A Big Data Guide for Noobs

If you’ve used the internet at all in the past decade or so, you would have come across this word quite a few times – Big data. Naturally, your interest would have peaked to find out what exactly is big data and why is everybody making such a big deal out of it. If that is the case, you’ve come to the right place, my friend.

What is Big Data?

Like its name suggests, Big Data is a huge collection of data that is drawn from a vast resource and is constantly growing on an exponential scale. This, however, is a watered down definition. In reality, big data is such a huge repository of data that the data management tools that we have been using for analytics, have been rendered obsolete and newer technologies have sprung up overnight to keep up with the scale. Given the magnitude of this technology, there is a lot of ground for us to cover. So let’s get started.

The Big Data Scale

With all the hype around the size of big data databases, one might be wondering, how big is big data really. It is estimated that every day around 2.5 quintillion bytes of data is generated. That is 2.5 followed by 18 zeros. If that is hard for you to assimilate, it is around 2.5 billion gigabytes. That is an impressively eerie amount of data, especially considering the fact that a healthy percentage of that could be generated from even from the silly banter on the Youtube comments section. Anyway, we are not here to talk about one of the millennial generation’s favorite hobbies, but to talk about the data that governs almost everything we do today.

Types of Big Data

Big Data can be classified into three major types.

1. Structured Data

This is data that has fixed format and length. Structured data is usually comprised of numbers, dates, and strings. Structured data is obtained from a myriad of sources, including machine-generated data, human-generated data, and sensor-based data. Experts estimate that around 20 percent of the data available today could be classified as structured data. This is also the data that is being processed in the most comprehensive manner and from which most value is derived.

2.Unstructured Data

Contrary to the concept of structured data, this type of data is comprised of non-uniform or non-field based data. It includes all text and multimedia-based data such as word documents, audio files, video files, and other documents. This is the most abundant form of data. It is estimated that unstructured data accounts for around 80 to 90 percent of data generated by organizations today. While this form of data is quite difficult to analyze, the actionable insights acquired by doing so could yield vital actionable insights that could be leveraged to cope with the competition.

3. Semi-structured Data

This type of data displays certain properties of being structured in form but it is not defined in a relational database as with structured data. XML and non-SQL based data are good examples of semi-structured data. Such data will be easier to analyze, allowing us to leverage better insights.  

Other Characteristics of Big Data that you Need to be Aware of

Diversity

One of the most defining aspects of big data is its diversity. From text to audio and video, there are a lot of data varieties. Handling such a wide variety of data on such a large scale is a messy tedious affair. Even storing such data will require a wider range of storage tools to match the scale, let alone the nature of the data. Maintaining any sort of consistency when it comes to big data is nothing more than a fantasy. Especially when you consider the fact that each browser, platform, and the web page has its own specific data, the versatile nature of the data really exasperates things. Processing this data, therefore, could pose as an insurmountable task. One of the most significant ones is the loss of vital parts of each piece of data while processing and analyzing. This beats the very purpose of big data. While traditional computing methods fail at the first whiff of such data, agile based technologies have proven to be quite effective when it comes to analyzing and processing big data. There are several programs to this end that should be explored. Perhaps we could cover those in another article.

Velocity

Given the large volume of data that floats around the internet these days, speed is a factor that plays a key role in making anything out of it. The idea behind big data has always been to leverage it to use every bit of data available. And given the density of competition, companies have to get their big data sorted out fast. Keeping track of the frequency or velocity of data generation will allow companies to gain insights on the growth of data and how fast it is being relayed to various ends.

Volume

Volume is the most significant attribute of big data. Today there are a lot of sources for data generation and these sources are generating an unfathomable amount of data. Even labeling a collection of data as ‘big data’ depends on its volume. So, this is a crucial attribute that needs to be taken into consideration before delving deeper into the data.

Conclusion

Big data is undoubtedly a technology that has been revolutionizing various vertically of enterprise for nearly a decade now. As newer technologies are developed to process and analyze big data, the valuable insights that are driving millions of businesses today will become more insightful as well as productive.

A Brief Look at the Role of Artificial Intelligence in Logistics

Modern businesses rely a lot upon a good logistics system. Keeping up with the sheer volume of business that companies deal with today doesn’t make things any easier. This is where all the automated goodness of AI plays a major role as a disruptor. Given the expanse of functions within the logistics industry, it is no doubt that there is a lot that AI could do and to a great extent is already doing.

For the logistics industry, the journey has been long and eventful, starting in the early days of pioneers looking for trade routes to procure tea and spices. At this juncture of the 21st century, however, logistics has evolved to its fullest as far as infrastructure is concerned. Furthermore, there are very few industries today that does not apply logistics in one form or the other. There are quite a few aspects of logistics that AI could be applied to. Here are a few areas where artificial intelligence is applied in logistics.

Automating Transport Management

The heart and soul of the logistics industry is transport. Without transport, every other process in logistics is just redundant. While the 20th century could make do with paper and ledger based management systems, transport in the 21st century requires a giant leap. This is where AI’s role is pretty cut and dry. Before getting into AI’s role in transport management, we need to address the current scenario surrounding this technology. Transport being a rather physical function, most logistics companies address it as such and consider it quite a priority and with good reason. There are several hassles and risks involved with transportation which go beyond the pen and paper strategies devised in offices. This is the reason why most companies leave the data based technological bits to third-party logistics companies or 3PLs. This has slowed down the widespread adoption of technology in general.

This trend of outsourcing technological details to 3PLs has also limited the influence of mobile technology which plays a huge role in AI-based applications. So, coming back to automation, the advancements made in optimizing transport management has been monumental. As far as AI in transport management is concerned, the idea is to automate as much of the entire process as possible. And to this end, there are quite a few remarkable devices and applications out there.

1. Live Tracking

The fast pace at which the world moves today demands fast systems for anything and everything to cope with our lifestyle. In logistics live tracking has been quite a disruptor. There are several automated tracking applications for all platform that collect and relay a lot of vital data. From scheduling trips to providing live updates of the cargo’s position, AI-based applications ensure an open and intuitive line of communication between the bases and mobile units. Even information such as traffic, potential delays and so on are relayed using predictive analysis. Furthermore, advancements in the mobile industry and the level of simplicity they have facilitated therein have inspired various businesses to onshore many of their processes pertaining to this. Online retail companies such as eBay, Amazon and so on have already adopted this technology on a rather comprehensive level with their management capabilities going down to the door level.

2. Advanced Black Boxes

Black boxes in trucks have received an overhaul as well. Advanced back boxes provide a great deal of support for the operating companies as well as drivers. These devices are capable of collecting information such as engine temperature, the status of the load, tire pressure and so on. Despite their fundamental purpose as a disaster recording tool, these black boxes make this data analyzable in real time and allow operators to monitor and manage their transport systems well. Similar advancements have been made in the systems of cargo ships and airplanes along with advanced automated navigational tools.

WorkFlow and Process Automation

While transport is a major aspect of logistics, it is still just a part of a larger process comprised of several smaller processes. These are the processes where AI could manifest into more cognitive and at times even physical forms. There are various manual processes which take thousands of man-hours to get through, and after all that could still yield various levels of errors. Here are a few of those processes that have been optimized by AI.

1. Warehouse Management

Ask anyone who has worked in a warehouse and they will tell you how hectic the schedule there is. The volume of loading and unloading that take place alone is enough to drive Rain Man and John Nash crazy. Loading and unloading then again are just the tip of the iceberg. AI in warehouse management has simplified many of these processes. The use of AI-based sorting and labeling systems have allowed logistics companies to speed up these processes and minimized the margin of error.

2. Scheduling

The AI systems in logistics today are also capable of scheduling various tasks within the entire process. Automation of planning and scheduling is something that has revolutionized logistics. AI systems, based on various warehouse itinerary data schedule the transportation, organize pipelines for cargos, assign and manage various employees to particular stations and so on. Apart from the obvious con of potential layoffs, this particular application of AI in warehouses has facilitated a faster network for logistics processes.

3. Robots

While AI’s application in logistics has been in a minimalistic way to various mundane tasks, one of the most pronounced applications has been that of robots. Despite the constant opposition from various scholars and trade unions, robotics has been applied to a great extent in all fields and logistics has not been spared in the least. From working in warehouses to transferring freight at cargo airports, robots have their work cut out for them and they are doing it at speeds their flesh and blood counterparts could never achieve.

Conclusion

Despite the slow rate of progress in adopting AI as a logistics tool, the technology is slowly but surely seeping into the system. Machine learning has further allowed investors with a broader outlook on how AI could revolutionize logistics just as it has done in many other verticals. The issue that surrounds AI now is not lack of optimization but the dilemma of having to divert time towards setting it up. This in today’s context makes sense as most companies shy away from investing in newer technologies unless a dire need for it is presented.

How AI is Used in Business Development and Sales

We’ve been hearing the words artificial intelligence for a long time. For millennials the earliest memory of AI would be an Austrian accent uttering the words ‘Come with me if you want to live,’ and ‘I’ll be back’. Over the past decade, AI has been around everywhere and has contributed to a great number of advancements in many verticals. Through that period one vertical remained virtually unaffected by the AI scourge – Business Development and Sales. All that however changed in early 2016 when the industry adopted AI as a mainstream disruptor. In business and sales, AI serves a singular purpose, sorting, analyzing and processing data. Automation of data related processes in various aspects of sales has been enabled a huge increase in productivity as well as the introduction of several sub-verticals to effectively conduct business.

Virtual Sales Assistants 

One of the advantages of using AI in sales is the prospect of increasing productivity without increasing or investing in additional workforce. One of the ways this is achieved is through the use of virtual sales assistants. These programs can make use of sales data to generate leads and then contact the leads through emails. Technologies such as natural language processing and natural language generation have played a huge role in defining virtual sales assistants.

Furthermore, predictive analytics have enabled businesses to effectively automate processes such as lead generation. Efforts by some of the leading sales data companies have yielded great results which have fetched data to such comprehensive depths that was never previously achievable even with traditional methods of automation. The amount of accuracy, as well as the details offered by the use of AI, have enabled sales representatives to improve their efficiency by large margins and also make the most of the data provided to them. It is as though every single lead given to them is a Glengarry lead.

AI in Customer Relationship Management

CRM is an intricate part of sales that uses a great deal of information on customers. This process governs both the possibility of new customers as well the prospect of retaining current ones. Which of course means a thorough understanding of individuals in either demographic. While in the past a lot of effort has been put into gathering information on clients, it has not been quite enough and relied purely upon the wits and talent of the sales representative. So, for quite a long time it has been a touch and go process. Today however with AI in the picture, CRM has been kicked into overdrive. With each interaction, AI systems are able to gather vital data that can be used to enhance the entire process and so far it has been working out quite well.

Enhanced Customer Service

Customer Service is a territory where AI has been almost creating miracles for companies large and small alike. There is a lot that artificial intelligence has to offer to various customer service processes. Both on the business-to-customer as well as the business-to-business fronts AI has been able to engage with the customer on a much deeper level than ever before. Using the cognitive technologies available today, devices such as Amazon’s Echo and Google Home have been able to reach out to customers on a personal level, understanding their preferences and programming themselves to them. Through machine learning, these devices are capable of answer more complex queries as well as predict other contingencies. The use of chatbots is also something that has enhanced the customer service experience. The speed at which customers’ queries and needs are solved have made it quite desirable for companies to rely upon AI. A good customer service experience also reflects on the other parts of the sales funnel including the ones implied above.

The Downside?

As with every AI discussion we have to always address the potential of complete automation and the resulting loss of jobs that would bring about. So will AI based automation in sales cause sales representatives to lose their jobs? At this point, the answer is ‘no’. The use of automation in sales could actually decrease the workload for sales reps. The many hours they spend on market research and data collection once automated could allow them to focus more on lead conversion. Even with automation of leads conversion processes, the salesperson will have fill in managerial and supervisory roles. While minor layoffs owing to the redundancy of certain human roles are inevitable, there is no end in sight as of now for sales reps. Besides most customers still, prefer the human touch with such processes and may continue to do as we have experienced after dealing numerous times with automated query systems.

Is Artificial Intelligence in Government a Good Idea?

It may be hard for most of us to believe but there used to be a time when people were actually scared of machines with cognitive abilities, and that magical period was called the 60s. If you were living in the 60s this subject would have sparked so much debate and controversy. Well at that time so did talk of aliens, women’s rights, racism and going to school. Hey, wait a minute that stuff happens today as well, so I must tread carefully. Yes indeed the 60s were fun times, but what’s not strange is the reality that the robots are here and by the looks of things it seems they might be starting to govern us already. Things are escalating quite fast indeed, the good news here is that we will all be still alive to see the rise of the machines, and that’s the bad news as well.

What Process in Government Could AI Take Over?

Beyond the obvious stereotypes, there are a lot of processes within the government that happen on an unfathomable scale. The fact is unless you are going through that process you wouldn’t realize the sheer volume of documents government agencies process every day. We often forget the fact that the government deals with documents of millions of people and most of the time these are in hard copy format. It is no wonder that government employees are grumpy all the time. If we were to break down the government’s functioning, it would take us days to cover all of them. So, for the sake of brevity, we’ll narrow it down to three major functions.

Automated Document Based Processes

At the heart of any public sector are the processes revolving around documents. And as mentioned earlier there are a lot of them. So, most of the jobs in government are based on document processing. This is a repetitive process with a certain mechanical edge to it. The rather mechanical nature of the process makes it optimal for automation. Machine learning is a technology that could be applied quite effectively here to prepare the systems for any contingency. Again, as with most machine-based processes, the margin of error is largely reduced. However, there are concerns over how well an automated system could be used to process handwritten documents. Not everything today is in digital format and the use of good old pen and paper are still quite popular and in a government agency, an AI system will have to face such tasks often. Natural Language Processing has its own limitation and the range of variety in handwritten documents could prove quite challenging for even the most advanced AI systems.

Predictive Analysis

This is a technology that is in particular useful in fields such as Income Tax where detection on various levels is mandatory. While filing taxes, it is quite common for people and companies to cheat, which cannot be determined using traditional methods. Machine Learning again plays a major role in coming up the right algorithms to process all such data, particularly ones that are based on Natural Language. Natural Language Processing is a vital part of such government processes. However, the threat here again is the AI’s limitations. Here if the machine misreads information it could cost dearly as people could be convicted of an error the machine made. Ironically this is not unprecedented. Weak AI in the 90s have been applied to a great extent and has lead to plenty of dispute and criticism. With access to IoT today we could run documents through several stages of cross-checking to ensure that nothing is missed out or misinterpreted.

Sensor-Based Technology

This is where AI stops being a helpful tool and starts bordering on the level of menacing. It is often debated whether or not the use of AI could replace humans. When AI really takes over on a mainstream level the first jobs to go will probably be on the maintenance and security levels. As far as maintenance goes we already have several automated devices that work 24/7 with various tasks both on the domestic as well as enterprise levels. However, the use of AI in security has largely been on a monitoring level with visual, touch as well as audio cues being constantly tracked and later processed. That is not saying that the use of automated physical security is unprecedented. There have been several instances where the use AI in security has gone beyond the mere monitoring level, particularly in defense services. However, as far as the government is concerned the requirement for such security has not yet arisen with the exception of the postal workers going ‘postal’ back in the 90s. But that was an internal strife and there is no man-made machine that can stop the wrath of the postmen. Anyway sensor based security in government could be used in the widespread implementation of tracking services such as the RFID chip and biometric transaction/enrolling services.

Conclusion

While the potential for AI in government yet again is quite vast, adoption in this sector is not as swift as other verticals. This is quite understandable considering how slowly digitalization seeped into government and the process is not yet complete in developed countries let alone countries that don’t yet have widespread internet access. So, AI in government is something that could bring about huge changes along with massive layoffs, but the time it would take for such an event to come to pass would be much like waiting at the ATMs back in 2016 when the generous government of India decided to alleviate all our monetary issues. What fun times we live in indeed.

How Big Data is Revolutionizing the Entertainment Industry

Entertainment has become a necessity of life for us. We constantly yearn for that shot of endorphins we get from entertainment materials. And there are enough shows and content today to keep us engaged for every moment of the rest of lives. So no matter which war breaks out or what strategic attack may be launched against us, one thing we can be sure of is that no one is going to die of boredom in the near foreseeable future. The internet and the rise of online streaming platforms have paved way for a new golden age of Television. Production House originals have been selling like hot cakes. It may come as a surprise, but, there is a good possibility that old reliable T.V. could be permanently replaced by the modern marvels of the internet.

Today platforms such as Amazon Prime, Netflix and Hotstar have been growing at an alarming rate. These platforms are both proliferating into the global market as well as evolving in terms of technology. One of the biggest disruptors for this sudden growth is Big Data. To begin looking at what big data has been doing in the entertainment industry we need to first have a look at one of the earliest examples of online mainstream entertainment-youtube.

Where it all Started

Online Entertainment as we perceive it today was born on Valentine’s day in 2005 when YouTube came to be. Thus started the online journey of mass-produced entertainment. Youtube has always been a free forum for every Tom, Dick, and Harry to upload whatever they deemed fit to be called entertainment. Yet, the amount popularity it has amassed is astounding. It is so far reaching that some YouTubers could easily buy and sell several actors, musicians, and entertainers who have been around since the 70s. The key here is audience engagement and Big Data is the magic tool that facilitates this. Big Data offers valuable insights into audience’s temperaments and their preferences which can further be used to create strategize content creation. From marketing strategy to create the content itself, there are a lot of aspects that are influenced deeply by Big Data.

What is Data Analytics?

If you are a YouTuber, a vlogger, a blogger or have anything to do with marketing, you would be quite familiar with this term. For any kind of marketing, understanding the audience’s disposition is quite essential. In the case of visual media, in today’s context, the content itself is considered a marketed product and the idea is to ensure that maximum exposure is achieved for videos, shows, and movies. There are several levels at which these marketing strategies work. Data Analytics offers a lot of vital information pertaining to the target demographic, their preferences, for creative content, various products, services and so on. This analyzed data can then be leveraged to create marketing strategies, create ads, and even create the content itself for T.V. shows, videos and so on. Considering today’s population, this is mandatory because the idea as with everything today is to maximize proliferation. So let us have a look at how Big data influences the entertainment industry.

1. Ad Targeting:

Long gone are the days of 3-second radio jingles. The modern media industry is host to an audience of over 7 billion and given the competition within the industry, a strong placement strategy is prudent. Usually, the length and the frequency of ads play a huge role in the advertising. However for advertisers today the main focus is the time when their particular audience view content on a particular platform. Analytics provides them with accurate data on where their target audience go for a majority of their entertainment needs, what programmes they like to watch and what time they log on to those. Furthermore, data from their habits of skipping ads and such can be used to determine what aspects of advertisements put them off and how they can be rectified. In short Big Data has reduced the number of guesswork advertisers usually have to put themselves through to understand the consumer.

2. Viewer Behaviour Prediction:

Big Data also offers a breakdown of viewers’ viewing habits such as how long people like to view ads, how long they watch shows for, what kind of content they skip and so on. Other statistical data such as age group of the audience on different multimedia platforms and devices used are also acquired through big data. YouTube is a good example of this technology is leveraged to understand the audience produce content that appeals to them. Naturally, this has been quite a boon for YouTube creators and they have managed to achieve the success that many mainstream shows have failed to in recent years.

3. Video Rating:

Online channels like Netflix and Amazon Prime also have had a huge advantage from using big data. Much like YouTube these also depend a lot on behavior prediction. However, their processes are much more comprehensive and directly dictate the production and retention of shows. Netflix uses viewer retention data for each show and uses it to decide whether or not to renew a show after each season and if they did the budget they will be allocating. This same method is applied before starting a new show.

4. Stream Scheduling:

A concept that has been growing in popularity in recent years is Live Streaming of content on social media and mass media platforms. Platforms like twitch have allowed for online content creators to connect with their audience in real time while they are broadcasting. These streams have proven to be quite effective opportunities for advertisers to place their products. Analytics has been used to predict the most optimal period to stream with the most viewers as well as what kind of products would connect the most with viewers.

Final Word

There is no doubt that Big Data has had a huge impact on entertainment and advertising. Data analytics has allowed independent content creators across the world to publish their content for the entire world to view and make substantial monetary gains often rivaling mainstream production houses. These tactics have also created wonders for T.V. show and movie producers who have been able to understand their target audience on a deep level. The marketing and advertising opportunities that have evolved therein have allowed for a revival of the quality Television Programming era which was just over a decade ago on the brink of fading away. All we need to do now is sit back, relax, enjoy the show and look to the horizon for the new wonders that the entertainment industry might throw our way.

Artificial Intelligence’s Role in Customer Service

Customer service has always been a vital aspect of any business and often dictates the manner in which a company is perceived in a market. For today’s businesses, however, customer service is a much more important process which also serves as a comprehensive part of their sales and marketing strategies. Being a continuous and often mechanical process, most aspects of the customer service process are being automated. A study by Oracle from 2016 reports that approximately 8 out of 10 businesses in the world today have either switched or are planning to switch to Artificial Intelligence for their customer service needs by the year 2020. The recent developments in AI have largely attributed to a surge in investments into the technology. For customer service, there are various aspects that today apply AI and automation on some level.

AI beyond Chatbots

The most common features of any website today are chatbots. Chatbots are those small pop-ups that appear when you log on to a website assisting you with the basic queries. As of now, that is the extent of their purpose. Responding to FAQs from a pre-programmed set of answers and contingencies, Chatbots could be considered the beginnings of Artificial Intelligence, although calling them that would be a gross overstatement. Yet, chatbots have allowed companies to improve the efficiency of their customer service teams. Being available 24/7 they offer an edge for companies in maintaining a strong presence in the market. No matter how advanced AI may be than chatbots, it is meant to serve only these two purposes and all AI designed for Customer Care function along these lines. However, AI adds layers to those functions often allowing for several functions that involve augmented AI-Human coordinated efforts.

AI-Assisted Human Agent

This is a model that companies follow as an alternative the Chatbots. This model offers the best of both worlds as human agents work hand in hand or rather hand on the mouse. Here the AI serves as a relay between the customer and the human agent. Using data and predictive analysis the AI analyses various previous conversation pertaining to the query in question and generates a custom answer based on the current inputs from the customer. The human agent then edits the answer to make it sound more ‘human’ and delivers it in a simple and understandable manner. After each query is answered the AI learns from the edited answer offered by the agent to enhance its own database the next time it answers. The advantage of this model is its versatility across platforms as well as media.

Unlike chatbots, this model is not limited to text-based queries and can function on a voice level as well. By applying Natural Language Processing, the AI recognizes human features associated with a voice to authenticate calls as well as make suggestions. It is predicted that in the next couple of years a healthy percentage of companies using voice call based business processes will be enhanced by the use of AI.

Swift Response AI

If we had a rupee for every time we refused our patronage to a service because of bad customer care, all of us would be quite rich indeed. In the digital age, quick and efficient customer care is an essential need and people lose patience and loyalty towards a company when they are made to wait and put through a ton of procedures. The AI today are capable of interacting directly with customers for any requests pertaining to information or service and by drawing from a database, offers appropriate suggestions and processes them. If there are queries that are beyond the AI’s processing capabilities it automatically initiates the process and redirects the customer to the concerned human agent. This swift AI servicing is proving to be quite effective.

AI-Derived Insights

One of the most comprehensive applications of AI in customer service has been data analysis. Where humans often fail in general is when we are expected to see the big picture. We have done this for centuries and it seems we never learn. But, AI today is able to grasp each and every aspect of a conversation, transaction or interaction of any kind and process it to derive actionable insights. By using technologies such as natural language processing and machine learning, AI will thoroughly analyze all data available and checks for anomalies and vital information such as trends and patterns which could be used in the future to improve conversion rates and so on. While not used as widely as the previous two, this form of AI has been quite effective as a business accelerator and it is predicted that with the growing popularity of AI it will eventually become a customer service staple.

Conclusion

While the application of AI in customer service still has a long way to go, the changes it is has brought about so far have been substantial. Despite the ‘Don’t fix it, if it is not broken’ attitude that is symbolic of the customer service market, there has been quite a push for the adoption of AI. Given the various possibilities that AI presents, the customer service industry, for now, is in a safe haven with this technology by their side.

How Big Data is Influencing ECommerce

ECommerce in recent years has been quite popular amongst the public. People prefer the idea of choosing their desired products from the comfort of their homes. The massive success of the eCommerce industry could be attributed to the large amounts of data that is available today. Big data analysis, in particular, has been crucial in helping the retail sector keep up with the growing trends and mindsets of the consumers. The modern day consumer is truly a remarkable creature with a myriad of needs and expectation along with a constantly changing taste pallet influenced by the media. In today’s market, it is hard to remain relevant without staying up-to-date with the constantly turning tide of the consumer demographic mindset. This is patently obvious with the various products that have a total lifetime of just a few months in the market after which a newer, more improved product takes over. Long gone are the days of time-tested products serving a loyal customer base for decades often bought from the same dealer. So, e-commerce and the use of the various tools that support is more of a mandate today rather than a choice. In India alone, it is expected that the e-commerce industry is set to cross the $100 billion mark in revenue by 2020. Now, let us have a look at the technology that influences e-commerce the most.

Data Analysis and Actionable Insights

As we have witnessed with everything today, data always plays a huge role. E-commerce in particular relies almost entirely on data for many processes. And there is a colossal amount of it online and analyzing it is by no means an easy task. It is here that big data makes a huge difference for all professionals within the e-commerce industry to make the best out of the data available to them. Deriving actionable insights that will help them with various processes from planning marketing strategies to campaigns, product placement, inventory, budget management etc. Professionals are now able to understand where their business strategies are taking a hit and plan contingencies that could be used to rectify them. Choosing the most effective marketing channel, the right platform to place the product are just a few of the perks that data analytics offers.

Demand Prediction

Prediction services are one of the largest benefits of big data. Given the vast resource of information available through big data, it is now possible for a retailer to predict the sales prospects of a product and how it will fair in the market. All this is based on historical data of the products’ performance in your business as well as that of other retailers. Services like Amazon Web Service (AWS) analyze customers’ shopping habits and their browsing habits to achieve this.

Personalized Business

Using behavior tracking services retailers are able to understand the personal preferences and purchase patterns. This data is further leveraged to customize ad placements and products displayed by retailers on e-commerce websites. Personalized stores display all the products that customers are more likely to buy and in most cases have been looking forward to buying. Such data is acquired by monitoring the customers’ web searches, the topics they are researching and so on. Automated recommendation for the product they are searching for is displayed on their browsers and on most websites they visit. Sometimes a cue for such a process is given when a customer first clicks on a particular product while just window shopping. In some cases, personalized offers are given to customers based on the various sites they are visiting to get the best deal. This prompts the retailer to offer a better deal for that customer. This also works quite well while scaling. The idea here is to always stay ahead of the curve. Coupons and promotional codes are also passed on to customers as part of this process.

Customer Service Optimization

The 21st-century business world relies a lot on customer service and support. A bad experience with customer service and support could lead to your business or product getting a bad reputation. Besides customer service today is also a marketing opportunity for selling accessories and additional supplies. Big data offers a clear view of the customers by providing vital information that could be used to enhance the customer service experience. Furthermore, with online transactions, mistakes occur often and some of them could cost dearly for both parties. Using big data a customer care professional will have access to all the information leading up to his/her interaction with the customer. This miscommunication can be minimized and a solution can be provided speedily.   

The Big Data Game-Changer in Sports

Sport to many means many things. To some it is a hobby, to some, a passion, to some, it is a religion and to some, life. No matter what your definition of sport is, it is something that has millions upon millions of followers across the world. Often the distinguishing factor between the nerds and studs, sports truly has evolved beyond the stereotypes into the 21st century. A part of human culture that continues to remain the last testament to our integrity, morality, and spirit, sport, in general, is trying to blend in with the technological jungle we now call home. From the action replay to ball tracking and trajectory prediction, various sports have incorporated a lot of technology. The latest technology to make its own mark on sports is big data. Big data from where we stand today is a technology that has potential to take sports to the next level.

Big Data on the Field

Given the level of technology available to various sports, there is a lot of information that can be gathered from the game which was not possible a couple of decades ago. Information such as the speed of the ball, the speed of the players’ various movements etc. are readily available for teams and players in real time. When big data is introduced into the equation, the whole concept manifests on a whole different plane. Through predictive analytics, teams can now get deeper insights into the game as well as their own abilities and that of their opposition. Information such as weaknesses in a football formation, weakness in bowler’s action, most optimal positions to counter a particular defense and so on. Big data is even used in choosing the right material, weight, size and shape for sports jerseys, shoes, bats, balls and so on.

Strategizing is where big data and related analytics processes are leveraged to the greatest extent today. The analytics capabilities that sports teams today possess will allow them to strategize every single step of a match to a degree which will provide them with predictions on the chances of their success. With the way this technology is evolving all this may very well be the tip of the iceberg.

Big Data as a Recruitment Tool

Every sport has these players who despite their talents and abilities don’t get noticed and even if they do, don’t get their big break till towards the end of their careers. Today this is an issue that is even more menacing given the number of players available. So, big data again comes to the rescue. Coaches, scouts, and selectors can now leverage big data and data analytics to get detailed information on various up and coming players, their stats, their strengths, and weaknesses, playing style and even possibly in the future, detailed analysis of the prospects of hiring them.

The Fan Experience

Big Data, as the name suggests comes into the context of large volumes of data. This technology in its rudiments has been leveraged quite a bit in the field of marketing. This is where big data comes into play with the fan experience. There are a lot of aspects where big data is influencing fan experience. In cohesion with technologies such as mobile, big data has the prospect for a great number of possibilities. As of now, big data to this end is only being implemented on a marketing level. And as with other verticals that have enjoyed the benefits of data analytics, sports too have enjoyed a huge increase in marketing prospects. Beyond these, there are several potential opportunities for fans to engage in their favorite sports beyond their usual spectator positions. Fantasy teams and such could leverage data analytics to offer a more comprehensive experience.

Overtime

There would be a lot of you that might argue that sports need to stick to tradition and that all this technology is making them lose their essence. Well, if you do, you are not alone on that. Applying data to such an extent has the risk of confining the sports to just strategies and winning, thereby amputating the celebration of the human spirit that most fans often associate with them. Today this is quite a reality and the over-commercialization of an already largely commercialized affair is something that needs to be taken note of. Big data is a tool that is most adept at enhancing this. The use of big data also opens up an avenue for manipulation and misuse and manipulation of data as well the insights thus gathered. For now, these are just hypothetical scenarios with far-reaching possibilities of becoming a real threat. The present concern on the matter yet again remains on the fact that sports are slowly beginning to lose their charm, class, and character they were once known for.

On a positive note though, there are certain aspects of the application of big data that have drawn in a new generation of sports fans who despite the downfalls, like to subscribe to this new era of sporting. The opportunities that have opened up therein have also been welcomed by the sporting community where players now have a chance of getting noticed better. The biggest change that big data has brought about in sports is the overnight proliferation into various regions and diverse demographics that we have been witnessing in recent years.

So, how the global sporting community will find a balance with the use of this new technology is something we’ll have to tune in, wait and watch.

How Big Data is Revolutionizing Healthcare

The healthcare industry is by no means a vertical that is to be taken lightly. Ask any medical practitioner and they will tell you that the nerve-wracking moments that they go through every day in the OR and ER are not for the faint-hearted (no pun intended). Beyond the life or death situations that the industry has become so well accustomed to, there are several processes that revolve around the pre and post-treatment phases. Given the ever-growing population today there is a dire need for a system that will help ease these processes and allow some room for medical support staff to work with. This is where Big Data comes in. In an age where data governs most aspects of human activity, there is a lot that big data can offer to the healthcare sector.

Big Data in Healthcare Research

The amount of data collected during healthcare treatments and procedures is a gold mine for researchers. Apart from the data collected in hospitals, there are several other means through which data can be gathered. The basic devices we use today such as the heartbeat monitor, the calorie counter, and so on, most of which are either available or integrated into our smartphones. Such data once analyzed could be transmitted to various medical institutions where professionals could arrive at diagnosis and treatments for various potential and incipient ailments. The vast amount of data from various sources allows medical professionals to perform a comparative analysis of various different symptoms displayed by each of them.This could in turn aid in the creation of a more comprehensive treatment. This also keeps doctors updated on whatever ailments they are dealing with and will not be left in the dark when they stumble upon a new symptom.

Healthcare Records

One of the most difficult tasks after the treatment process is maintaining patients’ records. Again the massive population today make things infinitely difficult for professionals that maintain records. Even countries with well-established healthcare systems face issues related to this more often than one would care to anticipate in such a field. Mistakes in records have ranged from financial irregularities to complications in treatments and medication given. To resolve this big data has proven to be quite an effective tool. However, this is a process that is easier said than done, as integrating Electronic Healthcare Records into any healthcare systems is quite a daunting task with various drawbacks of its own. The benefits of EHRs far outweigh the efforts many institutions have to go through to adopt them.

There are several efforts being made by many countries to make this a mandatory part of the global healthcare community. Doing so would allow healthcare to be delivered more smoothly. Furthermore, such a system would allow doctors to get better insights on patients who were previously receiving treatment in other institutions or countries.

Clinical Trials

Big data plays a huge role in modernizing many of the methods in which clinical trials are carried out and allow researchers to get the most out of the data acquired therein. Big Data is used to pick the most suitable candidates for the trials. The desired traits that researchers are looking for are sorted out using big data. Likewise, the effectiveness of medication and the areas that they are most likely to affect are also determined using big data. Big data has been used a great deal in finding a cure for the so-far unconquerable cancer.

Wearable Healthcare Devices

There is no deficiency of wearable devices today that are capable of doing a great many things that would ‘make our lives easier’. From blood pressure to heartbeat there is a device you can wear in some form or the other that can monitor each and every one of your bodily functions. These devices are capable to transmit data directly to doctors or the institutions they represent. With advancements in IoT and automation, in the near future, it is quite possible we could have these devices connect with other publicly available healthcare automatons.

Data Security

With cybersecurity being the menace which it is today, data vulnerability is an issue that plagues every industry and healthcare, in particular, could incur huge losses on all levels. It is hard to imagine the horrors that could be unleashed if personal medical and insurance records are hacked, not to mention payment details such as credit cards associated with them. Big data is the key here and quite often could play an instigating role in the data breaches rather than dissuade them. So ultimately when it comes to records big data is a technology that the healthcare industry must tread carefully with.

IBM Watson

The application of IBM Watson in the healthcare sector was quite a revolutionary step. The AI which uses big data to derive various solutions. IBM Watson uses natural language processing and machine learning to help healthcare institutions arrive at the most optimal treatment for the symptoms that patients may display. Watson has access to a comprehensive database of medical records pertaining to previous treatments, clinical trials, personal updates from physicians and researchers on various ailments and so on. While there is a certain margin of error with the diagnosis and hypothesis provided by Watson, it predicts the level of success in the treatment based on that data as well. This is quite a useful tool which in the hands of a seasoned physician could be leveraged to a successful degree.

Conclusion

Big Data today brings a lot of promise for the healthcare sector. In the years to come, there are various possibilities that could be brought to life through the use of big data. However, it potential for security threats and so on is something that we must all be cautious of before jumping fully into the technological development bandwagon. This, as a matter of fact, is a risk that most healthcare sector leaders are well aware of and are to a certain degree prepared to address with caution. Only time will tell how much this seemingly life-changing technology could come to influence our lives.

Legal is a complex vertical with its own unique style of functioning unlike any other vertical. So, let us have a look at how Artificial Intelligence could influence the legal sector.