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Every day, we create 2.5 quintillion bytes of data — so much that 90% of the data in the world today has been created in the last two years alone. The digital universe has grown from 3.2 zettabytes to 40 zettabytes in only six years. From all this massive data, 88% is ignored.

A lack of analytics tools is one of the reasons companies ignore a vast majority of their data, as well as the simple fact that often it’s hard to know which information is valuable and which is best left ignored. Hence, data analytics.

The big question now is this; what is data analytics?

Data analytics is the process of examining data sets to conclude the information they contain (mostly with the aid of specialized systems and software). It is the process of inspecting, cleansing, transforming and modelling data to discover useful information, informing conclusions and supporting decision-making.

Data analysis involves sorting through massive amounts of unstructured information and deriving key insights from it. These insights are enormously valuable for decision-making at companies of all sizes.


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Data analytics techniques are widely used in commercial industries to enable organizations to make more-informed business decisions and by scientists and researchers to verify or disprove scientific models, theories and hypotheses.

P.S. Data analysis and data science are not the same. (Although they belong to the same family, data science is typically more advanced, it contains a lot more programming, creating new algorithms, and building predictive models)

Here is an overview of how data analysis works:

1. Define the question or goal behind the analysis: What are you trying to discover?

2. Collect the right data to help answer this question.

3. Perform data cleaning/data wrangling to improve data quality and prepare it for analysis and interpretation–getting data into the right format, getting rid of unnecessary data, correcting spelling mistakes.

4. Manipulate data using Excel or Google Sheets. It may include plotting the data out, creating pivot tables, among others.

5. Analyze and interpret the data using statistical tools (i.e. finding correlations, trends,

6. Present this data in meaningful ways: graphs, visualizations, charts, tables, etc. Data analysts may report their findings to project managers, department heads, and senior-level business executives to help them make decisions and spot patterns and trends.

Why build a career in Business Analytics?

The analytics industry is set for exponential growth far more exceptional than we are currently experiencing. With more and more data being available in digital form, the need for smarter, faster, data-based decisions is only going to increase.

As a data analyst, you can work in a wide variety of industries like healthcare, finance, marketing, fast food, retail IT, etc wherever you’re interested! It’s one of the new gold mines of the millenial century. Data is everywhere, and it’s inevitable in this new era.

Over the next posts, we’ll be going deeper and deeper into data analytics.