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Big Data Analytics is Data Science and No Rocket Science

In recent times, there has been considerable growth and availability of data, both structured and unstructured across businesses __ at high velocities and from innumerable new sources. This data, when harnessed, helps businesses predict about its customers wants and preferences. Big Data analytics means having to deal with data formation, storage, and retrieval of large quantities of data—often from many different sources. Insights from Big Data analytics help in understanding customer behaviour patterns and purchase decisions that is becoming critical to having sustainable competitive advantage for all industries. Data scientists at larger organizations are skilled to leverage speedy information and quick insights, letting them take action on instant opportunities, and helping to capitalize on cross-sell opportunities while enhancing a company’s competitive gain. By harnessing the value of your data, analytics can serve the following five advantages: Improved learning: Achieve new insights on your most dedicated and profitable customers. Data analytics can help track and gauge progress while focusing on servicing customers on the right channels for their needs. Enhanced customer retention: Cross-sell and up-sell through effective customer management. Indentify loyal customers and recognize the risk when certain customers will slip. Value-added marketing: Build more targeted marketing programs and lead generation campaigns that are aimed at the right audience at the right time. Alleviate risk: Improve your customer management activities by effectively tracking change in customer behaviour patterns and purchase decisions. Act right away: Respond appropriately after key data segments are recognized by taking corrective steps and measure the implications over time. Small and midsized businesses confront hard decisions on how to turn tons of customer data into actual profitability. The truth is that majority of businesses do not have the resources to hire a team of analysts to sort and sift through data. However, if you are considering to put in place a team with such skills, ensure you have the right talent and budget, an appropriate analytics partner and the right tools and  software to capture, protect, accumulate, search, share, analyze, and envisage your data.

Big Data Analytics is Data Science and No Rocket Science

11K
Big Data Analytics is Data Science and No Rocket Science

In recent times, there has been considerable growth and availability of data, both structured and unstructured across businesses __ at high velocities and from innumerable new sources. This data, when harnessed, helps businesses predict about its customers wants and preferences.

Big Data analytics means having to deal with data formation, storage, and retrieval of large quantities of data—often from many different sources. Insights from Big Data analytics help in understanding customer behaviour patterns and purchase decisions that is becoming critical to having sustainable competitive advantage for all industries.

Data scientists at larger organizations are skilled to leverage speedy information and quick insights, letting them take action on instant opportunities, and helping to capitalize on cross-sell opportunities while enhancing a company’s competitive gain.

By harnessing the value of your data, analytics can serve the following five advantages:

  1. Improved learning: Achieve new insights on your most dedicated and profitable customers. Data analytics can help track and gauge progress while focusing on servicing customers on the right channels for their needs.
  2. Enhanced customer retention: Cross-sell and up-sell through effective customer management. Indentify loyal customers and recognize the risk when certain customers will slip.
  3. Value-added marketing: Build more targeted marketing programs and lead generation campaigns that are aimed at the right audience at the right time.
  4. Alleviate risk: Improve your customer management activities by effectively tracking change in customer behaviour patterns and purchase decisions.
  5. Act right away: Respond appropriately after key data segments are recognized by taking corrective steps and measure the implications over time.

Small and midsized businesses confront hard decisions on how to turn tons of customer data into actual profitability. The truth is that majority of businesses do not have the resources to hire a team of analysts to sort and sift through data.

However, if you are considering to put in place a team with such skills, ensure you have the right talent and budget, an appropriate analytics partner and the right tools and  software to capture, protect, accumulate, search, share, analyze, and envisage your data.

Usha

Usha Sunil

Blog Author

Writing is Usha's hobby and passion. She has written widely on topics as diverse as training, finance, HR and marketing, and is now into technical writing and education. She keeps an interested eye on new trends in technology, and is currently on a mission to find out what makes the world go around.

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Master Big Data With A Hadoop Certification

Ever wondered how your social media posts or online transaction details are always available? And not just yours, but anyone who uses the internet can access their data whenever they want. With the advent of technology and the internet, the amount of data generated online is humongous. According to a report, almost 90% of the data that we have today have been created over the last couple of years. Whether it’s networking sites or weather reports, data is being generated every second and it needs to be stored for various purposes. How does this happen? Surely, all of these information can’t be stored in physical storage devices like pen drives or hard disks, unless there’s a huge football field to accommodate them. This is where ‘Big Data’ plays a huge role. Let’s find out more about it. What Is Big Data? Big data is a technology that can collate and store huge amounts of data every day. The information stored is important as it allows companies to assess their customers and market their products. For example, when a product or service is advertised on Facebook, users like or comment on the post. This data is then used by companies to judge the popularity of their product and further promote or improve their marketing campaigns accordingly. Big data, therefore, is one of the most important technologies of the modern world. However, most of the data is unstructured, which means it can’t be used for analysis and data mining. This where you need a software that can sort the unstructured data and provide data security. How Does Hadoop Help Big Data? Hadoop is an open-source, Java-based programming framework that’s capable of storing and processing large amounts of data. Hadoop makes use of a distributed computing framework, wherein data is formatted and stored in clusters of commodity hardware. Simultaneously, it also processes data by using cheap computers. This software is available for free download and is run and maintained by developers from all around the world. However, nowadays, commercial Hadoop software are being made available to suit the various data processing and storage needs of organizations. What Are The Advantages Of Hadoop? Apart from the fact that Hadoop can process and store data quickly, there are many other reasons that makes it the most preferred data storage choice. Let’s take a look at some of them: ● Hadoop offers you the flexibility of storing data as you want. For example, in traditional databases, you would have to first process the data and then store it. But, in Hadoop, you can store anything and then analyze it later and this includes unstructured data like images, videos, and texts. ● When you’re using the Hadoop framework, you can be assured that data will not be lost due to hardware failure. If any one of the nodes become faulty, the data will automatically distributed amongst other nodes. Also, several copies of the data will be made and stored automatically. ● It’s a free open-source software that relies on commodity hardware to process and store data. You can scale it up as per your needs by adding nodes. Why Take A Big Data And Hadoop Certification? If you want to begin a career in the IT industry or would like to become a data analyst, then you can’t do without big data. It’s everywhere and every internet-based service relies on big data technology to store their information and analyze it. No matter which field you choose, right from social media to weather reports, big data plays a big role in keeping them up and running. Therefore, it only makes senses that you take a certification in big data and Hadoop to add another point to your resume and eventually land better jobs. How Will The Certification Help Me? When you’re preparing for the certifying exam, you can take up a training course to better acquaint yourself with the subject. During your training, you will be taught about the various aspects of Hadoop and how it’s used to store big data. Some of the things that you’ll be learning during the training are: ● A clear understanding of the Hadoop ecosystem that includes Flame and Apache oozie workflow scheduler ● Mastery over the basic and advanced concepts of Hadoop 2.7 framework ● Learning to write MapReduce programs ● Conduct detailed data analysis with the help of Pig and Hive Apart from these, you will also be given training on setting up configurations for Hadoop clusters. With big data becoming an integral part of most businesses, mastering the Hadoop technology will help you land well-paying jobs. If you’ve been on the lookout for big data analytics jobs or want to become a software developer and architect, then a Hadoop certification will open up a world of opportunities for you.
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What Is Big Data and Why Use Hadoop?

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Types Of Big Data

Big Data is creating a revolution in the IT field, every year the use of analytics is increasing drastically every year. We are creating 2.5 quintillion bytes of data every day hence the field is expanding in B2C apps. Big Data has entered almost every industry today and is a dominant driving force behind the success of enterprises and organizations across the Globe. Let us first discuss- “What is Big Data?” “Data” is defined as ‘the quantities, characters, or symbols on which operations are performed by a computer, which may be stored and transmitted in the form of electrical signals and recorded on magnetic, optical, or mechanical recording media’, as a quick google search will show. The concept of Big Data is nothing complex; as the name suggests, “Big Data” refers to copious amounts of data which are too large to be processed and analyzed by traditional tools, and the data is not stored or managed efficiently. Since the amount of Big Data increases exponentially- more than 500 terabytes of data are uploaded to Facebook alone, in a single day- it represents a real problem in terms of analysis. Before we jump into the article, let's have a visual introduction on what is Big data and its types. (Structured Data, Semi-Structured & Unstructured Data) Types of Big Data: Classification is essential for the study of any subject. So Big Data is widely classified into three main types, which are- Structured Unstructured Semi-structured 1. Structured data Structured Data is used to refer to the data which is already stored in databases, in an ordered manner. It accounts for about 20% of the total existing data and is used the most in programming and computer-related activities. There are two sources of structured data- machines and humans. All the data received from sensors, weblogs, and financial systems are classified under machine-generated data. These include medical devices, GPS data, data of usage statistics captured by servers and applications and the huge amount of data that usually move through trading platforms, to name a few. Human-generated structured data mainly includes all the data a human input into a computer, such as his name and other personal details. When a person clicks a link on the internet, or even makes a move in a game, data is created- this can be used by companies to figure out their customer behavior and make the appropriate decisions and modifications. Let’s understand Structured data with an example. Top 3 players who have scored most runs in international T20 matches are as follows: Player Country Scores No of Matches played                Brendon McCullum New Zealand                                 2140                                           71                    Rohit Sharma India     2237          90 Virat Kohli  India      2167          65 2. Unstructured data While structured data resides in the traditional row-column databases, unstructured data is the opposite- they have no clear format in storage. The rest of the data created, about 80% of the total account for unstructured big data. Most of the data a person encounters belong to this category- and until recently, there was not much to do to it except storing it or analyzing it manually. Unstructured data is also classified based on its source, into machine-generated or human-generated. Machine-generated data accounts for all the satellite images, the scientific data from various experiments and radar data captured by various facets of technology. Human-generated unstructured data is found in abundance across the internet since it includes social media data, mobile data, and website content. This means that the pictures we upload to Facebook or Instagram handle, the videos we watch on YouTube and even the text messages we send all contribute to the gigantic heap that is unstructured data. Examples of unstructured data include text, video, audio, mobile activity, social media activity, satellite imagery, surveillance imagery – the list goes on and on. The following image will clearly help you to understand what exactly Unstructured data is The Unstructured data is further divided into – Captured User-Generated data a. Captured data: It is the data based on the user’s behavior. The best example to understand it is GPS via smartphones which help the user each and every moment and provides a real-time output. b. User-generated data: It is the kind of unstructured data where the user itself will put data on the internet every movement. For example, Tweets and Re-tweets, Likes, Shares, Comments, on Youtube, Facebook, etc. 3. Semi-structured data: The line between unstructured data and semi-structured data has always been unclear since most of the semi-structured data appear to be unstructured at a glance. Information that is not in the traditional database format as structured data, but contains some organizational properties which make it easier to process, are included in semi-structured data. For example, NoSQL documents are considered to be semi-structured, since they contain keywords that can be used to process the document easily. Big Data analysis has been found to have definite business value, as its analysis and processing can help a company achieve cost reductions and dramatic growth. So it is imperative that you do not wait too long to exploit the potential of this excellent business opportunity. Diagram showing Semi-structured data Difference between Structured, Semi-structured and Unstructured data       Factors      Structured data       Semi-structured data     Unstructured data Flexibility It is dependent and less flexible It is more flexible than structured data but less than flexible than unstructured data It is flexible in nature and there is an absence of a schema Transaction Management Matured transaction and various concurrency technique The transaction is adapted from DBMS not matured No transaction management and no concurrency Query performance Structured query allow complex joining Queries over anonymous nodes are possible An only textual query is possible Technology It is based on the relational database table It is based on RDF and XML This is based on character and library data Big data is indeed a revolution in the field of IT. The use of Data analytics is increasing every year. In spite of the demand, organizations are currently short of experts. To minimize this talent gap many training institutes are offering courses on Big data analytics which helps you to upgrade skills set needed to manage and analyze big data. If you are keen to take up data analytics as a career then taking up Big data training will be an added advantage .
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