INTRODUCTORY VIDEO (Text transcription at the bottom)
I’ve generated content I would like to share with you. My interest is to be able to help you better understand the world of Big Data and Data Analytics, which I have been part of for quite some time. For that reason, I have titled the presentations that I have generated “Data Analytics and Big Data”. But I have added an afterthought to make them even more understandable, "A deep enough explanation for non-technical roles."
Hi. During these last weeks I’ve generated content I would like to share with you. My interest is to be able to help you better understand the world of Big Data and Data Analytics, which I have been part of for quite some time.
For that reason, I have titled the presentations that I have generated “Data Analytics and Big Data”. But I have added an afterthought to make them even more understandable, "A deep enough explanation for non-technical roles."
The format is simple. They are presentations in PDF format you can download and share. Unlike other content on this page, this time I wanted to generate content with greater detail than usual. I think this topic is too dense and convoluted to be able to easily generate videos with the right quality and depth. In this way, I’ve chosen to create these presentations that condense by theme everything necessary to have a clear notion of the subject and sufficient resources to after consult videos about the subthemes on the web.
Obviously, I have decided to start with the basics. And that’s the title of this video that serves as a summary and content intro. For a few minutes I will explain what you will find in the presentation called "The Basics" or how to be able to maintain a conversation with geeks without feeling embarrased.
Thus, the presentation focuses on talking about the data, what they are, how they are or the importance they have for society. I also explain the different types of analysis we can do to the data and the analytical domains that exist to attack the data according to our different intentions and expertise level. And finally, I make a detailed review of existing analytical capabilities that allow us to get the most out of the data, as well as the roles that must accompany you to achieve it.
The first slides show us very revealing data about the volume and speed at which the data is generated today compared to a few years ago. I reflect on the exponentiality we are living in the new data era to end asking what the data is. I mention the unstructured, semi-structured and structured data with a special emphasis on the growth of the unstructured. Very related to this classification, I explain the differences, benefits and disadvantages of the different types of databases with special mention to a new class of databases: the NoSQL.
From here I make a subsection to understand how to manage that data to obtain value. This is the data life cycle that will show us the different steps we have to go through in order to do so. This scheme already shows many of the concepts will be treated later in successive presentations and will be managed in different disciplines such as data engineering, business intelligence, Business Analytics and data science.
At this point we go down to the detail of different analytics types that we need and must achieve. We speak here of descriptive analysis or the one that should answer the question of "What happened?". I also stop in the diagnostic analysis, both in its exploratory part (What is happening?) and explanatory (Why has it happened?). And I show you in a little more detail what the predictive and prescriptive analysis means, both of critical importance for the companies and organizations to growth aligned with the current digital strategy.
Perhaps the most interesting and valuable part of the presentation (in fact, all of them are) is the one that explains in detail the different artifacts that allow us to interact with data. These tools are encompassed within the different analytical domains can easily indicate the companies’ level in comparison with optimal management and data usage target. We begin with the Information Portal, the reports and dashboards realm, and continue through the analytical workbench where users are given more autonomy with tools such as visual data discovery. We go into much more detail in these two than data science laboratory and Artificial Intelligence Hub, more evolved stages that still few companies are embracing. Apart from showing the analytical capabilities of each of them, we place special emphasis on the people who must act at each stage, whether with a business or technology orientation.
And that's it in this first presentation. The basics. But with enough detail so you can continue and download the next one that talks about Big Data and Data Analytics in organizations.