4 Reasons Why You Should Invest in Big Data Analytics

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In 2018 alone there is expected to be an estimated shortfall of over 200,000 data analysts and data scientists in India. This means the opportunity to invest and grow in the data analytics field has never been greater.

But beyond helping to ease the lack of skilled data scientists, what other skills does a specialization in big data analytics offer? What other reasons do companies have for focusing on big data? Let’s take a closer look:

Big Data Will Be Instrumental to Today’s In-Demand Tech Careers

From software engineering to IT consultants, all aspects of the tech sector have seen a surge in the use and analysis of big data. Worldwide, we create roughly 2.5 quintillion bytes of data a day. From the GPS that’s collecting information on your route to work every morning to the tweets you send on Twitter at lunch, to the text messages, weather updates and sports score you check, all of it is being collected.

But simply collecting the data is not enough. Computers and networks are great at storing and sifting through ones and zeros, but a deeper, human understanding of that data and how it factors into overall company initiatives are where the need is most pressing.

In order to play a pivotal role in this burgeoning field, top technology industries will require data scientists, engineers, and architects to build and analyze the information collected, so that they, in turn, can make confident decisions that move their respective companies forward.

Data Accuracy and Data-Driven Decisions are Imperative in Today’s Workforce

These days, companies are extremely reluctant to make decisions without core data backing them up. Too many campaigns have been derailed and brands sullied by knee-jerk reactions fueled by emotion and “what we think people want”, instead of logic.

And while think-tanks, brainstorming sessions, and focus groups are beneficial, they also don’t present the raw confidence and practicality that data can.

The brilliant thing about data is that data doesn’t lie. It’s concrete, proven and powerful. In the right hands, data can do everything from illustrating the customer journey to improving the overall efficiency of bringing a new product or service to market.

For this reason, investing in an education that teaches the intricacies and complexities of big data analytics is crucial to a company’s overall success and growth. Companies who don’t take the initiative in embracing data-driven decisions are simply guessing — and guessing is a shaky infrastructure to build a profitable company on.

Smart CEOs and startups alike know this and are hungry for skilled employees who not only understand the many facets of the data being collected but know how to leverage it for maximum impact across all sectors of the company’s growth and overall stability.

Understanding Big Data Goes Beyond Marketing and Technology

When most people think of big data analytics, they understandably think of its use in information technology and marketing. Knowing what actions people are taking based on the information you collect about them are vital to creating campaigns and promotions that are relevant to them.

But big data is much more than just a marketing tool. Handled correctly, it can be used by sales to improve customer retention or by accounting to help decrease expenses and improve profitability.

The Harvard Business Review recently polled Fortune 1000 executives to determine not only how they’re using data, but where they’re getting the most value from it. Their findings are as follows:

As this is only a sampling of the overall market potential for big data, it’s easy to see how smart companies are taking steps to not only integrate data analytics, data architecture, and data engineering into their overall operations but also creating a company-wide culture that is fueled by big data findings and analysis — so that all roles in the company can take the initiative to learn from the data at hand.

Growth in Big Data Sectors Shows No Signs of Slowing Down

Of course, the formation of a data-driven culture doesn’t happen overnight, and even in areas where companies have started data-driven initiatives and not seen value yet doesn’t mean that there won’t be a payoff for their efforts in the future.

Data is continually being collected, collated and consumed. If anything, the plethora of mobile devices, kiosks and the Internet of Things are enabling even greater data consumption and collection.

The sooner companies are on board with investing in data-driven analytics, the sooner they can begin to glean insights and enjoy “quick-wins” that further motivate and push the boundaries of what’s possible with the information they’re collecting.

Those companies that neglect to invest in big data analytics do so at the risk of their own market growth and loss of their competitive advantage. For individuals with the foresight to see the strategic maneuvering of today’s top companies, there’s little doubt that having the right people with the right skills is pivotal to their success.

10 Facts About Cloud Computing to Make You Look Smart Around the Water Cooler

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Are you planning a start-up offering a SaaS in analytics? Maybe you head the technology division and are toying with the idea of using a hybrid cloud solution, or maybe you were caught asking “what cloud”! Whatever be the case, you will surely like to know more about Cloud Computing, the technology that is growing at the fastest pace among all others.

Here are 10 Cloud Computing facts you should know about.

1. Cloud Computing is just that, computing in the cloud!

Hey, if you are wondering how did cloud in the skies pop up in technology, no worries. Cloud Computing simply refers to the use of the internet to store data on remote servers. Instead of traditional storage of data locally on your hard disks or flash drives, you now store data on the cloud. Just as you store documents on Google drive or store emails and company correspondence with your company email provider.

2. Did you know spending on the Cloud is HUGE and rising at a rapid rate? 

It is projected that by 2019, worldwide spending in Cloud will exceed $141 billion. From large multinationals, IT companies, SMEs to individuals, every element of the work and personal spectrum touches upon the cloud. It is there in everyday life, in a 24/7 connected world, it is there for work communication, transport or logistics. Every work or industry you can think of uses the services of cloud in some forms or the other, and most likely in multiple avatars.

As businesses realize the value storing data in the cloud, for any time, everywhere access; investments in cloud technology are increasing at high rates.

3. Why is Cloud technology so “hot”?

It is the most secure way of data storage. You can access your data at a moment’s notice from any location in the world. Besides, if you use the benefits of cloud storage to store important data, it can never get lost. You can access the same even if your laptop has gone “hung” on you or you left the file back at home. Cloud computing also allows collaboration, with edits and changes by a disparate workforce, racing to complete a project on time. It is scalable to suits your needs, supports disaster recovery and cuts down hardware costs. What more could you ask?

4. It’s Public, Private and Hybrid, all at the same time.

There are three types of cloud technologies – Public Cloud, Private Cloud and Hybrid Cloud. As the term suggests, Public Cloud data is open to all, while Private Cloud has restricted access where the secure cloud environment can be operated only by the specified client. The Hybrid Cloud is an integration of on-premise, private cloud /third-party and public cloud services. Here, IT is based on-site, with storage and computation of secure data in the cloud.

5. Cloud is not all about storage, it offers services too!

The Cloud stores global data and offers service too. The 3 services offered by Cloud Computing for businesses are

• Platform as a Service (PaaS)

• Infrastructure as a Service (IaaS)

• Software as a Service (SaaS)

PaaS allows you to start your app building with little money as the PaaS clouds have robust development. IaaS allows an internet business to develop instantly and scale-up. SaaS takes the cake of all Cloud technologies! It allows the cloud software architecture to be used by the vendor while cutting costs of maintenance, as the application runs on the vending machines.

6. Spending on Cloud Computing grows at rates much higher than IT

Of all cloud solutions, SaaS logs the largest block of global spend between $25 and $40 billion in global cloud spend. In India, while cloud services have grows at a CAGR of 33.2 percent, SaaS alone logged 34.4 and IaaS 39.8 percent. This phenomenal growth in cloud services is driven by new IT computing products deployed using cloud models.

7. Cloud talent has been unable to keep up with Cloud growth

Now, this is something that is going to grab your attention. One of the biggest challenge for the Cloud today is a lack of expertise. According to a study by Right Scale, 32% of respondents said their IT departments were poorly equipped to handle the growing workloads in the cloud. What IT chiefs want is a growing workforce who is an expert in cloud Technologies, or at least has covered the basics in a Could computing course.

8. Did you know that server deployment is directly proportional to smart devices?

It does sound weird, but it is true. According to stats, for every 1,200 smartphones / 600 tablets booted up, the cloud adds an additional 1 – 5 servers. By the end of 2019 global smartphone use is expected to rise to over 2.6 billion. So where do you think so many servers will be stored? Why don’t you throw this question around and see how varied and interesting the answers can be?

9. Hang on, here comes 5G!

As the amount of data generated and stored grows rapidly, consumers expectation of faster connections from network providers are also increasing. With work on this front already underway, you can expect up-scaling from gigabyte LTE speeds to full 5G networks! Of course, enhanced network quality will better the performance of fast-loading services and apps. Every industry and business will benefit from faster network speeds, and the world will surely be a different place with this Cloud hyper-performance.

10. Security issues will challenge the Cloud

The security of cloud infrastructures has however become a concern with the increased sophistication of cyber attacks by specialized groups and state-sponsored actors. Businesses will need to ramp up their security, deploy malware detection systems, and implement healthy cybersecurity practices. The risks of BYOD (Bring Your Own Device) culture downloading software from unverified cloud services with be a great risk. Cloud services will attempt to fill the role with managed security service providers offering end-to-end complete security.


You don’t need to be a techie to understand the Cloud, but be Cloud-savvy so you can recognize the potentials of Cloud as a long-term investment opportunity. As an analytics or IT professional, what can you do? Rack up your Cloud Computing knowledge so the next time around, when there is talk around the office water cooler, you sure can pull a crowd.

How Artificial Intelligence is Going to Affect the Financial Industry in 2018?

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Give this a thought: Could the 2008-10 recession in the US been prevented if they could forecast the stock market,  predict risks or detect frauds using machine learning and artificial intelligence? The answer lies in the ability of machines to perform diverse, intelligent tasks for us in the form of machine learning and artificial intelligence.

But how do machine learning and artificial intelligence impact the financial industry? The International Data Corporation (IDC) has predicted AI revenues to surge past $47 billion in 2020 and is poised to become the most important technology in the financial sector in India. Interestingly, Prime Minister Narendra Modi on February 18, stated that with AI, bots, and robots, productivity will increase. He emphasised that AI should be “Made in India” and “Made to Work for India,” thus making a strong case for professionals who want to upskill via machine learning certification.

This is set to give a massive boost to careers in artificial intelligence and machine learning. People trained in different branches of science,  mathematics or just a degree in technical engineering, can take up a machine learning course to gain a valuable machine learning certification.

The financial industry in India or the Banking, Financial Services and Insurance (BFSI) sector in India is a fast-evolving one. How then, do banks and associated organisations save time, costs and yet add value to their operations for smooth functioning? In India, Artificial Intelligence (AI) has begun to play a major role in solving some of the most vital problems faced by both companies as well as customers.  Not just banks, but nearly every company whether public or private in BFSI has started using AI.

Let’s explore how machine learning and artificial intelligence will impact the financial industry in 2018.

Advisory: Robo-advisory at fingertips

Advisory is a critical component of the BFSI sector. A key trend in 2018 will be the rise of robo-advisory by wealth advisers. Once limited to the affluent, AI in the form of robo-advisers will now reach the masses. 5nance.com, a finance management firm already uses a robo-advisory platform. These digital platforms will now act as personalised financial advisers. They come with a range of benefits: continuous market monitoring and 24/7 access.  Professionals with a machine learning certification can easily work towards developing such tech. Today, a good, standardised training will combine a machine learning course and an artificial intelligence course and include robotics as an integral part of its curriculum.

Risk Management and Fraud Detection: AI saving millions in costs

Globally, AI has now become synonymous with automated fraud detection. Abnormalities in patterns can now be easily determined and organisations can smartly undertake fraud prediction. AI and machine learning can improve real-time approval accuracy and overall improve general regulatory compliance. Financial organisations can then be more efficient and accurate in their processes, besides saving immense cost to financial institutions. Hiring professionals with either a machine learning certification or artificial intelligence course will be in great demand, particularly in risk management.

Trading: Sophisticated, high-tech trading that rules the world’s markets

With India’s surging economy, technology in data science machine learning makes trading relatively experience for those heavily-vested in the area. Artificial Intelligence has the ability to augment rules, help take key trading decisions and process valuable data. Startups in India such as AccuraCap use a mathematical model that is based on Big Data Analytics and Artificial Intelligence. Many such fund management firms in India have implemented similar trading algorithms, that are based on critical insights from public sources. Coursework in most machine learning certification helps ideate on similar such applications of machine learning and artificial intelligence.

Customer Experience: AI’s story of incredible support and guidance

With finance at the core of the world’s economy, customers will be centric to every activity. Artificial Intelligence and its applications in customer services will only serve to stabilise the BFSI sector. In India, while still a majority of support today is manual, a lot of this will shift to automation. Any investment in artificial intelligence and machine learning will only increase customer base and make it attractive for customers to subscribe to those using AI. From recommendations on savings to the analysis of expenditure – AI will encourage banks to build products that serve basic customer needs with minimum human-to-human interaction. For banks themselves, AI has the ability to provide information about products and services, profit margins, and costs. Thus, a solid support environment based on AI can reduce risks while improving results.  A case in point is the use of chatbots used by banks to serve customers more efficiently, combining machine learning with AI. With the help of machine learning certification,  it’s also possible to develop models to analyse complex data to return accurate results.

Extensive funding in AI has further strengthened opportunities. In India, government-backed AI funding is already fuelling innovation in existing tech firms. Private firms are investing in smaller startups and academic communities are thriving on diverse aspects of AI in the financial sector. Interestingly, a report by Accenture has forecasted that Artificial Intelligence could add $957 billion to the Indian economy. This also denotes a growth in AI courses across Indian cities, where thousands of aspirants are already registering for an artificial intelligence course.

Good times for job-seekers

For job seekers, these trends signify immense potential for full-fledged careers, including specialisations. An artificial intelligence course, that is also available to pursue online is going to be in great demand. To make a career in machine learning or artificial intelligence, the previous background does not matter. Anyone can simply sign up for a machine learning course, whether you are from a multidisciplinary background or from the STEAM (science, technology, engineering, arts, and mathematics) sectors. Career growth and opportunity after you learn machine learning are plenty. Majority of jobs in data science machine learning require aspirants to be innovative in an industry that is poised for success.

R vs Python

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Do you enjoy statistics and programming? Are you currently studying math and statistical learning (such as machine learning)? Are you excited to learn the latest technologies and techniques in data science? If your answers are more than just one yes, your career path may take you to data analysis very soon.

Many students in programming and statistics can find a very remunerative career in data science, as it is an ever-growing field with a lot of potential. But right from the start, you have to ask yourself how you are going to approach this discipline and how you are going to tackle the programming challenges ahead of you. And here comes the question that all data scientists had to answer at the very beginning of their careers: should I learn Python or R programming to start working on data analysis?

This is a tough question since Python and R are both versatile programming languages in data statistics. They were born in the same period (the late 80s or the start of the 90s) and both have proven themselves as very useful tools in data mining. Here you can find the pros and cons of using the two languages, so you may decide which one best suits your needs.

Python was developed to offer a way to write scripts to automate some of the routine tasks encountered on a daily basis. However, as time went by, Python has evolved and become quite useful in many other fields, especially data analysis.
On the other hand, R is a programming language as well as an open source software for both graphics and data analytics. It has the advantage of running on any computer system and is used by data miners and statisticians for both presentation and analysis of their data.

Python vs R Programming for Data Analysis

It is a common challenge for a data scientist to decide whether to use Python or R for data analysis. While R was purely developed for statisticians, making it portray analysis a specific advantage for visualizing data, Python stands out with its general-purpose characteristics and the fact that it has a very regular syntax. Based on these differences, it is necessary to compare the two languages to determine which one suits them best.

Python Programming Language

  • – Python programming language was inspired by Modula-3, ABC and C languages
  • – Python focuses on code readability and productivity
  • – It is easier to develop code and debug because of its easy-to-use and simple syntax
  • – Code indentation affects its meaning
  • – All pieces of functionality are often written in the same style
  • – Python is very flexible and can also be used in web scripting.
  • – It has a relatively gradual and low learning curve for it focuses on simplicity and readability
  • – Suitable for those beginning to program
  • – Its Package index is called PyPi. Its Python’s software repository with libraries. Although users have the option of contributing to Pypi. It is difficult in practice.
  • – RPy2 is the library which can be used within Python to run R code. Used in providing a low level to R from Python.
  • – In 2014, Dice Tech Salary Survey showed the average salary of an experienced expert was $94139
  • – It is mainly applied when there is a need for integrating the data analyzed with a web application or the statistics is to be used in a database production
  • – The capability to handle data was a challenge for it in the past although it has improved, this was due to its package infancy in data handling
  • – You must use tools like pandas and NumPy to enable it to be used for data analysis
  • – IDEs available include Spyder, IPython Notebook.

R Programming

  • – S programming language inspired R.
  • – Emphasizes on data analysis methods, graphical models and statistics that are user-friendly.
  • – It is slightly hard to use since statistical models are only written using few lines.
  • – There exist R stylesheets, although they are rarely used
  • – There are many ways of representing or writing the same functionality piece.
  • – Offers the ease of using complex R formulas. For its many statistical models and tests.
  • – Has a learning curve that is steep at the beginning when learning the basics. But it becomes very easy to learn advanced topics later on
  • – Not very hard for expert programmers.
  • – Comprehensive R Archive Network (CRAN). CRAN is the R repository package that is easily contributed to by the users.
  • – The rpython package is used from R to run Python code.Call Python methods or functions and for getting data.
  • – In 2014, Dice Tech Salary Survey showed the average salary of an experienced expert was $115 531
  • – Mainly applied when the analysis requires independent computing or individual servers.
  • – Easier when used for a critical task for beginners. Employs few code lines to write statistical methods.
  • – Ideal for handling data from its large package number. Usable tests and the use of formulas.
  • – R does not require additional packages for basic analysis. It only requires packages like dplyr for big datasets.
  • – Uses R studio IDE

Analysis done by KDnuggets polls in 2014 for Python vs. R used together showed that:
R Programming = 58%
Python Programming = 42%
Python + R Programming = 23.45%

Python – Pros

  1. The IPython Notebook facilitates and makes it easy to work with Python and data. This is from the fact that you can share notebooks with other people without necessarily telling them to install anything. Which reduces code organizing overhead, hence allowing one to focus on doing other useful work.
  2. Given that it is a general-purpose language, it is intuitive and simple. It enables a data scientist with a flat learning curve which in turn allows him to increase his program writing skills. Python also has an inbuilt framework for testing which encourages improved test coverage, which in turn is a guarantee of one’s code being dependable and reusable
  3. It is a multi-purpose programming language bringing together people with various backgrounds, that is, statisticians and programmers.

Python – Cons

  1. Visualization is a crucial factor when determining the data analysis software to use. Python offers several libraries for visualization like Boken, Pygal, and Seaborn which may, in turn, be too many to pick. And unlike R, its visualizations are convoluted and not attractive to look.
  2. Python is just an R Challenger and doesn’t substitute the many R packages that are essential.

R – Pros

  1. R offers clear visualization of data, making the data efficiently designed and understood. Examples of its visualization packages are ggvis, ggplot2, rChart, and googleVis.
  2. R has a broad ecosystem of active community and desirable packages. The packages are available at Github, BioConductor, and CRAN.
  3. It was developed, for statisticians, by statisticians. Hence, they can communicate concepts and ideas through R packages and code.

R – Cons

  1. If you compare the speed of Python vs R, R is slow because of its code that is poorly written. Packages that can improve its performance include Renjin, PQR, FastR.
  2. R has a very steep non-trivial learning curve. Especially if you have a graphical user interface (GUI) background that were used for statistical analysis. Finding simple utilities and packages can be very hard.

It is clear that both the languages have their own advantages and disadvantages and it depends on your personal preferences to pick one that will solve your problems.

6 Ways Emotion AI is Changing Our Lives

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It’s a Wednesday, 11 am.
You and your best friend are just about to start brunch at your favorite restaurant.
Wait. Brunch in the middle of the week? Yes, but I’ll come back to that.
You are chatting frenetically.
Your shoulders are down, and in between bites, you look at your friend with eyes full of tears.
You and your boyfriend broke up last week.
She feels your pain and knows what can cheer you up.
This definitely calls for a meal at your favorite restaurant. So what if it’s expensive? If this isn’t an emergency, then I don’t know what is!

So basically, your best friend is someone who takes charge of everything, sorts out the mess and gives the only most practical solution.
Now, if only there was an application on your smartphone that could understand you as well as your best friend does!

What if, in the near future, there could actually be more than just an app that could interpret your feelings and emotions with one single swipe?

That’s where the future of Artificial Intelligence (or AI) is headed to, alongside human Emotional Intelligence.

Most of our communication consists of body language and verbal signals; signals that leave behind a ton of data that still has to be assessed by software and applications to understand and build a better customer experience. Our moods and our emotions speak more than a thousand words. And as you read this, many companies and startups are already heading towards this field to change the way we interact with the world, for good.

And here are 5 ways in which Artificial Emotional Intelligence is impacting our lives right now.

1. How Do You Feel Today?

AI technology is a field that is rapidly growing thanks to new devices that are taking their places in people’s homes, but what about their implementation in hospitals and in the healthcare system?

AI, supported by Emotional Intelligence, would be capable to understand vocal cues, and these devices would be able to highlight depression in a person or even detect chronic issues like heart disease.

Right now in Israel, a startup named Beyond Verbal is developing analytics tools aimed to dig data from behavioral and vocal patterns.

CEO Yuval Mor expressed his thoughts on the matter: “In the not so far future, our aim is to add vocal biomarker analysis to our feature set enabling virtual private assistants to analyze your voice for specific health conditions.”

In a not so distant future, hospitals could bank on the tools provided by Emotion AI as they evolve to tackle many different scenarios and uses.

For example, voice-enabled virtual assistants may become a crucial part in a clinician’s diagnosis. These smart devices may mark a new way to engage with patients and offer a better experience during the patient’s journey during his or her time spent in healthcare.

2. A New Frontier in the Game Industry

Another incredible and exciting improvement Emotion AI could bring to the table is ready for the gaming industry for the taking. Although there aren’t many mainstream players among game developers that are tackling this AI branch, many important studies are taking place as we speak to develop a whole new gaming experience for the players

James Ryan is a Ph.D. student at UC Santa Cruz, working in the Expressive Intelligence Studio. He’s leading a team of experts that are working on Talk of the Town, an AI platform created to bring to life interactive experiences featuring intelligent characters who have ongoing personalities encompassing emotions, beliefs, memories, and relationships.

“There are two core AI problems that Talk of the Town is tackling,” says Ryan. “How do you support autonomous characters who have ongoing subjective experience of the game world, and how do you support unconstrained conversational interaction between player and NPCs? We have systems that decide how people go about their daily routines, and how the various subjective phenomena should be triggered over the course of a character’s day – things like forming, propagating, misremembering knowledge or memories, and forming or evolving relationships.”

Another company to keep your eyes on is Mobius AI. Founded in 2015 by a team of video game veterans and AI researchers, including Aaron Reed, the company is developing an AI platform specifically for the mainstream games industry.

“We’re working on a project to provide game developers with a social AI engine that can power game characters with greater knowledge about the world and the ability to react and perform in dynamic situations, which has never been available before in a complete package like this.”

No matter what the technology is involved, Reed is looking forward a new gaming scenario where we’ll see the birth of new types of game – romantic comedies, character studies, coming-of-age stories.

3.Virtual Personal Assistants (VPAs)

Another sector that seems a natural fit for Emotional AI is the increasing industry of VPAs such as Apple’s Siri, Microsoft’s Cortana and Google Assistant. Devices available today employ natural-language processing and natural-language understanding to interact with their owners. And yet they still do not possess the ability to understand and respond to users’ emotional states. What would happen if VPAs devices could detect and interpret our emotional states, as well as our commands? By decoding data from facial expressions, voice intonation, and behavioral patterns, the user experience would be taken to a whole new level. You can already find these features in products such as VPA Hubble.

4. Look Ma, I’m Driving With No Hands!

While talking about cars, Emotional AI will have a big role to play in the self-driving cars industry. Yet, this automotive sector is still taking its first baby steps. In fact, it is mandatory for any self-driving car to be assisted by a human behind the wheel, as a sudden change may occur and a human control should be employed. One may get distracted during the journey or may decide to multitask. Based on the driver’s emotional state or wellbeing, he could be given a friendly suggestion to take control of the car again.

A self-driving car may be able to detect a moody driver and help him calm his nerves. As the ultimate goal of Emotional AI would be to improve customer experience, your next ride may just become a whole new journey altogether.

5. The Moment Your Fridge Suggests You What to Eat!

Recently at CES 2018, Annette Zimmermann, Research Vice President at Gartner said, “By 2022, your personal device will know more about your emotional state than your own family.”

As unbelievable as it may sound, this affirmation should be taken seriously after seeing the revolutionary products showcased at the CES edition. These products, like a smart fridge, may be able to interpret your moods and emotional state, and could, therefore, suggest the best food you may be craving at that very moment. Emotional AI is a stepping stone in the interpretation of the customer experience and buyer journey. As Emotional AI algorithms decode your emotional state, they could improve the choice of products you buy. Everyday objects, enabled by these new algorithms, may collect, analyze and interpret data from people’s feelings like happiness and love, fear and shame, or even anger. In turn, they may respond with the most appropriate buying suggestion or lead the person to the most appropriate choice of activity.

6. May I Have a Sandwich, Please?

So how does Emotional AI impact user experience? One of the bigger companies interested in this branch of AI is Uber. This Silicon Valley-based startup offers as a service to its customers the journey to their destination, and also provides meal delivery. But what if you could personalize the customer experience by adding a touch of emotional intelligence? Uber would stop being just a service; it would become a bilateral interaction. By employing Emotional AI, UberEats’ job would not just shift from delivering a meal, but to suggest the best possible dish to its customer, based on his mood of the moment.

AI will not only be able to create a persona, but it will also be able to create and understand a personality. The road has already been paved, and it is only a matter of time when it works wonders.

4 Ways Big Data is Transforming Supply Chain Management

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Big Data has found a variety of applications in this era, and supply chain was a natural choice. From improving delivery times by synchronizing shipments to identifying better ways to reduce the communication gap between manufacturers and suppliers, today, BDA is working as an evolutionary catalyst for the supply chain management in the following ways:

  1. Consumer Habits and Behaviours
    Most of the big telecom companies such as Vodafone Business Services, Reliance Communications, and Bharti Airtel Ltd. are actively investing in BDA and using it to analyze the usage patterns of their customers. Using the data collected from the analytics systems, they retain their subscribers and increase revenue significantly.
    While Airtel has introduced “data marts” for managing reports and gathering data that can help in the development of new products and consumer retention campaigns, Vodafone is using BDA technology to predict the network growth and plan network expansions efficiently.

  2. Customer Experience
    BDA has been found to be extremely helpful in opening new pathways to a better customer experience. For instance, BloomReach is one of the leading companies in this segment that has been researching and learning what people search for, click and share on the Internet.
    BloobReach offers a subscription model of four BDA-based mobile APIs that businesses can easily integrate into their platforms. One of these, Predictive Search takes into account the previous consumer interactions to predict what they are most likely to search for using just a few characters typed into a search box. Similarly, the Cross-Device Retargeting tracks the customers on different platforms to carry over their preferred experience- mobile, tablets, computers, etc.
    In another example, yatra.com, which is one of the biggest online travel agencies of India is using a combination of both in-house and third-party technologies to better understand the needs of its customers and offer a personalized experience to each.
  3. Streamlined E-Commerce
    Online retailers such as Flipkart or Snapdeal would be foolish to ignore BDA, and they are not. All the big players in this space have their teams of dedicated data scientists and state-of-the-art analytics systems to streamline their supply chain management system.
    For instance, Snapdeal uses a multi-tier system called Hadoop-based farm which is an open source data analytics software. Similarly, Flipkart strongly emphasizes on technology to ensure top-notch supply chain management. Its engineers are encouraged towards developing and improving algorithms for accurate delivery predictability, advanced mobile technology for route optimization, and increased automation in the warehouse, etc.
    In a notable example, when the Diwali festive sale of 2015 led to a 500% traffic growth for Snapdeal, it was already prepared, thanks to BDA. With predictive analytics, it was able to deal with the logistics and handling of massive volumes of concurrent orders with ease.
  4. Exceptional Inventory management
    A small retailer who runs a brick-and-mortar store isn’t really bothered by inventory management due to the small scale of the operation. However, big box retailers and top online stores deal with a sizeable inventory which poses several challenges. This is where BDA has emerged as a game changer.
    Operations managers now get a minute-to-minute overview of the operations and identify bottlenecks that slow down the system, using advanced BDA tools. Moreover, consumer trends also tend to go hand-in-hand in this and allow the business to promote the best-selling products and optimize inventory accordingly.
    Amazon is one of the leading players using BDA in its inventory management. Amazon selects warehouses based on the proximity of vendors and that of the customers to brings its distribution costs down. Using predictive analysis, it distributes the inventory based on the consumer demands and preferences in a particular area, thus keeping the flow of the supply chain at the maximum level.

BDA has tremendous potential, and while we have reached new heights in the supply chain management today, there is considerable progress lined ahead in this age of the fourth industrial revolution.

Also Read:

6 Reasons Why the Healthcare Industry is Investing in Analytics

4 Ways Big Data is Delivering Business Value to e-Commerce

4 Ways Big Data Analytics is Delivering Business Value to e-Commerce

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We are living in an age where data is exploding at an enormous pace. Every day, we create 2.5 Quintilian bytes of data – so much so that 90% of the data in the world today has been created in the last two years alone. Amidst this massive amount of data, pearls of wisdom lay hidden.

From helping companies understand customer preferences to increasing productivity and improving decision making, analyzing these huge data sets gives a competitive edge that is too significant to ignore. In fact, companies that disregard the potential of Big Data Analytics are at risk of being left behind.

Seizing the ‘Big’ Potential

While many industries are still at the nascent stages of figuring out what to do with the huge amount of data at their disposal, e-Commerce is one industry that is already reaping the rewards of their Big Data initiatives. Major players in this industry rely heavily on business analytics and their team of data scientists to compete in this fiercely dynamic space.

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Let’s look at the key areas where e-Commerce companies are deriving value from Big Data analytics:

  1. Personalized Offers: Analytics is enabling e-Commerce companies to better target their customers by improving search results. Relevant search results ensure that customers are easily able to spot the products that they are looking for, resulting in faster sale. With suggestions like, ‘similar products’, ‘frequently bought together,’ e-Commerce sites display contextual and relevant content to customers. Players also analyze past browsing history to provide accurate search suggestions.
  1. Running Promotions/Big Discount Days: e-Commerce players rely heavily on data analytics for the success of their promotional campaigns and big discount days. Companies turn to data to get answers to business-critical questions like when to run a promotion, how to segment customers, and more. A case in point is Snapdeal, which witnessed a 9-fold increase in sales volume on its Unbox Diwali Sale by using business analytics.
  1. Inventory Management: Real-time analysis of data empowers e-Commerce firms to better manage their inventory. They use predictive analytics to understand which products will not see an immediate sale and adjust their inventories accordingly. This helps them allot their funds in purchasing products that are more in demand, in turn increase profits. Analytics thus helps e-Commerce firms handle their biggest pain-point: Overstock. Take, for instance, Flipkart, which has improved its inventory utilization by 5% by using analytics in its day-to-day operations.
  1. Optimize Pricing: As e-Commerce is an extremely competitive space, players need dynamic pricing to ensure more sales and profit. Major players analyze factors like competitor pricing, available inventory, customer activity, etc. for real-time pricing.

While the e-Commerce sector is reaping the early-mover advantage of Big Data Analytics, it has only touched the tip of the iceberg in terms of benefits. Other sectors too are taking a cue from successful user cases in e-Commerce to intensify their Analytics initiatives. Building a team of skilled and certified data scientists has thus emerged as a top priority for companies to survive in this fast-paced environment.

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