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Drowning on Data, Big Data & Analytics Could Save You

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Drowning on Data, Big Data & Analytics Could Save You

Drowning on Data, Big Data & Analytics Could Save You

Big Data

Big Data helps us move from storing data to analyze information proactively.

It’s not the same data and information, data cannot be used for a particular purpose, are meaningless by themselves, when we describe it and put into context, is when they acquire meaning and become information. For example, if I speak of 1.78, 89, 37, 01120, 43. These data do not tell me anything. Is when I put in context that adds value. I am 1.78 meters, weight 89 kilos, I have 37 years, my zip code is 01120 and my home number is 43.

Analytics

Analytics is the discovery and communication of meaningful patterns in the data.

Therefore, we get to use the Big Data Analytics for the ability to analyze all the information that is available in the organization, both structured data, such as forms and unstructured, as any data that captures any data in the same place, to discover relevant trends, which allows results that improve the performance of the company.

Besides that, it has to be done effectively and efficiently.

Before, without Big Data’s ability to analyze data, what we had to transfer data from the source systems, i.e. where data is created in the systems that support the daily operation of the company.

Where is the data?

We must differentiate between what are transactional and analytical systems or systems that support decision making.

Transactional systems, support the daily operation of the company, examples of these systems are payroll, sales, warehouse, etc. Which they have to execute their tasks very fast and are focused on providing service to users without interruption, for transactions such as payments to employees calculate, record sales operations and control inputs and outputs of products in inventory.

In another category are the analytical systems, these are aimed at analyzing and making decisions based on the information. Here are the reports, when we know that stores sold more, what products are best sold, review the performance of the company through indicators or determine who are my best customers based on purchase history all my clients in the past 2 years, they are analysis tasks.

In nature are different objectives and questions we do to each type of system, which is why no big data needed to move data systems which were created to systems where analyzed. After starting the process to analyze the data, making it much time and resources were spent to get results. Although due to limitations in terms of processing power, it is not equal to review an Excel file with 1,000 records of customers who do it with a database that has information than 1,000,000 customers.

Sampling

It was even necessary to make sampling of the data, that is, select the data to be more representative of all because it was not possible to analyze everything at once. For this techniques to choose what data to include in the analysis and expect the results to be, they can be applied to everyone else used. For example, if I have 10 clients and I sampled, I select 3, based on its characteristics, whether they are married, single, have children, where they live; and I give me the results that I will expect analysis can also be applied to other customers.

Combining with Big Data Analytics however, can analyze all customers and combine information from other sites and social networks where my customers interact or geographic information media such as the National Institute of Statistics, Geography and Informatics. Without having to move data back and forth, this is like when copying information from a USB memory to another computer, the more files you have, the longer it takes to do so.

Summary

With the ability to analyze Big Data, we can apply analytics software to find better, more relevant results because they consider all existing and very important information, the findings are obtained in a timely manner for making decisions that allow us to be proactive, be able to anticipate market changes, customer tastes and risks that arise in the environment.

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