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Images are the new currency. Don’t believe me? Take a look at these statistics 95 million images are uploaded on Instagram daily – Source There are over 330 million active Twitter users and tweets with images receive 150% more retweets – Source 60 million emojis are used on Facebook daily – Source The proverb ‘a picture paints a thousand words’ has become even more relevant in today’s sharing age. But why? We could say that it is easier to express a feeling with an image than text. Or that the human brain processes visual content faster. We are now living in the world of hashtags, emojis, limited text characters. Images complete the stories these are trying to tell. Images are not only enhancing text but are often standing in place of it entirely. And wouldn’t it be a shame if organizations today did not leverage the information from this huge ocean of visual data? It is estimated that by 2025, the global image recognition market is expected to touch $38.92 Billion. The video analytics market is expected to touch USD 8.55 Billion by 2023. So where does image analytics feature here? Much like how sentiment analysis raised the bar for social monitoring, image analytics is raising the bar for social listening. There is a goldmine of data stored in these images as these help organizations understand visual sentiment especially when text is absent. Take this tweet as an example. The entire sentiment of the tweet is summed up in the hashtag #PerfectDay. Nowhere is the airline brand mentioned but only the word ‘plane’. Now imagine the kind of opportunity Emirates could have created by leveraging logo recognition resulting from image analysis! A tweet like “Thank you for the click! You’re such an amazing mom. We hope you and your son had an amazing time plane spotting”.  It is this human-to-human kind of conversations that customers are looking for. So, what exactly is image analytics? Image analytics isn’t some futuristic technology. You are probably using it without even knowing it. Your smartphone is already categorizing your photos. Your iPhone will tell you easily which photos are from your office party or from your latest adventure trip. What image analytics essentially does is categorizes images from different sources and sorts them according to contexts such as facial expression, age, action, topics, sentiment, gender, and brand logo. How does it do so? Quite simply by leveraging automatic algorithmic extraction and consequent logical analysis of information found in the image employing digital image processing techniques. And why should you care about image analytics? Fritz Venter and Andrew Stein say that the objective of image analytics is to “bring an unstructured rendition of reality in the form of images and videos into a machine analyzable representation of a set of variables.” Here are a few reasons why image analytics is something to look out for Source authentic data There is data everywhere. But how authentic is that data? Organizations across the US spend almost $10 billion each year on third-party authentic data. This sourced data has its accuracy limitations and yet forms the basis of many personalized marketing campaigns. The result? Limited accuracy. With image analytics, the data organizations source will not be mere numbers from a survey, but actual customer data derived from first-hand sources, think facial expressions. The data is also captured real-time when the customer is experiences something, often before she makes a purchase. Such data becomes more relevant when personalizing offers as the insights derived are deeper, more accurate and also real-time. Improve customer journeys The age of digital transformation puts the customer in the centre of all focus. And for that, it is imperative to improve customer journeys and customer experience. Leveraging facial recognition businesses can create a positive impact on the same in several different ways. The airline industry, for example, can leverage image analytics to replace passport checks or deliver travelers from annoyance caused by straggling passengers. Changi airport, for example, is putting facial recognition technology to work to find lost passengers, detect and find people who are on a particular flight or leverage camera-based scanners to automate passport gates. The airport is also using this technology to offer self-service at check-in, immigration, and boarding. Insurance companies can use image analytics and facial recognition to improve the insurance claims process. An insurance provider in the US is doing the same by allowing customers to upload pictures of their damaged vehicles. The company analyzes these pictures and processes the claims. Insurance companies can also leverage this technology to reduce liabilities from workmen injuries from dangerous risk assessment areas such as rooftops and use drones instead. They can also determine the extent of damage and claims estimation reporting and enable faster assessment of claims especially in the wake of natural disasters. The retail sector can unlock the power of image analytics to validate customer identity at stores using cards such as Mastercard. This can help prevent cart abandonment by eliminating challenges such as OTP’s sent via text messages. Facial recognition and image analytics can also be leveraged to gauge customer dissatisfaction by analyzing the customers’ facial expressions and movement. It can also be used to activate customer loyalty programs and preferences and improve the customer experience and customer journey. Optimize retail initiatives Image analytics has a huge potential in the retail industry. The Consumer-Packaged Goods industry, for example, could leverage some technology advantage to optimize shelf monitoring, store checks and audits. With this technology, retail outlets can Effectively track and monitor in-store operations using shelf images. Get real-time insights into key performance indicators such as stock outs, on-shelf availability, compliance metrics or pricing changes. Improve store coverage for field sales representatives by replacing manual checks with image recognition. The analytics derived from accurate in-store insights help to optimize store coverage, identifying performance issues and thereby improving retail execution and recovering lost sales. Improved sentiment analysis Sentiment analysis is gradually becoming an essential contributor to improving customer journeys. Using image

For the past few years, several industries have leveraged the power of data science in marketing, risk management, fraud detection, business operations, supply chain, business model innovations, and many other such areas. By using statistics, predictive modeling, and machine learning, data science helps enterprises resolve various challenges within the industrial sectors and for the economy at large. When implemented correctly, data science is a powerful tool and brings positive results. We already witnessed massive changes in how the western countries and industries work and how data science is empowering that change. But when it comes to India, we are still lagging behind the western counterparts. Dr. Avik, Head, Data Analytics, Niti Ayog, stated that “We are trying to make sense of the operational data to get a good picture of the state of the economy.” He also shared his challenges over the collection and implementation of data, as most of the collected data is highly unstructured and difficult to make any sense. Data science, as we understand, is the study of information such as where it comes from, what it represents, and how to transform it into a valuable resource in the creation of business and economic strategies. It also includes mining of extensive structured and unstructured data. It identifies the patterns which can help businesses or government bodies to minimize costs, increase efficiency, recognize new opportunities, and take other development advantages. While the data collection is vital, it is only the first step in the process. The ultimate use of data is to derive meaningful and actionable insights. How data science can transform India For centuries, data has been the backbone of all the research. Now with the proliferation of advanced technologies, programming languages, and availability high computing power, its use has spread to countless businesses as well as for formulating better government policies. Let’s take a look at various ways data science can transform India – Conserving Water According to the Niti Ayog water index report, 21 cities of India including four major cities namely Delhi, Hyderabad, Bengaluru, and Chennai, will run out of groundwater by 2020. Every summer, we see drought, and the situation is worse in rural areas. The water usage is also increasing, and less rainfall could end up turning it into a severe crisis very soon. The Indian government can use data science to predict water level and the water usage patterns in certain areas. The government can also organize water supply tanks in time after making an informed decision with the help of collected data. Armed with more information and data, it can become easier for the government bodies to come up with more ideas for rainwater harvesting and to increase the groundwater level. Controlling Air Pollution As per the World Health Organization report, 11 out of the 12 most polluted cities are in India. India ranked 141 out of 180 nations in the environmental performance index. This shows that the problem is grave, and the authorities need a permeant solution to control air pollution as soon as possible. With the help of data science, the government can take preventive measures to control different variables like pollution from vehicles, crop burning, industry fuel, and biomass burning. Generating Electricity Did you know? India is the third largest producer and consumer of electricity? India’s gross electricity consumption during the 2017-18 was 1,149 kWh per capita. The demand for power is soaring every year. The Government of India has also launched the “Power for All” program to provide adequate electricity supply to all the people in the country. Data science can help authorities to understand the consumption pattern of the households classified by states, districts, cities, regions, streets. After interpreting the demand and the pattern of consumption, the authorities can predict the usage of electricity and take appropriate measurements to organize for the required electricity demands and optimize the usage. Improved Healthcare Indian healthcare system offers a unique approach to leverage data science to conduct research, clinical trials, and medical data to plan public policies. The healthcare system providers generate data through various resources, including the biometric, patient’s record, medicines, prescriptions, and many others. When stored centrally and analyzed in real-time, this data can provide actionable insights, predict outcomes, and help in better planning of treatment protocols for improved public health. Enhanced National Security The security situation in India is unpredictable. The general security officers face a lot of challenges to understand how the information is analyzed, collected, and implemented to protect the country against unforeseen situations. They can use data science to collect information and identify the gray areas associated with security. The police and security agencies can collect and analyze the data and tackle crimes, attacks, and other dangerous circumstances in the country. Traffic Jam Solutions Long traffic jams are frequent in India. The Niti Aayog team is working to understand the causes of traffic jam and options to deal with them. The government bodies can use big data to analyze some of the significant aspects of traffic jam like choke points, narrow or broken roads, lack of traffic management professionals, failure of traffic lights, and other such elements. The traffic policemen can use CCTV cameras and sensors to monitor traffic and immediately solve the problem. Road accidents kill more than 400 people every day. The figure is disappointing, and the traffic authorities can use data science to analyze the pattern and take immediate action to prevent accidents. Build a Better Nation Data is an essential asset and backbone for the smooth functioning of various government bodies. Government agencies collect a significant amount of data from surveys, programs, public banks, and administration. The roadmap to build an analytical framework to integrate data in public departments is quite promising and will transform the entire nation digitally. The government needs a strategy which merges industries to build successful data and analytical capabilities. This would offer a wide range of social benefits to the citizens and improve their lives considerably.

The finance ministry, in February 2018, cleared National Institution for Transforming India (NITI) Aayog’s Rs 7,000-crore plan. The funding will support a national Artificial Intelligence (AI) program. This program will include the identification of certain projects as well as initiatives, in which the government is going to implement AI technology. This announcement was based on the launch of a Task Force on Artificial Intelligence for India’s Economic Transformation by the Commerce and Industry Department of the Government of India in 2017. Before we proceed ahead, here’s a brief about AI in India – The AI industry is currently valued at $230 Million in revenue approximately. There are tremendous opportunities and substantial economic impact of AI, which can boost India’s annual growth rate by 1.3% by 2035. This roughly translates to the addition of approximately one trillion USD to India’s economy. There are about 40,000 AI professionals in India, with Bengaluru being one of the leading AI hubs in the country. Over 1000 firms in India claim to work on AI in some form. The Central Board of Secondary Education (CBSE) has approved the implementation of AI as a subject for students in classes 8,9 and 10. While these have set things in motion, it is the national strategy for AI that has everyone hooked. The NITI Aayog has carefully evaluated several sectors that are going to be impacted by AI. In no particular order, these are: AI in Healthcare Healthcare in India is one of the most crucial sectors in the country, which is expected to grow to USD 280 billion by 2020. The challenges in this sector include accessibility, quality, and affordability for the         majority of the population. So, what is being done in this direction? The Union Health Ministry is working towards creating an effective roadmap of public healthcare in the country by addressing various gaps. It is realizing the economic impact of AI and prioritizing the building of AI technology capabilities. For instance, one of the initiatives that the central government has undertaken includes – an Imaging Biobank for Cancer. Through this initiative, the NITI Aayog, along with the Department of Biotechnology, can realize the goal of building an extensive database of cancer-related radiology and pathology images. This database will be inclusive of over 20,000 profiles of cancer patients. This can be effective in the treatment of the disease at a lower cost by enhancing the decision-support. Another application of AI in healthcare is that of Diabetic Retinopathy. NITI Aayog is already working with tech-biggie Microsoft and Forus Health for rolling out a technology that can lead to early detection of the disease. All in all, it will help the country progress towards more proactive healthcare services. AI in Agriculture Back in February 2016, Hon’ble PM Shri. Narendra Modi launched the Prime Minister’s Crop Insurance Scheme or Pradhan Mantri Fasal Bima Yojana. This scheme covered various long-standing issues of the farmers, including calamities, loss of crops, germination risks, and so on. Several agencies were roped in for the pilot projects using AI. Sample this, NITI Aayog and IBM have partnered to bring forth a crop yield production model using AI. This will be used to offer real-time advisory to the farmers. The predictive analysis will generate insights which can enhance soil yield, crop productivity, and also offer early warnings from remote sensing, weather prediction, crop phenology, etc. The farmers are all set to benefit from accurate advice. The strategy here is to use AI for solutions that will involve projects from several agricultural, technology-based startups. AI in Education Education is undeniably the most important sector in India. The country, at present, faces challenges that include low-retention rates in rural schools as well as poor learning outcomes. To rectify this, India needs to leverage AI tools to overcome these issues. These can be in terms of interactive tutoring systems and adaptive learning tools that can customize learning for the students. For this purpose, the IITs are partnering with MHRD for democratizing education with the IIT -PAL initiative. The aim is to create an AI-powered education sector, which will cater to the students in smaller cities and towns. Another case in point is that of a hackathon that was conducted by the NITI Aayog, which featured ReadEx, an Android application that generates questions in real-time using NLP (Natural Language Processing), creates flashcards, and offers content recommendations. Also, the AP government has teamed up with Microsoft to predict the number of dropouts in the state based on ML and analytics. Based on the reports, they can take measures such as conducting programs and counseling sessions to bring down the overall drop out rate. AI for Smart Cities and Infrastructure Let’s face it. The urban population is growing by leaps and bounds, and in the coming decades, the number will be exponentially high. According to data by the UN, the world population will reach up to a limit of 9.7 billion by the end of 2050. Therefore, when there is a surge in unplanned urbanization as well as issues like congestion, over pollution, and sub-par living standards, then it is going to create a major issue for the citizens in the future. AI can be handy in urban planning, efficient utility distribution, enhanced delivery of services, and help with many more solutions for the smart city and infrastructure woes. The Government of India is on a mission to set up several Smart Cities Pan India to drive better quality of life and economic growth. For this, 99 cities have been selected with the investment of INR 2.04 lakh crores. The strategy to be implemented in these cities includes the city extension, city renewal, city improvement, and also, covering the cities with smart solutions. One of the key initiatives for this purpose is the Atal Mission for Rejuvenation and Urban Transformation. AI plays a major role here by helping with predictive intelligence and augments the features of smart cities. These features can be – crowd management, smart parks, public facilities, and

A we enter the era of data-intensive computing, terms such as data science, machine learning and artificial intelligence are used almost every day, often even interchangeably. In comparison to a few years ago, where the role of data scientists was limited to research and R&D purposes, today, they have become mainstream and this is creating a huge demand in the industry. And the scope of AI has evolved from simple problem solving to offering human-like reasoning capabilities. This evolution is mainly due to questions that are plaguing business leaders on a daily basis: how do we drive innovation within the company? How do we provide insightful, out-of-the-box data analytics and boost the productivity and efficiency of our workforce? How do we ensure a higher success rate of our products and services than our competitors? What’s causing the growth of AI Artificial intelligence is being adopted into the enterprise at a rapid pace, and adoption is likely to surge in the coming years. According to IDC, global spending on AI and cognitive technologies will hit $52.2 billion by 2021. Two factors are driving this growth: first, a vast amount of data is becoming available in more and more application areas, and second, affordable infrastructure like Cloud is enabling enterprises to store, retrieve, and share data, which was unthinkable, inaccessible, or even impossible a few years ago. The growth of IoT is only making AI more mainstream; when combined with sensors, real-time localization technologies, and near-field communication, AI is able to drive value like never before. This massive shift into digitalization is allowing businesses to unify knowledge from scientific research with vast amounts of multidisciplinary data, complete tasks based on a stipulated set of rules, and create business wisdom like never before. Why AI and data science need each other The massive amount of data being generated by organizations today presents a huge opportunity to identify, categorize, and unearth patterns applicable to each business.  AI, with its immense capability, can automate many tasks that Data Scientists and Data Engineers perform on a daily basis, including preparing and cleansing data, checking for correctness, identifying issues, making data available to teams, and building hundreds or thousands of variations of models. And although it can automate lower-level steps in data preparation and visualization, it cannot, by itself, truly understand what a specific set of dta means for an organization, its business and in the context of the industry it operates in. The learnings from problem-solving, deduction and pattern recognition from each area have to be conjoined with other areas, and this can be done only using Data Science. Here are two major reasons why Data Science and AI need to work together: Feeding the right data to get the right insights: Whether it is analyzing billions of transactions to detect fraud or forecasting consumer demand to plan for growth, since AI systems primarily perform human-like functions such as logical reasoning, and learning from experience, they have to be fed with the right computing power and data to empower them to think like humans. Hence, instead of inputting large amounts of unnecessary data into AI, data science helps extract knowledge or insights from large volumes of data and feeds only the relevant bits into AI systems. This enables businesses to find appropriate and meaningful information from huge pools of data faster and more efficiently. Making the right decisions from the insights unearthed: Although AI systems, through all the reasoning, provide insights into operations, business performance, customer behavior, and market trends, they also need Data Scientists to help decision-makers understand what the insights really mean. Insights, together with human judgment can enable organizations to make timely, efficient, data-driven decisions. By generating hundreds or thousands of variations of models with different prediction features, they can create iterative simulations to choose the best variation. Such a dynamic, multifaceted decision process, will make AI intelligent assistants to Data Scientists, allowing them to run more complex data simulations, make key business decisions, and respond to problems in real-time. Match made in heaven In the age of data overload, you need more than just a cool AI platform to drive smarter decisions; what you need is a solid team of data scientists who can successfully run teams and steer what-if scenarios before your competitors do. Considering the many benefits that AI and Data Science bring to the table, instead of only focusing on feeding systems with a consistent set of data to measure past performance and plan for the future, you need to focus on data science and use a combination of analytics and machine learning techniques to draw inferences and insights out of the massive sea of data. This will help solve higher order questions and enable you to derive far greater business value than you ever imagined. AI and Data Science – truly a match made in heaven!

The term ‘Future of work’ has been dominating all the discussions related to the way we will live and work in the future. It has particularly gained prominence during COVID-19. So, what is the future of work, and how is it different from the way we work today? Deloitte defines the future of work as “The growing adoption of artificial intelligence in the workplace, and the expansion of the workforce to include both on- and off-balance-sheet talent.” It essentially means that future workplaces will leverage more technologies such as AI, robotics, and automation to bring in a change in the way we work. McKinsey’s Global Institute Report states that more than 30% of the activities associated with the majority of the occupations in the US will be automated. Unlike traditional work, the future of work will be technology-driven, lack fixed hierarchy, and will have a workforce that will cut across different generations. However, technology will not be the only driver of the future of work. The only way employees can thrive in a fast-paced future of work is by adopting the following skillsets. Future Skills for Future of Work Critical Thinking Every company has its share of challenges and problems. Critical thinking enables employees to resolve them effectively. It encourages teams to identify the problems, come up with solutions, and implement them. Critical thinking focuses on problem-solving rather than arriving at unsubstantiated conclusions and judgments. To promote critical thinking in the workplace, employees will have to make it a part of their work. One way to begin is by focusing on smaller problems first. That will boost confidence and encourage employees to take up bigger issues. Creativity and Innovation What sets companies like Amazon or Netflix apart from their competitors is their ability to innovate continuously. Unlike traditional workplaces where the focus was on following procedures and working mechanically, the future of work is more flexible and creative. A workplace that fosters curiosity, creativity, and innovation can keep employees motivated and inspire them to design new strategies to stay ahead of the competition. Innovation and creativity cannot be acquired overnight. Employees have to take efforts to make it a part of their work. Companies need to create an environment that boosts these qualities. Employees should be able to brainstorm new strategies to ensure that innovation and creativity do not get side-lined in regular work. Initiatives such as intrapreneurship could be established to encourage employees to come up with innovative solutions to solve some of the pressing issues. Collaboration Collaboration helps employees across different locations, teams, and skillsets to come together to achieve a shared goal. It capitalizes on of each team member’s strengths, so they can think together as a single unit and come up with concrete solutions and ideas to solve problems. To cultivate collaboration, employees should work with an open mindset, encourage others to share ideas without any inhibitions, and cross-collaborate with members of different teams. A collaborative culture helps employees to stay motivated at work and grow as an individual. Collaborative tools can help employees to exchange ideas and work together irrespective of their location or time zones. Flexibility and Adaptability The future of work is no longer about sticking to a particular job until retirement. Employees need to don multiple hats, learn several new skills, and be willing to work in different roles throughout their careers. They should be willing to learn and adapt their working style to changing situations. They must learn to be out of their comfort zone, accept new challenges, and be flexible enough to start from scratch, if necessary. They must be willing to accept failures, learn from those, and move ahead quickly. A company with a flexible and adaptable culture will be more responsive to the changes in customer needs and cater to it quickly and efficiently. Leadership In traditional workplaces, leaders were more focused on the outcome of the task, bottom line profits, etc. They believed in a rigid hierarchy and impersonalized relationships with co-workers. Today’s leaders are more transparent, inclusive, and focus on building a strong team that encourages each other to achieve goals. Their relationship with their team exudes trust and confidence. To cultivate leadership skills, leaders will have to learn the art of listening actively rather than just give out orders. They must communicate clearly, be transparent, become a part of the team, and set an example by showing a willingness to improve daily. Proactive Business situations will constantly change, as proven by current pandemic, climatic changes, and the proliferation of technology. Proactive employees will stay a step ahead and be prepared to solve these problems. Instead of waiting to react to the challenges, they take pre-emptive measures to address those. To become proactive, employees must develop foresight, learn to anticipate problems, and be prepared to resolve it. They must acquire an analytical bend of mind to recognize common patterns that exist in the business and use them to solve future problems. They must avoid reacting impulsively during an unforeseen circumstance. Employees who are prepared for the unforeseen are the ones who can help companies tide over difficult challenges and keep it ahead of the competition. Accountability and Responsibility Every company desires to work with employees who take ownership of their work and its outcome as it makes the employees responsible and mindful of the way they work. A lack of accountability and responsibility in the workplace makes employees complacent, disengaged, and demotivated. The only way to deliver better work is by making each employee accountable for their job. Employees can define the goals they plan to achieve to stay focused. Managers can set key metrics to measure their accountability and accuracy of work. They can also encourage employees to use tools to assess their progress and improve the quality of their work. The future of work calls for efficiency and accuracy in work – both of which can be achieved only if employees take responsibility for their work. Technology may take some of our jobs,

The world around us is changing fast, and we need to understand the seasonality behind the way the world works. Let’s understand this with the help of some examples. Imagine analyzing rainfall data, predicting crop growth, or even the fluctuations in air ticket pricing during carnival to Rio. One can easily decipher that there is a certain amount of seasonality to these data points, and they go in cycles. These seasonal aspects also have a direct bearing on all the other facets like sales of related products. Even the stock market is susceptible to seasonal upheaval. These give rise to the relevance of the analysis of the time-series data. In simple terms, this means collecting data at a regular interval with the time stamp on it to understand how the data varies with changing time. This analysis helps in predicting whether the data is stationary, is there a seasonality, or are they autocorrelated. Suppose the metallurgy department wants to predict the amount of rainfall that is going to happen this year based on the historical data. This is called forecasting and is a bit more complicated than a regular modeling task. To do this, one needs to first visualize the time series, make it stationary, and find whether there is an autocorrelation or not. Based on that, the model that fits them the best needs to be decided and applied. Once all these have been completed, the predictions can be made, and rainfall can be forecasted with some accuracy. Models for Time Series Analytics Let us dive deeper into the various models which play a crucial role in Time Series analytics. The most popular ones are the ARIMA/SARIMA model, Seasonal Decomposition, exponential smoothing, and GARCH. The most popular method is the simple exponential smoothing method. The principle is to analyze the past observations on the basic weighted average, which decreases exponentially as we go back in time. In case the data shows some seasonality, then the whole equation needs to be broken down into the – the seasonal, trend, and the remainder component. The ARIMA/SARIMA model is the short form of Auto-Regressive Integrated Moving Average and Seasonal ARIMA. This model combines the linear combination of the past variable values along with the error values in the forecast. Another methodology is the GARCH methodology, which factors in the fact that the error terms will change over some time. Methods to follow in Time Series Analytics While the modeling process for time series analytics is extremely complicated, there are a couple of basic steps that need to be followed to make it simple. The first step is to identify the correct problem statement. For instance, the problem statement could be predicting stock prices. Stock prices of various companies vary over time and are the best fit for the time series analysis. Another example could be to understand electricity usage over a period or predict air quality – for all these cases we need to employ Time Series Analysis. Once the problem statement is identified, the next step is to decide on the tool which can be leveraged for doing the analysis. By tools, we mean R, Python, etc. The choice depends upon the Data practitioner’s level of comfort. Once the tool has been zeroed down, the Data practitioners download all the libraries that are required to do the analysis. The next step is to gather all the relevant data from all the internal as well as external sources. It is possible that the data sets could have duplicities and may need to be scrubbed and cleaned for further analysis. Once the data sets are analytics-ready, the next step is to do the exploratory analysis. For this, the data points are first represented on a graph. Any time-series data, by its basic nature, will have spikes and drops in its graph plots. It is essential to employ the exponential smoothing process to cut out the noise. In some instances, employing it just one time may not effective, and it may need to be carried out more than once to smoothen it effectively. Post that, one needs to employ the ARIMA, SARIMA model. This step includes defining the parameters and generating a list of possible combinations. SARIMA can be used to train the system to find out the best possible combination and the model. By leveraging this model, one can find out how effective it is to predict the numbers. If it lies within a designated tolerance limit, it is leveraged for future prediction and forecasting. Examples where Time Series Analytics is Employed When data changes over some time, it can be used to predict the future. Some real-world use cases of this include, Field of economics and share market The government agencies employ social scientists to follow the population’s growth data, school enrollment data, etc. Healthcare organizations need to observe data about seasonal diseases over a period of time to effectively and accurately predict the spread of the diseases and be better prepared to handle those. The field of life sciences is waking up to this phenomenon. Blood pressure, sugar data collected via wearables over a period of time help companies evaluate the efficacy of the drugs and their dosages. Consumers also show seasonality and cyclical nature in their buying patterns. By understanding the time-series aspect of it, organizations can plan better for the future. With their understanding of how much time it takes to manufacture a particular product and how its demand is going to vary over time, they can better plan the production and operation. This also has a severe impact on various aspects, such as inventory planning, distribution, supply chain, etc. have severe implications. Is your enterprise ready to leverage the power of data to predict the future?

The use of the term data science has become very common nowadays, but what does it exactly mean? What is the future of data scientists? What is the scope of work for transitioning to a data scientist? Is everything rumored around the glamour of becoming a data scientist true? Let us get insights into these questions eventually. First, let us see what data science is. As the world entered the era of big data, more and more organizations have started processing and analyzing the data at their disposal to derive meaningful insights. The discovery of hidden patterns from raw data is achieved with a blend of various tools, algorithms, and machine learning principles. This entire process of deriving meaningful insights from raw data is called Data Science. This work is done by data scientists, and as more and more industries are changing to data-driven results, the value of data scientists is also increasing as they are the ones who know how to tease actionable insights out of gigabytes of data. It is becoming more common by the day that there is considerable value in processing and analyzing data – and that is the reason data scientists are in the spotlight. We often hear how science is a cool industry, and how data scientists are like modern-day superheroes, but most of us are still unaware of the value a data scientist holds in an organization. Various organizations have already put data science in action. It is helping them provide personalized customer experience by understanding the audiences at a granular level. It is helping them find when and where their products sell best. It is helping them in product and business model innovations. And it is also helping them in mitigating risks and frauds. As more and more organizations are shifting to data-driven results. The shift in the outlook of organizations has created a huge demand for data scientists, and hence several students and professionals want to shift into a data science career. As with anything new, several myths are bubbling around making a career in data science. Let us see if the transition to becoming a data scientist is smooth or filled with hurdles. Myth 1: You need a Ph. D. degree to become a Data Scientist It is a common belief that it is mandatory to have a Ph.D. to become a data scientist, or a full-time data science degree is a must for making the transition to data scientist. With the amount of interest, data science started developing in the past few years. People assumed that the best way to distinguish themselves as better suited for data science roles is by getting these degrees advertised by institutes. This is a myth created by the educational institutes to run their business. In the vast and complex area like data science, practical experience is the key. There are numerous problems out there that can be picked up and solved by various data science techniques. There is plenty of online material available for study, which can help you transition to becoming a data scientist. Myth 2: Previous experience will automatically translate to data science role This is a myth because there are two aspects to this story. Firstly, if you are changing your role entirely from, say, being a developer or analyst to a data scientist role, then your experience is counted as zero. When given real-world data for that role, you don’t have any experience of how it works and leads to failure. Secondly, if you have some domain knowledge and switch to data science, then you already know about the nuisances of that domain. That will be factored in as an experience and help achieve a better role and salary as a data scientist. Myth 3: Data Science is for Geeks with a degree in Computer Science, Statistics, or Mathematics Most of the folks that you come across as data scientists will either be from Mathematics, statistics, or programming background, but that does not mean that people coming from different backgrounds cannot be a data scientist. The only difference is a person from a background other than the ones above might have to work harder during the initial phase of career and learn everything from scratch. Organizations now are also warming up to the concept of Citizen Data Scientists where domain experts are provided with easy to use tools and are recognized as data scientists delivering business insights. Myth 4: Developing expertise with a specific technology is enough to become a successful data scientist The myth is that being able to write code using NumPy, sci-kit-learn, etc. should be enough to call yourself an expert. That is not true as data science needs a combination of multiple skills, and programming is just one of them. They should have other skills like problem-solving, structured thinking, communicating the right problem, and understanding the right data. Myth 5: Participating in various hackathons and contests (and not working on real-world projects) is enough to be a good data scientist Data science competitions and hackathons are excellent starting points for the career journey. Hackathons and competitions in data science have increased multi-fold in the last few years, and individuals who are aspiring for data science roles are putting these in their resume considering them as good as working in real-life projects.  This concept is a myth, and recruiters have started paying lesser attention to this aspect of the resume. This is because working with real data science projects is way different than working on hackathon projects. For example, in competitions, you will work with spotless data, whereas; in real-world projects, you will have to deal with unclean and unstructured raw data that needs to be processed to make it usable. Anyone can become a data scientist with the right guidance, training, and technology expertise. It is important to not go after the make-belief myths around it. Data Science has a long way to go, and it is good to see that educational institutions around

Almost every organization, government or otherwise, is going through a digital transformation journey. Many organizations are at different points in their maturity models. Digital Transformation enables optimal resource utilization as well as increased customer satisfaction. For instance, sectors like automobile have found out avenues to monetize the data on driving patterns by selling them to insurance companies. Essentially, data is opening up new revenue generation avenues for companies. Gut instinct-based decision making is a thing of the past now, and every major strategic decision is data-driven. In such a scenario, data becomes a priceless organizational asset. 90 percent of the data available worldwide has been generated in the last two years. If the analysts are to be believed, then the revenue of any company can be increased by 66 percent by following best data quality and analysis practices. But before embarking on the journey of being data-driven, one has to understand the key building blocks of a strong foundation. Data – Data has already been categorized as an organizational asset used for making a strategic decision. As per Gartner, by 2025, for 90% of the organizations, data will guide business decisions. Already 69 percent of the organizations are using data from newer sources to understand their market. 64% of organizations have started using predictive and other forms of advanced analytics in their business. 67% are exploring new types of analytics to consume the data they have with them. Analytics – The most common types of analytics include descriptive, prescriptive, and predictive analytics. Descriptive includes day to day reporting or operational reporting. Prescriptive analytics helps in coming up with recommendations. Predictive, as the name suggests, helps in predicting the future so that the organizations can reap the maximum benefits out of it. Technology – Storage, collection, processing of this huge amount of data is being made possible due to the huge strides we have made in technology. There are tools like Python, Spark, Power BI, which enable us to collate, process, store, and visualize data. People – This is one crucial aspect, as people resources and skill form the basis of any significant shift. Becoming data ready requires Data Scientists, Domain experts, and Technology experts. One has to be sure that these relevant skill sets are on board. There is a certain amount of cultural change which has to be brought in and is the result of getting the buy-in of the folks who will be a part of the job day in and day out. Organizations need to understand that this is a team effort, and not one single individual can change the organizational leanings in overnight. Processes – The whole value chain has to become data-centric. Elaborate and a foolproof method of collecting and processing data has to be set up. Data quality is of utmost importance, and the role of CDO has to be created who can ensure that there is a certain methodology in place to ensure sanity in the data process. Strategy and Vision – What does the company want to achieve in the long run? The strategy and vision of an organization could vary across a broad spectrum of activities. It might want to reduce costs and become leaner. Whereas on the other hand, it might want to reach out to newer markets or develop newer products. Recently companies have also started exploring newer avenues of revenue generation. Depending on the needs, the data attributes being captured, and the metrics being measured change. Operating Model – To succeed in the quest of becoming data-driven, one must understand that a robust target operating model should be set up. From the data office who will be interacting with whom and collecting what data and who will be responsible for the data quality – everything becomes extremely important for the smooth steady-state running of the operations A strong data-driven culture, availability of the right data for analysis across the value chain, easy to use tools that can allow quick slicing and dicing of data, and more awareness around data-driven decision-making are some of the key factors for the success of data-driven enterprises. Where are you on your data journey?

The phenomenon known as big data has been growing exponentially. Reports suggest that, by 2020, there will be around 40 trillion gigabytes of data, and the big data analytics market is set to reach $103 billion by 2023! This has been further fueled by the fact that it has become extremely economical to store and analyze petabyte & zettabyte sized data. Once the data is stored and analyzed, organizations need someone to crunch the numbers and provide actionable insights on that. This is where Data Science comes into the picture. With the growing amount of data, the need for qualified data scientists is also growing. No wonder, Data Scientist has been termed as the sexiest job of the 21st century. The demand for the role of Data Scientist is only going to increase from here on, and if various research reports are anything to go by, then we already have a shortage of Data Science talent. The demand for the skill has far surpassed the supply of the same. The growing interest in the field also fuels a lot of misconceptions. While there is already a lot of literature about what Data Science really means, in this blog, we will take a look at what Data Science is not. Data Science is NOT AI/ ML Quite often, the terms data science, artificial intelligence, and machine learning are used interchangeably. However, they are not the same. We do agree on the fact that there is a lot of overlap amongst the three. Data Science is more of a general field where we require a lot of human intelligence to work in tandem with the machines. Use cases to emphasize on this case in point are fraud detection, churn prediction, etc. AI and ML become more niche and are used for repetitive tasks, which, as of now, do not require human intervention. AI use cases like chatbots, and ML use cases like recommendation engines can throw light on the difference. Data Science is NOT a silver bullet Organizations often make a mistake of thinking that Data Science is the panacea to all the problems they are facing. Like what Simon Sinek said, one needs to first understand “why”. Before starting their data science initiatives, organizations need to first define their business case and the problem they plan to solve using data science. This definition then helps in defining the data attributes to be collected and the analysis of the same. Without a clear problem statement, investments in skills, domain knowledge, and top management involvement, there are high chances it will end up being another failed initiative. Data Science is NOT just about Visualization Visualization of data, powered through tools such as Power BI or Tableau, is all good, but it should aid the process of decision-making, and only then it can have a direct bearing on the bottom-line. Data Science, by its very meaning, implies that human intelligence becomes imperative for drawing insights from them. Visualization aids that, but data science is not only about visualization. Visualization is about reporting of the various data points available with the organization. However, it cannot be either predictive or prescriptive without the Data Scientist with domain understanding adding value. Data Science is NOT just R or Python There is a common perception in the market that people with expertise in languages such as Python, R, SPSS, etc. are data scientists. This is very far from the truth. These languages are merely tools for doing data analysis. But one needs to know the problem statement to know which algorithm, model, or which methodology will best suit the purpose. For example, for an OOT provider, Data Scientist is supposed to draw a decision tree to understand what the customers are watching on an OTT channel, create clusters to understand the demographics and do a regression to understand the causal effect relationship. Data Science is NOT only about Data Data ingestion plays a significant role in data science. To get a complete view, organizations need to analyze data available internally, and data also needs to be collected from sources outside the organization. It is possible that the internal data is lying in siloes across various systems such as ERP, CRM, etc. For a complete analysis, all the data needs to be collected and reconciled. But like all the others, this is a crucial subset of Data Science but not data science per se. Based on the end goal and what analysis needs to be conducted, the tools, as well as the data needed for the tools, will change. One needs to acknowledge that Data Management is a huge ask. There are several tools available in the market that can help organizations manage their data. This includes cleansing, scrubbing, de-duplication of the data. Data management is essential for data science and is an integral part of the process, but needless to say, this is not data science in its entirety. Data Scientists are aided by the quality of the data, which is enhanced through robust data management. Data Science is NOT Statistics An ace Data Scientist needs to possess three critical skills – 1) Strong programming knowledge and skills to run the models 2) Understanding of statistics to run the correct statistical tools, and 3) Strong domain knowledge to come up with the correct inference. Many believe that knowledge and understanding of any one of these skills can make you a Data Scientist, which is not valid.   How many such myths did you believe about data science?

Love it or hate it, the fact is that data is gold. As Geoffrey Moore likes to put it, “Without big data, you are blind and deaf and in the middle of a freeway.” And you wouldn’t want to be there, especially when data is fueling businesses in ways more than ever. Qualitative data helps drive businesses in ways more than one. In the hyper-connected world of digital devices and systems, if you are not leveraging data for business, you are lagging the game for no good reason. Also read: Data is NOT Oil – Data Is the New Soil In this article, we will discuss how data-powered business models are carving their way into the future. Data-powered business models: Overview A data-powered business model is the one where businesses utilize data to improve processes, make better business decisions, offer more personalized customer service, design the revenue model based on data, and/or offer products or services completely dependent on data. Data-powered business models are different from the ones that use data in order to accomplish a part of the business process. The data-driven business models make active use of data to make decisions in place of emotions or instincts. Every business leader hunts for data-centric insights to improve the business outcomes, and going the data-powered way seems like a wise choice. Top benefits of having a data-driven business model include: Optimized processes and internal communication Improved workflows for improved efficiency Increased sales and customer retention Enhanced the customer experience with more personalized recommendations Businesses that unlock the true potential of data and place it in the center of the business strategy can be truly termed as a data-powered business. Let us have a look at a top few examples of data-powered business models that are future-leaning and future-ready. Smart Cities Sustainable, environment-friendly, and hyper-connected smart cities are in vogue. Smart cities leverage IoT sensors to collect data from various sources and utilize the data to drive systems within the city. The objectives can be multi-fold, such as improving the energy efficiency and consumption or revamping operations across the city – from traffic management to town planning, waste management, to crime detection. Data can be incremental in planning a smart city for estimating the inflow on public transport, helping to decongest the cities, identifying areas that require more public health penetration with AI-based prediction, and enhancing the overall governance in the city. Connected Cars Connected cars are cars (and other vehicles like buses) that can connect to the other cards or vehicles on the road enabling other cars to share data via IoT devices. More importantly, connected cars usher in driver safety, security, ease of navigation for a simple day-to-day problem, including finding a parking spot. Connected cars generate extensive data about the car performance, speed, and door locking logs, which make it a heavily data-driven and data-dependent model that is as futuristic as it can get. OTT (Over-The-Top Media Services) OTT media services or video streaming is one of the most rapidly expanding entertainment channels at the moment. The OTT services such as Netflix make active use of customer viewing data to predict customer preferences based on their demographics as well as geographies. Making use of this data, the OTT media service providers can improve the customer experience, offering them exactly what they’d like to view instead of exploring a sea of content and being overwhelmed. Netflix for instance, not only collects viewing data but also the ratings, view times, and user ratings to curate a more customized viewing experience as well as onboarding content types that truly sell. Also Read: How Big Data and Analytics Can Transform the World of OTT Subscription-based retail and fashion (eCommerce personalization) Renting out, sharing economy, personalized economy or subscription economy is gaining rapid traction, especially in the developed countries. According to a study,  48% of participants prefer using subscription clothing where experts curate and deliver items based on previous purchases. Clearly, utilizing data for driving personalized recommendations is on the surge in the retail industry. The subscription-based retail businesses work on data-driven models to understand customer preferences and curate unique experiences for them. In fact, data-powered retail has resulted in a replenishment economy where products are intelligently shipped to the customer right before they are replenished, studying vast amounts of data from previous purchase cycles. Dynamic learning and education The educational reward and ranking system have been conventionally limited to the exam performance, assignments, and projects. But over the course of spending years in schools, students exhibit several qualities that are not accurately demonstrated or judged in traditional exam systems. By making use of big data, student achievements (academic or otherwise) can be mapped throughout to create a more holistic and diverse student portfolio. It also helps educators to determine the hurdles in understanding, challenges in learning, and other behavioral traits in students. Big data can be used to reduce student dropouts through prediction analysis. Teachers can study patterns of previous dropouts and provide assistance before it’s too late. Data-driven educational models can be incremental in offering customized programs to improve learning. To summarize… Data-driven businesses and process optimization is a change the world appreciates. But without identifying the right tech, creating a data-driven mindset, overcoming silo thinking, and addressing privacy concerns, the journey can be futile. Approaching with an agile mindset with modern tech and compliance can help data-powered business models to thrive.