Big Data Analytics in Action

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.

Techniques and Methods That Help to Make Big Data the Simplest Recipe for Success

Data analytics has grown in a machine learning context. Whatever the reason data is used or exploited, customer segmentation or marketing targeting, it must be processed first and represented on feature vectors. Many algorithms, such as clustering, regression, classification, and others, need to be represented and clarified in order to facilitate processing and statistical analysis. If we have seen, through the previous chapters, the importance of big data analysis (the Why?), as with every major innovation, the biggest confusion lies in the exact scope (What?) and its implementation (How?). In this chapter, we will take a look at the different algorithms and techniques analytics that we can use in order to exploit the large amounts of data.

Big Data, Who Are You?

When you hear “big data” probably you think like most, to a rather heavy file that includes diverse information, or a real iceberg that hides a large portion of its real mass. But we have not to think so about big data. Yes! You can worry about it. But you can very well choose to see it as a potential which allows you to create value. I would say even more that you must choose to take advantage of this large amount of data! Data or big data are primarily a way to segment your target. It is a collection of information that is analyzed, processed, and arranged to be profitable. So, do not let data phobia hold you back, just keep calm because this first chapter gives you an overview of what the concept big data encompasses, and you will realize by yourself that it is an exciting field.

First of All, Understand Data Analytics Context and Changes

Big data marks a major turning point in the use of data and is a powerful vehicle for growth and profitability. A comprehensive understanding of a company's data, its potential can be a new vector for performance. It must be recognized that without an adequate analysis, our data are just an unusable raw material. In this context, the traditional data processing tools cannot support such an explosion of volume. They cannot respond to new needs in a timely manner and at a reasonable cost. Big data is a broad term generally referring to very large data collections that impose complications on analytics tools for harnessing and managing such. This chapter details what big data analysis is. It presents the development of its applications. It is interested in the important changes that have touched the analytics context.

Understanding Data Analytics Is Good but Knowing How to Use It Is Better!

Collecting the data and being able to generate value from it: this is certainly the key success factor of tomorrow's champions, one that will allow you to innovate and create new business models. Faced with the 3Vs of big data, many companies are embarking on big data projects with the main objective: generating value. The goal is to succeed, by the detailed analysis of large amounts of data, to lift the veil and discover hitherto hidden models and barely perceptible correlations, as many new business opportunities that companies must grasp. The key to the success of any big data analytics initiative is to define your goals, identify specific business questions that a suitable technical architecture will need to answer, and use the data experts to generate value from data by using specific algorithms.

Big Data Applications in Business

Nothing seems to stop the big data revolution. At the same time a promise of a better world and anguish of a possible big brother, big data is the new reality of the digital economy: it is the new territory of development and creation of value for the companies. The opportunities seem endless, which is why we must appropriate the data to better understand and tame it, in order to prepare for the future towards which it seems to lead us. After the theory, let's go to the “fun” part with some examples of big data uses that you may know without realizing it. We will see in this chapter some examples of using big data in a dynamic improvement of the business strategy in order to generate value.

Plan and Rules for Data Analysis Success

Adapting the complex big data into your projects will be one of your strengths! Your mission to integrate big data is not limited to the use of sophisticated tools to solve your problems, but you must align the requirements of your activities with data lake or data warehouse through clear and correct strategies, taking into account your business as a goal. This provides support to your companies in all stages of your projects: from defining and taking requirements to start production and subsequent maintenance. Finally, it will help you create sustainable and stable competitive advantages.

Entrepreneurship and Big Data

The range of possibilities opened up by big data technologies offers companies in all sectors a remarkable opportunity for development and transformation. And if the majorities are convinced of its strategic interest, many are wondering about the implementation of such a project. Today, companies using big data are search engines like Google; social networks like Facebook, Twitter, or LinkedIn; e-commerce websites like eBay, Ali Baba, or Amazon, etc. But, it would be premature to conclude that big data is reserved for large companies only and that they alone can gain added value from its use. Indeed, as the motorist uses the highway without having built it, the commercial or public organizations, whatever their size and their field of interests, will be able to benefit from the use of big data.

What the 3Vs Acronym Didn't Put Into Perspective?

Data sizes have been growing exponentially within many companies. Facing this size of data—meta tagged piecemeal, produced in real-time, and arrives in continuous streams from multiple sources—analyzing the data to spot patterns and extract useful information is harder still. This includes the ever-changing landscape of data and their associated characteristics, evolving data analysis paradigms, challenges of computational infrastructure, data quality, complexity, and protection in addition to the data sharing and access, and—crucially—our ability to integrate data sets and their analysis toward an improved understanding. In this context, this second chapter will cover the issues and challenges that are hiding behind the 3Vs phenomenon. It gives a platform to complete the first chapter and proceed to different big data issues and challenges and how to tackle them in the dynamic processes.