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The Basics Of Data Analysis and its Importance

According to Wikipedia Data analysis is the process of inspecting, cleansing, transforming, and modelling data with the goal of discovering useful information, suggesting conclusions, and supporting decision-making.

Data Analytics techniques are used in commercial industries to make more informed data-driven decisions, Solve real business problems and Do better planning.

Believe it or not, Data Analysis is all around you. You must have noticed that Facebook keeps suggesting new friends to you, Google can complete your search before you’ve even typed the third letter, and Netflix predicts which television shows you’d likely enjoy. These all results have been derived with the help of Data Analytics.

Even teams and owners in IPL use Data Analytics to get best out of the auctions and improve team’s performance by analyzing the past data.

Data Analysis is not New:

In fact, it has a pretty long history. It is said that the beginning of Statistical Data Analysis was seen in ancient Egypt as it took a periodic census for building pyramids.

Throughout history, statistics has played an important role for governments all across the globe, for the creation of censuses, which were used for various governmental planning activities (including, of course, taxation).



With the data collected, we can move on to the next step, which is the analysis. Data analysis is a method that begins with retrieving data from various sources and then analyzing it with the goal of discovering useful information.

The practice of data analysis has slowly developed over time and has gained its momentum from the evolution of computing. Nowadays Data Analytics is taking on an increasingly larger role in all sizes of companies including small startups.


Less known fact about Data Analysis:

According to Forbes report, Data is growing faster than ever before and by the year 2020, about 1.7 megabytes of new information will be created every second for every human being on the planet. By then, our accumulated digital universe of data will grow from 4.4 zettabytes today to around 44 zettabytes, or 44 trillion gigabytes. Every second we create new data. For example, we do 40,000 search queries every second (on Google alone), which makes it 3.5 searches per day and 1.2 trillion searches per year.

You see we are surrounded by Data and studies show that companies who leverage the full power of Data using Analytics could increase their profit margins by as much as 60%.


The Process of Data Analysis:

  • Define Your Question – The First step would be to define the question or set a measurable objective, which should be clear and concise. This question can be a Business problem like Why were the sales low this quarter? Or How can we increase the sales?

  • Collect the Required Data – To get answers to your question you need to collect data to build better models and find more feasible insights. For example to answer the Question “How can we increase the Sales?” you should start collecting Data of sales volumes, margins, market information etc.

  • Process the Data – After collecting the required data you must process the data, organize it and make it ready for analysis, this process is called Data Cleaning. In this process duplicates are removed, errors are corrected and unusual data are removed.

  • Manipulate the Data – After the data cleaning, it’s time for analysis. Where data is manipulated in a number of different ways such as finding correlations or creating Pivot tables in Microsoft Excel (A pivot table lets you sort and filter data by different variables and let’s find feasible insights)

  • Implement the Algorithms – Mathematical formulas or Models called Algorithms are applied to data. During this step, data analysis tools and software are extremely helpful. Visio, Minitab and Stata are all good software packages for advanced statistical data analysis. However, usually, nothing quite compares to Microsoft Excel in terms of decision-making tools.

  • Interpret the Results – After analyzing your data and possibly conducting further research, it’s finally time to interpret your results. Here the data analyst may consider data visualization techniques to help effectively communicate the message to the decision makers (CXOs and Business Heads).

  • Take Data Driven Decisions – Now, it’s time to take decisions based on the results of the Analysis and answer the question that was defined in Step 1. With practice, your data analysis gets faster and more correct – meaning you make better, more informed decisions to run your organization most effectively.

But, Data Analysis is very complicated and Technical



A lot of people think that Data Analysis is very complicated and too technical for them to carry out and understand it’s concepts. They think one has to learn programming languages, write complex codes. But that is not true people have analyzed the data well before the computers were invented. Programming languages like Python and R and software like SPSS do make things easier to carry out but there are many other tools available that can help solve the variety of business problems. One of the most widely used tools is the Microsoft Excel.

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