Data Analytics is a concept that groups the technologies and mathematical developments that are dedicated to store, analyze and cross all that information to try to find patterns of behavior. Or what is the same, tie the ends of what our habits and customs are. More and more companies are working under this new paradigm, since with it they can get to know their users better and offer them an experience, be it the sector that is, much more personalized.
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Data Analytics: we are data
Data analytics is an approach that involves the analysis of data (big data, in particular) to draw conclusions. By using data analytics, companies can be better equipped to make strategic decisions and increase their business volume.
The main objectives of a data analytics approach are:
- Improve operational efficiency
- Improve and optimize the UX and customer experience
- Perfecting the business model
Every day are generated in the world a multitude of data that the administration or private companies store, about 2.5 trillion bytes globally and, to be more exact, more than a trillion queries in Google, about 250 million tweets, 60 hours of videos uploaded per minute on YouTube, 800 million updates on Facebook, 10,000 credit card transactions per second. In addition, the cities are full of sensors that collect all kinds of weather, telephone, traffic information, in short, we are data.
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Many companies or even public administrations already use the benefits of mass data analysis to try to offer us a better service and, at the same time, optimize their objectives. The Walmart supermarkets store hourly data of up to one million transactions of their customers to be able to predict in advance, for example, what products they will demand if it is raining.
The people in charge of the Data Analytics are the Data analysts. They are becoming the most valued workers in the world, according to salary.com, in the United States they are quoted at $ 55,000 at the lowest levels and can reach $ 110,000 at the highest levels, such as Architect of data.
Key and characteristic processes of Data Analysis
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Although the capture, storage, search and analysis of extensive data sets are essential elements in any data analysis process, the differential value of a data analysis is fundamentally found in the following key processes:
- Exploratory data analysis: identification of trends, graphs, histograms, classification of groups, etcetera.
- Modeling and algorithms:
- Based on statistical information: means, trends, deviations, maximums, minimums, regressions, t-tests, z-tests and other techniques applicable to the company.
- Based on heuristic algorithms or business logic (when we know a priori some cause or determining relationship in the result).
- Based on artificial intelligence or machine learning.
- Reports and data visualization: the information can be delivered to be understood through reports and graphs that are managed from Excel, PowerBI, Reporting Services, custom applications, QlikView, PeriscopeData, Tableau, Google Data Studio, etc. The information must be delivered in a way that offers the KPIs, insights and key data to the different departments and areas of the company, from the areas of management and strategy to departmental ones: commercial, marketing, financial, operations, technical areas, and so on.