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.

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.

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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 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.

Big Data applications in Cyber Security

As businesses and business transactions are growing so are the threats of cyber crimes. There used to be a time when if someone wanted to steal from a bank, they had to go through an elaborate planning montage featuring eleven people which included a small Chinese gymnast.

But, today the game has changed quite a bit. Today the only bank robbery montage you have to go through is that of a socially awkward guy in a hoodie sitting in a coffee shop with his laptop. Whoever thought that would be a ‘cool’ thing to do? Well, the answer is pretty much anyone with a laptop, an internet connection and a basic knowledge of Kali Linux. The cavalier approach to this very much real threat which is seasoned with the occasional media outburst is quite understated but justified.

The reason is that there is only so much that can be done to stop cyber crimes. Which is also true for real crimes. So, the only thing that can be done is beef up security by using newer and more advanced systems. But, let us not forget the fact that for every new security feature that professionals come up with, there is a hacker somewhere trying to counter it.

One of the most interesting developments and reasons for debate on this topic is Big Data. As we have seen in recent years, Big Data has opened up several opportunities for all industries and it is being used in every which way conceivable. The argument with big data in cybersecurity is that it is capable of hurting security systems just as much as it is helping it. For something as sensitive as cybersecurity this is not a gamble we could afford to take. So, let us explore whether this argument holds water.

The Cyber Security Threats Today

Every year for the past decade we have had this incident where a massive breach of security has the whole world in an uproar. The past two years, in particular, have been filled with such incidents, from the alleged incident of Russians Hacking the U.S. elections to the Worldwide WannaCry malware. The more recent Cambridge Analytica scandal is a good example of how data breaches don’t necessarily have to involve hacking.

This sheds light on a whole new perspective on data security. With big data at the helm of their marketing strategies, many multinational corporations have access to a lot of information. So, let us move from the data we are trying to hide and have a look at data that we are giving away. With the ongoing Facebook scandal, we have learned one thing, that all the data that we are comfortable with sharing is as exploitable as the sensitive stuff we hide. Big Data here plays a huge role as it has become the go-to data analytics tool. On a global scale, this can become a very notorious issue.

The risks of having a corporation tapping into your published data through seemingly legal means have caused such an issue. Imagine the extent of damage that could be caused if such data fell into the hands of real threats like ISIL. Well, you don’t need to imagine that hard to perceive such a scenario as such hacks are a common thing today. Yet again, it is big data that comes to the forefront as a promoting tool for cyber crimes. Before we can go throwing blame around on corporations, we must remember that they have to maintain and secure several exabytes of data. Which means that they have to constantly analyze potential threats as well as breaches. This is by no means an easy task and the introduction of big data-based technologies in this equation only makes things infinitely worse.

How Big Data helps the cause of Cyber Security?

Risk management and intelligence that kicks into action when required are common outcomes of a big data analysis. It would always be good to have tools that can analyze and automate data so that it is available easily and the analyzed data is transferred to the right people at the right time is of the essence.

By doing this, analysts get to categorize threats without investing long hours. If the data comes in after a long delay, the response that comes in would be irrelevant to the attack that the company is under.

Big data comes in handy in the manner that it helps analysts to visualize cyber-attacks by considering the complexity of data from a vast network of data and simplifying the patterns that have been understood into visualizations.

Big data is an excellent method of detecting Trojan horses that come from employee devices. It does so by identifying anomalies in employee and contractor devices also. Where big data is impressively useful is when certain tangible steps are taken towards improving cybersecurity.

Big data allows you to automatically respond to threats noticed in the data that comes in and also enables you to trust the accuracy of the data. This is what is said to be the X-factor behind what makes big data security all the more proficient.

To sign off

In conclusion, it comes as no surprise that now companies are investing heavily in high-end and advanced infrastructure to enhance cybersecurity thereby detecting threats far efficiently and effectively. Some believe that big data will solve the varied issues of the cybernetics industry in a blink of an eye. The truth, however, is that as these attacks keep getting stronger and advanced, the security installed to avoid them are getting stronger too. Considering the many ways in which big data can be leveraged for cybercrime, it is important for corporations to use their resources to this end responsibly.

Applications of Big Data in E-commerce

There was a point in time when lack of data was an issue. Now, times have changed and it is the overabundance of data that seems to be complicating the matter all the more. Especially in the e-commerce sector, where metrics like GMV, CLV, cart abandonment rate, and AOV are tracked diligently by online retail companies to gauge its performance.

Using the traditional methods to organize, store and study data are no longer feasible. Fortunately, Big Data is going mainstream and offers a range of advantages that will aid the data and analytics needs of the e-commerce companies. Here’s how big data is changing the face of e-commerce:

1. Enhanced customer service experience

The larger your customer base, larger is the data generated and the more you’d need to invest in infrastructure for storing data. This being the traditional method has been leading to poor customer service and unsatisfied customers overall. This can be easily avoided by using big data.

Big data holds the potential to track not just customer information but also maintain their experience records. Companies can then backtrack each customers experience and see what it is that is going wrong repetitively and strive to improve it.

2. Secure payments online

While paying online, big data has a significant role to play. Big data makes paying over the net a more secure and faster process. Big data integrates various payment platforms into one centralized platform. It aids not only the customers but also keeps fraudulent activities at bay.

3. Mobile commerce

Smartphones have turned out to be an extended organ of humanity. An organ so essential that imagining anything over an hour without it would seem impossible. Big data is in favor of mobility simply because anything with an IP address and the ability to transfer and receive data is compatible with Big data. Google also is turning partial towards those sites that are mobile responsive, and we don’t see why it wouldn’t be doing so.

4. VR advancements

When you combine another buzzword in the IT field such as VR and merge it with big data, you get something that holds the potential to reconstruct the future. This merger not only changes the way a consumer would go about with his/her day but it would also revolutionize the way sellers sell things.

Virtual reality would materialize things right in front of your eyes without the dependence on anything remotely human and for that, big data would be of help. Are we looking at the insurgence of Artificial intelligence here as VR and big data score so high on compatibility charts? It is for us to wait for not too long and watch.

Parting thoughts

Seemingly the King Midas of the IT sector, big data is changing to gold whatever it comes in contact with. E-commerce companies are increasingly relying on big data to get that much needed competitive edge and enhance the overall customer service experience that they can deliver.

How Big Data can help HR with hiring decisions

The Human resources (HR) function is usually not the first thing that comes to mind when we think of big data. That’s because it is still a developing concept and a majority of the companies are still using traditional methods to perform HR related tasks.

Organizations should make the most of big data to find the right level of adaptability to offer work-life balance to its employees as well as to determine the right benefits and bonuses in order to encourage employee loyalty towards its company. It can also be used to increase the quality and usefulness of regular training programs, thus maximizing the use of their human capital.

Big data has proven itself fruitful in many businesses; be it sales, marketing or accounting. Today we explore how big data will impact the HR function:

1. Amplify the quality of new recruits

Hiring the wrong person for the job is probably the worst mistake an HR team can make. With the help of big data, recruiters can be more analytical and strategic when it comes to finding the ideal candidate for the job.

If they get access to online employee resume databases, employment records, social media profiles, tests and other profiles, it will help the recruiter find the best candidate with the highest potential by sorting this information and narrowing down the talent pool.

Take the case of Royal Dutch Shell; they made their employee play specially designed video games in order to analyze the best idea generators in their team. As a result, the team found it easier to recruit employees that had the 6 main qualities the company needed i.e. mind wandering, social intelligence, goal-orientation fluency, implicit learning, task-switching ability, and conscientiousness.

2. Promote better training and employee success rate

Training can be an expensive affair if the overall employee retention is unsuccessful. Big data allows businesses to measure the effectiveness of the training program so they can make better investments when it comes to training and development of their employees. If regular performance evaluations are conducted, the efficiency can be measured via big data and it will help the HR understand the effectiveness of their employee development programs.

Check out IBM’s strategy in this context. Traditionally an outgoing personality has been seen as a key trait, but IBM compared worker surveys and tests with manager assessments and found that the most important characteristic of sales success was actually emotional courage. Successful salespeople may or may not be outgoing, but they do need to be persistent, and not take no for an answer.

3. Prevent employee attrition

Making strategic workforce decisions without data to back them up is like guessing, and it’s an issue that has prevented the HR from making a bigger impact on business outcomes. Workforce analytics is the art and science of connecting data to discover and share insights about your workforce that will lead to better business decisions. In order to reduce employee turnover, HR needs to become more data-driven, looking past simple descriptive analytics and towards more exploratory analytics, predictive analytics.

To conclude

These are a few ways in which big data can help the HR perform at a much more efficient rate. It is an infant concept but once companies truly start implementing it in their recruitment processes, it will yield great results.