by alerting them to potentially fraudulent transactions. It can accurately predict if an account is at risk through factors such as the number of compromises endured in the past, etc. Using appropriate machine learning algorithms, AI can also help in the reduction of false positives. This not only improves the fraud detection efficiency but also improves customer satisfaction.
  4. AI-powered Chatbots – Thanks to AI, chatbots have become more sophisticated in recent times. Apart from communication purposes, they are being used as customer-facing tools. Customers can avail the services of these AI-driven chatbots for requests and queries related to their accounts. These include details such as balance enquiries, personal information, spending habits, etc.

Bank of America is known for using an AI-powered chatbot called Erica. Within the first 3 months of its launch, Erica’s services were used by 1 million of the bank’s customers.

  1. Algorithmic Trading – Algorithmic trading is all about data analysis and fast decision-making. These are the areas where AI thrives. Machine learning algorithms can easily analyze data irrespective of its size and density. These self-learning algorithms can detect patterns that are difficult to spot by a human. Based on the insights derived from the data, AI can execute trades automatically and faster than human traders.


The advancement in AI and machine learning has produced smart systems that can learn and adapt from data. The financial industry is leading the way with respect to the adoption of such AI-based systems with existing Fintech applications. These systems have brought automation to the most mundane financial tasks. Through predictive analysis, they can safeguard sensitive financial data by detecting fraud and mitigating risks. They also play a huge role in helping financial marketers enrich the customer experience and increase the ROI on their marketing spend.

The future of Fintech and the financial industry is thus bright and exciting, thanks to Artificial Intelligence.

Top 10 Books on Artificial Intelligence for Beginners

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Top 10 Books on Artificial Intelligence for Beginners

Artificial Intelligence (AI) has taken the world by storm. Almost every industry across the globe is incorporating AI for a variety of applications and use cases. Some of its wide range of applications includes process automation, predictive analysis, fraud detection, improving customer experience, etc.

AI is being foreseen as the future of technological and economic development. As a result, the career opportunities for AI engineers and programmers are bound to drastically increase in the next few years. If you are a person who has no prior knowledge about AI but is very much interested to learn and start a career in this field, the following ten Artificial Intelligence Books will be quite helpful:

  1. Artificial Intelligence – A Modern Approach (3rd Edition)

By Stuart Russell & Peter Norvig

This book has been considered by many as one of the best AI books for beginners. It is less technical and gives an overview of the various topics revolving around AI. The writing is simple and all concepts and explanations can be easily understood by the reader.

The concepts covered include subjects such as search algorithms, game theory, multi-agent systems, statistical natural language processing, local search planning methods, etc. The book also touches upon advanced AI topics without going in-depth. Overall, it’s a must-have book for any individual who would like to learn about AI.

  1. Machine Learning for Dummies

By John Paul Mueller and Luca Massaron

Machine Learning for Dummies provides an entry point for anyone looking to get a foothold on machine learning. It covers all the basic concepts and theories of machine learning and how they apply to the real world. It introduces a little coding in Python and R to tech machines to perform data analysis and pattern-oriented tasks.

From small tasks and patterns, the readers can extrapolate the usefulness of machine learning through internet ads, web searches, fraud detection, and so on. Authored by two data science experts, this book makes it easy for any layman to understand and implement machine learning seamlessly.

  1. Make Your Own Neural Network

By Tariq Rashid

This book provides its readers with a step-by-step journey through the mathematics of neural networks. It starts with very simple ideas and gradually builds up an understanding of how neural networks work. Using Python language, it encourages its readers to build their own neural networks.

The book is divided into three parts. The first part deals with the various mathematical ideas underlying the neural networks. Part 2 is practical where readers are taught Python and are encouraged to create their own neural networks. The third part gives a peek into the mysterious mind of a neural network. It also guides the reader to get the codes working on a Raspberry Pi.

  1. Machine Learning: The New AI

By Ethem Alpaydin

Machine Learning: The New AI gives a concise overview of machine learning. It describes its evolution, explains important learning algorithms, and presents example applications. It explains how digital technology has advanced from number-crunching machines to mobile devices, putting today’s machine learning boom in context.

The book gives examples of how machine learning is being used in our day-to-day lives and how it has infiltrated our daily existence. It also discusses about the future of machine learning and the ethical and legal implications for data privacy and security. Any reader with a non-Computer Science background will find this book interesting and easy to understand.

  1. Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies

By John D. Kelleher, Brian Mac Namee, Aoife D’Arcy

This book covers all the fundamentals of machine learning along with practical applications, working examples, and case studies. It gives detailed descriptions of important machine learning approaches used in predictive analytics.

Four main approaches are explained in very simple terms without using many technical jargons. Each approach is described by using algorithms and mathematical models illustrated by detailed worked examples. The book is suitable for those who have a basic background in computer science, engineering, mathematics or statistics.

  1. The Hundred-Page Machine Learning Book

By Andriy Burkov

Andriy Burkov’s “The Hundred-Page Machine Learning Book” is regarded by many industry experts as the best book on machine learning. For newcomers, it gives a thorough introduction to the fundamentals of machine learning. For experienced professionals, it gives practical recommendations from the author’s rich experience in the field of AI.

The book covers all major approaches to machine learning. They range from classical linear and logistic regression to modern support vector machines, boosting, deep learning, and random forests. This book is perfect for those beginners who want to get familiar with the mathematics behind machine learning algorithms.

  1. Artificial Intelligence for Humans

By Jeff Heaton

This book helps its readers get an overview and understanding of AI algorithms. It is meant to teach AI for those who don’t have an extensive mathematical background. The readers need to have only a basic knowledge of computer programming and college algebra.

Fundamental AI algorithms such as linear regression, clustering, dimensionality, and distance metrics are covered in depth. The algorithms are explained using numeric calculations which the readers can perform themselves and through interesting examples and use cases.

  1. Machine Learning for Beginners

By Chris Sebastian

As per its title, Machine Learning for Beginners is meant for absolute beginners. It traces the history of the early days of machine learning to what it has become today. It describes how big data is important for machine learning and how programmers use it to develop learning algorithms. Concepts such as AI, neural networks, swarm intelligence, etc. are explained in detail.

The book provides simple examples for the reader to understand the complex math and probability statistics underlying machine learning. It also provides real-world scenarios of how machine learning algorithms are making our lives better.

  1. Artificial Intelligence: The Basics

By Kevin Warwick

This book provides a basic overview of different AI aspects and the various methods of implementing them. It explores the history of AI, its present, and where it will be in the future. The book has interesting depictions of modern AI technology and robotics. It also gives recommendations for other books that have more details about a particular concept.

The book is a quick read for anyone interested in AI. It explores issues at the heart of the subject and provides an illuminating experience for the reader.

  1. Machine Learning for Absolute Beginners: A Plain English Introduction

By Oliver Theobald

This book explains the various theoretical and practical aspects of machine learning techniques in a very simple manner. It makes use of plain English to prevent beginners from being overwhelmed by technical jargons. It has clear and accessible explanations with visual examples for the various algorithms.

So these were the top 10 artificial intelligence books that we recommend to start with. Under Artificial Intelligence, we have Machine Learning, Deep Learning, Computer Vision, Neural Networks and many other concepts which you need to touch upon. To put machine learning in context, some basic Python programming is also introduced. The reader doesn’t need to have any mathematical background or coding experience to understand this book.

Top 9 AI Startups in India (April 2019)

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Artificial Intelligence (AI) and Machine Learning (ML) are technologies that have seen a tremendous uplift both in terms of investment and innovation in the past three years. They are now being implemented across diverse verticals such as manufacturing, healthcare, fashion, agriculture, real estate, voice-based solutions, etc.

In India, AI-based startups started emerging in the year 2016. Over the next two years, the growth in the AI startup space has been staggering. A recent report estimated that around $87.85M was alone raised in the AI space in India’s startup capital, Bengaluru. Owing to the spectacular growth in the AI sector, India has emerged as a nation with a robust ecosystem that enables AI startup professionals and founders to kick-start their ventures.

Therefore, let’s take a look at the top 9 AI startup companies in India (April 2019):

  1. Manthan

Manthan is an AI-based analytics company that was founded in 2003 with an initial investment of $98 million. The company’s AI-powered retail analytics platform provides prescriptive and descriptive analytics for users, grow customer engagement, and recommend actions.

Manthan’s AI platform caters to 170 customers across 21 countries. It helps them in various areas such as marketing, customer targeting, inventory, pricing, and promotions. The Bengaluru-based company also has a Natural Language Processing (NLP) engine called Maya. Maya acts as a business assistant and can answer questions such as sales trends, last month’s profits, etc.  In 2018, Forrester Research named Manthan as a strong performer in its Customer Analytics Solutions Wave Q2 report.

  1. SigTuple

By raising $25 million in 2015 from investors, SigTuple began its operations in Bengaluru. Using AI and machine learning, the startup develops medical diagnostic solutions. The company’s AI platform is built to perform screening and advanced diagnosis of urine, blood, semen samples, along with retinal scans and X-rays.

A patient’s samples are first analysed by SigTuple’s AI algorithms. They are then sent to a pathologist for review and finally sent back to the point-of-care in 5 minutes. So in this process, initial diagnosis is done quickly and ambulances can be dispatched to carry emergency medicines to patients. The company is also known for creating an automated microscope to make up for the lack of pathologists.

  1. Mad Street Den

Mad Street Den is one of India’s first AI and computer vision startups. The Chennai-based company aims to bring AI into the daily lives of customers in verticals such as IoT and connected cars, user engagement & analytics, online fashion, mobile gaming, social media, etc. is the company’s flagship product which serves retailers, specifically in fashion, across the globe. It also makes the shopping experience more meaningful to customers by showcasing products that are most relevant to them across sites, apps, and stores.

The company caters to clients across Asia, Middle East, US, and Europe. Its clientele includes brands such as Voonik, Tata, Zilingo, Craftsvilla, etc. Mad Street Den was also included in Bloomberg’s 2017 list of the world’s 50 most promising startups.

  1. Haptik

Haptik specializes in developing AI-based chatbots for enterprises, service companies, and consumers. The company was founded in 2013 in Mumbai with a $12 million funding. Haptik provides specialized bots for various industry applications. It also provides customers with a hybrid interface for human-to-AI transitions, a drag-and-drop bot builder, and detailed conversation analytics.

In 2018, Haptik entered into a partnership with Amazon AWS. This partnership will enable the company to incorporate AWS’s cloud offering, AI tools, and advanced database framework. The company’s global clientele includes well-known names such as Coca Cola, Amazon Pay, and Samsung.

  1. Flutura

Flutura is an IoT startup based out of Bengaluru. The company is a provider of big data analytics solutions with a vision to transform operational outcomes by monetizing machine data. The company’s flagship product is called Cerebra. Through AI, Cerebra provides diagnostics and prognostics to unlock new business value for energy and engineering customers across the world.

The company has clientele such as Hitachi, Henkel, Sodexo, GTT, etc. It also has notable partnerships with Bosch, Halliburton, and Siemens. Flutura was recognized by CIO Review magazine as one of the Top 20 Most Promising Big Data companies in 2015.

  1. Uncanny Vision

Another Bengaluru-based startup, Uncanny Vision uses deep learning-enabled vision algorithms that run on Edge servers, to develop surveillance solutions. The company delivers scalable and cost-effective security for next generation smart industries and safe cities. For high-value customers in banking and retail, Uncanny Vision also enables real-time actionable surveillance and analytics.

The company graduated from the Target Accelerated Program in 2016. It was also part of the third edition of Nasscom Innotrek 2016. At the Nasscom Awards 2017, it was selected as one of India’s Most Innovative Top 50 Emerging Software Product Companies.

  1. is a Mumbai-based AI startup that helps other AI startups solve complex problems at a much faster pace. The company caters to industry verticals such as Banking, Insurance, Medicine and Healthcare, Retail, and Oil and Gas.

Through its core product, Vega, Arya helps in building complicated systems really fast and helps users in automating multiple things. For example, using the company’s platform, insurance firms can process insurance claims in minutes. In the banking industry, Arya’s platform helps in cheque automation. Apart from being based in India, the company is also spread across the UK and Singapore.


Founded in 2017, helps in automating HR systems and procedures complete with virtual assistants and replicating cognitive HR functions. Using AI and big data, the company powers virtual assistants and drives HR for businesses by mimicking cognitive functions related to HR. Bash’s AI chatbot helps companies get a real-time and highly accurate way to automate conversation with employees.

The AI-powered HR chatbot can be accessed using instant messengers such as Skype, Slack, and Facebook Messenger. Currently, Bash.Ai offers modules such as ticketing, HR helpdesk, post-hire orientation, organize HR activities, employee engagement, and payslips related queries. The company’s main aim is to create an ecosystem where humans and AI co-exist efficiently in the HR settings.

9. Niki.Ai

Niki.Ai was founded in 2015 with seed money of $2.4 million. The company’s main product is an AI-based shopping assistant and chatbot. The AI assistant helps customers with the help of NLP and ML by helping them shop for products and services over a chat interface. It also helps in automating things like online ordering, phone recharges, and payments.

The company currently caters to over 2 million consumers and provides 20+ services. The intelligent chatbot is now available on Android, iOS, and Facebook Messenger.

To explore a career in Artificial Intelligence, click here.

15 reasons why AI will create more jobs than it takes

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Artificial Intelligence (AI) is gaining a lot of traction in recent times. This is mainly because it has broken the bottleneck of human efficiency, reduced repetitive work, and enhanced work efficiency. As a result, almost every industry in the world is planning to leverage AI or has already implemented it in their business.

However, implementing AI can also lead to the elimination of a lot of human jobs. In fact, in areas such as medical diagnosis, speech translation, and accounting, AI has outperformed humans in every way imaginable. Thus, there is this underlying fear that AI will one day take over most of the jobs in the world.

According to accountancy firm PwC, over 7 million jobs will be displaced by AI between 2017 and 2037. However, it will also lead to the generation of 7.2 million jobs, which is a net gain of 200,000 jobs.

Taking all of this into account, here are 15 reasons why AI will create more jobs than it takes:

1. The demand for man-machine hybrid teams

The future of employment will depend on how well humans and AI blend to create augmented hybrid teams. An AI has strengths such as speed, accuracy, computation, etc. A human has strengths such as empathy, judgment, cognition, etc. This role will combine these strengths of both AI and humans to generate better business outcomes. Last year’s Accenture report estimated that new applications of AI-human collaboration could boost employment by 10% by 2020.

2. Worldwide employment

Companies that effectively implement AI can generate more money for their businesses. This, in turn, leads to higher employee wages, better technology tools, and greater efficiency. With such success, companies can actually spread their reach across the world. As a result, they will require a global workforce which again generates huge employment opportunities.

3. AI’s role in marketing and sales

AI has already started playing a substantial role in marketing and sales. Through the development of smart databases, AI can help in building effective, large-scale sales forces. Tools such as Zendesk and Salesforce, are using AI to develop insights that can help companies in recruiting the right sales teams for their business.

4. AI will help companies scale up

If AI and machine learning algorithms can wisely use large amounts of big data, it will help companies perform better. It will also increase the employee retention rate and help in new customer acquisition. This will create new job opportunities as companies begin to scale up and grow, and benefit the industry as a whole.

5. AI will create new job positions

A recent World Economic Forum (WEF) report estimated that nearly 50% of the full-time workforce of companies will shrink over the next few years. Although AI will eliminate jobs, it will create a demand for new job positions. For AI to function properly, humans will be needed to check work, improve it and manage it. The WEF indicated that the new positions will require additional skills for managing the interface between technology and humans.

6. AI maintenance jobs

AI is helping businesses operate more efficiently especially in areas such as customer service, automation, etc. Some companies have even integrated AI in all their crucial systems. As AI gets implemented in every industry, the demand for an AI maintenance workforce is going to skyrocket. Companies would need large amounts of AI developers and engineers to maintain their systems.

7. AI in Robotics

The area of robotics will see massive growth in the next few years. AI-based robots such as stationary robots, non-humanoid land robots, and fully automated aerial drones, are gaining significant business interest from companies globally. This increased demand is bound to open a lot of job roles for AI robotics engineers.

8. AI Facilitator roles

AI’s increased adoption in companies will see a demand for AI facilitators who will provide businesses with the flexibility to compile their own AI environments. This role will involve the creation of automated environments as well as build systems such as virtual assistants. The AI-based environments will help employees work better without compromising on security, governance, data control and compliance.

9. AI in Healthcare

Healthcare is one area where there is a lot of scope for AI applications. PwC estimates that it would be one of the biggest winners from AI, where job opportunities could increase by nearly 1 million. In the near future, AI-powered healthcare will be available at scale and on-demand to everyone. Hence, the requirement for AI-assisted healthcare technician jobs will see an upward surge.

10. AI in the Gaming industry

The gaming industry is one area where AI applications are immense. Right from game design and development to in-game operations and maintenance, AI plays an indispensable role that is reaping huge rewards for gaming companies. With virtual reality games becoming highly popular, companies are bound to invest a lot of money on AI-based systems. Thus, AI developer jobs in gaming will be in high demand.

11. AI in Automated Transportation

AI is already playing a major role in the automated transportation sector. Companies like Uber and Google are investing millions of dollars into AI-driven self-driving cars and trucks. As this mode of transportation picks up in the future, it is going to create plenty of vacancies for AI and machine learning engineers.

12. AI in Financial Services

Financial Services companies such as banks require AI engineers to develop systems that can identify and mitigate fraud. They are also using machine learning based anomaly detection models to monitor transaction requests and identify suspicious activity. Apart from security, AI is being increasingly used in financial marketing. Financial companies are developing systems that can orchestrate customer journeys on their most preferred channels and at the right time. Thus, in the Financial Services sector, the AI job scenario is highly positive.

13. AI in Education

PwC’s recent report revealed that the education sector is poised to get almost 200,000 extra jobs. The education industry is slowly shifting towards e-learning. Concepts such as smart content, personalized learning, and other data-driven operations depend heavily on AI. An eSchool News study indicated that the application of AI in education will be increased by 47.5% by 2021. For facilitating such growth, the industry will need to hire plenty of AI and machine learning experts.

14. AI in the Entertainment industry

AI and machine learning have seen massive potential in the entertainment industry. Streaming service companies such as Spotify, Amazon Prime, Netflix, Hulu, etc. are already using AI technology in their business model. By identifying the online behaviour of consumers, AI algorithms help in recommending media which suits the consumer’s preferences. This is just one of the umpteen use cases that AI offers for this industry. The industry will therefore definitely hire a huge AI workforce to fulfill all their requirements.

15. AI and Digital Assistants

Digital assistants such as the Google Assistant, Siri, Alexa, and Bixby are getting smarter by the year. Global IT behemoths such as Google, Samsung, and Amazon are pouring billions of dollars into making digital assistants perform complex tasks. These tasks include learning about our daily routines, speech recognition, etc. According to Zion market research, by 2025, the global intelligent virtual assistant market will reach $19.6 billion. This estimate purely indicates that a large amount of AI jobs will get generated in this industry.

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20 Practical Ways to Implement Data Science in Marketing

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Over the past decade, online information consumption has drastically shot up due to the wide affordability of the World Wide Web. It is estimated that there are over 6 billion devices connected to the internet right now. Around 2.5 million terabytes of data are generated every single day. By 2020, for every single person, there will be 1.7 MB of data created every second.

For marketers, this staggering amount of data is a gold mine. If this data could be properly processed and analyzed, it can deliver valuable insights which marketers can use to target customers. However, decoding huge chunks of data is a mammoth task. This is where Data Science can immensely help.

Data Science is a field that extracts meaningful information from data and helps marketers in discerning the right insights. These insights can be on various marketing aspects such as customer intent, experience, behavior, etc. that would help them in efficiently optimizing their marketing strategies and derive maximum revenue.

Let’s take a look at 20 practical ways in which Data Science can be implemented in Marketing:

  1. Marketing Budget Optimization

Marketers are always under a strict budget. The main goal of every marketer is to derive maximum ROI from their allotted budgets. Achieving this is always tricky and time-consuming. Things don’t always go according to plan and efficient budget utilization is not accomplished.

By analyzing a marketer’s spend and acquisition data, a data scientist can build a spending model that can help utilize the budget better. The model can help marketers distribute their budget across locations, channels, mediums, and campaigns to optimize for their key metrics.

  1. Marketing to the Right Audience

Generally, marketing campaigns are broadly distributed irrespective of the location and audience. As a result, there are high chances for marketers to overshoot their budget. They also may not be able to achieve any of their goals and revenue targets.

However, if they use data science to analyze their data properly, they will be able to understand which locations and demographics are giving them the highest ROI.

  1. Identifying the Right Channels

Data science can be used to determine which channels are giving an adequate lift for the marketer.  Using a time series model, a data scientist can compare and identify the kinds of lift seen in various channels. This can be highly beneficial as it tells the marketer exactly which channel and medium are delivering proper returns.

  1. Matching Marketing Strategies with Customers

To derive maximum value out of their marketing strategies, marketers need to match them with the right customer. To do this, data scientists can create a customer lifetime value model that can segment customers by their behavior.  Marketers can use this model for a variety of use cases. They can send referral codes and cashback offers to their highest value customers. They can apply retention strategies to users who are likely to leave their customer base and so on.

  1. Lead Targeting

Marketers can use data science to narrowly target leads and know all about their online behavior and intent. By looking at historical data, marketers can determine their business requirements and the type of brands they’ve been associated with, in the past year.

  1. Advanced Lead Scoring

Every lead that a marketer procures doesn’t convert into a customer. If the marketer can accurately segment customers as per their interest, it will increase the sales department’s performance, and ultimately, revenue.

Data science enables marketers to create a predictive lead scoring system. This system is an algorithm that is capable of calculating the probability of conversion and segmenting your lead list. The list can be categorized into the following: eager customers, curious prospects, and not interested customers.

  1. Customer Personas and Profiling

While marketing a product/service, marketers look at creating customer personas. They are constantly building specific lists of prospects to target. With data science, they can accurately decide which personas need to be targeted. They can figure out the number of personas and the kind of characteristics they need to create their customer base.

  1. Content Strategy Creation

Marketers always have to deliver relevant and valuable content to attract their customers. Data science can help them pull audience data that will in turn help in creating the best content for every customer. For example, if a customer came via Google by searching for a certain keyword, the marketer will know to use that keyword more in their content.

  1. Sentiment Analysis

Marketers can use data science to do sentiment analysis. This means that they can gain better insights into their customer beliefs, opinions, and attitudes. They can also monitor how customers react to marketing campaigns and whether or not they’re engaging with their business.

  1. Product Development

Data science can help marketers gather, aggregate, and synthesize data on their products for several different demographics. Based on the insights provided by this data, they can develop products and create highly targeted marketing campaigns to their intended demographic.

  1. Pricing Strategy

Data science can help marketers when it comes to improving their pricing strategy. By focusing on factors such as individual customer preferences, their past purchase history, and the economic situation, marketers can identify exactly what drives the prices and the customer’s buying intent for each product segment.

  1. Customer Communication

By properly analyzing data, marketers can determine the right time to communicate with their prospects and customers. For example, they may be able to understand that a customer reads and responds to emails but isn’t very receptive on SMS. Such insights can help marketers understand the right time and channel for communication.

  1. Real-Time Interaction Marketing

Data science can produce information about real-time events and allow marketers to tap into those situations to target customers. For example, marketers of a hotel company can use data science in real-time to determine travelers whose flights were delayed. They can then target them by sending ad campaigns directly to their mobile devices.

  1. Improving Customer Experience Using Data

Providing a rich customer experience has always been an important factor in achieving marketing success. With data science, marketers can collect user behavior patterns that will predict who may want or need specific products. This allows them to market efficiently and provide customers with enriching experiences.

  1. Customer Loyalty

Loyal customers are those who help in sustaining a business. They are less expensive than new customer acquisition. Data science can help marketers improve marketing to existing customers and thus boost their loyalty. For example, Target used data science to gain a profile of pregnant women based on their purchases before pregnancy. The company then targeted these customers with product offers during their pregnancies. This marketing strategy turned out to be a huge success in terms of purchases and loyalty for the company.

  1. Social Media Marketing

Nowadays, customers are highly active on social media sites like Facebook, LinkedIn, and Twitter. Marketers can use data science to see which leads are exploring their social media page, what content they clicked on and more. With insights such as these, they can formulate a proper social media engagement strategy.

  1. Community Groupings

Data science can be used to target specific social media groups for accessing customer feedback. This is done by helping marketers identify the most frequently discussed topics based on keyword frequency.

  1. Going Beyond Word Clouds

For analyzing social conversations, marketers always relied on word clouds. However, word clouds were useful when there was a high level of social activity. If the level of social activity was less, marketers often ended up using irrelevant keywords. With data science and natural language processing algorithms, they can go beyond word clouds by contextualizing word usage and delivering meaningful insights.

  1. Ad Offerings

Marketers can use data science to specifically target ads to customers and measure clicks and results of campaigns. It can ensure that the right people are seeing the banner ads and improve the chances of being clicked.

  1. Email Campaigns

Data science can be used to figure out which emails appeal to which customers. How often are these emails read, when to send them out, what kind of content resonates with the customer, etc. Such insights enable marketers to send contextualized email campaigns and target customers with the right offers.

  1. Digital Marketing Platforms

Digital marketing platforms thrive on data. Marketers can garner better insights by feeding these platforms with refined data. Data science can improve digital marketing platforms by providing the right data and thereby enabling marketers to determine what they have to do to achieve their marketing goals.

To explore a career in Data Science, click here.

To explore a career in Business Analytics, click here.

How Cloud Computing is Transforming the Gaming Industry

Reading Time: 3 minutes

The shift towards online and digital gaming, as opposed to physical copies of games, started as early as the middle of 2010. This marked the point when more games were bought and played online than otherwise, according to an NPD report about digital downloads ( ). This was largely down to the phenomenal growth and success of websites like Steam (

The evolution of Cloud computing has since taken over most of the gaming industry and seems set to provide us with further innovations. So, let’s take a look at:
– What is Cloud computing
– How it took over the gaming industry
– Benefits of Cloud technology for the gaming industry and consumers
– Predictions for the future

What is Cloud Computing?

The term ‘Cloud computing’ may sound unfamiliar and technical to those who haven’t heard it before, but it’s actually something you’ll be using as you browse the internet (‘the Cloud’ being almost, but not exactly, synonymous with the internet), and, as we’ll discuss, play games online through gaming as a service.

The official definition is:

“Cloud computing is the on-demand availability of computer system resources, especially data storage and computing power, without direct active management by the user. The term is generally used to describe data centers available to many users over the Internet.” –

Google CEO Eric Schmidt first coined the term as early as 2006, when he introduced the term at a conference, according to MIT Technology Review ( ). Clouds available to the public allow anybody to access information, including downloading games from another server, which could be anywhere in the world.

How Cloud Computing took over the Gaming Industry?

Gamers often struggle with the high cost of equipment as an initial outlay for their gaming experience. And that’s before they shell out for games! The fact is that now Cloud computing is so predominant, it’s actually more difficult and expensive to set up with physical games. Read on for a break-down of the benefits that led to Cloud computing’s success.

Benefits of Cloud Technology for the Gaming Industry (and you)

– Your information is protected by industrial security standards (so little to no opportunity for hackers).

– If your computer breaks, you won’t lose your game progress.

– The Cloud allows users to play high-end games on low-end consoles, so you can experience excellent quality even on a cheap laptop.

– It’s cheaper for gaming companies, as they only pay for bandwidth actually used by their traffic.

– Users can play and save their progress with multiple games, and switch between them with ease, meaning more sales.

– The same game can be made available on multiple platforms.

– Without physical copies, piracy is much harder, so companies make more money.

– Basically, any game, on any device, at any location. What can beat that?

Predictions for the future

Statista predicts that by 2021, there will be 2.73 billion gamers worldwide ( With games like Red Dead Redemption 2 selling 17 million copies in less than a week, it’s not difficult to see the trend. Cloud computing has solved many of the problems experienced by both gamers and the companies who work hard to please them. So, it was no surprise in 2018 when Google and Microsoft announced a shift to Cloud gaming (now available as Google Stadia and Project xCloud). But is everyone as excited by the shift?

Some realists are looking at the big picture behind the hype, like writer Angelo M. D’Argenio over at Gamecrate ( ). He points out that even the internet has limits for processing speed. It can work at the speed of light, yes — but even light takes time to travel halfway around a planet. We might see a big change in the future to solve this problem. Companies building and renting servers spaced around the world would mean everyone uses local servers. At that point, you’d probably reach a lag-less gameplay perfection. Of course, there’s still the problem of locations with low internet speed to solve, but perhaps the solution to that is coming soon.

A Peek into Global Artificial Intelligence Strategies

Reading Time: 5 minutes

Artificial Intelligence (AI) offers a wide range of capabilities that are being used by almost every industry in some form or the other. Due to incredible technological advancements in this field, AI has generated considerable interest in countries across the world. They have realized its immense potential and are, therefore, promoting its use and development for economic growth.

Every country has its own unique strategy for AI. Each strategy focuses on various aspects such as data and digital infrastructure, skills and education, scientific research, public and private sector adoption, talent development, etc.

Let’s take a look at some of the AI strategies adopted by countries globally:


India, one of the fastest growing Asian countries, is planning to leverage AI for both economic growth and social inclusion. Last year, the country’s think tank, NITI Aayog, developed a national strategy called “#AIforAll”. The strategy is focused on three things:

  1. – Train and equip Indians with the skills to find quality jobs
  2. – Maximize social impact and economic growth by investing in research
  3. – Market and scale in-house AI solutions to the developing world

NITI Aayog proposed an integrated two-tier approach for boosting AI research in India. The first approach involved the setting up of Centres of Research Excellence (COREs) which will focus on fundamental AI research. The second approach involved the usage of COREs as technology feeders for the International Centres for Transformational AI (ICTAIs).

ICTAIs will focus on developing AI-based applications in areas which have societal importance. Agriculture, Healthcare, Education, Smart Mobility, and Smart Cities were chosen as the priority sectors which will benefit on a social level from AI applications. The main goal of NITI is to set up India as an “AI Garage”. The idea here is to enable the developing world to gain access to Indian-made AI solutions.


According to a Govini report, $7.4 billion was spent by the Pentagon in 2017 on AI and its supporting fields such as cloud computing and big data. In June 2018, the Pentagon announced a new Joint Artificial Intelligence Center that will have a total of $1.7 billion to spend over five years.

In Feb 2019, President Trump signed an executive order on “Maintaining American Leadership in Artificial Intelligence.” The order called on US companies to “drive technological breakthroughs in AI across the Federal Government, industry, and academia in order to promote scientific discovery, economic competitiveness, and national security”.

The order further encourages agencies to “enhance access to high-quality and fully traceable Federal data, models, and computing resources, promote sustained investment in AI R&D and train the next generation of American AI researchers and users“.


In March 2017, Canada became the first country to announce a national AI strategy. The Canadian government’s federal budget detailed the launch of a five-year Pan-Canadian Artificial Intelligence. It planned to invest C$125 million in AI research and talent.

The national AI strategy is being led by the Canadian Institute for Advanced Research along with the Canadian government and three new AI Institutes: the Vector Institute in Toronto, MILA in Montreal, and the Alberta Machine Intelligence Institute (AMII) in Edmonton.

Four main goals were outlined for the country’s AI strategy:

  1. – Establish three clusters of scientific excellence
  2. – Increase the number of AI graduates and researchers
  3. – Support the national research community on AI
  4. – Develop thought leadership on the economic, ethical, policy, and legal implications of AI

The key difference in Canada’s AI strategy from other government strategies was that it primarily focused on research and talent. It didn’t include policies found in other strategies such as investments in skills development, data and privacy, and other sectors. The country’s end vision is to enhance Canada’s international profile as a leader in AI research and training.


The Chinese government announced its AI strategy by issuing the New Generation Artificial Intelligence Development Plan (AIDP) in July 2017. The plan earned the distinction of the most comprehensive of all national AI strategies, with goals and initiatives for industrialization, R&D, education and skills acquisition, standard setting and regulations, talent development, security, and ethical norms.

The AI strategy followed a three-step approach:

  1. – Make the AI industry of China world-renowned and “in-line” with competitors by 2020
  2. – Lead the world in some AI fields by 2025
  3. – Become the premier center for AI innovation by 2030

The aim is to develop an AI industry worth 1 trillion RMB by 2030, with related industries worth 10 trillion RMB. The AI strategy also lays out a plan to strengthen the training of the domestic AI labour force, recruit the world’s best AI talent, and lead the world in laws, regulations, and ethical norms that promote AI development.

The Chinese government has also planned to develop AI research and industrial leadership by partnering with national technology companies. It has already begun work on a $2.1 billion AI research technology park in Beijing.


In the field of AI, Russia’s private sector has achieved success in image and speech recognition. The public sector’s AI research involved the development of a variety of weapons as missiles, aircraft radars, electronic warfare, and unmanned systems.

In 2018, several initiatives were launched by the Russian government to better unify private and public AI research. Finally, in January 2019, President Putin instructed his government to create a national strategy for Artificial Intelligence with a June 15th deadline.

As per Putin’s instruction, the government is aiming to create an “AI infrastructure”. Creating this infrastructure would include the complete unification of the public and private sectors to mobilize an AI breakthrough. To compete with its compatriots, it’s also planning to spend millions of dollars specifically on AI research and development.

Through its national AI strategy, the country’s vision is to become a modern digital economy and turn into a major science and technology development power.


At the 2018 World Economic Forum Annual Meeting, the Theresa May government announced that it was determined to establish the UK as a world leader in AI. Consequently, in April 2018, the government released the AI Sector Deal. The deal aimed to advance the UK’s AI ambition and position itself as a global leader in the field.

The deal’s key policies were to:

  1. – Invest £725 million in new Industrial Strategy Challenge Fund programs to capture the value of innovation
  2. – Raise the total research and development (R&D) investment to 2.4% of GDP by 2027
  3. – Establish a technical education system that rivals the best in the world
  4. – Invest an additional £406 million to help address the shortage of science, technology, engineering and maths (STEM) skills
  5. – Boost the digital infrastructure with over £1 billion of public investment
  6. – Allocate £300 million to fund research related to data science and AI


In March 2018, France announced its plan to transform France into a global leader in AI. Presented at the “AI for Humanity Summit” in Paris, the plan had four main components.

  1. – Strengthen France’s AI ecosystem and attract international talent
  2. – Drive the adoption and application of AI by developing an open data policy. This will be done in sectors where France has the potential for AI excellence, such as healthcare
  3. – Create a regulatory and financial framework to support the development of domestic “AI champions”
  4. – Develop regulations for ethics to ensure that the use and development of AI is transparent, non-discriminatory, and explainable

The Emmanuel Macron government will invest a total of €1.5 billion in AI in the next four years. 60% of the funds will be governmental and 40% will come from external investment. Out of the budget, €700 million will go towards AI research, $400 million to industrial projects in AI, €100 million to AI startups and companies, and €70 million annually through France’s Public Investment Bank.


AI has become the new focus of international competition.  The amount of investment and research that each county has placed on this technology is astounding. Using this technology to proliferate innovative weapons may give a tactical military advantage, which is only temporary. The true winners will be those countries that use AI for the societal and economic growth of their nations.

To explore a career in Artificial Intelligence, click here.

Artificial Intelligence Can Win Football Matches

Reading Time: 4 minutes

Football is a sport that is universally loved and adored by people from all walks of life. It is one of the most popular sports in the globe with around 4 billion fans. Its highly competitive nature instills a crazy frenzy in teams to try and win each and every game. As a result, football managers find it very challenging to devise successful strategies against their opponents.

For formulating these strategies, managers typically look at all kinds of data about their opponents. This includes data such as the playing style and formations of the opponent team, the strategy they followed in their previous matches, the pros and cons of each opponent player, the tactics used by their best players in difficult situations, etc. In recent years, managers have started employing innovative technologies to help them analyze this data and create winning formulae for their teams.

Football and Technology

For a long period of time, there was an aversion towards using any kind of technology in football. The International Football Association Board (IFAB) deemed that the “beautiful game” didn’t require the assistance of technology with in-game decisions. However, with a large number of refereeing errors in global events such as the 2010 FIFA World Cup, the IFAB decided to take a re-look at how technology could assist referees in making the right decisions.

Goal Line Technology (GLT) is one of the first technologies to be implemented in football. Introduced in 2012, GLT can help in accurately assessing if a ball has crossed the goal line or not.

Another popular technology that has been making waves in the footballing world is Video Assistant Referee (VAR). VAR can be used to review decisions made by the head referee with the use of video footage and a communication headset. This technology made its debut in the 2018 FIFA World Cup in a group stage match between France and Australia.

In the same world cup, a cool technology called Electronic Performance and Tracking Systems (EPTS) was introduced. EPTS, a tablet-based system, enables teams to share information with their coaches in real-time.

The Rise of Artificial Intelligence (AI)

In recent years, Artificial Intelligence and machine learning have seen a lot of traction in the footballing world. AI came to be associated with football for predicting the outcome of matches. By analyzing big data, machine learning algorithms have the capability to predict the success and failure of football games.

In 2017, UK-based sports betting company, Stratagem, built an AI-based system to predict the results of football matches. The system worked by pairing human analysts with deep neural networks. In the same year, another startup company called Unanimous AI made headlines when it successfully predicted the exact score of the National Football League’s Super Bowl results.

2018: A landmark year for AI in football

2018 turned out to be a landmark year for AI in football. The 2018 FIFA World Cup became the first global competition to incorporate the use of various AI-based technologies.

Telstar, the football used in the world cup, came embedded with an NFC (Near Field Communication) chip powered by AI. Using a smartphone, the chip enabled fans to interact with the ball along with other undisclosed advanced features.

Computer Vision, an AI-based tool, was extensively used in applications such as smart ticketing, video highlights, security, fine-grained control of devices such as cameras, etc. Even existing digital technologies such as GLT and VAR were made far superior, faster, and efficient using AI. 

Creating Winning Scenarios Using AI

AI is now being used by data scientists to help teams come up with spot scenarios and objective measures. AI-based intelligent algorithms have the capability to simulate a large number of events. This feature enables analysts to translate insights derived from the simulations. These insights can help in making recommendations to what will happen on the pitch.

This, in turn, helps coaches make informed decisions on players while preparing for an upcoming game. By analyzing the opposition’s historical data, AI can be used to determine tactics that can immensely help in selecting the right team for a particular game and eventually winning it.

Selecting the right team is the key

JUST ADD AI, a German-based AI company, helped a Bundesliga team win matches by selecting the right players. The company achieved this by building an AI tool that extracted insights from unstructured data and putting them in a single dashboard. IBM’s Watson AI was leveraged by the company for providing a deep perspective on players. Watson was trained to understand scouting reports and draw out the most relevant details.

Another interesting AI use case is injury prediction. What if football coaches could use AI to predict when their best players could get injured? This information would be highly valued when it comes to critical games. Last year, data scientists at the University of Pisa, Italy, developed a machine learning algorithm to predict injuries. The algorithm accurately predicted 9 out of the 14 injuries sustained by an elite Italian team in a single season. 

In January this year, a London football club partnered with an AI firm called The Big Bang Fair. The partnership’s main aim was to install an AI “coach” that could help in selecting the team’s formations and tactics. Based on game results, the AI learned on the job by evolving its thinking over the course of a season.


AI and machine learning have enabled faster and better decision-making in the world of sports such as football. AI-powered algorithms can derive actionable insights that add even greater value to the players and coaching staff. Artificial Intelligence has unlocked many use cases that will be paramount for everyone involved in this beautiful game. The future seems bright for AI in football. It will be exciting to see the extent to which it is leveraged by teams to devise match winning scenarios. 

To explore a career in Artificial Intelligence, click here.