Big Data Technologies and Tools

An organization is all about the data it beholds and to make a decision that is valid for years, a massive amount of data is required. This brings us to today’s topic ‘how to handle data influx with Big Data‘ and what are pointers that you should know about Big Data.

Power of the Big Data can be used to elevate the business to new levels and capture market opportunities. Big Data is the term which is used for massive data. As the data inputs are received from a variety of sources, it is diverse, massive, and beyond the capacity of the conventional technologies.

Such quantum of data requires computationally advanced skills and infrastructure to handle it. Once equipped with the appropriate infrastructure the data must be analyzed for patterns and trends. Such trends and patterns aid in formulating marketing campaigns.

Following are some industries that are already ahead in leveraging Big Data for regular operations:

• Government organizations trace social media insights to get the onset or outbreak of a new disease.
• Oil and gas companies fit drilling equipment with sensors to assure safe and productive drilling.
• Retailers use Big Data to track web clicks for identifying the behavioral trends to adjust their ad campaigns.

Below we have listed few Big Data Technologies and big data tools that ought to be aware of

1. Predictive analytics

This technology helps you to discover, assess, optimize, and deploy predictive models, which will improve business performance by moderating business risks.

2. Stream analytics

Stream analytics analyzes the varied data in different formats that come from disparate, multiple, and live data sources. This method helps to aggregate, enrich, filter, and analyze a high throughput of data on a regular basis.

3. NoSQL database

NoSQL database is having an exponential growth curve in comparison to its RDBMS counterparts. This database offers increased customization potential, dynamic schema design, scalability, and flexibility which is a must for storing Big data.

4. In-memory data fabric

This technology lets you process data in bulk and provides low-latency access. Also, it distributes data across SSD, Flash, or dynamic random access memory (DRAM) of a distributed computer system.

5. Data Virtualization

If you require real-time or near real-time analytics to be delivered from various big data sources such as Hadoop and distributed data sources, data virtualization is your best way out.

6. Data integration

Data integration includes tools that enable data orchestration across solutions such as Apache Pig,  Apache Hive, Amazon Elastic Map Reduce (EMR), Couchebase, Hadoop, MongoDB, Apache Spark, etc.

These tools are discussed in detail for you to understand below:

a) Apache Spark

Apache Spark is the fastest and general engine for Big Data processing. It has built-in modules for SQL support, graph processing, streaming, and machine learning. It supports all major Big Data languages including Java, Python, R, and Scala.

The main issue with data processing is the speed. A tool is required to reduce the waiting time between the queries and time taken to run the program. Apache Spark complements to computational computing software process of Hadoop but it is not the extension of the latter. In fact, spark uses Hadoop for storage and processing only.

It has found its utilization in industries which aim to track fraudulent transactions in real time like Financial institutions, e-commerce industry, and healthcare.

b) Apache Flink

Apache Flink was introduced by Professor Volker Markl- Technische University, Germany. Flink is a community-driven open source framework which is known for accurate data streaming and high performance.

Flink is inspired by MPP database technology for functioning like Query Optimizer,  Declaratives, Parallel in-memory, out-of-core algorithms, and Hadoop MapReduce technology for functions like User Defined functions, Massive scale out,  and Schema on Reading.

c) NiFi

NiFi is a powerful and scalable tool with the capacity to process and store data from a variety of sources with minimal coding. Also, it can easily automate the data flow between different systems.

NiFi is used for data extraction and filtering data. Being an NSA project, NiFi is commendable in its performance.

d) Apache Kafka

Kafka is a great glue between various systems from NiFi, Spark, to third-party tools. It enables the data streams to be handled efficiently and in real time. Apache Kafka is an open source, fault-tolerant,  horizontally scalable, extremely fast and safe option.

In the beginning, Kafka was a distributed messaging system built initially at LinkedIn, but today it is part of the Apache Software Foundation and is used by thousands of known companies including Pinterest.

e) Apache Samza

The main purpose to design the Apache Samza is to increase the capabilities of Kafka and is integrated with the features like Durable messaging, Fault Tolerant,  Managed State, Simple API, Processor Isolation, Extensibility, and Scalability.

It uses Kafka for messaging and Apache Hadoop YARN for fault tolerance. Thus, it is a distributed stream processing framework which comes with a pluggable API to run Samza with other messaging systems.

f) Cloud Dataflow

With a simple programming model for both batch-based and streaming data processing tasks, Cloud Dataflow is a native Google cloud data processing integrated service.

This tool cuts your worries about operational tasks including resource management and performance optimization. With its fully managed service, resources can be dynamically provisioned to maintain high utilization efficiency while minimizing latency.

Final Words

All of these tools contribute to real-time, predictive, and integrated insights which are exactly what big data customers want now. For gaining a competitive edge with big data technologies, one needs to infuse analytics everywhere, develop a speed differentiator, and exploit value in all types of data.

For doing all this, an infrastructure is required to manage and process massive volumes of structured and unstructured data. Thus, data engineers require the above mentioned tools to set patterns for data and help data scientists examine these huge data sets.

How to build a career in Data Science

Today, data scientists are one among the highest paid professionals. Technology is soon advancing and it is necessary that you constantly pay attention to upgrade your skills and expertise.

Tech giants such as Google, Facebook, Apple etc, all of them are looking for data science experts to build intelligent and path-breaking products.

If you are planning to become a data scientist, then you need to be well-versed in some programming languages. In this blog, we list the top 11 skills that you must possess to become a successful data scientist.

1. Education

Data scientists are usually from the highly educated crowd in the college. As a matter of fact, 46% of them have PhDs while 88% of them have a Master’s degree.

You could be from any stream like social science, physical science, computer science,  or statistics in order to be a data scientist. The common field of studies are as follows:

• Mathematics and Statistics (32%),
• Computer Science (19%)
• Engineering (16%).

A degree course in the above fields helps you to develop skills you need to analyze big data. It is highly recommended to obtain a Master’s or Ph.D. after successful completion of the Bachelor’s program. To transit into the data science field, you will require to pursue your master’s degree in Mathematics, Data Science, Astrophysics or any such related field.

2. R Programming

R programming is specially designed for data science needs. Any problem in the field of data science can be solved with R. Currently, 43% of data scientists use R to solve statistical problems. Therefore, it is recommended to learn R.

However, R is tricky to learn especially if you have already mastered a programming language. An online learning program should be taken up to learn R.

3. Python Coding

Along with Java, C/C++, Perl, Python is the most common coding language and is perfect for data scientists. Around 40% of the data scientists use Python as their major programming language. Python is a versatile language and can be used in almost all the steps of the data science processes.

With Python, you can easily import SQL tables into your code and also process various forms of data. Further, it allows you to create your own datasets.

4. Hadoop Platform

This is not a pressing requirement but it is highly preferred in many cases. Also, if you have experience with Pig or Hive or familiarity with cloud tools such as Amazon S3, you will be preferred over other applicants.

Why Hadoop platform is important?

There might be a situation when the volume of data to be processed exceeds your system’s memory and you will require to send data to different servers. In such a situation, you can use Hadoop to transfer your data to various points. Also, Hadoop can be used in data sampling, data exploration, data filtration, and summarization.

5. Apache Spark

Apache Spark is faster than Hadoop with the same big data computation framework. The reason why Apache spark is faster than Hadoop is that Spark caches the computations in memory while Hadoop reads and writes to disk.

Apache Spark helps data scientists to handle complex unstructured data sets and saves time by processing the data faster. It can be used on one machine or a bunch of machines, at once.

6. SQL Database/Coding

SQL stands for Structured Query Language. SQL is a programming language which enables you to carry out operations like delete, add, and extract data from a database. Also, it helps in transforming database structures and carrying out analytical functions.

For becoming a successful scientist, you need to be proficient in SQL. SQL will help you to access, communicate and also work on data. It has brief commands that can help you lessen the amount of programming you need to perform. Additionally, it will help you comprehend relational databases and boost your experience profile.

7. Data Visualization

For a data scientist, it is essential to visualize data to make it easier to understand. This can be done with data visualization tools such as d3.js, Tableau, ggplot, and Matplottlib. These tools can convert data into easy formats.

Data visualization is the need of the contemporary corporate world because of the insights delivered. These insights indicate which business opportunities to grab and how to stay ahead of the competition.

8. Machine Learning and AI

Machine Learning can give you an edge over others as with this you can transform the way data science is functioning. Most data scientists are not proficient in this field. To stand ahead of others, you must learn decision tree, supervised machine learning, logistic regression, etc. Read here for more information on which Machine Learning Algorithm to pick. 

A proficiency in Machine Learning helps you in solving complex data science problems that are based on predictions.

Other examples of advanced machine learning skills that you should consider are Unsupervised machine learning, Natural language processing, Outlier detection, Time series, Recommendation engines, Survival analysis, Reinforcement learning, Computer vision, and Adversarial learning.

9. Unstructured data

A data scientist must essentially be able to work with unstructured data. Basically, the unstructured data are undefined content that can not be put into database tables.

For instance, customer reviews, videos, blog posts,  video feeds, social media posts, audio etc. Such heavy data is difficult to sort because they have no order.

Unstructured data is also referred to as ‘dark analytics’ because of its complex nature. Ability to comprehend and discern unstructured data from several platforms is the prime attribute of a data scientist. It helps you interpret the insights that are useful for decision making.

Apart from the above mentioned technical skills, following non-technical skills will help you to achieve your goals faster.

10. Intellectual curiosity

Curiosity provides you with the thirst to learn something new every day. As a data scientist, you will counter new problems every now and then, at this moment, curiosity will motivate you to find solutions to your problems.

On average, data scientists spend about 80% time in discovering and preparing data. In order to keep pace with the evolving world of data science, you need to keep learning.

11. Communication skills

Data scientists make complex data understandable for normal people which is why it is essential for them to have smooth communication skills. With fluent communication skills, they will be able to explain their technical findings to non-technical teams such as Sales or marketing department.

Thus, with these 11 skills, you will be able to launch your career as a Data Scientist. Even if you are someone who is planning to shift technologies, just spend some time to learn programming languages such as R, Python and the Apache suite and you will be in a good position to start off a career in data science.

Effects of GDPR on Marketing Data

For every business, data is the center of marketing nowadays. The scope of sales and conversions depend on the incorporation of data in marketing activities. That is why every business needs to understand the implications of GDPR on their marketing practices.

GDPR or General Data Protection Regulation is a collection of privacy and data laws for the activities conducted in the digital world. The idea behind GDPR is to bring better governance and transparency in the online world. Companies are required to become responsible in terms of data collection and its use.

Core laws of GDPR

There are 8 major rules that GDPR highlights regarding the use of data and privacy in the digital world:

• Informing transparently about the use of collected personal data.
• Allowing individuals to access their own personal data.
• Allowing the ability to rectify incomplete or inaccurate data.
• Allowing individuals to remove their data whenever seem right.
• Providing the right to restrict the use of collected personal data.
• Allowing individuals to use their own data for personal purposes.
• Providing an option to say “no” to the use of data for marketing purposes.
• Giving individuals a chance to separate themselves from data-based automated processing.

Impact of GDPR on marketing

From B2B to B2C marketing, businesses are required to comply with the regulations of GDPR. No matter if you are based in or out of the EU, it would be wise to comply with these conditions.

Here are all the major marketing elements that will get affected by the laws of data collection, data processing, and data use.

1. Data collection has to be transparent

Using cookies on your website has been an effective marketing practice for a long time. Marketers collect cookies to find out how their consumers think. Similarly, there are many other ways such as email marketing where companies collect data from consumers.

Now, GDPR asks companies to inform and ask for permission before collecting data. In fact, you are required to tell your consumers about how you plan to use that data. You can’t use sign-ups, transactions or account creations as a permission. There has to be clear information to the users about data collection. You need to gain consent from the users in order to collect their data.

This big change is going to change how marketers approach the digital world. You need to communicate in an encouraging way to get consent from your consumers.

2. Processing data for permitted purposes only

It is a necessity that you use the collected data for the permitted purposes only. For instance, you can’t use personal data for email marketing, if the user opted for activity tracking only. There has to be another consent for email marketing.

3. Holding data for a limited period only

As you are allowed to use data for specific purposes, the collected data has to be deleted after it has fulfilled its purpose. If you want to hold on to the data, GDPR requires a legitimate reason for that.

If your business depends on brands and consumer relations, GDPR is an upgrade for your marketing activities.

Business Intelligence in FinTech

With the ever-increasing competition in the Fintech landscape, investors are seeking ways to make meaningful investments. The market for this new revolution has always been diverse, unique, and unconventional. According to global reports, Fintech has experienced a sudden decline from $46.7 to $24.7 billion within a span of one year, i.e. 2015 to 2016. While these reports narrate one half of the story, some of the other stats reflect a different side.

Tapping into the right channels

It’s pretty clear that Fintech offers a host of possibilities to investors. However, they have to be careful and invest in areas which aren’t yet saturated. If you wish to make meaningful investments and leverage available resources, it would be imperative to tap into profitable channels.

From innovative startups and tech giants to traditional institutions, every single business entity is vying for attention thus grabbing significant market share. Governments of various countries are also trying hard to enforce regulations on Fintech. In a nutshell, it is crucial to identify the avenues that change the face of Fintech and help you make the most of your resources.

The emergence of Business Intelligence

Business Intelligence and data analytics are two of the most effective resources in this regard. Leveraging these innovations will help you stay ahead of the growth curve thus accelerating your bottom lines. Here’s how BI can unlock new avenues for Fintech:

1. Business Intelligence helps in monitoring user behavior

Gaining profound information about user behavior will benefit the Fintech industry to a great extent. Since Business Intelligence helps you track and monitor user behavior, you will have the opportunity to make the right investments.

Most importantly, data analysis can assist in the decision-making process thus helping you devise useful strategies. Some of the leading robot-advisor startups like ‘Betterment’ and ‘Wealthfront’ disrupted investments by leveraging Business Intelligence.

2. Uncovering market trends

When it comes down to making investments, it becomes imperative to understand the market and its specific trends. That’s what Business Intelligence helps you do, thus eliminating chances of making wrong moves and taking improper decisions.

3. Improved security with Business Intelligence

We have seen many instances in the past where breaching the security encryption for a FinTech company data isn’t a big deal for notorious fraudsters. Business Intelligence strengthens security and enables successful behavioral analytics. As a result, fraudulent behaviors are easily tracked and investments are less likely to get laundered.

4. Unmatched user experience

Tech firms gain a competitive advantage over traditional funding institutions when it comes to ensuring unparalleled user experience. FinTech has reached great heights since the arrival of dynamic apps and online services. Disruptive technologies were the need of the hour and that’s exactly where BI and data analytics came into the picture.

Amazon’s popularity, growth, and revenues are results of the user experience it provides to consumers. Needless to say, the company had considered BI integration long ago.

Parting thoughts

Although the performance of the Fintech Industry isn’t that impressive in the US, reports from Asia state a different story altogether. The continent records a total investment of $7.1 billion which is huge and magnanimous. So, BI can prove to be the game-changing force for the Fintech industry if implemented in the right way.

Influence of Data Science on Web Development

The whole ideology behind technology is that it is subject to constant change. It has to be dynamic so as to pave way for something more optimized, beneficial, and less demanding. It is an inevitable path of progress, which has undoubtedly led us to where we are right now. The internet has been one of the turning points of this century. Its implications via technologies like data science are rippling through every sector conceivable including web development.

Enter the concept of data science in web development

Our reliance on web apps has increased over the years. This is because of the ease and convenience they bring to us in the long run. Web apps are software-applications that run with the help of the internet. They do things for you so that you can cut down on doing them manually.

So far, web apps have had a certain way of functioning. Apps were based on focus groups, surveys, and specific educated guesses. This would help programmers have an idea what the masses want. But this traditional method of web app development is soon going to belong to the archived files of modern day technological history.

Developers now have zettabytes of data at their disposal, owing to IoT (Internet of Things). People are constantly plugged in to the net and this generates data every second of every minute. This generated data is carefully analysed which then transforms into precious insights that can be acted upon. Here are some ways applying data science would transform web development for the better

1. Software production

Traditionally, which is also how things happen currently, a software development programmer would decide what goes on in the app menu, based on his or her understanding of what the users would like and appreciate. Data science, on the other hand, would generate data from similar apps and would then suggest what seems to be opted by the users more. Imagine it to be the Autocomplete feature going industrial, in terms of web development.

2. Upgrades would be automatic

Upgrades would no longer be something commanded by a team of programmers. Machine learning would compile the data generated by users using a certain app and introduce version upgrades as and when it seems appropriate. It is like the app learns from you and betters itself to help you better.

3. Personalization to the max

Apps will now have a more accurate AI (Artificial Intelligence) system integrated into its functionality. This would mean that your apps understand you better. Already, your smartphone gives you suggestions based on your location, previous searches, past likes, etc.

Your apps will now interact with you in a more customized manner as it will learn, based on the information collected from the other apps. How users use an app and would contribute to how you use it and the data generated from your usage would improve your user experience even further.

4. Foreseeing the impact

It is hard to estimate how far-reaching the impact of Data science would be in our day to day lives, but one thing remains of certainty that nothing would be the same as it is now. Factors like speed and functionality and security would perhaps remain the same, but what would be interesting to see is how an app ends up adapting to your interests.

These are some of the interesting ways in which data science is expected to transform the web development sector.

How big data can influence the Income-tax filing process

The term big data has been doing the rounds quite often these days. That is solely because of the endless amount of potential it holds. Big data is, quite commonly, associated with the internet. It basically is extremely large sets of data that may be analyzed by advanced computing to uncover patterns and trends that would otherwise go unnoticed.

(more…)

5 Open Source Big Data Tools to Get You Started

As effective as it is in almost every field today, the term big data has turned into a scourge of sorts that seems to be floating around every corner you turn. People have been so talked out with big data that there is not much left for us to discuss unless of course, we were to discuss the various tools that are employed in processing and analyzing it. There are several platforms, tools, and extensions available today that are meant to serve each and every need.

1. Hadoop

When it comes to big data, there is no platform or tool that asserts itself as Hadoop does. Of course, the cute little yellow elephant mascot is both symbolic of Hadoop as well as big data in general. An open-source platform for big data, Apache’s Hadoop serves as the go-to place for everything big data, from the most fundamental aspects to more complex processes. With a myriad of different modules to offer, Hadoop sets the bar quite high for versatility. The Hadoop framework itself contains four modules.

Hadoop Common: A module comprised of Java libraries and utilities, Hadoop common supports other Hadoop modules. The libraries of this module contain java files and scripts required to run Hadoop.  

HDFS: Hadoop distributed file system or HDFS as it is commonly known is the primary storage system employed in all Hadoop applications.

YARN: Short for Yet Another Resource Negotiator, YARN is Hadoop’s cluster management system. It serves as a buffer between the HDFS’ and the processing engines.

Map Reduce: This is Hadoop’s data conversion module which converts data sets and breaks them down into individual elements called tuples. This is one of the most vital aspects of Hadoop and defining it in two sentences does not do it justice by the least. Perhaps we could cover this topic in detail in the future.

2. OpenRefine

One of the most fundamental tasks when it comes to big data is data sorting or data refining. As a software applied in an underrated aspect of big data, OpenRefine is an open source big data sorting tool that allows you to sort out the most contrived and unstructured of business data. One of the most desirable aspects of this open source tool is its simplicity and the user-friendly nature of its interface which will allow even data newbies to get right into the big data experience. However, as with anything and everything in technology, a little of experience and technology would be most recommended to get the most out of data using Open Refine.

3. Talend

An open source application that runs on Hadoop and NoSQL databases, Talend is a comprehensive tool to harness the best out of big data. It equips the user with various graphics tools and wizards to use Hadoop effectively with data quality, data integration, and management. Furthermore, the simplicity of this application is an added bonus for beginners. Talent offers a visual representation of the data and the processes rather than the source code in a simple drag and drop system, which while desirable to some, could become quite a hassle during certain processes. Talend also offers TalendForge, a collection of open source extensions to support and enhance the experience with various products offered by them.

4. MongoDB

When it comes to non-relational databases, there is none like Mongo for big data. A document-oriented, cross-platform database, Mongo comes with full index support and a great deal of flexibility. Horizontal Scalability and third-party log tool support are some of the additional features that Mongo offers. This database also comes with a replication facility called the Replica Set which provides automatic data failover and data redundancy.

5. Tableau Desktop and Server

Yet another data visualization tool for data analysis and other big data related processes, Tableau is quite an impressive piece of technology with a twist. It offers a new perspective of the data by allowing the user to slice it up into bits and even merging it with other data to gain yet another perspective. Tableau harnesses the power of both Apache’s Hadoop as well as Hive to offer an immersive and interactive process.

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.

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.

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.