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While it often uses fairly complex algorithms, the goal of differential privacy is pretty simple: make sure that people whose data is collected have as much privacy as they would if the data had never been recorded. You should never be able to identify someone just by looking at a set of stored information about them.

How Differential Privacy Works

Since data about us is being collected at an unprecedented rate and people are getting uncomfortable with it, the idea that your privacy can be mathematically proven is starting to look pretty good. Companies like Microsoft, Google, Apple, Facebook, and Uber have either implemented it in some form or are exploring their options, but even before big tech got interested, it was being used for things like sensitive research data, medical records, and even parts of the U.S. census.

It does this by adding noise, either to the stored data itself or to the results that get returned when someone queries it – messing up individual pieces of data but maintaining the overall shape. “Noise” is essentially irregularity, or unexplained variability, in data, and the goal here is to insert noise into individual data points while keeping overall measures like the mean, median, mode, and standard deviation close to where they were before.

Simple Differential Privacy

Let’s imagine that you’ve been selected to participate in a groundbreaking social science study. Here’s the catch, though: some of the questions are going to be potentially embarrassing, incriminating, or otherwise inconvenient for you. Let’s just say you’d prefer not having anyone see your name next to a checkmark in the column labeled “Actually liked the last season of Game of Thrones.”

Luckily, the researchers have anonymized the study. Instead of names, you get a random number, but even then, people can use your responses and narrow it down to you.

That’s a problem that’s actually come up quite a bit in the real world, perhaps most famously when researchers were able to not only identify Netflix users but even find out about some of their political preferences. But what if we could rig that data, as well as our survey, so that no one reading the results could know for sure what each person said?

Adding noise with coin flips

Here’s a technique we can use to both maintain your privacy and get results that, in aggregate, look like they would if everyone told the truth:

We won’t be looking at the coin, so we won’t know whether or not it told you to lie. All we know is that you had a 50% chance of telling the truth and a 50% chance of saying “Yes” or “No.”

Your answer is then recorded next to your name or ID number, but you now have plausible deniability. If someone accuses you of enjoying that last Game of Thrones season, you have a defense that is backed by the laws of probability: the coin flip made you say it.

The actual algorithms most tech companies are using for differential privacy are much more complex than this (two examples below), but the principle is the same. By making it unclear whether or not each response is actually valid, or even changing responses randomly, these algorithms can ensure that no matter how many queries someone sends to the database, they won’t be able to concretely identify anyone.

Not all databases treat this the same way, though. Some only apply the algorithms when the data is queried, meaning the data itself is still being stored in its original form somewhere. This obviously isn’t the ideal privacy scenario, but having differential privacy applied at any point is better than just pushing raw data out into the world.

How is it being used? Apple

The Mean Count Sketch algorithm

used by Apple

for differential privacy

Apple uses differential privacy to mask individual user data before it’s ever submitted to them, using the logic that if a lot of people submit their data, the noise won’t have a significant impact on the aggregate data. They use a technique called “Count Mean Sketch,” which essentially means the information is encoded, random pieces are changed, and then the “inaccurate” version is decoded and sent to Apple for analysis. It informs things like their typing suggestions, lookup hints, and even the emojis that pop up when you type a word.


RAPPOR data flow from the project’s GitHub

Google’s first big foray into differential privacy was RAPPOR (Randomized Aggregatable Privacy-Preserving Ordinal Response), which runs the data through a filter and randomly changes pieces of it using a version of the coin-flip method described above. They initially used it to gather data on security issues in the Chrome browser and have since applied differential privacy elsewhere, like finding how busy a business is at any given time without revealing individual users’ activity. They’ve actually open-sourced this project, so there may be more applications popping up based on their work.

Why isn’t all data being treated this way?

Differential privacy is currently a bit complex to implement and it comes with an accuracy tradeoff that can negatively impact critical data in some circumstances. A machine-learning algorithm using privatized data for sensitive medical research might make mistakes big enough to kill people, for example. Nonetheless, it’s already seeing real use in the tech world, and given increasing public awareness of data privacy, there’s a good chance that we’ll see mathematically-provable privacy being touted as a selling point in the future.

Image credits: RAPPOR data flow, Server-Side Algorithm for Hademard Mean Count Sketch, Dataset-survey R-MASS package, Tree of probabilities – flipping a coin

Andrew Braun

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Share And Receive Large Confidential Files With Titanfiles

Have you ever wanted to share a large and confidential file with your colleagues or clients but can’t find the appropriate tools to do it? While there are plenty of online services that allow you to send/receive large files, the main concern is still the security issue.

Once you are logged in, the dashboard will show your recent activity. It will be blank if you are logged in for the first time.

Sharing Files

To share a file, first go to the Files tab to upload your files. During the uploading process, you can define the expiry date of the file. After the expiry date, the link that you sent to your friends/clients will not work anymore.

Receiving Files

Other than sharing files with others, you will also be given a sub-domain URL where you can share with others. People can then visit the site and drop file to your account.

Adding Members

Another feature of TitanFile is that you can add members to your account (for premium account only). This is best suited if you have several people working on a project.


Damien Oh started writing tech articles since 2007 and has over 10 years of experience in the tech industry. He is proficient in Windows, Linux, Mac, Android and iOS, and worked as a part time WordPress Developer. He is currently the owner and Editor-in-Chief of Make Tech Easier.

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Boon From Big Data Or Loss Of Privacy?

Today’s post is going to be different.

There is no technical subject matter I am going to talk about. But the article is far more thought provoking than any of the article I have written till date.

[stextbox id = “section”] A real life incident:[/stextbox]

Let me start with a real life example to get your thinking process started:

About 6 months back, I bought a top end Android smartphone. After using it for a month or so, I accidently started Google Now on the phone. The interface looked very simple on first look (nothing more than a search bar and weather update). So I moved back from the application and started living my usual life.

I would have almost forgot this instance like multiple other applications which come with the phone and I don’t use. However, Google had something else in store. A week after I opened the application for first time, I got a notification on my home screen, suggesting that I am 15 minutes away from Home and the traffic on route is normal!

The notification took me by surprise. I never told my phone where my home is! Over the next few days, the application identified my Office, commute place, friends place, the websites I visit frequently. It now integrates my searches across devices. So if I search a restaurant on my laptop, my phone shows me the route to same restaurant!

The incident above is like a dream come true for a lot of analysts and a scary incident leading to loss of privacy to a lot of customers.

As an analyst and some one who specializes in predictive modeling, I am usually a proponent of big data and the changes it is bringing to our day to day life. However, I have to admit that Google took me by surprise and has made me think and reflect a lot more on how life is changing. It has ensued a debate between 2 sides of my personality.

[stextbox id = “section”]Two sides of debate:[/stextbox]

My first personality is that of a common man. I want my privacy, specially during some personal moments. These moments could be the time I spend with my family or when I am reading or may be talking to a friend. I don’t want interruptions or suggestions from any third party during this period. I want to relish the moment as it is. After going through the experience mentioned above and many more like that, I am not sure whether these moments will remain as pristine and unadulterated as I would want them. Would my reading experience be marred by suggestions about different things I might like? Would the phones pop up notifications about my friend when I am talking to them? or may be when I am talking about them to my wife? The possibilities are limitless!

The other side of my personality is a big proponent of technology and Analytics. I remain excited about how technology can be used to solve day to day problems. I come out with innovative ways of using data to create value (for customers as well as Organizations). I continuously think how behavioural modeling can help customers in breezing through day to day chores? How can I predict something before it actually happens.

[stextbox id = “section”]How do we resolve this?[/stextbox]

The second personality needs to be cognizant about the presence of first personality and take actions which are in sync with values of first personality. Here are some rules I have come out with, which every analyst needs to keep in mind while designing a product or working on his next big data project:

[stextbox id = “section”]1. Transparency:[/stextbox]

This is the biggest takeaway. The bare minimum an analyst needs to make sure is that the customer is aware about what data is being collected and how can this be used. This needs to come out clearly. This is similar to apps (on smartphones) asking permissions before installing them. If you are collecting data with out asking customer explicitly, you are headed for disaster.

So, instead of using data through a pre-selected tick box (buried somewhere is my phone settings), I would have appreciated if the app reminded me of the data it will use, when I started it for the first time.

[stextbox id = “section”]2. Develop a character of your Organization by keeping customer at the heart:[/stextbox]

Let me try and explain. Years before Google started collecting information about usage from Android phones, Microsoft started this for MS Office. They asked me whether I would want to share my usage patterns with Microsoft, which will help them improve user experience further. I almost always declined. When Google asks me same thing, I am more open to sharing information.

It might be a personal choice. However, the reality is that I am more open to sharing data with Google because I can relate to the benefits they have provided me by using this information. I have benefited by sharing some of this information with Google.

The message is that if you don’t provide the benefit of this information back to the consumer, they will stop sharing this information.

[stextbox id = “section”]3. Make change in subtle manner:[/stextbox]

Big changes in user interface or the way new product gets rolled out can take customer by surprise. You have to build in these changes in subtle manner. In a way such that the customer still feels as much at home as possible. I think Google does a nice job at it. Here are some best practices:

Provide an option to user to switch back to old proposition, if it is not working for him

Try and keep as much user interface unchanged as possible.

[stextbox id = “section”]4. Test and roll-out:[/stextbox]

Irrespective of how good an idea is, you should avoid making complete roll-outs without testing. There are multiple benefits from this:

You actually act based on how customer feels about the product

You can size the benefit / loss you have seen by moving to a new product.

I think until and unless Organizations and analysts adhere to these rules, it might only be a question of time before they face a bunch of disgruntled customers.

If you like what you just read & want to continue your analytics learning, subscribe to our emails or like our facebook page.


5 Free Privacy Tools For Protecting Your Personal Data

Stop mobile trackers with Lockdown

Lockdown is a free and simple utility for iOS and Mac that prevents apps from connecting with these data trackers. I’ve had it on my iPhone since January and in that time it’s blocked more than 1 million tracking attempts in a completely unobtrusive way. Until sitting down to write this newsletter, I’d forgotten that I’d set it up already.

Keep in mind that Lockdown is not a VPN, so it’s not routing any of your internet traffic through its own servers to mask your location, but I think that’s mostly a positive since it doesn’t interfere with connectivity. If you do want a VPN service, however, Lockdown sells it as an add-on subscription. (One other note: The app can hinder your ability to log into Facebook Messenger or WhatsApp, but turning it off while logging in seems to solve the problem.)

Mask your email address with Abine Blur

Jared Newman / IDG

For marketers that want to track your online behavior, your email address is the ultimate prize. Once you log into a website or app, that site can use tracking cookies to follow you around and associate the data with your email. And, for less scrupulous marketers, opting out of their emails can be a major hassle.

Abine Blur isn’t the only tool of its kind. Apple offers a “Hide My Email” for users with paid iCloud+ subscriptions and DuckDuckGo offers masked emails through its mobile apps and browser extensions. But I like that Abine Blur works across platforms and doesn’t require changing your default search engine.

Opt out of tracking cookies with Super Agent

Jared Newman / IDG

If you’re tired of seeing those annoying “accept cookies” prompts while browsing the web, a browser extension called Super Agent is the best solution I’ve seen yet for making them go away. While other extensions merely hide the pop-ups in a way that can break some websites, Super Agent automatically fills out and dismisses cookie consent forms on your behalf.

Super Agent works with every major desktop browser and is also available as a Safari extension on iOS. The developers make money by selling code to websites that want to integrate with the extension and promise never to sell your data.

Protect your phone number with Google Voice

Jared Newman / IDG

Next time a business asks for your phone number and you’re not comfortable giving it out, consider handing out a number from Google Voice instead. When you sign up for Google Voice, you claim a phone number from an area code of your choosing. Incoming phone calls will then forward to your real number and you can check text messages through the Google Voice website or mobile app.

Jared Newman / IDG

Similar to how Google Voice can mask your real phone number, chúng tôi lets you use virtual credit cards for online stores and subscription services. You can then put spending limits on each virtual card or even designate them as single-use cards, preventing untrustworthy vendors from running off with the card info.

This isn’t just form of payment protection, though. It’s also a privacy tool that prevents credit card companies from tracking and selling your shopping habits. Combine this with a masked phone number and email address and vendors will have a much tougher time mining that data. (Check out my colleague Ian Paul’s review for more details.)

Want more tips and tricks like these? Sign up for Jared’s Advisorator newsletter, where a version of this article originally appeared.

Apple Touts Health Data Privacy In New Whitepaper And Clever ‘The Waiting Room’ Video

As it continues to focus on its commitment to privacy, Apple is launching an all-new campaign today emphasizing how Health data on iPhone is protected. As part of this, Apple has shared a new Health Privacy whitepaper as well as a clever new video that aims to highlight the importance of health data privacy.

New Health data whitepaper

In the new Health Privacy Overview whitepaper, Apple goes in-depth on how the Health app and the HealthKit framework for developers protects user privacy. For instance, Apple touts that all Health data is stored using end-to-end encryption:

Health data, stored in HealthKit, is encrypted on-device and is only accessible with your passcode, Touch ID or Face ID. Medical ID is still available when your device is locked to help first responders access your critical medical information from the Lock Screen in an emergency. For users with two-factor authentication, a device passcode, and a device running iOS 12 or later – Health app data synced to iCloud is also not readable by Apple. As of August 2023, over 95% of active iCloud users have two-factor authentication enabled.

Apple also explains the relationship between a user’s health data and third-party apps. “Apps can’t see any Health app data, or add any data to the Health app, without your permission. Before accessing any data, apps have to prompt you to access Health app information,” Apple explains. “You have fine-grained control over precisely which Health app data you want to share with a third-party app. By default, no data is selected.”

There are also specific requirements in place for apps that want to request access to health data:

Apps must meet certain criteria in order to request access to Health app data through HealthKit, and these requirements are detailed in the App Store Review Guidelines and the Developer Program License Agreement. HealthKit information may only be requested by third parties that provide a health or fitness service, and you must give permission for your data to be shared.

Another key to protecting health data highlighted by Apple include on-device processing and control. “iPhone and Apple Watch generate the metrics shown in the Health app entirely on-device. Sensors built into Apple Watch, like the optical heart sensor, or built into iPhone, like the gyroscope, feed information to the operating system,” Apple says “The operating system then locally computes the health summaries stored in HealthKit and are ultimately shown to you on your Apple Watch and in the Health app.”

Apple’s full Health data privacy whitepaper can be found on its website. It includes additional details on features like health records, health sharing, and more.

New ad campaign

Alongside the new whitepaper, Apple is also expanding its long-running “Privacy on iPhone” ad campaign with a new video focused on Health data. “Worried your most personal data might get into the wrong hands? The Health app on iPhone helps you control who sees your health data—and who doesn’t. Because when it comes to your health, privacy matters,” Apple explains.

The ad, which is narrated by actress and comedian Jane Lynch, is set in a waiting room and imagines a world in which everyone knows the exact reason why you’re at the doctor’s office.

FTC: We use income earning auto affiliate links. More.

What Is Big Data? Why Big Data Analytics Is Important?

What is Big Data? Why Big Data Analytics Is Important? Data is Indispensable. What is Big Data?

Is it a product?

Is it a set of tools?

Is it a data set that is used by big businesses only?

How big businesses deal with big data repositories?

What is the size of this data?

What is big data analytics?

What is the difference between big data and Hadoop?

These and several other questions come to mind when we look for the answer to what is big data? Ok, the last question might not be what you ask, but others are a possibility.

Hence, here we will define what is it, what is its purpose or value and why we use this large volume of data.

Big Data refers to a massive volume of both structured and unstructured data that overpowers businesses on a day to day basis. But it’s not the size of data that matters, what matters is how it is used and processed. It can be analyzed using big data analytics to make better strategic decisions for businesses to move.

According to Gartner:

Importance of Big Data

The best way to understand a thing is to know its history.

Data has been around for years; but the concept gained momentum in the early 2000s and since then businesses started to collect information, run big data analytics to uncover details for future use.  Thereby, giving organizations the ability to work quickly and stay agile.

This was the time when Doug Laney defined this data as the three Vs (volume, velocity, and variety):

Volume: is the amount of data moved from Gigabytes to terabytes and beyond.

Velocity: The speed of data processing is velocity.

Variety: data comes in different types from structured to unstructured. Structured data is usually numeric while unstructured – text, documents, email, video, audio, financial transactions, etc.

Where these three Vs made understanding big data easy, they even made clear that handling this large volume of data using the traditional framework won’t be easy.  This was the time when Hadoop came into existence and certain questions like:

What is Hadoop?

Is Hadoop another name of big data?

Is Hadoop different than big data?

All these came into existence.

So, let’s begin answering them.

Big Data and Hadoop

Let’s take restaurant analogy as an example to understand the relationship between big data and Hadoop

Tom recently opened a restaurant with a chef where he receives 2 orders per day he can easily handle these orders, just like RDBMS. But with time Tom thought of expanding the business and hence to engage more customers he started taking online orders. Because of this change the rate at which he was receiving orders increased and now instead of 2 he started receiving 10 orders per hour. This same thing happened with data. With the introduction of various sources like smartphones, social media, etc data growth became huge but due to a sudden change handling large orders/data isn’t easy. Hence a need for a different kind of strategy to cope up with this problem arise.

Likewise, to tackle the data problem huge datasets, multiple processing units were installed but this wasn’t effective either as the centralized storage unit became the bottleneck. This means if the centralized unit goes down the whole system gets compromised. Hence, there was a need to look for a better solution for both data and restaurant.

Tom came with an efficient solution, he divided the chefs into two hierarchies, i.e. junior and head chef and assigned each junior chef with a food shelf. Say for example the dish is pasta sauce. Now, according to Tom’s plan, one junior chef will prepare pasta and the other junior chef will prepare the sauce. Moving ahead they will hand over both pasta and sauce to the head chef, where the head chef will prepare the pasta sauce after combining both the ingredients, the final order will be delivered. This solution worked perfectly for Tom’s restaurant and for Big Data this is done by Hadoop.

Hadoop is an open-source software framework that is used to store and process data in a distributed manner on large clusters of commodity hardware. Hadoop stores the data in a distributed fashion with replications, to provide fault tolerance and give a final result without facing bottleneck problem. Now, you must have got an idea of how Hadoop solves the problem of Big Data i.e.

Storing huge amount of data.

Storing data in various formats: unstructured, semi-structured and structured.

The processing speed of data.

So does this mean both Big Data and Hadoop are same?

We cannot say that, as there are differences between both.

What is the difference between Big Data and Hadoop?

Big data is nothing more than a concept that represents a large amount of data whereas Apache Hadoop is used to handle this large amount of data.

It is complex with many meanings whereas Apache Hadoop is a program that achieves a set of goals and objectives.

This large volume of data is a collection of various records, with multiple formats while Apache Hadoop handles different formats of data.

Hadoop is a processing machine and big data is the raw material.

Now that we know what this data is, how Hadoop and big data work. It’s time to know how companies are benefiting from this data.

How Companies are Benefiting from Big Data?

A few examples to explain how this large data helps companies gain an extra edge:

Coca Cola and Big Data

Coca-Cola is a company that needs no introduction. For centuries now, this company has been a leader in consumer-packaged goods. All its products are distributed globally. One thing that makes Coca Cola win is data. But how?

Coca Cola and Big data:

Using the collected data and analyzing it via big data analytics Coca Cola is able to decide on the following factors:

Selection of right ingredient mix to produce juice products

Supply of products in restaurants, retail, etc

Social media campaign to understand buyer behavior, loyalty program

Creating digital service centers for procurement and HR process

Netflix and Big Data

To stay ahead of other video streaming services Netflix constantly analyses trends and makes sure people get what they look for on Netflix. They look for data in:

Most viewed programs

Trends, shows customers consume and wait for

Devices used by customers to watch its programs

What viewers like binge-watching, watching in parts, back to back or a complete series.

For many video streaming and entertainment companies, big data analytics is the key to retain subscribers, secure revenues, and understand the type of content viewers like based on geographical locations. This voluminous data not only gives Netflix this ability but even helps other video streaming services to understand what viewers want and how Netflix and others can deliver it.

Alongside there are companies that store following data that helps big data analytics to give accurate results like:

Tweets saved on Twitter’s servers

Information stored from tracking car rides by Google

Local and national election results

Treatments took and the name of the hospital

Types of the credit card used, and purchases made at different places

What, when people watch on Netflix, Amazon Prime, IPTV, etc and for how long

Hmm, so this is how companies know about our behavior and they design services for us.

What is Big Data Analytics?

The process of studying and examining large data sets to understand patterns and get insights is called big data analytics. It involves an algorithmic and mathematical process to derive meaningful correlation. The focus of data analytics is to derive conclusions that are based on what researchers know.

Importance of big data analytics

Ideally, big data handle predictions/forecasts of the vast data collected from various sources. This helps businesses make better decisions. Some of the fields where data is used are machine learning, artificial intelligence, robotics, healthcare, virtual reality, and various other sections. Hence, we need to keep data clutter-free and organized.

This provides organizations with a chance to change and grow. And this is why big data analytics is becoming popular and is of utmost importance. Based on its nature we can divide it into 4 different parts:

In addition to this, large data also play an important role in these following fields:

Identification of new opportunities

Data harnessing in organizations

Earning higher profits & efficient operations

Effective marketing

Better customer service

Now, that we know in what all fields data plays an important role. It’s time to understand how big data and its 4 different parts work.

Big Data Analytics and Data Sciences

Data Sciences, on the other hand, is an umbrella term that includes scientific methods to process data. Data Sciences combine multiple areas like mathematics, data cleansing, etc to prepare and align big data.

Due to the complexities involved data sciences is quite challenging but with the unprecedented growth of information generated globally concept of voluminous data is also evolving.  Hence the field of data sciences that involve big data is inseparable. Data encompasses, structured, unstructured information whereas data sciences is a more focused approach that involves specific scientific areas.

Businesses and Big Data Analytics

Due to the rise in demand use of tools to analyze data is increasing as they help organizations find new opportunities and gain new insights to run their business efficiently.

Real-time Benefits of Big Data Analytics

Data over the years has seen enormous growth due to which data usage has increased in industries ranging from:







All in all, Data analytics has become an essential part of companies today.

Job Opportunities and big data analytics

Data is almost everywhere hence there is an urgent need to collect and preserve whatever data is being generated. This is why big data analytics is in the frontiers of IT and had become crucial in improving businesses and making decisions. Professionals skilled in analyzing data have got an ocean of opportunities. As they are the ones who can bridge the gap between traditional and new business analytics techniques that help businesses grow.

Benefits of Big Data Analytics

Cost Reduction

Better Decision Making

New product and services

Fraud detection

Better sales insights

Understanding market conditions

Data Accuracy

Improved Pricing

How big data analytics work and its key technologies

Here are the biggest players:

Machine Learning: Machine learning, trains a machine to learn and analyze bigger, more complex data to deliver faster and accurate results. Using a machine learning subset of AI organizations can identify profitable opportunities – avoiding unknown risks.

Data management: With data constantly flowing in and out of the organization we need to know if it is of high quality and can be reliably analyzed. Once the data is reliable a master data management program is used to get the organization on the same page and analyze data.

Data mining: Data mining technology helps analyze hidden patterns of data so that it can be used in further analysis to get an answer for complex business questions. Using data mining algorithm businesses can make better decisions and can even pinpoint problem areas to increase revenue by cutting costs. Data mining is also known as data discovery and knowledge discovery.

In-memory analytics: This business intelligence (BI) methodology is used to solve complex business problems. By analyzing data from RAM computer’s system memory query response time can be shortened and faster business decisions can be made. This technology even eliminates the overhead of storing data aggregate tables or indexing data, resulting in faster response time. Not only this in-memory analytics even helps the organization to run iterative and interactive big data analytics.

Predictive analytics: Predictive analytics is the method of extracting information from existing data to determine and predict future outcomes and trends. techniques like data mining, modeling, machine learning, AI are used to analyze current data to make future predictions. Predictive analytics allows organizations to become proactive, foresee future, anticipate the outcome, etc. Moreover, it goes further and suggests actions to benefit from the prediction and also provide a decision to benefit its predictions and implications.

Text mining: Text mining also referred to as text data mining is the process of deriving high-quality information from unstructured text data. With text mining technology, you uncover insights you hadn’t noticed before. Text mining uses machine learning and is more practical for data scientists and other users to develop big data platforms and help analyze data to discover new topics.

Big data analytics challenges and ways they can be solved

A huge amount of data is produced every minute hence it is becoming a challenging job to store, manage, utilize and analyze it.  Even large businesses struggle with data management and storage to make a huge amount of data usage. This problem cannot be solved by simply storing data that is the reason organizations need to identify challenges and work towards resolving them:

Improper understanding and acceptance of big data

Meaningful insights via big data analytics

Data storage and quality

Security and privacy of data

Collection of meaningful data in real-time: Skill shortage

Data synching

Visual representation of data

Confusion in data management

Structuring large data

Information extraction from data

Organizational Benefits of Big Data

Big Data is not useful to organize data, but it even brings a multitude of benefits for the enterprises. The top five are:

Understand market trends: Using large data and  big data analytics, enterprises can easily, forecast market trends, predict customer preferences, evaluate product effectiveness, customer preferences, and gain foresight into customer behavior. These insights in return help understand purchasing patterns, buying patterns, preference and more. Such beforehand information helps in ding planning and managing things.

Understand customer needs:  Big Data analytics helps companies understand and plan better customer satisfaction. Thereby impacting the growth of a business. 24*7 support, complaint resolution, consistent feedback collection, etc.

Improving the company’s reputation: Big data helps deal with false rumors, provides better service customer needs and maintains company image. Using big data analytics tools, you can analyze both negative and positive emotions that help understand customer needs and expectations.

Promotes cost-saving measures: The initial costs of deploying Big Data is high, yet the returns and gainful insights more than you pay. Big Data can be used to store data more effectively.

Makes data available: Modern tools in Big Data can in actual-time presence required portions of data anytime in a structured and easily readable format.

Sectors where Big Data is used:

Retail & E-Commerce

Finance Services



With this, we can conclude that there is no specific definition of what is big data but still we all will agree that a large voluminous amount of data is big data. Also, with time the importance of big data analytics is increasing as it helps enhance knowledge and come to a profitable conclusion.

If you are keen to benefit from big data, then using Hadoop will surely help. As it is a method that knows how to manage big data and make it comprehensible.

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