Data Analytics in Clear Language

You have allocated a budget to innovate; you’re on the verge to work with the new “oil” Data. You decide to take data scientists. A good start, and now? Where to begin? What tools they should use and what data sources are appropriate? What is the duration of a data science process if the result is acceptable and how you bring a beautiful model in practice?

Big Data, Artificial Intelligence, Machine Learning, Deep Learning; Nowadays you can not ignore. But … what do these terms actually is and how it is used in the business?

What is Data Science really?

In recent years, the amount of collected and available data grew exponentially. Until 2020, the data will grow by approximately 42% per year. Here we not only talk about data and numbers in tables (structured data) but also documents, chats, posts, photos, videos, audio clips, etc. (<a href="https://learningsimp

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You have allocated a budget to innovate; you’re on the verge to work with the new “oil” Data. You decide to take data scientists. A good start, and now? Where to begin? What tools they should use and what data sources are appropriate? What is the duration of a data science process if the result is acceptable and how you bring a beautiful model in practice?

Big Data, Artificial Intelligence, Machine Learning, Deep Learning; Nowadays you can not ignore. But … what do these terms actually is and how it is used in the business?

What is Data Science really?

In recent years, the amount of collected and available data grew exponentially. Until 2020, the data will grow by approximately 42% per year. Here we not only talk about data and numbers in tables (structured data) but also documents, chats, posts, photos, videos, audio clips, etc. (unstructured data).

To take advantage of these data, it is important to translate these data into useful information. The transformation of the quantity of data to information is done through data analytics. This newly acquired knowledge can then be used to make-driven systems, processes, and decisions.

Data Analytics Process

An average data analytics process can be divided into three phases: data processing, transformation, and visualization.

The process starts with the business itself, where a set of data is available where information can be removed. You can consider data from your customer base, from financial systems or, for example, logistics administration. It is also possible to integrate external data sources into your analysis, such as weather data and social media.

Processing

In this phase, there is scooped outline in a large amount of data is present, so that it can be used for the transformation and the visualization step. This processing phase is the most time-consuming, it takes on average 70% of the time. During this period, the data is put in the correct format.

Transformation

During the transformation phase, different data are combined and transformed into analytics models. In this way, new information extracted from the data. The analytics models search for hidden trends and relationships so that you understand about your business.

Visualization

In the visualization phase, the results of the transformed data will be made transparent to the user by means of visualization. By using the appropriate visualization methods and tools, it is possible to understand and use the information hidden in your data.

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