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What is Big Data – Characteristics, Types, Benefits & Examples

Updated on 20 February, 2024

187.73K+ views
19 min read
What is Big Data

Lately the term ‘Big Data’ has been under the limelight, but not many people know what is big data. Businesses, governmental institutions, HCPs (Health Care Providers), and financial as well as academic institutions, are all leveraging the power of Big Data to enhance business prospects along with improved customer experience.

Simply Stating, What Is Big Data?

Simply stating, big data is a larger, complex set of data acquired from diverse, new, and old sources of data. The data sets are so voluminous that traditional software for data processing cannot manage it. Such massive volumes of data are generally used to address problems in business you might not be able to handle.

IBM maintains that businesses around the world generate nearly 2.5 quintillion bytes of data daily! Almost 90% of the global data has been produced in the last 2 years alone.

So we know for sure that the best way to answer ‘what is big data’ is mentioning that it has penetrated almost every industry today and is a dominant driving force behind the success of enterprises and organizations across the globe. But, at this point, it is important to know what is big data? Lets talk about big data, characteristics of big data, types of big data and a lot more.

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What is Big Data? Gartner Definition 

According to Gartner, the definition of Big Data – 

“Big data” is high-volume, velocity, and variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making.”

This definition clearly answers the “What is Big Data?” question – Big Data refers to complex and large data sets that have to be processed and analyzed to uncover valuable information that can benefit businesses and organizations.

However, there are certain basic tenets of Big Data that will make it even simpler to answer what is Big Data:

  • It refers to a massive amount of data that keeps on growing exponentially with time.
  • It is so voluminous that it cannot be processed or analyzed using conventional data processing techniques.
  • It includes data mining, data storage, data analysis, data sharing, and data visualization.
  • The term is an all-comprehensive one including data, data frameworks, along with the tools and techniques used to process and analyze the data.

Types of Big Data

Now that we are on track with what is big data, let’s have a look at the types of big data:

Structured

Structured is one of the types of big data and By structured data, we mean data that can be processed, stored, and retrieved in a fixed format. It refers to highly organized information that can be readily and seamlessly stored and accessed from a database by simple search engine algorithms. For instance, the employee table in a company database will be structured as the employee details, their job positions, their salaries, etc., will be present in an organized manner. 

Read: Big data engineering jobs and its career opportunities

What is big data technology and its types? Structured one of the types of big data is easy to input, store, query and analyze thanks to its predefined data model and schema. Most traditional databases and spreadsheets hold structured data like tables, rows, and columns.

This makes it simple for analysts to run SQL queries and extract insights using familiar BI tools. However, structuring data requires effort and expertise during the design phase. As data volumes grow to petabyte scale, rigid schemas become impractical and limit the flexibility needed for emerging use cases. Also some data like text, images, video etc. cannot be neatly organized in tabular formats.

Therefore, while structured data brings efficiency, scale and variety of big data necessitates semi-structured and unstructured types of digital data in big data to overcome these limitations. The value lies in consolidating these multiple types rather than relying solely on structured data for modern analytics.

Unstructured

Unstructured data refers to the data that lacks any specific form or structure whatsoever. This makes it very difficult and time-consuming to process and analyze unstructured data. Email is an example of unstructured data. Structured and unstructured are two important types of big data.

Unstructured types of big data constitutes over 80% of data generated today and continues to grow exponentially from sources like social posts, digital images, videos, audio files, emails, and more. It does not conform to any data model, so conventional tools cannot give meaningful insights from it. However, unstructured data tends to be more subjective, rich in meaning, and reflective of human communication compared to tabular transaction data. 

With immense business value hidden inside, specialized analytics techniques involving NLP, ML, and AI are essential to process high volumes of unstructured content. For instance, sentiment analysis of customer social media rants can alert companies to issues before mainstream notice.

Text mining of maintenance logs and field technician reports can improve future product designs. And computer vision techniques on image data from manufacturing floors can automate quality checks. While analysis requires advanced skill, the unstructured data’s scale, variety, and information density deliver new opportunities for competitive advantage across industries.

Check out the big data courses at upGrad

Semi-structured

Semi structured is the third type of big data. Semi-structured data pertains to the data containing both the formats mentioned above, that is, structured and unstructured data. To be precise, it refers to the data that although has not been classified under a particular repository (database), yet contains vital information or tags that segregate individual elements within the data. Thus we come to the end of types of data. Lets discuss the characteristics of data.

Semi-structured variety in big data includes elements of both structured and unstructured data. For example, XML, JSON documents contain tags or markers to separate semantic elements, but the data is unstructured free flowing text, media, etc. Clickstream data from website visits have structured components like timestamps and pages visited, but the path a user takes is unpredictable. Sensor data with timestamped values is semi-structured. This hybrid data abstraction effortlessly incorporates the variety and volume of big data across system interfaces. 

For analytic applications, semi-structured data poses technical and business-level complexities for processing, governance, and insight generation. However, flexible schemas and object-oriented access methods are better equipped to handle velocity and variety in semi-structured types of digital data in big data at scale. With rich contextual information encapsulated, established databases have expanded native JSON, XML, and Graph support for semi-structured data to serve modern real-time analytics needs.

Characteristics of Big Data

Back in 2001, Gartner analyst Doug Laney listed the 3 ‘V’s of Big Data – Variety, Velocity, and Volume. Let’s discuss the characteristics of big data.
These characteristics, isolatedly, are enough to know what is big data. Let’s look at them in depth:

1) Variety

Variety of Big Data refers to structured, unstructured, and semistructured data that is gathered from multiple sources. While in the past, data could only be collected from spreadsheets and databases, today data comes in an array of forms such as emails, PDFs, photos, videos, audios, SM posts, and so much more.

Variety is one of the important characteristics of big data. The traditional types of data are structured and also fit well in relational databases. With the rise of big data, the data now comes in the form of new unstructured types. These unstructured, as well as semi-structured data types, need additional pre-processing for deriving meaning and support of metadata.

2) Velocity

Velocity essentially refers to the speed at which data is being created in real-time. In a broader prospect, it comprises the rate of change, linking of incoming data sets at varying speeds, and activity bursts. The speed of data receipt and action is simply known as velocity. The highest velocity for data will stream directly into the memory against being written to the disk. Few internet-based smart products do operate in real-time or around real-time. This mostly requires evaluation as well as in real-time.

Learn: Mapreduce in big data

The velocity of variety in big data is crucial because it allows companies to make quick, data-driven decisions based on real-time insights. As data streams in at high speeds from sources like social media, sensors, mobile devices, etc., companies can spot trends, detect patterns, and derive meaning from that data more rapidly. High velocity characteristics of big data combined with advanced analytics enables faster planning, problem detection, and decision optimization. For example, a company monitoring social media chatter around its brand can quickly respond to emerging issues before they spiral out of control.

3) Volume

Volume is one of the characteristics of big data. We already know that Big Data indicates huge ‘volumes’ of data that is being generated on a daily basis from various sources like social media platforms, business processes, machines, networks, human interactions, etc. Such a large amount of data are stored in data warehouses. Thus comes to the end of characteristics of big data.

The data volume matters when you discuss the big data characteristics. In the context of big data, you will need to process a very high volume of low-density or unstructured data. This will be data related to an unknown value. Example data feeds on Twitter, clickstreams on web pages or mobile apps, or even sensor-based equipment. For a few organizations, it means ten times a few terabytes of data. For some others, it could mean hundreds of times petabytes.

Advantages of Big Data (Features)

  • One of the biggest advantages of Big Data is predictive analysis. Big Data analytics tools can predict outcomes accurately, thereby, allowing businesses and organizations to make better decisions, while simultaneously optimizing their operational efficiencies and reducing risks.
  • By harnessing data from social media platforms using Big Data analytics tools, businesses around the world are streamlining their digital marketing strategies to enhance the overall consumer experience. Big Data provides insights into the customer pain points and allows companies to improve upon their products and services.
  • Being accurate, Big Data combines relevant data from multiple sources to produce highly actionable insights. Almost 43% of companies lack the necessary tools to filter out irrelevant data, which eventually costs them millions of dollars to hash out useful data from the bulk. Big Data tools can help reduce this, saving you both time and money.
  • Big Data analytics could help companies generate more sales leads which would naturally mean a boost in revenue. Businesses are using Big Data analytics tools to understand how well their products/services are doing in the market and how the customers are responding to them. Thus, the can understand better where to invest their time and money.
  • With Big Data insights, you can always stay a step ahead of your competitors. You can screen the market to know what kind of promotions and offers your rivals are providing, and then you can come up with better offers for your customers. Also, Big Data insights allow you to learn customer behaviour to understand the customer trends and provide a highly ‘personalized’ experience to them.

Read: Career Scope for big data jobs.

Who is using Big Data? 5 Applications

The people who’re using Big Data know better that, what is Big Data. Let’s look at some such industries:

1) Healthcare

Big Data has already started to create a huge difference in the healthcare sector. With the help of predictive analytics, medical professionals and HCPs are now able to provide personalized healthcare services to individual patients. Apart from that, fitness wearables, telemedicine, remote monitoring – all powered by Big Data and AI – are helping change lives for the better.

The healthcare industry is harnessing big data in various innovative ways – from detecting diseases faster to providing better treatment plans and preventing medication errors. By analyzing patient history, clinical data, claims data, and more, healthcare providers can better understand patient risks, genetic factors, environmental factors to customize treatments rather than follow a one-size-fits-all approach.

Population health analytics on aggregated EMR data also allows hospitals to reduce readmission rates and unnecessary costs. Pharmaceutical companies are leveraging big data to improve drug formulation, identify new molecules, and reduce time-to-market by analyzing years of research data. The insights from medical imaging data combined with genomic data analysis enables precision diagnosis at early stages.

2) Academia

Big Data is also helping enhance education today. Education is no more limited to the physical bounds of the classroom – there are numerous online educational courses to learn from. Academic institutions are investing in digital courses powered by Big Data technologies to aid the all-round development of budding learners.

Educational institutions are leveraging big data in dbms in multifaceted ways to elevate learning experiences and optimize student outcomes. By analyzing volumes of student academic and behavioral data, predictive models identify at-risk students early to recommend timely interventions. Tailored feedback is provided based on individual progress monitoring.

Curriculum design and teaching practices are refined by assessing performance patterns in past course data. Self-paced personalized learning platforms powered by AI recommend customized study paths catering to unique learner needs and competency levels. Academic corpus and publications data aids cutting-edge research and discovery through knowledge graph mining and natural language queries.

Knowledge Read: Big data jobs & Career planning

3) Banking

The banking sector relies on Big Data for fraud detection. Big Data tools can efficiently detect fraudulent acts in real-time such as misuse of credit/debit cards, archival of inspection tracks, faulty alteration in customer stats, etc.

Banks and financial institutions depend heavily on big data in dbms and analytics to operate services, reduce risks, retain customers, and increase profitability. Predictive models flag probable fraudulent transactions in seconds before completion by scrutinizing volumes of past transactional data, customer information, credit history, investments, and third-party data. Connecting analytics to the transaction processing pipeline has immensely reduced false declines and improved fraud detection rates. Client analytics helps banks precisely segment customers, contextualise engagement through the right communication channels, and accurately anticipate their evolving needs to recommend the best financial products.

Processing volumes of documentation and loan application big data types faster using intelligent algorithms and automation enables faster disbursal with optimized risks. Trading firms leverage big data analytics on historical market data, economic trends, and news insights to support profitable investment decisions. Thus, big data radically enhances banking experiences by minimizing customer risks and maximizing personalisation through every engagement.

4) Manufacturing

According to TCS Global Trend Study, the most significant benefit of Big Data in manufacturing is improving the supply strategies and product quality. In the manufacturing sector, Big data helps create a transparent infrastructure, thereby, predicting uncertainties and incompetencies that can affect the business adversely.

Manufacturing industries are optimizing end-to-end value chains using volumes of operational data generated from sensors, equipment logs, inventory flows, supplier networks, and customer transactions. By combining this real-time structured and unstructured big data types with enterprise data across siloed sources, manufacturers gain comprehensive visibility into operational performance, production quality, supply-demand dynamics, and fulfillment. Advanced analytics transforms this data into meaningful business insights around minimizing process inefficiencies, improving inventory turns, reducing machine failures, shortening production cycle times, and meeting dynamic customer demands continually.

Overall, equipment effectiveness is improved with predictive maintenance programs. Data-based simulation, scheduling, and control automation increases speed, accuracy, and compliance. Real-time synchronization of operations planning with execution enabled by big data analytics creates the responsive and intelligent factory of the future.

5) IT

One of the largest users of Big Data, IT companies around the world are using Big Data to optimize their functioning, enhance employee productivity, and minimize risks in business operations. By combining Big Data technologies with ML and AI, the IT sector is continually powering innovation to find solutions even for the most complex of problems.

The technology and IT sectors pioneer big data-enabled transformations across other industries, though the first application starts from within. IT infrastructure performance, application usage telemetry, network traffic data, security events, and business KPIs provide technology teams with comprehensive observability into systems health, utilization, gaps and dependencies. This drives data-based capacity planning, proactive anomaly detection and accurate root cause analysis to optimize IT service quality and employee productivity. User behavior analytics identifies the most valued features and pain points to prioritize software enhancements aligned to business needs.

For product companies, big data analytics features of big data logs, sensor data, and customer usage patterns enhances user experiences by detecting issues and churn faster. Mining years of structured and unstructured data aids context-aware conversational AI feeding into chatbots and virtual assistants. However, robust information management and governance practices remain vital as the scale and complexity of technology data environments continue to expand massively. With positive business outcomes realized internally, IT domain expertise coupled with analytics and AI skillsets power data transformation initiatives across external customer landscapes.

6. Retail

Big Data has changed the way of working in traditional brick and mortar retail stores. Over the years, retailers have collected vast amounts of data from local demographic surveys, POS scanners, RFID, customer loyalty cards, store inventory, and so on. Now, they’ve started to leverage this data to create personalized customer experiences, boost sales, increase revenue, and deliver outstanding customer service.

Retailers are even using smart sensors and Wi-Fi to track the movement of customers, the most frequented aisles, for how long customers linger in the aisles, among other things. They also gather social media data to understand what customers are saying about their brand, their services, and tweak their product design and marketing strategies accordingly. 

7. Transportation 

Big Data Analytics holds immense value for the transportation industry. In countries across the world, both private and government-run transportation companies use Big Data technologies to optimize route planning, control traffic, manage road congestion, and improve services. Additionally, transportation services even use Big Data to revenue management, drive technological innovation, enhance logistics, and of course, to gain the upper hand in the market.

The transportation sector is adopting big data and IoT technologies to monitor, analyse, and optimize end-to-end transit operations intelligently. Transport authorities can dynamically control traffic flows, mitigating congestion, optimising tolls, and identifying incidents faster by processing high-velocity telemetry data streams from vehicles, roads, signals, weather systems, and rider mobile devices. Journey reliability and operational efficiency are improved through data-based travel demand prediction, dynamic route assignment, and AI-enabled dispatch. Predictive maintenance reduces equipment downtime. Riders benefit from real-time tracking, estimated arrivals, and personalized alerts, minimising wait times.

Logistics players leverage big data for streamlined warehouse management, load planning, and shipment route optimisation, driving growth and customer satisfaction. However, key challenges around data quality, privacy, integration, and skills shortage persist. They need coordinated efforts from policymakers and technology partners before their sustainable value is fully realised across an integrated transportation ecosystem.

Big Data Case studies

1. Walmart

 Walmart leverages Big Data and Data Mining to create personalized product recommendations for its customers. With the help of these two emerging technologies, Walmart can uncover valuable patterns showing the most frequently bought products, most popular products, and even the most popular product bundles (products that complement each other and are usually purchased together).

Based on these insights, Walmart creates attractive and customized recommendations for individual users. By effectively implementing Data Mining techniques, the retail giant has successfully increased the conversion rates and improved its customer service substantially. Furthermore, Walmart uses Hadoop and NoSQL technologies to allow customers to access real-time data accumulated from disparate sources. 

2. American Express

The credit card giant leverages enormous volumes of customer data to identify indicators that could depict user loyalty. It also uses Big Data to build advanced predictive models for analyzing historical transactions along with 115 different variables to predict potential customer churn. Thanks to Big Data solutions and tools, American Express can identify 24% of the accounts that are highly likely to close in the upcoming four to five months.

3. General Electric

In the words of Jeff Immelt, Chairman of General Electric, in the past few years, GE has been successful in bringing together the best of both worlds – “the physical and analytical worlds.” GE thoroughly utilizes Big Data. Every machine operating under General Electric generates data on how they work. The GE analytics team then crunches these colossal amounts of data to extract relevant insights from it and redesign the machines and their operations accordingly.

Today, the company has realized that even minor improvements, no matter how small, play a crucial role in their company infrastructure. According to GE stats, Big Data has the potential to boost productivity by 1.5% in the US, which compiled over a span of 20 years could increase the average national income by a staggering 30%!

4. Uber

 Uber is one of the major cab service providers in the world. It leverages customer data to track and identify the most popular and most used services by the users. Once this data is collected, Uber uses data analytics to analyze the usage patterns of customers and determine which services should be given more emphasis and importance.

Apart from this, Uber uses Big Data in another unique way. Uber closely studies the demand and supply of its services and changes the cab fares accordingly. It is the surge pricing mechanism that works something like this – suppose when you are in a hurry, and you have to book a cab from a crowded location, Uber will charge you double the normal amount!  

5. Netflix

Netflix is one of the most popular on-demand online video content streaming platform used by people around the world. Netflix is a major proponent of the recommendation engine. It collects customer data to understand the specific needs, preferences, and taste patterns of users. Then it uses this data to predict what individual users will like and create personalized content recommendation lists for them.

Today, Netflix has become so vast that it is even creating unique content for users. Data is the secret ingredient that fuels both its recommendation engines and new content decisions. The most pivotal data points used by Netflix include titles that users watch, user ratings, genres preferred, and how often users stop the playback, to name a few. Hadoop, Hive, and Pig are the three core components of the data structure used by Netflix. 

6. Procter & Gamble

Procter & Gamble has been around us for ages now. However, despite being an “old” company, P&G is nowhere close to old in its ways. Recognizing the potential of Big Data, P&G started implementing Big Data tools and technologies in each of its business units all over the world. The company’s primary focus behind using Big Data was to utilize real-time insights to drive smarter decision making.

To accomplish this goal, P&G started collecting vast amounts of structured and unstructured data across R&D, supply chain, customer-facing operations, and customer interactions, both from company repositories and online sources. The global brand has even developed Big Data systems and processes to allow managers to access the latest industry data and analytics.

7. IRS

Yes, even government agencies are not shying away from using Big Data. The US Internal Revenue Service actively uses Big Data to prevent identity theft, fraud, and untimely payments (people who should pay taxes but don’t pay them in due time).

The IRS even harnesses the power of Big Data to ensure and enforce compliance with tax rules and laws. As of now, the IRS has successfully averted fraud and scams involving billions of dollars, especially in the case of identity theft. In the past three years, it has also recovered over US$ 2 billion.

Careers In Big Data

Big data characteristics are seemingly transforming the way businesses work while also driving growth through the economy globally.

Businesses are observing immense benefits using the characteristics of big data for protecting their database, aggregating huge volumes of information, as well as making informed decisions to benefit organizations. No wonder it is clear that big data has a huge range across a number of sectors.

For instance, in the financial industry, big data comes across as a vital tool that helps make profitable decisions. Similarly, some data organizations might look at big data as a means for fraud protection and pattern detection in large-sized datasets. Nearly every large-scale organization currently seeks talent in big data, and hopefully, the demand is prone to a significant rise in the future as well.

Wrapping Up

We hope we were able to answer the “What is Big Data?” question clearly enough. We hope you understood about the types of big data, characteristics of big data, use cases, etc. 

Organizations actually mine both unstructured as well structured data sets. This helps in leveraging machine learning as well as framing predictive modeling techniques. The latter helps extract meaningful insights. With such findings, a data manager will be able to make data-driven decisions and solve a plethora of main business problems.

A number of significant technical skills help individuals succeed in the field of big data. Such skills include-

  •       Data mining
  •       Programming
  •       Data visualization
  •       Analytics

If you are interested to know more about Big Data, check out our Advanced Certificate Programme in Big Data from IIIT Bangalore.

Learn Software Development Courses online from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs or Masters Programs to fast-track your career.

Frequently Asked Questions (FAQs)

1. What are the different jobs that professionals learning Big Data can get?

The increase in the usage of Big Data in companies and industries has led to the rise in the demand for Big Data professionals. Many jobs need the knowledge of Big Data and its applications. Data Scientists are the most common and well-known Big Data job profiles. They organise, clean, and consolidate data from many sources. Data engineers are computing experts who are in charge of planning, constructing, and maintaining extensive data infrastructure. While data engineers efficiently handle data, data scientists focus on data analysis. Machine Learning engineers are hard-core computer programmers who create and apply algorithms for analysing Big Data analytics trends. As a result, the system makes decisions based on historical data trends. There are many more jobs for Big Data professionals such as Hadoop scientists, Hadoop engineers, etc.

2. What are the responsibilities of Big Data professionals?

Big Data experts are in charge of importing data from many sources, performing high-speed queries, and recommending best practices and standards. Designers, builders, installers, configures, and supporters of Hadoop are anticipated. They manage and implement HBase while also ensuring data security and privacy. These experts also analyse and unearth insights from a large variety of data repositories. Big Data developers are in charge of developing data-tracking web services that are scalable and high-performing. They also create detailed designs for complex technological and functional needs.

3. How can I become a successful Big Data Developer?

The first step in becoming a successful Big Data Developer is to learn Hadoop. Hadoop is a comprehensive ecosystem, not just a single phrase. The Hadoop ecosystem includes a variety of technologies that serve various objectives. Secondly, Learning Spark, a real-time distributed processing framework with in-memory computing capabilities, is the ideal choice for Big Data developers who are proficient in any of the real-time processing frameworks. You must have the knowledge of any coding language such as C, SQL, R, or Python too.

4. What are the different jobs that professionals learning Big Data can get?

The increase in the usage of Big Data in companies and industries has led to the rise in the demand for Big Data professionals. Many jobs need the knowledge of Big Data and its applications. Data Scientists are the most common and well-known Big Data job profiles. They organise, clean, and consolidate data from many sources. Data engineers are computing experts who are in charge of planning, constructing, and maintaining extensive data infrastructure. While data engineers efficiently handle data, data scientists focus on data analysis. Machine Learning engineers are hard-core computer programmers who create and apply algorithms for analysing Big Data analytics trends. As a result, the system makes decisions based on historical data trends. There are many more jobs for Big Data professionals such as Hadoop scientists, Hadoop engineers, etc.

5. What are the responsibilities of Big Data professionals?

Big Data experts are in charge of importing data from many sources, performing high-speed queries, and recommending best practices and standards. Designers, builders, installers, configures, and supporters of Hadoop are anticipated. They manage and implement HBase while also ensuring data security and privacy. These experts also analyse and unearth insights from a large variety of data repositories. Big Data developers are in charge of developing data-tracking web services that are scalable and high-performing. They also create detailed designs for complex technological and functional needs.

6. How can I become a successful Big Data Developer?

The first step in becoming a successful Big Data Developer is to learn Hadoop. Hadoop is a comprehensive ecosystem, not just a single phrase. The Hadoop ecosystem includes a variety of technologies that serve various objectives. Secondly, Learning Spark, a real-time distributed processing framework with in-memory computing capabilities, is the ideal choice for Big Data developers who are proficient in any of the real-time processing frameworks. You must have the knowledge of any coding language such as C, SQL, R, or Python too.

Did you find this article helpful?

Abhinav Rai

Abhinav is a Data Analyst at UpGrad. He's an experienced Data Analyst with a demonstrated history of working in the higher education industry. Strong information technology professional skilled in Python, R, and Machine Learning.

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The consumer market today is becoming more and more competitive and companies are struggling to offer something unique to their consumers. To be able to do that, companies need to understand the consumers better. The primary way to get meaningful consumer insights is to analyse the existing data collected from users. These insights can then be used not only to continue selling the products but provide customised events and service, which are available at a premium. This trend is fairly common in new age industries such as e-commerce, even traditional, centuries-old industries greatly benefit from big data and analytics applications. For example, by installing sensors and subsequently analysing them, a railway operator can analyse their fixed and rolling assets. Big data analytics can identify when to carry out preventive maintenance on assets such as bridges and railway lines, increasing economic life and reducing downtime. Hence, data is not just benefitting new-age industries, but the traditional industries as well. Here are some of the most commonly used big data applications around you, across industries: Retail Companies collect data of individual customers, the type of purchases they’re making and more importantly where they’re making the purchases. Based on this information, companies are able to segment customers according to their buying behavior. They then make predictions on what they will be buying in the future. This data is also used to cross-sell or upsell items, with the help of attractive offers on these new items. Location Another big use of data in analytics is to map areas or locations, as well known by everyone who uses Uber or Ola or Google Maps. Even food delivery apps and other apps that deliver goods to your doorsteps know where you live/work, etc. A huge amount of data gets captured every time you order and it includes all location characteristics in it. This information is also mined from a public policy perspective to look for traffic jams and also for taking decisions like setting up public transportation facilities such as metro stations. Explore our Popular Software Engineering Courses Master of Science in Computer Science from LJMU & IIITB Caltech CTME Cybersecurity Certificate Program Full Stack Development Bootcamp PG Program in Blockchain Executive PG Program in Full Stack Development View All our Courses Below Software Engineering Courses Energy The advent of big data has had a huge impact on the energy sector. Big data involves a large number of sensors and data collection methodologies which have allowed for the setting up of large systems for preventive maintenance. It enables better forecasting of demand. For example, ten years ago, there were no smart meters. Now, the power utility sector has very good information on how their consumers are consuming their power, the time, and the load that is consumed. This is actually helping them to make their investment decisions much faster. These industries are becoming more efficient both in terms of cost and in operation. Telecom Every operator is searching for new ways to increase profits during a time of stagnant and competitive growth in the industry. Here is where telecom companies are advancing rapidly in terms of being able to capture data and use it wisely for a variety of uses. Companies around the world are using big data to gain market share with targeted promotions, combating fraud, improving customer experiences and designing newer product offerings. Explore Our Software Development Free Courses Fundamentals of Cloud Computing JavaScript Basics from the scratch Data Structures and Algorithms Blockchain Technology React for Beginners Core Java Basics Java Node.js for Beginners Advanced JavaScript Automotive This sector is actually now trying to become more connected. Self-driving cars that we all already know about is one of the biggest buzzwords. Underneath it, to make this possible, there is a huge amount of technology that vehicles are collecting, gathering and using in conjunction to come up with these advancements. Increased government encouragement of electric vehicles requires location analytics to establish charging stations. In-Demand Software Development Skills JavaScript Courses Core Java Courses Data Structures Courses Node.js Courses SQL Courses Full stack development Courses NFT Courses DevOps Courses Big Data Courses React.js Courses Cyber Security Courses Cloud Computing Courses Database Design Courses Python Courses Cryptocurrency Courses What lies ahead? The only thing that is going to hold back the Big Data industry is the number of people who are skilled in it. The big data applications are actually limitless. There is a huge demand for skilled people at all levels from project managers to raw beginners. As a practitioner who’s been in this industry for some time, I can tell you that there is a huge demand. Companies are facing a talent problem at all levels and the solutions also have to come from different sources, such as increased access to education, training initiatives by companies, awareness spreading by the government. The 11-month BITS Pilani and UpGrad program for working professionals is exactly the type of program that we need to help people who are ambitious, keen on furthering their careers and following their passions. I think a course like this is very useful because you have a large number of people who come from the industry and are excited to teach. Students will benefit a lot from learning hands-on and through practitioners directly. I am fairly certain that it will involve a lot of problem-solving and casework type methodology. So, I think people are going to have fun while they’re at it. I think that’s especially important when you are doing something on your weeknights and weekends. Read our Popular Articles related to Software Development Why Learn to Code? How Learn to Code? How to Install Specific Version of NPM Package? Types of Inheritance in C++ What Should You Know? Views shared in this blog are the author’s personal views and they do not reflect the official stance of The Boston Consulting Group (BCG) or any of the author’s clients. Conclusion If you are interested to know more about Big Data, check out our Advanced Certificate Programme in Big Data from IIIT Bangalore. Learn Software Development Courses online from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs or Masters Programs to fast-track your career.
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by Sanjay Sinha

22 Dec'17
How Big Data and Machine Learning are Uniting Against Cancer

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How Big Data and Machine Learning are Uniting Against Cancer

Cancer is not one disease. It is many diseases. Let us understand the cause of cancer by a simple example. If you take a photocopy of a document, due to some issues, other dots or smears appear on it even though they are not present in the original copy. In the same way, in gene replication processes, errors occur inadvertently. Most of the time the genes with errors will not be able to sustain and will ultimately perish. In some rare cases, the mutated gene with mistakes will survive and get further replicated uncontrollably. Uncontrollable replication of mutated genes is the primary cause of cancer. This mutation can happen in any of the twenty thousand genes in our body. Variation in any one or a combination of genes makes cancer a severe disease to conquer. To eradicate cancer, we need methods to destroy the rogue cells without harming the functional cells of the body; which makes it doubly hard to defeat. Cancer and its complexity Cancer is a disease with a long tail distribution. Long tail distribution means there are various reasons for this condition to occur and there is no single solution for eradicating it. There are diseases which affect a large percentage of the population but have a sole cause of occurrence. For example, let us consider Cholera. Eating food or drinking water contaminated by the bacterium Vibrio Cholerae is the cause of cholera. Cholera can occur only because of Vibrio Cholerae, and there is no another reason. Once we find out the only cause of a disease, then it is relatively easy to conquer it. What if a condition occurs because of multiple reasons? A mutation can occur in any of the twenty thousand genes in our body. Not only that, but we also need to consider their combinations. Cancer may not just happen because of a random mutation in a gene but also because of a combination of gene mutations. The number of causes for cancer becomes exponential, and there is no single mechanism to cure it. For example, a mutation of any of these genes ALK, BRAF, DDR2, EGFR, ERBB2, KRAS, MAP2K1, NRAS, PIK3CA, PTEN, RET, and RIT1 can cause lung cancer. There are many ways for cancer to occur and that’s why it is a disease with long tail distribution. In our arsenal for waging this war on cancer and conquering it, big data and machine learning are critical tools. How can big data help in fighting this war? What does machine learning have to do with cancer? How are they going to help in fighting a disease with many causes, a condition with a long tail distribution? Firstly, how and where is this big data generated? Let us find answers to these questions. Gene Sequencing and explosion in data Gene sequencing is one area which is producing humongous amounts of data. Exactly how much data? According to the Washington Post, the human data generated through gene sequencing (approximately 2.5 lakh sequences) takes up about a fourth of the size of YouTube’s yearly data production. If all this data were combined with all the extra information that comes with sequencing genomes and recorded on 4GB DVDs, it would be a stack about half a mile high. Explore Our Software Development Free Courses Fundamentals of Cloud Computing JavaScript Basics from the scratch Data Structures and Algorithms Blockchain Technology React for Beginners Core Java Basics Java Node.js for Beginners Advanced JavaScript The methods for gene sequencing have improved over the years, and the cost for the same has plummeted exponentially. In the year 2008, the cost of gene sequencing was 10 million dollars. As of today, it is only a 1000 dollars. In the future, it is expected to reduce further. It is estimated that one billion people will have their genes sequenced by 2025. So, within the next decade, the genomics data generated will be somewhere between 2 – 40 exabytes in a year. An exabyte is ten followed by 17 zeros. Before coming to how data will help in curing cancer, let us take one concrete example and see how data can help in conquering a disease. Data and its analysis helped in finding out the cause of one infectious disease and fight it, not now but in nineteenth-century itself! Yes, in the nineteenth century! The name of that disease is Cholera. Clustering in the Nineteenth Century – the Cholera breakthrough John Snow was an anesthesiologist and cholera broke out in September 1854 near Snow’s house. To know the reason for cholera, Snow decided to note the spatial dimensions of the patients on the city map. He marked the location of the home address of patients on London’s city map. With this exercise, John Snow understood that people suffering from cholera were clustered around some specific water wells. He firmly believed that a contaminated pump was responsible for the epidemic and against the will of the local authorities replaced the pump. This replacement drastically reduced the spread of cholera. Snow subsequently published a map of the outbreak to support his theory, showing the locations of the 13 public wells in the area, and the 578 cholera deaths mapped by home address. This map ultimately led to the understanding that cholera was an infectious disease and quickly spread through the medium of water. John Snow’s experiment is the earliest example of applying the clustering algorithm to know the cause of illness and help eradicate it. In the nineteenth century, John Snow could apply clustering algorithm on a London city map with a pencil. With cancer as the target disease, this level of analysis is not possible with the same ease as John Snow’s Analysis. We need sophisticated tools and technologies to mine this data. That is where we leverage the capabilities of modern technologies like Machine Learning and Big Data. Explore our Popular Software Engineering Courses Master of Science in Computer Science from LJMU & IIITB Caltech CTME Cybersecurity Certificate Program Full Stack Development Bootcamp PG Program in Blockchain Executive PG Program in Full Stack Development View All our Courses Below Software Engineering Courses Big data and Machine learning – tools to fight cancer Vast amounts of data along with machine learning algorithms will help us in our fight with cancer in many ways. It can help us with diagnosis, treatment, and prognosis. Mainly, it will help customise the therapy according to the patient, which is not possible otherwise. It will also help deal with the long tail of the distribution. Given the enormous amounts of Electronic Medical Records (EMR), data generated and recorded by various hospitals; it is possible to use ‘labelled’ data in diagnosing cancer. Techniques like Natural Language Programming (NLP) are utilised for making sense of doctor’s prescriptions and Deep Learning Neural Networks are deployed to analyse CT and MRI scans. The different types of machine learning algorithms search the EMR databases and find hidden patterns. These hidden patterns will help in diagnosing cancers. A college student was able to design an Artificial Neural Network from the comfort of her home and developed a model that can diagnose breast cancer with a high degree of accuracy. In-Demand Software Development Skills JavaScript Courses Core Java Courses Data Structures Courses Node.js Courses SQL Courses Full stack development Courses NFT Courses DevOps Courses Big Data Courses React.js Courses Cyber Security Courses Cloud Computing Courses Database Design Courses Python Courses Cryptocurrency Courses Diagnosis with Big Data and Machine Learning Brittanny Wenger was 16 years old when her older cousin was diagnosed with breast cancer. This inspired her to make the process better by improving the diagnostics. Fine Needle Aspiration (FNA) was a less invasive method of biopsy and the quickest method of diagnosis. The doctors were reluctant to use FNA because the results are not reliable. Brittanny thought of using her programming skills to do something about it. She decided to improve the reliability of FNA which would enable the women to choose less invasive and comfortable diagnostic methods. Brittanny found public domain data from the University of Wisconsin that included Fine Needle Aspiration. She coded an Artificial Neural Network (ANN) which is inspired by the design of human brain architecture. She used cloud technologies to process the data and train the ANN to find the similarities. After many attempts and errors finally, her network was able to detect breast cancer from an FNA test data with 99.1% sensitivity to malignancy. This method is applicable for diagnosing other cancers as well. The accuracy of diagnosis is dependent upon the amount and quality of the data available. The more the data available, the more the algorithms will be able to query the database, find similarities and come out with valuable models. Treatment with Big Data and Machine Learning Big data and Machine learning will be helpful not only for diagnosis but treatment as well. John and Kathy were married for three decades. At the age of 49, Kathy was diagnosed with stage III breast cancer. John, CIO of a Boston hospital helped plan her treatment with the help of big data tools that he designed and brought into existence. In 2008, five Harvard affiliated hospitals shared their databases and created a powerful search tool known as ‘Shared Health Research Information Network’ (SHRINE). By the time of Kathy’s diagnosis, her doctors could sift through a database of 6.1 million records to find insightful information. Doctors queried ‘SHRINE’ with questions like “50-year-old Asian women, diagnosed with stage III breast cancer and their treatments”. Armed with this information doctors were able to treat her with chemotherapy drugs by targeting the estrogen-sensitive tumour cells by avoiding surgery. By the time Kathy completed her chemotherapy regimen the radiologists could no longer find any tumour cells. This is one example of how big data tools can help in customising the treatment plan according to the requirement of each. As cancer is a long tail distribution a ‘one size fits all’ philosophy will not work. For customising treatments depending on the patient’s history, their gene sequence, results of diagnostic tests, a mutation found in their genes or a combination of their genes and environment, big data and machine learning tools are indispensable. upGrad’s Exclusive Software Development Webinar for you – SAAS Business – What is So Different? document.createElement('video'); https://cdn.upgrad.com/blog/mausmi-ambastha.mp4   Drug Discovery with Big Data and Machine Learning Big data and Machine learning will not only help in diagnosis and treatment but also will revolutionise drug discovery. Researchers can use open data and computational resources to discover new uses for the drugs which are already approved by agencies like FDA for other purposes. For example, scientists at University of California at San Francisco found by number crunching that a drug called ‘pyrvinium pamoate’ which is used to treat pinworms – could shrink hepatocellular carcinoma, a type of liver cancer, in mice. This disease which is associated with the liver is the second highest contributor to cancer deaths in the world. Not only is big data used for discovering new uses for old drugs but can also be used for detecting new drugs. By crunching data related to different drugs, chemicals, and their properties, symptoms of various diseases, the chemical composition of the drugs used for those conditions and side effects of these medications collected from different media; new drugs can be devised for various types of cancer. This will significantly reduce the time taken to come up with new medicines without wasting millions of dollars in the process. Using big data and machine learning will no doubt improve the process of diagnosis, treatment and drug discovery in treating cancer, but it is not without challenges. There are many stumbling blocks and problems on the road ahead. If these blocks are not removed, and these challenges are not faced, then our enemy will get the upper hand and will defeat us in the future battle. Read our Popular Articles related to Software Development Why Learn to Code? How Learn to Code? How to Install Specific Version of NPM Package? Types of Inheritance in C++ What Should You Know? Challenges in using Big Data and Machine Learning to fight Cancer Digitisation Except for a few large and technically advanced hospitals, most of them are yet to be digitised. They are still following the old methods of capturing and recording data in massive stacks of files. Due to lack of technical expertise, affordability, economies of scale and various other reasons, digitisation has not taken place. Provision of open source EMR software, teaching how helpful these digital records could be in treating the patients and how profitable it is to the hospitals are some steps in the right direction. Data locked in enterprise warehouses As of today, only a few hospitals can digitally capture patient records. This apparatus too is locked away in enterprise warehouses and inaccessible to the world at large. Hospitals are reluctant to share their databases with other hospitals. Even if they are willing, they are plagued by the different database schemas and architectures. Critical thinking is required on this front about how hospitals can share their databases among themselves for their mutual benefit without being suspicious of each other. A consensus needs to be reached about the schema in which this data should be shared as well, for the benefit of all hospitals. This patient data should be democratised and utilised for the betterment of the future of mankind.   Patient data should not be allowed to be employed for the growth of a single organisation. Utmost care should be taken to anonymise the individual to whom the data belongs. If a person’s lipstick preference is leaked, then there is not much harm. If a person’s medical history is leaked, then it will have a significant impact on his life and prospects. The government should take positive steps in this direction and should help create a big data infrastructure for storing medical records of patients from all hospitals. It should make it compulsory for all hospitals to share their database within this shared infrastructure. Access to this database should be made free for patient treatment and research. Improvement in efficiency of Machine Learning Algorithms Machine learning is not a magic pill for cancer diagnosis and treatments. It is a tool that if used well can help in our journey to conquer cancer. Machine learning is still in a nascent stage and has its disadvantages. For example, the data on which these algorithms are trained needs to be very close to the data on which they are utilised for producing results. If there is a huge difference in them, then the algorithm will not be able to provide meaningful results which can be employed. There are many machine learning algorithms which exist with their own peculiar assumptions, advantages, and disadvantages. If we can find a way to combine all these different algorithms for achieving the results required by us, i.e. curing cancer, needless to say, we would have found a hugely beneficial outcome. The famous machine learning scientist Pedro Domingos calls it “The Master Algorithm”, who also wrote a popular science book of the same name. According to Pedro, there are five different schools of thought in machine learning. The symbolist, connectionist, Bayesian, evolutionaries and analogisers. It is difficult to go into all these different types of machine learning systems in this article. I will cover all the five types of machine learning systems in one of my future blogs. For now, we need to understand that all these different methods have advantages and disadvantages of their own. If we can combine them, then we can derive highly impactful insights from our data. This will be immensely useful not only for all kinds of predictions and forecasts but also for our fight against a vengeful enemy – cancer. To summarise, cancer is a formidable enemy which keeps changing its form frequently. We do possess new weapons in our arsenal now in the form of big data and machine learning, however, to face it competently. But to demolish it entirely we need a more powerful weapon than what we presently possess. The name of that weapon is ‘The Master Algorithm’. We also need to make some changes in the strategies and methods with which we are fighting this enemy. These changes are creating a big data infrastructure, making it compulsory for hospitals to share anonymised patient records, maintaining the security of the database and allowing free access to the database for patient treatment and research to cure cancer. Get data science certification from the World’s top Universities. Learn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career. Wrapping up If you are interested to know more about Big Data, check out our Advanced Certificate Programme in Big Data from IIIT Bangalore. Learn Software Engineering degrees online from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career.
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Piyush Kumar of MakeMyTrip explains Big Data Operations

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Piyush Kumar of MakeMyTrip explains Big Data Operations

Piyush Kumar is the Head of Data Platform Engineering at MakeMyTrip. He heads the Data team (Data platform, Data Science, and Business Intelligence functions) at MakeMyTrip to support various Lines of Business such as Flights, Hotels, Holidays, and Ground. Along with defining Big Data strategy, he looks after designing and building a scalable and distributed machine learning platform for Big Data systems with real-time streaming and batch processing for Clickstream, Mobile, Transactional, CRM (Customer relationship management) & user feedback or reviews data. In an exclusive interview, Piyush provides valuable insights to UpGrad about how MakeMyTrip has leveraged Big Data, in line with current trends, to upgrade and enhance its product offerings. In this first video, Piyush talks about how MakeMyTrip uses Big Data to solve critical business problems in the area of customer segmentation, personalisation, building data pipelines, etc. He also explains the architecture of the Big Data system at MakeMyTrip. In the second video, Piyush shares insights on career planning for Big Data enthusiasts highlighting different career paths available in Big Data and the necessary skill sets required. So, Piyush spoke about how MakeMyTrip uses Big Data in their operations. He provided valuable insights to UpGrad about how MakeMyTrip is leveraging Big Data, in line with current trends, to upgrade and enhance its product offerings. He shared insights on career planning for big data enthusiasts highlighting the necessary skill sets required. Explore Our Software Development Free Courses Fundamentals of Cloud Computing JavaScript Basics from the scratch Data Structures and Algorithms Blockchain Technology React for Beginners Core Java Basics Java Node.js for Beginners Advanced JavaScript Are you planning a big data career? If you want us to cover other topics and interview other industry experts please let us know your thoughts in the comments section. Explore our Popular Software Engineering Courses Master of Science in Computer Science from LJMU & IIITB Caltech CTME Cybersecurity Certificate Program Full Stack Development Bootcamp PG Program in Blockchain Executive PG Program in Full Stack Development View All our Courses Below Software Engineering Courses If you are interested to know more about Big Data, check out our Advanced Certificate Programme in Big Data from IIIT Bangalore. In-Demand Software Development Skills JavaScript Courses Core Java Courses Data Structures Courses Node.js Courses SQL Courses Full stack development Courses NFT Courses DevOps Courses Big Data Courses React.js Courses Cyber Security Courses Cloud Computing Courses Database Design Courses Python Courses Cryptocurrency Courses Learn Software Engineering degrees online from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career. Read our Popular Articles related to Software Development Why Learn to Code? How Learn to Code? How to Install Specific Version of NPM Package? Types of Inheritance in C++ What Should You Know?
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by Mohit Soni

17 Jan'18
The Business of Data Security is Booming!

5.25K+

The Business of Data Security is Booming!

This is an excerpt from the book ‘Breach: Remarkable Stories of Espionage and Data Theft and the Fight to Keep Secrets Safe’ by Nirmal John. Nirmal John has worked in advertising and journalism. He was earlier the assistant editor of Fortune. This book brings to light several incidents which till now were brushed under the carpet. It has instances of piracy, data theft, phishing, among many others. Even though he focuses on India, Nirmal John takes great pains to show links between underground international networks working to undermine data security. This excerpt has been taken from the chapter, ‘WHITE HAT Is GrEEnBACK’. This excerpt throws light on the normal routine of Saket Modi, a young CEO of a data security company, Lucideus. Fear. Urgency. Desperation. Panic. The themes that dominate that call for help are almost always the same. Pretty much everyone working in the cybersecurity business knows what it is to get that call, especially in the middle of the night. There used to be a time when break-ins were reported first to the police. But with the crime itself changing in nature, the way it is reported is changing too. The cops aren’t in control when it comes to new-age crime and theft of data. Dialling 100 may not get you far when it comes to data breaches. Saket Modi has been receiving these calls for a few years now. Modi is a baby-faced young man in his twenties who boasts an easy charm. His company is named Lucideus. It is a mash-up of two names from the ancient scriptures— Lucifer, the Latin word which came to be used to describe the devil, and Zeus, the supreme Greek deity who, among other things, dispensed justice. The mash-up is meant to be a reference to how the ‘bad’ and the ‘good’ come together online. Modi’s earlier office in Safdarjung Development Area market near IIT in Delhi was small and tastefully appointed in white (perhaps to accentuate the idea of the white hat hacker). He has since moved to a new, much larger space in Okhla, still tastefully appointed, still in white. He started out when he was in his teens, helping companies investigate breaches and shore up their cybersecurity. His carefully constructed reputation as a young white hat hacker brought him many projects over the years. These days he is among those advising the Government of India on matters of cybersecurity. Most of his projects for companies started with a call from a panic-laden voice. Modi particularly remembers one call from nearly five years back. It was the chief executive of one of India’s largest services companies at the other end of the line. The CEO introduced himself. He had met Modi on the sidelines of a conference; they’d exchanged visiting cards, and the chief executive had fished out Modi’s card to call him. ‘We think we are in major trouble. How quickly can you fly to Bengaluru?’ Modi was used to such requests from panic-stricken executives. He asked for a bit more context on what exactly had gone wrong. ‘The CEO of one of my top five clients, who is a huge name internationally, called me earlier today. He asked me to immediately stop all the operations I was doing for his company. He didn’t explain why. He just said that he will be calling me later to explain further.’ This was a client that contributed a very significant chunk to the Indian company’s top line. There were hundreds of employees from the Indian company working on the client’s projects. ‘I suspect there has been a breach, because of which all this could be happening. There are a few other things that would explain this reaction from the client. The truth is, I can’t afford to lose this client under any circumstances,’ the executive confessed. Explore Our Software Development Free Courses Fundamentals of Cloud Computing JavaScript Basics from the scratch Data Structures and Algorithms Blockchain Technology React for Beginners Core Java Basics Java Node.js for Beginners Advanced JavaScript Saket Modi took the next flight to Bengaluru. It was when he reached the office of the chief executive that Modi realized he wasn’t the only one who had got a call from him. There, sitting in the conference room and waiting to be briefed, were cyber-forensics experts from big accounting firms and other security researchers like himself. ” upGrad’s Exclusive Software Development Webinar for you – SAAS Business – What is So Different? document.createElement('video'); https://cdn.upgrad.com/blog/mausmi-ambastha.mp4 ”   Even though this was par for the course when it came to how Indian companies reacted in such situations, Modi says he was taken aback. He says this has become a common practice when it comes to investigating breaches—the targeted company invites the names known to have cyber- forensics experience for a briefing post an incident and then gives the job to whoever bids the lowest. The question he asks is whether matters of security can be treated like other supplier relationships, especially in a crisis situation? This is probably how things work in many Indian corporations but, as he points out with evident displeasure, that is not how security and breach protocol should roll, particularly in a crisis situation. ‘security is not an L1 business.’ The chief executive briefed the gathering about the situation. There had indeed been a breach. He was looking for partners who could immediately deploy resources to find the vulnerabilities that had led to the breach and could help plug them. That was the only way he could convince the client not to terminate the contract. Modi ended up with the project even though his quoted fee was high. He flew in his team from New Delhi and, during the investigation, found several vulnerabilities in the organization that had resulted in the breach. In-Demand Software Development Skills JavaScript Courses Core Java Courses Data Structures Courses Node.js Courses SQL Courses Full stack development Courses NFT Courses DevOps Courses Big Data Courses React.js Courses Cyber Security Courses Cloud Computing Courses Database Design Courses Python Courses Cryptocurrency Courses The team started by pouring over the access logs which list the requests for individual files from a website. They then isolated the sectors which were compromised and sandboxed them. That meant that they used a separate machine, not connected to the company’s main network, to run programmes and test the behaviour of the malicious code. The idea behind doing this was to deduce if there were patterns in the type of data that was being compromised. If they could unearth a pattern, it could theoretically lead them to the hacker. Unfortunately, as in many such instances, Modi says, he couldn’t identify the source of the breach as its origins were from beyond Indian borders and hidden in a complex trail of IPs. His team couldn’t definitively pinpoint the location, but they pushed the chief executive and his company to shore up every single facet of its security protocol. Explore our Popular Software Engineering Courses Master of Science in Computer Science from LJMU & IIITB Caltech CTME Cybersecurity Certificate Program Full Stack Development Bootcamp PG Program in Blockchain Executive PG Program in Full Stack Development View All our Courses Below Software Engineering Courses The client continued the shutdown of the handling of his operations by the Indian company for a month, while Modi and his team worked on overhauling the Indian company’s security system. A month later, Modi had a call with the CEO of the company’s international client to detail the steps they had taken to make sure that breaches such as the one that had happened would not recur. Later, the client sent a team to audit the changes, and only when it was satisfied did the client allow the company to resume work on its projects. It cost the Indian company thousands of billable hours, not to mention damage to their standing in front of the client. If you like this excerpt and want to read real-life thriller stories full of hackers, police, and corporates, you can read the book; ‘Breach’ by Nirmal John. Read our Popular Articles related to Software Development Why Learn to Code? How Learn to Code? How to Install Specific Version of NPM Package? Types of Inheritance in C++ What Should You Know? Conclusion If you are interested to know more about Big Data, check out our Advanced Certificate Programme in Big Data from IIIT Bangalore. Learn Software Engineering degrees online from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career.
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by upGrad

01 Feb'18
Big Data: What is it and Why does it Matter?

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Big Data: What is it and Why does it Matter?

If you’re a complete newbie in the world of Big Data, the term itself might be slightly confusing. Before we move to the technicalities, let’s ask two essential questions: How big? What data? The answer to the first question isn’t fixed – it would’ve changed by the time you’d have completed reading this line. For all we know, by the time you’ve read through the article, the total amount of data in the world would have soared by quite a bit. According to IBM, we create roughly 2.5 quintillion bytes of data per day – To put things in perspective, that is the capacity you’ll need to hold around 530,000,000 MP3 songs. Look at that number again, there are quite a lot of zeros in there. Now, let’s talk about the “what”. What data is this? It’s almost like the famous song by The Police, which goes something like… “Every breath you take, every move you make, every bond you break, every step you take, I’ll be watching you.” And that’s what they’re doing. By they, we simply mean the ones who’re in charge of collecting this data. Everything you do on the internet is adding to this colossal mountain of data. Your Facebook posts, Tweets, Snapchat stories, and whatever the kids are using these days – are just bricks in the huge wall of Big Data. Watch Youtube video. So, to answer your second question – the data in question is the very data you’re producing every passing moment. Every time you book a cab, or order food online, or even do a very basic google search – It’s all going on top of the heap. Everything is being collected. That’s what is making this big data, bigger – every passing minute.   Now that you’re in control of the situation, let’s dive a little deeper into the ocean of Big Data. Further, we’ll look at why exactly does Big Data matter so much, and who’re the ones benefiting from it? Explore our Popular Software Engineering Courses Master of Science in Computer Science from LJMU & IIITB Caltech CTME Cybersecurity Certificate Program Full Stack Development Bootcamp PG Program in Blockchain Executive PG Program in Full Stack Development View All our Courses Below Software Engineering Courses What is Big Data? By now, we’re clear that Big Data is just an extremely large volume of data – both structured and unstructured – collected through a variety of sources and in a variety of formats. For the sake of a formal definition, you can have a look at how IBM defines “Big Data”: According to the data scientists at IBM, Big Data can typically be characterized by 4 V’s – Volume, Variety, Velocity, and Veracity. Volume Very simply, volume means how “big” the Big Data is. Like we said earlier, there’s no specific number to it, it’s ever-increasing. Variety The data we’re talking about comes from a number of sources, hence it is in numerous formats. We’re talking about data in the form of audio, video, pdf, email, and more! Most of this data is unstructured – implying not much sense can be made out of it without a proper study.   Explore Our Software Development Free Courses Fundamentals of Cloud Computing JavaScript Basics from the scratch Data Structures and Algorithms Blockchain Technology React for Beginners Core Java Basics Java Node.js for Beginners Advanced JavaScript Velocity The flow of Big Data from the variety of sources we discussed above is massive and un-ending. Like we said, by the time you’ve read this article, the amount of Big Data in the world would have increased drastically. If you don’t believe us, listen to the guys at IBM who claim that by 2020, there’ll be 5,200 GB of data for each and every person on Earth. Yeah, talk about velocity! Veracity Veracity in context of Big Data simply refers to the noises and anomalies present in the data. When dealing with Big Data, veracity is one of the biggest challenges that data analysts face. By now, it’s clear that there’s a lot of data around us, almost too much to even think about! Making sense of this data is quite a daunting task in itself. For this, we have data analysts – the heart and soul of any organization’s analytics team – but how exactly do businesses use data to power their operations? Let’s see. In-Demand Software Development Skills JavaScript Courses Core Java Courses Data Structures Courses Node.js Courses SQL Courses Full stack development Courses NFT Courses DevOps Courses Big Data Courses React.js Courses Cyber Security Courses Cloud Computing Courses Database Design Courses Python Courses Cryptocurrency Courses Big Data matters – but why? The organizations which earlier had to rely on the data collected through archaic spreadsheets now have access to tonnes of data on their customers. Data that can be used to overhaul their business and make profits like never before. Watch Youtube video. Sherlock Holmes puts it right – “It’s a capital mistake to theorize before one has data!” And today, businesses HAVE data – a lot of it. But how exactly does it help them? By carefully examining the data at hand, organizations are performing the following kinds of intricate analytics to gather actionable insights and perform better in the market: Social listening It gives the organizations the power to know the real-time feedback of their consumers. The days of polls or surveys are long gone – sentiment analysis provides much more comprehensive and actionable feedback. Tools like HootSuite, TweetReach, Klout, and BuzzSumo are just a few examples of social listening tools that help the organizations stay a step ahead by knowing what the consumers have to say, their sentiments, and feedback. Comparative analysis Thanks to Big Data, organizations can now compare their products, services, and overall brand image with their competitors by examining user-behavior metrics in real-time. Marketing analytics This helps organizations in promoting new products and services to the target audience in a much more informed and innovative way. There are various sophisticated tools dedicated to Marketing Analytics which help organizations keep a close eye on how their product is being received in the market. Some common tools for this include – Marketing Evolution, Predictive Modeling, Lattice Engines – all of which aim to improve the organization’s ROI by leveraging Big Data. Targeting Using this stream of Big Data analytics, organizations can dive into social media activity on any subject, based on a variety of sources, all in real-time. For example, let’s say you want to target specific customer groups and provide them with exclusive special offers – you can do that now, using Big Data. It’s a win-win situation for both the organization as well as the customers. The same tools as the ones discussed in Social Listening can be used for this purpose as well. Customer satisfaction Organizations can boost customer engagement manifold by analyzing Big Data from a multitude of sources. Also, using these metrics, they’re able to figure out, and eventually iron out any potential customer issues that might go viral – preserving brand loyalty and improving customer service, at the same time. Who’s using Big Data – Real-world applications It’s safe to say that no domain of business today is untouched by the magic that is Big Data. From banking, to healthcare, to social-media, to education, to even government sectors – the list can go on – everyone is trying their best to make sense of the data at hand and outperform their competition. Let’s see some major industries that are affected by the giant that is Big Data: Healthcare Providers Asia’s largest healthcare group – Apollo hospitals – is using Big Data and analytics to control HAI (hospital-acquired infections). Education Big data is used quite extensively to improve higher education. Take the example of the University of Tasmania. It has deployed a management system that tracks things like the time at which a student logs on to the system, time spent on different pages of the system, and even the overall progress of the student. Government Operations Big Data has a wide range of applications in government operations and services. They include energy exploration, fraud detection, environmental protection, financial analysis, and health-related research. We can go on and on about each and every industry, but we think you get the gist. Big Data analytics is being used wherever it is possible. And frankly, there’s no domain that can’t use a little data analytics to improve their operations. Because at the end of the day, data is all that’s there, and all there will ever be. To wrap things up… It’s safe to say that Big Data is not just a fad – it’s a revolution. It’s always better to stay on your toes when you’re in the middle of a revolution, or you’ll be left behind before you know it. What makes one particular organization stand out from the rest is the way they deal with their data. Having said that, it’s only fair to conclude by saying that the demand for good data scientists is, and will keep on, increasing. So, buckle up while you can, and get started with exploring the mad but genius world of Big Data! If you are interested to know more about Big Data, check out our Advanced Certificate Programme in Big Data from IIIT Bangalore. 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by Mohit Soni

05 Feb'18