Autor: Ryuma Nakano

What are data? And how should we manage them to obtain the best benefit within our organizations? These two questions, which at first glance seem straightforward, turn out to be vital for analytics. Conversely, their answers are not as easy as we might think, as they encompass a set of concepts, techniques, and tools that, if not correctly implemented, could lead to overcosts and unfriendly experiences.

I'm Ryuma Nakano, holding two master's degrees, one in data science and another in business intelligence, with over 12 years of experience in this wonderful world of data. Over the next few weeks, I'll be writing a series of short and easily digestible articles to provide answers to the questions posed in this introduction. I'll share what I've learned during this journey while exchanging opinions and generating knowledge collectively for the entire community.

Analytics is one of the fastest-growing trends in recent years thanks to topics such as machine learning, AI (artificial intelligence), deep learning, and more. But none of these would be possible without data. Data is the most basic and important input to make rapid, accurate, and reliable decisions. Moreover, data has allowed us to create new trends and technologies that offer users facilities in our daily interactions, from unlocking a phone using a camera or fingerprint, watching a series or movie recommended according to our particular tastes, optimizing fuel consumption in our vehicles, making an emergency call automatically in case of an accident, to more complex issues such as disease forecasting, national security, public health analysis, and countless other applications.

We must be clear that an analytical solution always arises from a need, whether from an individual or a group of people such as organizations, sports teams, nations, and more. It is crucial to understand this because unless we have an infinite budget, the collection, management, and consumption of data have associated costs that could drain our budget, not to mention the effort, time, and wear and tear of each person involved in the process. Therefore, before mistakenly investing resources and time, let's first be clear about what we want to solve. For example: I want to identify which task generates the most reprocessing within my production chain, what products should I place in my stores to generate higher profitability?

In our upcoming deliveries,

we'll start exploring how to move from data to knowledge generation through analytics.

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