Big Data Analytics in Action: Examples

Big Data Analytics in Action: Examples

Copyright: © 2019 |Pages: 29
DOI: 10.4018/978-1-5225-7609-9.ch009

Abstract

Have you ever wondered how companies that adopt big data and analytics have generated value? Which algorithm are they using for which situation? And what was the result? These points will be discussed in this chapter in order to highlight the importance of big data analytics. To this end, and in order to give a quick introduction to what is being done in data analytics applications and to trigger the reader's interest, the author introduces some applications examples. This will allow you, in more detail, to gain more insight into the types and uses of algorithms for data analysis. So, enjoy the examples.
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Introduction

The secret of getting ahead is getting started. The secret of getting started is breaking your complex overwhelming tasks into small manageable tasks, and starting on the first one. (Mark Twain)

If you ask a data scientist about which algorithm is best for analyzing such a problem, he will ask you to try several and see which one works best depending on your case (Sedkaoui, 2018a).

So, it depends on:

  • The data quality

  • Parameters that will be used

  • Data source

  • Execution time required

  • Available parameters to influence the performance of the algorithm

  • Etc.

So, for each type of analytics question, a specific group of methods called algorithms. The algorithms present a sort of real evolution. They make our programs smarter by allowing them to learn automatically from the data we provide. We noticed this importance again during a participation in several events, meets and conferences around the world during these last few years. Personally, I was able to perform even better, thanks to several applications, how do the algorithms work? And how value id generated?

With the several examples illustrated in this last chapter, you can see and experience how data analytics will generate added knowledge and how it turns ideas into business opportunities. Big data analytics revolutionizes businesses by mixing the immensity of big data to draw unique observations and deductions, never envisaged, to better predict the next act of their clients. Everything they have always wanted to know about their clients without ever daring to ask.

The aim of these examples is to show you how you can read your data, apply tools and methods and visualize your results. Going from the simple example, the aim is then to help you learn to practice in this universe.

Far from the big abstract speeches, the author will make you, through this chapter; discover the practices of data scientist. And it will be the opportunity for you also to put your hand in this field, with just enough theory to understand what involves the methods of data analysis used, but especially with your computer, some free and powerful big data software and technologies, as well as a little thought, you will participate actively in this passionate exploration

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How Can Small Business Use Big Data? Practical Examples And Application Cases

Through the collection, analysis and the data exploration, big data is synonymous with innovation in term of use and contribution in the enhancement of competitive advantage of your business activity. Applications taking advantage of big data are announced as numerous, diverse and very promising. Reading the press, we hear often about the recommendation systems of US giants like Google and Amazon, decryption of the human genome, monitoring logs or behaviors, retargeting, etc.

Davenport and Kim (2013) and Brynjolfson and McAfee (2012) and others suggest that large companies have used big data analytics to increase their performance, and some uses can be extracted:

  • Processing a large amount of data generated through internal operational systems and processes.

  • Capturing external sources of data and using them to enrich the data generated internally.

  • Scanning the external environment and industry for information on what is being said by competitors and customers, using technologies such as sentiment analysis

Key Terms in this Chapter

MapReduce: Is a programming model or algorithm for the processing of data using a parallel programming implementation and was originally used for academic purposes associated with parallel programming techniques.

Missing Values: Occur when no data value is stored for the variable in an observation.

Classification: In machine learning and statistics, classification is the problem of identifying to which of a set of categories (sub-populations) a new observation belongs, on the basis of a training set of data containing observations (or instances) whose category membership is known.

Regression Analysis: Is a set of statistical processes for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables when the focus is on the relationship between a dependent variable and one or more independent variables (predictors).

Algorithm: A set of computational rules to be followed to solve a mathematical problem. More recently, the term has been adopted to refer to a process to be followed, often by a computer.

Outliers: An outlier is an observation that lies an abnormal distance from other values in a random sample from a population. In a sense, this definition leaves it up to the analyst (or a consensus process) to decide what will be considered abnormal. Before abnormal observations can be singled out, it is necessary to characterize normal observations.

Correlation: A mutual relationship or connection between two or more things.

Cluster Analysis: A statistical technique whereby data or objects are classified into groups (clusters) that are similar to one another but different from data or objects in other clusters.

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