What to Know about Data Analytics? 6 important things

In a simple statement, Data Analytics is the science of analyzing unfurnished or raw data to generate probable conclusions about the related information. The prime goal of Data Analytics is to discover useful information and providing conclusions that help in decision making. Data Analytics allows us to make sound decisions rather than guessing wayward. So, lets us know what to know about data analytics before diving into it.


Data Analytics can reveal trends and metrics related to a particular business. These metrics then help the Analysts to make proper decisions and improve the efficiency of the system or a business. For example, if we consider a manufacturing unit where production time, maintenance time and overall efficiency of machines has to be analyzed. In this case, there is always a predefined metric of measurement, which assists in planning the next production plan keeping in mind factors like machine runtime, workload and the target to be achieved within a certain duration.


Today, running any business is not only about marketing, sales and profit. It has gone way beyond that. Nowadays businesses focus more on what the customer expects and how to drive sales through customer insights.  Businesses are going data centric.  A general stat report only tells you about the sales, profit and customer base. But this is only a small picture of the case. These stats don’t explain:

  • Who is buying the product?
  • Customers lost & gained.
  • Why lost and gained how?
  • What to do to stay competitive?

These stats are not available, but they are extremely crucial factors in terms of running a business. Here the concept of Data Analytics is vital. A good analysis can tell you what you need to change to gain a particular set of customers and keep them attached to you in the longer run. IT gives you an idea to innovate fearlessly and cater to customer needs directly. Companies that have a sound trust in Analytics, are moving forward and understanding each customer individually. Because every customer is important, they are doing everything to help them do business with them. Thus, beating the competitors first hand.


As a Data Analyst, your prime responsibilities will be as follows:

  • Data interpretation, analyzing results by the use of statistical analysis methods.
  • Implementation of data analysis, data collection systems and other strategies that optimize statistical efficiency and quality.Why lost and gained how?
  • Gathering data from various data sources and maintaining complete databases.


Descriptive Analytics

This gives a general breakdown of information such as:

  • What happened in a given time period?
  • What are the stats today as compared to last month or quarter?
  • What is the sales figure this month?

Diagnostic Analytics

This focusses on why something happened. This involves predictive hypothesis with the help or diverse data inputs.

  • What is the impact of the last discount offers?
  • Did the competitor’s strategy beat ours?

Predictive Analytics

An analysis of what might happen in the upcoming future.

  • What did we achieve last year during this time?
  • What is the expected sales prediction this financial year?

Prescriptive Analytics

Here we get to choose a particular course of action. If we are to achieve a figure of $50000 this year, we need to push the production by 25% in addition to the existing plan.

What to Know about Data Analytics beforehand?

How can Data Analytics ensure a proper business strategy?

In the simplest words, Data Analysis can help you build a proper customer engagement strategy, market your product more effectively and eventually boosts your sales. It helps you analyse the problem, gets the stats corrected, builds a right working system and finally executes and deliver the promised service to your customers. All the changes right from the very basic matter a lot in implementing a proper strategy.

Business strategy is built by segregating your data into measurable components. This is known as segmentation. In terms of customers, you can segment them according to:

  • Age.
  • Location.
  • Interests.
  • Shopping Habits.


Data Gathering

What to analyze, how to analyze and why to analyze? What type of data is to be analyzed? How to measure the analysis?

Data Sorting

As soon as you get the data, you can now work on what to measure. Collecting data based on requirement is a must. This collected data must be organized or processed for the Analysis task.

Data Cleaning

Simply, eradicating out the useless data. Keeping what is of use only. No errors, no duplicity of records. Avoiding recurring data sorting.

Data Analysis

As you perform analysis of any kind you manipulate the data to get the desired outcome. You may get the exact information in the collected data, or you may need to extract more data.

Data Interpretation

After analysis, comes the interpretation. The analyzed data can be put up into a report in terms of text, tables or flowchart as required.

Data Visualization

The most common forms of data visualization are graphs, bar charts, flow charts etc. By comparing multiple data sets we can easily find out meaningful information.

To Conclude

There are a lot of opportunities in various sectors relating to Data Analytics. Your challenge is how you can open the doors of those opportunities for yourself. You may have to add on to existing skills according to the industrial domain variation, but the basics are all the same. So, get started and start analysing your skills and build on them to develop your career as a Data Scientist.

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