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How Big Data Is Influencing HR Analytics for Employees and Employers, Both

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How Big Data Is Influencing HR Analytics for Employees and Employers, Both

HR analytics powered by big data is aiding talent management and hiring decisions. A Deloitte 2015 report says 35% of companies surveyed revealed that they were actively developing suave data analytics strategies for HR. Moreover, big data analytics isn’t leaving us anytime soon; it’s here to stay for good.

Now, with that coming, employers are of course in an inapt position: whether to use HR analytics or not? And even if they do use the data, how are they going to do that without violating any HR policies/laws or upsetting the employees?

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Health Data

While most of the employers are concerned about healthcare and wellness programs for their employees, a whole lot of other employees have started employing HR analytics for evaluation of the program’s effectiveness and addressing the gaps in healthcare coverage with an aim to improve overall program performance.

Today, data is the lifeblood of IT services. Adequate pools of employee data in conjunction with company data are aiding discoveries of the best benefit package for employees where they get best but affordable care. However, in the process, the employers need to be very careful and sensitive to employee privacy at the same time. During data analysis, the process should appear as if the entire organization is involved in it, instead of focusing on a single employee or sub-groups.

Predictive Performance Analytics

For talent management, HR analytics is a saving grace. Especially, owing to its predictive performance. Because of that, more and more employers are deploying this powerful skill to determine future hiring needs and structure a strong powerhouse of talent.

Rightfully so, predictive performance analytics use internal employee data to calculate potential employee turnover, but unfortunately, in some absurd cases, the same data can also be used to influence decisions regarding firing and promotion – and that becomes a problem.

Cutting edge machine learning algorithms dictate whether an event is going to happen or not, instead of what employees are doing or saying. Though it comes with its own advantages, its better when people frame decisions based on data. Because, people are unpredictable and so are the influencing factors.

Burn away irrelevant information

Sometimes, it may happen that employers instead of focusing on the meaningful things end up scrutinizing all the wrong things. For example, HR analytics show that employees living close to the office, geographically, are less likely to leave the office premise early. But, based on this, can we pass off top talent just because they reside a little farther from the office? We can’t, right?!

Hence, the bottom line is, whenever it comes to analyzing data, analysts should always look for the bigger picture rather giving stress on minute features – such as which employee is taking more number of leaves, and so on. Stay ahead of the curve by making the most productive decisions for employees as well as business, as a whole.

In the end, the power of data matters. HR analytics help guide the best decisions, but it’s us who are going to make them. We shouldn’t forget that. Use big data analytics responsibly to prevent any kind of mistrust or legal issues from the side of employees, and deploy them in coordination with employee feedback to come at the best conclusions ever. 

Those who are inclined towards big data hadoop certification, we’ve some droolworthy news for you! DexLab Analytics, a prominent data science learning platform has launched a new admission drive: #BigDataIngestion on in-demand skills: data science and big data with exclusive 10% discounts for all students. This summer, unfurl your career’s wings of success with DexLab Analytics!

 

Get the details here : www.dexlabanalytics.com/events/dexlab-analytics-presents-bigdataingestion

 

Reference:

The article has been sourced from https://www.entrepreneur.com/article/271753

 

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A Comprehensive Guide on Clustering and Its Different Methods

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A Comprehensive Guide on Clustering and Its Different Methods

Clustering is used to make sense of large volumes of data, structured or unstructured, by dividing the data into groups. The members of a group are ‘’similar’’ between them and ‘’dissimilar’’ to objects in other groups. The similarity is based on characteristics such as equal distances from a point or people who read the same genre of book. These groups with similar members are called clusters. The various methods of clustering, which we shall be discussing subsequently, help break up data into logical groupings before analyzing the data more deeply.

If a CEO of a company presents a broad question like- ‘’ Help me understand our customers better so that we can improve marketing strategies’’, then the first thing analysts need to do is use clustering methods to the classify customers. Clustering has plenty of application in our daily lives. Some of the domains where clustering is used are:

  • Marketing: Used to group customers having similar interests or showing identical behavior from large databases of customer data, which contain information on their past buying activities and properties.
  • Libraries: Used to organize books.
  • Biology: Used to classify flora and fauna based on their features.
  • Medical science: Used for the classification of various diseases.
  • City-planning: identifying and grouping houses based on house type, value and geographical location.
  • Earthquake studies: clustering existing earthquake epicenters to locate dangerous zones.

Clustering can be performed by various methods, as shown in the diagram below:

Fig 1

The two major techniques used to perform clustering are:

  • Hierarchical Clustering: Hierarchical clustering seeks to develop a hierarchy of clusters. The two main techniques used for hierarchical clustering are:
  1. Agglomerative: This is a ‘’bottom up’’ approach where first each observation is assigned a cluster of its own, then pairs of clusters are merged as one moves up the hierarchy. The process terminates when only a single cluster is left.
  2. Divisive: This is a ‘’top down’’ approach wherein all observations start in one cluster, and splits are performed recursively as one moves down the hierarchy. The process terminates when each observation has been assigned a separate cluster.

Fig 2: Agglomerative clustering follows a bottom-up approach while divisive clustering follows a top-down approach.

  • Partitional Clustering: In partitional clustering a set of observations is divided into non-overlapping subsets, such that each observation is in exactly one subset. The main partitional clustering method is K-Means Clustering.

The most popular metric used for forming clusters or deciding the closeness of clusters is distance. There are various distance measures. All observations are measured using one particular distance measure and the observation having the minimum distance from a cluster is assigned to it. The different distance measures are:

  • Euclidean Distance: This is the most common distance measure of all. It is given by the formula:

Distance((x, y), (a, b)) = √(x – a)² + (y – b)²

For example, the Euclidean distance between points (2, -1) and (-2, 2) is found to be

Distance((2, -1), (-2, 2)) 

  • Manhattan Distance:

This gives the distance between two points measured along axes at right angles. In a plane with p1 at (x1, y1) and p2 at (x2, y2), Manhattan distance is |x1 – x2| + |y1 – y2|.

  • Hamming Distance:

Hamming distance between two vectors is the number of bits we must change to convert one into the other. For example, to find the distance between vectors 01101010 and 11011011, we observe that they differ in 4 places. So, the Hamming distance d(01101010, 11011011) = 4

  • Minkowski Distance:

The Minkowski distance between two variables X and Y is defined as

The case where p = 1 is equivalent to the Manhattan distance and the case where p = 2 is equivalent to the Euclidean distance.

These distance measures are used to measure the closeness of clusters in hierarchical clustering.

In the next blogs, we will discuss the different methods of clustering in more details, so make sure you follow DexLab Analytics– we provide the best big data Hadoop certification in Gurgaon. Do check our data analyst courses in Gurgaon.

 

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How Big Data is Revolutionizing Political Campaigns in America

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How Big Data is Revolutionizing Political Campaigns in America

No doubt that big data is altering the manner in which politicians win elections in America, but it is also breaking American politics. So was the verdict in a column by NBC’s Chuck Todd and Carrie Dann.

According to Todd and Dann, recent technological advancements give access to detailed voter information and demographic data, like what they watch, what they shop and what they read; campaign initiators are completely aware of the preferences of voters. Hence, it enables them to target people who are most likely to vote for them through ads and other relevant content. They don’t feel the need to persuade the ones who are less likely to agree with their ideologies. Clearly, this is a crisis situation that fuels polarization within a governing system. It is encouraging campaigns to appeal to their most likely supporters rather than all their constituents. Also, this process is cheaper and faster.

Eitan Hersh, a notable professor of political science at Yale University conducted research on the role of big data and modern technology in mobilizing voters. So, let’s find out if his research work indicates the situation to be as adverse as Todd and Dann claims it to be.

New sources of data:

Earlier, campaigns relied on surveys to generate their data sets, which were based on a sample of the entire population. Now campaigns can use data that is based on the entire population. The data sets that are looked into include voter registration data, plenty of public datasets and consumer databases. Zonal data, like neighborhood income, can be accessed via the Census Bureau. Information about a voter, like her party affiliation, gender, age, race and voting history is often listed in public records. For example, if a democratic campaign is aware that a person has voted for a Democratic party previously, is Latino or of African origin and is under 25 years, then it is highly probable that this person will vote for them.

Once campaigns chalk out their team of supporters, they employ party workers and tools like mails and advertisements to secure their votes.

Hacking the electorate:

According to Eitan Hersh, it is truly impossible to completely understand the interests of the entire population of voters. However, campaigns are focusing heavily on gathering as much data as possible. The process consists of discovering new ways existing data can be utilized to manipulate voters; asking the right questions; predicting the likeliness of a group to vote for a particular candidate, etc. They need to find sophisticated ways to carry out these plans. The ever increasing volume of data is definitely aiding these processes. Campaigns can now customize their targeting based on individual behavior instead of the behavior of a standard constituent.

Types of targeting:

There are chiefly 4 methods of targeting, which are not only used for presidential elections but also for targeting in local elections. These are:

  1. Geographic targeting: This helps target people of a particular zip code, town or city and prevents wastage of money, as ads are focused on people belonging to a specific voting area.
  2. Demographic targeting: This helps targeting ads to specific groups of people, such as professionals working in blue-chip companies, men within ages 45 and 60 and workers whose salaries are within $60k per year for example.
  3. Targeting based on interest: For example, ads can be targeted to people interested in outdoor sports or conservation activities.
  4. Targeting based on behavior: This is basically the process in which past behavior and actions are analyzed and ads are structured based on that. Retargeting is an example of behavioral targeting where ads are targeted to those who have interacted with similar posts in the past.

To conclude, it can be said that victory in politics involves a lot more than using the power of big data to reduce voters to ones (likely voters) and zeros (unlikely voters). Trump’s victory and Clinton’s defeat is an example of this. Although, Clinton targeted voters through sophisticated data-driven campaigns, they might have overlooked hefty vote banks in rural areas.

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References: 

https://www.vox.com/conversations/2017/3/16/14935336/big-data-politics-donald-trump-2016-elections-polarization

https://www.entrepreneur.com/article/309356

 

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10 Key Areas to Focus When Settling For an Alternative Data Vendor

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10 Key Areas to Focus When Settling For an Alternative Data Vendor

Unstructured data is the new talk of the town! More than 80% of the world’s data is in this form, and big wigs of financial world need to confront the challenges of administering such volumes of unstructured data through in-house data consultants.

FYI, deriving insights from unstructured data is an extremely tiresome and expensive process. Most buy-sides don’t have access to these types of data, hence big data vendors are the only resort. They are the ones who transform unstructured content into tradable market data.

Here, we’ve narrowed down 10 key areas to focus while seeking an alternative data vendor.

Structured data

Banks and hedge funds should seek alternative data vendors that can efficiently process unstructured data into 100% machine readable structured format – irrespective of data form.

Derive a fuller history

Most of the alternative data providers are new kid in the block, thus have no formidable base of storing data. This makes accurate back-testing difficult.

Data debacles

The science of alternative data is punctured with a lot of loopholes. Sometimes, the vendor fails to store data at the time of generation – and that becomes an issue. Transparency is very crucial to deal with data integrity issues so as to nudge consumers to come at informed conclusions about which part of data to use and not to use.

Context is crucial

While you look at unstructured content, like text, the NLP or natural language processing engine must be used to decode financial terminologies. As a result, vendors should create their own dictionary for industry related definitions.

Version control

Each day, technology gets better or the production processes change; hence vendors must practice version control on their processes. Otherwise, future results will be surely different from back-testing performance.

Let’s Take Your Data Dreams to the Next Level

Point-in-time sensitivity

This generally means that your analysis includes data that is downright relevant and available at particular periods of time. In other cases, there exists a higher chance for advance bias being added in your results.

Relate data to tradable securities

Most of the alternative data don’t include financial securities in its scope. The users need to figure out how to relate this information with a tradable security, such as bonds and stocks.

Innovative and competitive

AI and alternative data analytics are dramatically changing. A lot of competition between companies urges them to stay up-to-date and innovative. In order to do so, some data vendors have pooled in a dedicated team of data scientists.

Data has to be legal

It’s very important for both vendors and clients to know from where data is coming, and what exactly is its source to ensure it don’t violate any laws.

Research matters

Few vendors have very less or no research establishing the value of their data. In consequence, the vendor ends up burdening the customer to carry out early stage research from their part.

In a nutshell, alternative data in finance refer to data sets that are obtained to inject insight into the investment process. Most hedge fund managers and deft investment professionals employ these data to derive timely insights fueling investment opportunities.

Big data is a major chunk of alternative data sets. Now, if you want to arm yourself with a good big data hadoop certification in Gurgaon then walk into DexLab Analytics. They are the best analytics training institute in India.

The article has been sourced from – http://dataconomy.com/2018/03/ten-tips-for-avoiding-an-alternative-data-hangover

 

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How Data Exhaust is Leveraged for Your Business

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How Data Exhaust is Leveraged for Your Business

Big data is the KING of corporate kingdom. Every company is somehow using this vital tech tool; even if they are not using it, they are thinking of it.

A 2017 survey says, around 53% of companies were relying on big data for their business operations. Each company focuses on a particular variant of data. Some of the data types are considered most important, while others are left out. Now what happens to the data that is kept aside?

Data exhaust can be a valuable addition for a company – if leveraged properly.

Let’s Take Your Data Dreams to the Next Level

Explaining Data Exhaust

It entirely deals with the data that is leftover but produced by the company itself. Keep in mind, when you try collect information from a specific set of data, a whole lot of information is also collected at the same time. So, many organizations might be sitting on a gold mine of data but without acknowledging the importance of that data. In instances like this, data exhaust can be very helpful across numerous business development channels.

Market Research

The best way to use data exhaust is through extensive market research. Know your audience is the key. Customers are crucial for effective marketing and product development. Nevertheless, the former involves manual research as well as analytical research, which once again leads us to analytics.

Through data exhaust, you get to know everything your customers do on your website – thus, can understand what they like better.

Cyber Security

As a potent threat, cyber crime results into potential costs to businesses all across the world. So, what role does data exhaust play? At best, it can help determine risk across different databases to develop superior cyber security plan.

Product Development

Importantly, businesses work on a plethora of projects at the same time. As a result, the issue of time crunch pops up. No one can do everything all at once, and data exhaust helps in sharpening whatever is important. Like, if your excess data says that most of your viewers visit your site through mobile device, it’s better to develop a mobile app to serve the customers better.

All Data Is Not Important

All data is not useful. Though data exhaust is useful, yet there would be times when you will come across bad data. You need to shed off those data, and get rid of data of that manner that is meaningless. Ask data experts which data to keep and which is irrelevant. Data that is of no use needs to be destroyed, because a company cannot keep trash for long.

Be Responsible for Data

Its clear data exhaust is all good and great for business, but it’s always suggestible to be cautious and responsible. There can be many legal implications, hence its suggestible to consult a data professional who have the desired know-how, otherwise things can get a bit complicated.

In this world of competitive technology, businesses have to be very careful about how they are using data to avoid any kind of negative outcomes. Be responsible and use data correctly; big data help frame a highly effective business strategy.

Looking for good big data courses? We have good news rolling your way – DexLab Analytics offers excellent big data training in Gurgaon. If interested, check out the course itinerary RN.

The blog is sourced from – http://dataconomy.com/2018/03/how-data-exhaust-can-be-leveraged-to-benefit-your-company

 

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Discover: Interesting Ways Netflix Relies on Big Data

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Discover: Interesting Ways Netflix Relies on Big Data

Netflix boasts of over 100 million subscribers – a humongous wealth of data is stored and analyzed to enhance user experience. Big data makes Netflix the King of Stream; it keeps the customers engaged and content.

Big data recommends Netflix a list of programs that interests the viewers and this system actually influences 80% of content that is available on Netflix. Estimates say the cutting edge algorithms save $1 billion a year in value from customer retention – undoubtedly, a whopping figure for the entertainment industry.

Big data is used extensively all through Netflix application, BUT the Holy Grail is the prediction part: what the customers want to watch and enjoy matters the most. Moreover, big data is the fuel that powers up the recommendation engines that are created to serve the purpose.

netflix-and-devices-243

Healthy prediction of viewing habits

Efforts started way back in 2006, when Netflix was primarily into DVD-mailing business. It initiated the Netflix prize, rewarding $1 million to any group, which can devise the best algorithm to predict how a customer would rate a particular movie, based on previous ratings. Today, though the algorithms are constantly updated, but the principles still remain a key characteristic of a recommendation engine.

In the beginning, analysts were left with very little data about their customers, but as soon as streaming became more mainstream, new data points about their customers became easily available. What affects a particular movie had on a viewer could be assessed, as well as models were built to predict the ‘perfect storm’ situation for customers who were served with the movies they like.

Infographic-Netflix-knows-when-youre-Hooked

Identifying the next smashing series

Of late, Netflix has broadened its scope to include content creation, instead of limiting itself to being a distribution method for movie studios and other channels. This strategy is of course backed by meaningful data – which highlighted how its viewers are hungry for content directed by David Fincher and starring Kevin Spacey.

Every minute part of the production of the series is structured on data, including the colors used on the cover image of the series to draw in subscribers.

Netflix

For a quality experience

Netflix takes the quality aspect into great consideration. It closely monitors and analyzes the various factors that affect user behavior. Even, it develops models to explore how they perform. While, a large number of shows are hosted internally on its own distributed network of servers, they are also reflected around the world by ISPs and other hosts. Along with improving the user experience, efficient content streaming reduces costs for ISPs – shielding them from the cost of downloading data from Netflix server.

Big data and analytics have positioned themselves in the right order to dictate the operations across all Netflix platforms. They surely lead the pack of data by taking over distribution and production networks and re-modifying them through constant evolution and innovation of data.

Not only this, Netflix has reduced its promotional campaign budgets by targeting only the most relevant and interested people at the same time. All possible because of big data.

So, next time, when you peruse through your favorite shows in Netflix, do think and thank the power of big data. Because, big data is much more than what you think!

DexLab Analytics, a renowned big data training institute in Gurgaon is the best place to start a big data certification endeavor. The consultants are proficient in what they teach, the course curriculum is comprehensive and flexible course modules are suitable for everyone, irrespective of professionals or students.

The article has been sourced from:                                 

https://insidebigdata.com/2018/01/20/netflix-uses-big-data-drive-success

http://dataconomy.com/2018/03/infographic-how-netflix-uses-big-data-to-drive-success

https://www.linkedin.com/pulse/amazing-ways-netflix-uses-big-data-drive-success-bernard-marr

 

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AI is enhancing careers: How can you gain advantage in this AI-era?

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Artificial intelligence has a significant impact on our lives. Several AI powered automation tools are already in use such as customer service applications and voice-powered assistants, like Apple’s Siri and Amazon’s Alexa. Adoption of AI will benefit the business by improving the quality and consistency of work. Based on a discussion between Forbes Agency council members, we have listed the ways in which artificial intelligence can help workers improve their career.

  1. More valuable insights

AI will bring positive changes in the job of PR professionals. AI technology will take over manual jobs such as news monitoring, researching, reporting and making media lists. AI based predictive analytics will help PR professionals make better market predictions. They will reduce manual workload and help in strategic and creative thinking.

  1. Replace mundane tasks

AI, automation and machine learning will replace daily low-quality cognitive tasks such as scheduling calendar invites, daily food ordering, determining whether to answer/review/delete emails based on facts. They will eventually aid in quality tasks such as identifying connections, analyzing correlation and drawing inferences.

  1. Act as concierge

Popularity of Alexa, Watson, and Einstein suggest that consumers will expect tech to provide concierge services in the future. As AI techs evolve post their purchase, it will anticipate an individual’s daily tasks and provide highly personal recommendations.

  1. Make marketing smarter

AI will enable companies develop stronger relationships with their customers. IBM’s Watson and other cognitive technology will help analyze unstructured text, audio, images and video. AI’s ability to perceive and process personality, tone and feelings will help deliver better personal recommendations. It will help companies carry out conversations using chatbots.

  1. Automate customer support

The availability of chatbots round the clock will save a lot of time. They answer customer questions, give recommendations and guide customers to the next step. They will reduce the workload of customer support systems. Bots can draw insights on the needs, engagements and emotions of customers.

  1. Unleash the full potential of your mind

Workers will be spared from carrying out mundane tasks. They will have the time to focus on productive tasks, which require problem-solving skills and creativity.

  1. On-the–fly video editing

AI will eventually edit videos in real time.  Real- time user engagement will perform multiple instantaneous tasks such as changing sound effects on the fly.

  1. Create jobs and assimilate workflow

AI will interfere with regular workflow but in return it will create new jobs. It will help integrate the workforce. Humans will be instrumental in helping the AI work in harmony with the employees.

  1. Improve future strategies

Humans will always be a part of the PR industry, as they are crucial in maintaining a healthy customer relationship. The data that is collected through AI will enable making more informed decisions for the future. AI will help companies stay abreast of information related to their competitors through better media monitoring.

  1. Shrink 40 hours of analysis to 4 minutes

Manual analysis is very time consuming. The future of marketing efficiency lies in automation tools that will drastically reduce the time taken to analyze data and form strategies.

  1. Productivity even during commute

AI has made automated driving a reality. Driving in autopilot mode greatly reduces driver fatigue and can affect productivity during commute, especially to and from work.

  1. Improve brand engagement

AI can help devise customized experiences in real time. It interprets customer interactions and instantly creates customized content.

  1. Make routine processes easier

Entrepreneurs describe AI as the ultimate efficiency driver. The day to day tasks can be entrusted to digital hands, which enable human hands to be more productive. AI driven technology is benefitting manufacturing processes as well as advertising platforms.

  1. Give edge in competition

Businesses using AI will have a competitive edge over their clients. This is because AI implementation replaces manual processes of sorting complex data, drawing key insights and chalking out an action plan. AI improves decision-making, ROI, operational competence and cost savings.

AI related employment opportunities are on the rise. Compared to the demand, there is a lack in the number of professionals proficient in AI. It is predicted that by 2020, 20 percent of companies will need their workers to monitor and direct neural networks. About 2 million jobs in the cyber security sector are about to go vacant in the coming years.

So it is absolutely imperative to future-proof your career for the imminent AI era. Broaden your skill set and increase your proficiency by taking professional training in Machine Learning, Business Analytics and Data Science. Get an edge in your career by joining the Data science and machine learning certification course offered by Dexlab Analytics- a premier institute offering multiple courses on data science.

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5 Examples that Show Artificial Intelligence is the Order of the Day of Daily Life

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Artificial Intelligence is no more an elusive notion from science fiction; in fact, it’s very much in use in everyday life. Whether you realize it or not, the influence of AI has grown manifold, and is likely to increase further in the coming years.

5 Examples that Show Artificial Intelligence is the Order of the Day of Daily Life

Here are a few examples of AI devices that lead you to a brighter future. Let’s have a look:

Virtual Personal Assistants

The world around you is full of smart digital personal assistants – Google Now, Siri and Cortana though available on numerous platforms, such as Android, Ios and Windows Mobile strives to seek meaningful information for you, once you ask for it using your voice.

In these apps, AI is the power giver. With the help of AI, they accumulate information and utilize that data to better understand your speech and provide you with favorable results that are tailor-made just for you.  

Smart cars

Do you fantasize about reading your favorite novel, while driving to office? Soon, it might be the reality! Google’s self-driving car project and Tesla “autopilot” characteristic are two latest innovations that have been stealing the limelight lately. In the beginning of this year, there was a report that, Google developed an algorithm that could potentially allow self-driving cars learn the basics of driving just like humans, i.e. through experience.

Fraud detection

Have you ever found mails asking if you have made any particular transaction using your credit card? Several banks send these kinds of emails to their customers to verify if they have purchased the same to avoid frauds being committed on your account. Artificial Intelligence is employed to check this sort of fraud.

Like humans, computers are also trained to identify fraudulent transactions based on the signs and indications a sample shows about a purchase.

Buying pattern prediction

Distinguished retailers, like Amazon do make a lot of money, as they anticipate the buyer’s needs beforehand. Their anticipatory shipping project sends you products even before you ask for them, saving you from the last-minute online shopping. If not online retailers, brick-and-mortar retailers also use the same concept to offer coupons; the kind of coupons distributed to the shoppers is decided by a predictive analytics algorithm.

Video games

Video games are one of the first consumers of AI, since the launch of the very first video games. However, over the years, the effectiveness and intricacies of AI has doubled, or even tripled, making video games more exciting, graphically and play wise. The characters have become more complex, and the nature of game-play now includes a number of objectives.

No matter, video games are framed on simple platforms, but as industry demand is burgeoning at an accelerating pace, a huge amount of money and effort are going into improving AI capabilities to make games more entertaining and downright exciting!

Fact: Artificial Intelligence is serving millions of people on earth today. Right from your smartphone to your bank account, car and even house, AI is everywhere. And it is indeed making a huge difference to all our lives.

To gain more knowledge on AI, enroll in Big Data Certification Gurgaon by DexLab Analytics. Their big data and data analytics training is of high quality and student-friendly. The prices of the course are also fairly convenient.

The blog has been sourced from – https://beebom.com/examples-of-artificial-intelligence

 

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How Big Data Plays the Key Role in Promoting Cyber Security

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The number of data breaches and cyber attacks is increasing by the hour. Understandably, investing in cyber security has become the business priority for most organizations. Reports based on a global survey of 641 IT and cyber security professionals reveal that a whopping 69% of organizations have resolved to increase spending on cyber security. The large and varied data sets, i.e., the BIG DATA, generated by all organizations small or big, are boosting cyber security in significant ways.

How Big Data Plays the Key Role in Promoting Cyber Security

Business data one of the most valuable assets of a company and entrepreneurs are becoming increasingly aware of the importance of this data in their success in the current market economy. In fact, big data plays the central role in employee activity monitoring and intrusion detection, and thereby combats a plethora of cyber threats.

Let’s Take Your Data Dreams to the Next Level

  1. EMPLOYEE ACTIVITY MONITERING:

Using an employee system monitoring program that relies on big data analytics can help a company’s human resource division keep a track on the behavioral patterns of their employees and thereby prevent potential employee-related breaches. Following steps may be taken to ensure the same:

  • Restricting the access of information only to the staff that is authorized to access it.
  • Staffers should use theirlogins and other system applications to change data and view files that they are permitted to access. 
  • Every employee should be given different login details depending on the complexity of their business responsibilities.

 

  1. INTRUSION DETECTION:

A crucial measure in the big data security system would be the incorporation of IDS – Intrusion Detection System that helps in monitoring traffic in the divisions that are prone to malicious activities. IDS should be employed for all the pursuits that are mission-crucial, especially the ones that make active use of the internet. Big data analytics plays a pivotal role in making informed decisions about setting up an IDS system as it provides all the relevant information required for monitoring a company’s network.

The National Institute of Standards and Technology recommends continuous monitoring and real-time assessments through Big Data analytics. Also the application of predictive analytics in the domain of optimization and automation of the existing SIEM systems is highly recommended for identifying threat locations and leaked data identity.

  1. FUTURE OF CYBER SECURITY:

Security experts realize the necessity of bigger and better tools to combat cyber crimes. Building defenses that can withstand the increasingly sophisticated nature of cyber attacks is the need of the hour. Hence advances in big data analytics are more important than ever.

Relevance of Hadoop in big data analytics:

  • Hadoop provides a cost effective storage solution to businesses.
  • It facilitates businesses to easily access new data sources and draw valuable insights from different types of data.
  • It is a highly scalable storage platform.
  • The unique storage technique of Hadoop is based on a distributed file system that primarily maps the data when placed on a cluster. The tools for processing data are often on the same servers where the data is located. As a result data processing is much faster.
  • Hadoop is widely used across industries, including finance, media and entertainment, government, healthcare, information services, and retail.
  • Hadoop is fault-tolerant. Once information is sent to an individual node, that data is replicated in other nodes in the cluster. Hence in the event of a failure, there is another copy available for use.
  • Hadoop is more than just a faster and cheaper analytics tool. It is designed as a scale-out architecture that can affordably store all the data for later use by the company.

 

Developing economies are encouraging investment in big data analytics tools, infrastructure, and education to maintain growth and inspire innovation in areas such as mobile/cloud security, threat intelligence, and security analytics.

Thus big data analytics is definitely the way forward. If you dream of building a career in this much coveted field then be sure to invest in developing the relevant skill set. The Big Data training and Hadoop training imparted by skilled professionals at Dexlab Analytics in Gurgaon, Delhi is sure to give you the technical edge that you seek. So hurry and get yourself enrolled today!

 

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