Data Analytics in Clear Language

//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. (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.


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.


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.


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.

By |2017-07-06T14:25:00+00:00July 6th, 2017|Tips & Tactics|2 Comments


  1. Daniel Omwancha September 20, 2017 at 10:20 am - Reply

    Thank you for this insightful article. With the fast growth of digital technology, the adoption of internet of things and the more pronounced use of technology in our daily lives, the growth of data will only continue exponentially over the years. In light of this, one aspect of digital transformation that organizations struggle to get right is the identifying, capturing, managing and analyzing of big data. Across all industries, organizations are keen to use this data and the work of data scientists to discover the insights that will drive strategic business decisions. CIOs today need analytics expertise as well as an understanding of the data sciences and algorithmic approaches that will provide data analytics to their companies. This article clearly defines the steps and according to me, organizations should shift their focus to data analytics as a skill development area. However, just because you can do something doesn’t mean you should. Today’s data-gathering capabilities must be used with care and consideration to prevent the creation of a heap of useless information. Organizations must be strategic in how they approach the collection, management and analysis of their data if they want to find the gems of insight that will provide them with a competitive edge.

  2. Johanna Hernandez December 23, 2017 at 2:40 pm - Reply

    This is such an informative article. Data Science to manage a business will provide an analysis of what an organization needs to become a data-science organization. Beyond having data intelligence or a team of first level data scientists, the first step would be to establish a data-oriented culture, effective and strongly rooted in the company. This information is so usefull for those who want to build a business in midst of digital technology. Good post!

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