Big Data is a collection of large amounts of data that not only grows exponentially every day but is complicated for a traditional data manager to manage, analyze and process.
The New York Stock Exchange (NYSE) is an example of Big Data. About One Terabyte of data is generated everyday at the NYSE.
There are three types of Big Data:
1. Structured
2. Unstructured
3. Semi-structured
1. Structured
Structure Big Data refers to the data that can be easily accessed, stored and organized in a specific format. Over the decades, computer science has taken a turn and has achieved great success in introducing such techniques that help in working with such data and also, derives value from the data.
2. Unstructured
Unstructured data is that which does not have any specific format or structure. Often, if the size of the data is huge, unstructured data becomes difficult to process and derive value out of it. Today, organizations have a lot of raw data available but they are not able to derive complete value from it because it is in an unstructured format.
3. Semi-structured
Semi-structured data can contain both types of data, structured as well as unstructured. Semi-structured data is mostly seen in a structured form.
Characteristics of Big Data a.ka. The five Vs of Big Data
1. Volume
As the name suggests, Big Data represents data of a large size. This implies that the volume ought to be large too.
2. Variety
Variety to the sources of the data and the nature of it i.e. if the data is structured or unstructured. Programs or the directory were the only nature of data considered earlier but now, PDFs, audios, videos, emails, photos, etc. are also considered as a source for data and in the analysis application. This type of data sometimes creates issues in storing, interpreting, analysing and managing the data.
3. Velocity
Velocity refers to the speed at which the data is processed by the server to meet the demands of the customer. Any large business or organization is very keen to know at what speed the company data can be managed, interpreted and used to make business decisions about the betterment of the business. The data is very useful for the company because they are able to track down the behavior of the customer fast and easy.
4. Variability
This refers to the variations that the data handler faces while managing and analyzing the big data. This is the inconsistency that is there in data making it difficult to work on.
Analytics is defined as a way to find meaningful and useful patterns from the given set of data, it is a scientific method used to make reports of the business. It is a process of converting raw data and deriving something meaningful out of it to get a better look at the business. It helps in making better business decisions for the future. The process of Data Analytics is based on computer programming, statistics, research to quantify, and then, they are able to make a report and look at a better future for their future.
Data Analytics is very important for large corporations and businesses. Analytics helps us spot the mistakes or patterns that are hidden in the big data. Analytics has successfully changed the world of sports, science, health, education and many more fields.
With the help of Data Analytics, we can now find out hidden patterns like consumer behavior, team performance, and other activities like the weather, climate, diseases and many other things.
Mostly, Businesses use Analytics to focus on the following areas-
● Risk Analysis
● Fraud Analytics
● Web Analytics
● Market Modeling
● Advertisement and Marketing
● Optimizing the Marketing performance
The present 4.0 era is a fusion of the real world and the virtual world. The digital revolution that we are looking at is a result of Technologies like Big Data and Artificial Intelligence to help nurture automatic learning systems. Companies and Businesses seem to seek help to achieve business intelligence by compiling, managing, analyzing and then sharing data across all domains to produce excellent business decisions.
The internal connection between the systems and computers as well as the potential to analyze large quantums of data is made possible with the help of the intelligent machines that we have today. We have machines that can make correct and informed decisions without any human intervention. The values that we are able to extract by using Big Data is, indeed, a new level of Technology.
The amount of information that is produced by the manufacturing systems should be translated into some kind of actionable idea. This is why Big Data analyses the given data and gives the appropriate insights for the future too.
● Improvement in Warehouse Processes
Through the help of sensors and portable devices, companies are now able to improve their operating efficiency by reading into the errors, performance quality controls and assembly routes.
● Elimination of processes that hold up
Big Data firstly identifies issues that can be caused during the process and can affect the performance and guides the manufacturers to look into it without any cost.
● Predictive Demand
Through Big Data, we are able to make more accurate predictions about customer preferences and trends that are followed currently around the world. This helps the company to work on its product portfolio.
● Maintenance
The sensors installed in the machinery identifies the issues that it might face before the machine goes down and sends an alert to the equipment for it to react in time.
There are other great things that you can do with the help of Big Data Analytics. These include improving security, loading optimization, supply chain management, prediction of the weather, climate, traffic, etc.
Applications of Big Data Analytics With constant advancements in technology, we are looking forward to a world where nothing is impossible. As mentioned earlier, Big Data Analytics has helped large organisations and businesses make favourable decisions about their company.
This is a list of some of the applications of Big Data in large organisations:
1. Tracking your customers’ spending habits and shopping behavior
In major retail stores like Amazon, Walmart, Big Bazaar, etc. the data management team tracks the spending patterns of the customers. They find out how customers spend their money, which product they prefer, what they look for in the store and more. They keep track of all the information to ensure that those products are available for the customer at all times at their stores.
The Banking sector keeps track of debit cards, credit cards with discounts, and cashback that the customer is using at certain places to buy things. By using this way, they can give the customer the right type of offers at the right time.
2. Recommendation
By tracking the customers’ shopping behavior and spending habits, big retail stores will be able to identify products that the customer searches for. Post which, the store can send a recommendation of that particular product and the various brands to the customer.
For example, you are looking for a skin moisturizer, say Neutrogena. Amazon’s data will show that you are interested in buying a product of the same brand and whenever you go to browse your Google page, you will see different recommendations of the product available under the same brand.
3. Smart Traffic System
Data about the state of different roads is collected with the help of cameras installed at every corner of the city. All the data is analyzed and roads that have less traffic or no traffic are recommended to the driver of the car, saving them a lot of time. Smart traffic systems can be built in the city with the help of Big Data Analysis.
4. Auto Driving Car
Big Data Analysis can help a person drive a car without any human involvement. Sensors are placed in various spots of the car camera that gather data about the surroundings of the area like the size of the cars around you, obstacles if any, distance from other cars, etc. This data is then analyzed and certain calculations are made such as the number of angels that have to be changed, the speed of the car, etc. These calculations and predictions help in taking necessary action immediately.
5. Virtual Personal Assistant Tool
Big Data helps the virtual assistant to answer questions that the users have like Siri in Apple, Cortana in Windows and Google Assistant in Android. This tool keeps a track of the location, the weather, their local time, season, etc of the user helping the virtual assistant answer the questions.
Try saying: “Is it going to rain today?” The tool will then access your location, collect the data around the location and give you the right answer.
6. IoT
IoT is the Internet of Things. Manufacturing companies install IoT sensors in their machines. By installing IoT, they are able to access till when the machine will work without any problem, when it requires maintenance or service and when the machine will need a changing of the parts. This saves a lot of cost to the company by letting them know prior before the machine gets totally down.
● Big Data helps in arranging data that is too large for an organization to manage from multiple sources including the Internet, Social Media platforms, the company’s databases, etc.
● It also helps in actual prediction as well as monitors the data of the business and the market.
● It identifies problems and crucial points that are hidden in large data influencing the decisions of the organization.
● It works on the risks and threats that come with a large set of data immediately, optimize it and lowers the risks.
● It identifies the issues in the system and the processes used in the business.
● Finds out what the customers like the most or which product they are looking for, discounts, cashback, offers, services, etc. Are also predicted by Big data.
● Provides a speedy delivery of the product giving the customer utter satisfaction and meeting his expectations.
● Big Data Analytics is very good for your large business or organization because it keeps a track of what goes on in the market, enabling you to build new strategies, products and services for a better customer experience.
Python has been one of the most liked languages by developers around the world since it was introduced to the coding world. The reason why Python is loved is it’s an easy to learn and easy to follow language that is also fast. Since Python’s development of analytical and statistical libraries like NumPy, SciPy, etc. It is known to be one of the most powerful data analytics tools.
Spark is an open-source analytics tool that helps in the processing of data that is not well structured or very large amounts of data. Spark became very popular in the last few years due to its ability to integrate properly with Hadoop making Apache Spark a very famous Analytics tool. Spark also has its own Machine Learning library.
Storm is the tool of choice for Big Data which helps in the movement of data or if the data comes in as a continuous stream.
Storm is the best tool for immediate analytics and stream processing.
Excel is one of the most widely used data analytics tools in the world. Even if you are an expert in any other data analytics tool like Tableau or R, you will still use Excel for the grunt work. Professionals from a non-analytics background might not have access to SAS or R on their systems. But every professional has Excel.
Splunk is known to be more popular than some other famous Analytics tools like Cloudera and Hortonworks. Splunk started as a “Google for log files” meaning the primary use of the tool was to machine log file data. But through its advancements, it has become much more than that. Splunk also has great visualization and web interface making it easy to use.
Careers In Big Data Analytics
Big Data is taking over businesses and the growth in large data analysis throughout the world is a sign of things to come. Big Data is an advancement of technology helping people with large data to manage, process and organize. There are a lot of career options that you can go for if you are interested in Big Data Analytics and its technologies.
The following is a list of career options with their salaries and the responsibility of the job-
1. Big Data Engineer
The expected salary of a Big Data Engineer is Rs. 7,00,000 to 10,00,000. A Big Data Engineer is similar to a Data Analyst where they have to convert large amounts of data into insights that an organization or a business can use to make smart business decisions. Then again, an engineer is also tasked with retrieving, interpreting, organizing, and reporting on the data. A Big Data Engineer is also expected to create the company’s hardware and software architecture including the systems and processes that are needed to work the data.
2. Data Architect
The average salary of a Data Architect is around Rs. 20,00,000 per year. A Data Architect has to design the structural framework of a database as well as building and maintaining these databases. A Data Architect also develops various strategies for some of the organisation’s key subject areas and communicates plans, the status of the work, and issues to the company’s executives.
3. Data Warehouse Manager
The expected salary of a Data Warehouse Manager ranges from Rs. 10,00,000 to Rs 12,00,000.
A Data Warehouse Manager is responsible for the storage and analysis of data of a facility. Professional data managers use performance and usage metrics to evaluate data, analyze the load of data and also, monitor job usage. They identify potential risks to data storage and transfer.
4. Database Manager
The average salary of a Database Manager in India is around Rs. 15,00,000. A Database Manager will identify the problems or the issues that occur in the databases. He or she also takes steps to correct those issues, helps with the design and the implementation of the storage hardware and maintenance of the system.
These professionals team up with other database developers and can also train and teach a lower-level staff.
5. Business Intelligence Analyst
The average salary of a Business Intelligence Analyst in India ranges from Rs. 6,50,000 to 10,00,000 based on the experience of the person. A Business Intelligence Analyst is expected to turn the company’s data into different insights that the company executives and other people could read easily and make informed decisions. A Business Intelligence Analyst should have sufficient experience in analytical and reporting tools. The person should also be experienced with database queries, stored procedure writing as well as Online Analytical Processing (OAP) and possess data cube technology skills.
6. Data Scientist
The average salary of a Data Scientist in India ranges from Rs. 6,50,000 to Rs 9,00,000 based on one’s level of experience. A Data Scientist has to design and build new procedures for data mining, modeling and production. The person should be experienced in data mining techniques like clustering, regression, etc. He or she must also conduct data studies as well as product experiments and are given tasks such as the development of prototypes, algorithms, predictive models, etc.
7. Data Modeler
The average salary of a Data Modeler in India ranges from 6,00,000 to 12,00,000 per year based on the level of experience of the person. A Data Modeler turns large data into insights, such as the macro and micro trends which are then put together in a report for business purposes. Data Modelers should be skilled in both information science and statistical analysis with knowledge of the Programming languages.
8. Data Analyst
The average salary of a Data Analyst in India ranges from 3,25,000 to 9,00,000 based on the level of experience of the person. He or she works with a very large amount of data, turning them into insights that businesses can use to make better decisions and choices. They can work across different industries; from healthcare to technology. A Data Analyst will fit anywhere. He or she will work on making systems and improving on them, making it easier to scale and reduce the data easily in the coming future.
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