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Derive Solutions from the Information Obtained from Analytics

  • July 08, 2023
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Meet the Author : Mr. Bharani Kumar

Bharani Kumar Depuru is a well known IT personality from Hyderabad. He is the Founder and Director of Innodatatics Pvt Ltd and 360DigiTMG. Bharani Kumar is an IIT and ISB alumni with more than 18+ years of experience, he held prominent positions in the IT elites like HSBC, ITC Infotech, Infosys, and Deloitte. He is a prevalent IT consultant specializing in Industrial Revolution 4.0 implementation, Data Analytics practice setup, Artificial Intelligence, Big Data Analytics, Industrial IoT, Business Intelligence and Business Management. Bharani Kumar is also the chief trainer at 360DigiTMG with more than Ten years of experience and has been making the IT transition journey easy for his students. 360DigiTMG is at the forefront of delivering quality education, thereby bridging the gap between academia and industry.

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Why is data analytics becoming important?

The era of the internet is upon us. Since its inception in the late 1990s, the internet has been a phenomenon, but it has never been as necessary as it has been in recent years. The internet has made the majority of our everyday demands easier to meet, and we find it impossible to fathom life without it. The idea is that the internet has changed from being a luxury to a necessity.

The majority of businesses wanting to take advantage of this potential and give customers speedier service rely heavily on automation made possible by the internet. Now, due to the fast increase in demand for and use of the internet and digital services, massive volumes of data are being generated. Terabytes of data are generated every day across all industries.

Instead of just letting the data go to waste, several of the firms sought for a method to use them and extract the required information from them. As a result, the idea of data analytics was created.

Data analytics is simply the practise of carefully analysing the provided data to extract relevant information. Data scientists use a variety of intricate mathematical and statistical models on the gathered data to uncover patterns and information that are otherwise impossible to detect through routine processing.

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Obtaining solutions from the information collected

The process of data analytics is harder than it sounds. Unlike common belief, the hardest part of data analytics is not the processing of the data, but to obtain useful information from the results of the analysis. The results of data analysis are mathematical and statistical and mean nothing to the business strategists. The data analyst needs to obtain information from the results to devise the business plan and strategy.

For obtaining the required information from the results, one must have great creativity and insight into the given data. It is not an easy process and certainly not something one can acquire just by completing a course. This is a very involved process and the analyst must be clear about certain steps from the onset of the process to make sure that they get the desired result at the end of the process.

To gain the necessary information from the results we need to know about another process known as ‘Data Interpretation’.

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What is Data Interpretation?

Data interpretation is the process of using multiple techniques to extract the necessary knowledge from the data that has been gathered through data analysis. Without data interpretation, the results are worthless; in other words, data interpretation gives context to the findings of data analysis.

Since it is possible that the data will come from numerous sources and be handled randomly, the process of data interpretation needs to be quite comprehensive. This becomes clear that various organisations and consumers will have varied needs for data collection, processing, and interpretation. Depending on the sort of business it belongs to, the data will differ in type.

We must be certain of the scope of the interpretation even before we begin, and only then can we go on to the topic of qualitative and quantitative data analysis.


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The most commonly used data interpretation scales are:

Ordinal Scale: This scale consists of the categories which are exhaustive and exclusive and are arranged in a logical order. For example agreement and quality, ratings fall under the category of an ordinal scale.

Nominal Scale: The nominal scale consists of categories that cannot be quantitatively ranked or compared and contains variables that are exhaustive and exclusive.

Intervals: This is the most basic scale and under this, the data are grouped according to their categories and equally spaced. A point is arbitrarily chosen as the zero point.

Ratio: This scale combines the features of all the above mentioned three types of scales.

The interpretation of data is a very complicated matter and the analyst should be careful that the interpretation does not involve any kind of partial or biased influences. However, the analyst needs to consider all the factors which have directly impacted the results.

Though the data interpretation is aimed at extracting meaningful information from the numerical results of the analysis, it should be remembered that there should be a single approved base interpretation method used by all the departments in a single company. If different departments apply different data interpretation methods on the same set of data, then the results might be conflicting in nature and can cause major confusion about the final results.

The most widely used methods of data interpretation are qualitative and quantitative. Both the methods of data interpretation for obtaining solutions from the analytics are explained below.

A Quantitative Method of Data Interpretation

To provide a fundamental understanding of what quantitative interpretation is, it may be summed up as any type of data interpretation including numerical numbers or variables. Thus, any form of numerical data and values may be interpreted using a quantitative approach of interpretation. Quantitative analysis frequently uses statistical modelling techniques to explain the analytics' findings since it works with numerical things.

Common statistical techniques including mean, median, standard deviation, and frequency distribution are frequently utilised in quantitative methods. It's not too difficult to interpret quantitative data in order to get answers. To effectively develop a company plan, one merely has to develop statistical models of the numerical values pertaining to the key factors and determine the necessary relationship between them.

In order to assess all the outcomes and ultimately select the most suited and successful choice, many sets of data can be analysed.

Qualitative Method of Data Interpretation

Just like the name, the qualitative data interpretation is concerned with interpreting the given data according to the various categories in which it is classified. Unlike quantitative analysis, this interpretation method does not take into consideration the numerical values and patterns associated with the data but solely relies on the descriptive information available.

The Techniques Involved in the Qualitative Data Interpretation Method are given below in detail.

Documents: They play an important role as they can help in categorizing the type of information available effectively at the source itself. This makes the classification process easier.

Observation: Apart from numerical values all the data sets have some sort of behavioural patterns that can vary like the frequency of the activity or the amount of time spent on that particular data. This provides us with the necessary contextual data about the different categories of data.

Interview: The most efficient way of segregating data is to categorize it based on the topics and the nature of the interaction which helps us in effectively analyzing the behavioural patterns in a shorter time.

The main difference between quantitative and qualitative analysis of the data is in the fact that for qualitative analysis the data needs to be classified and divided into categories before any interpretation techniques are applied to it.

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Why is Data Interpretation Necessary?

Data analytics is frequently used to examine customer trends and improve services in a more individualised way. The production process may be optimised and sales income can be increased by using the knowledge that data analysis gives. However, in order to achieve this, the strategists must comprehend the findings of the data analysis. The analysis's findings are presented in statistical and mathematical formats and are unable to provide any descriptive details.

The data from the findings must be transformed into something informative that can be shared with the interested parties. The actual meaning of the outcomes is extracted with the aid of data interpretation. Data interpretation may foresee future trends or occurrences, in addition to displaying consumption patterns, which can help an organisation prepare ahead of time.

Finding troublesome areas in the manufacturing process and helping to eliminate or optimise them to lower production costs without impacting the company's output is another important cause for data interpretation.

Along with many benefits, data interpretation also has a number of drawbacks that seriously impair decision-making. It is frequently possible to infer that many events are interdependent rather than merely linked. Since the analysis findings are essentially the output of statistical models, it may be exceedingly challenging to distinguish between them. Since the study findings are just based on statistical models and do not include any type of contextual information, it can be quite challenging to distinguish the differences.

Biassed interpretation and becoming perplexed by irrelevant facts are two of the most deadly issues with data interpretation. evidence analysts may develop ideas in their heads and search for evidence to support those assumptions. They could reject the actual facts throughout this procedure if it contradicts the notion they have in mind.

We are constantly inundated with massive volumes of data since we are living in the digital era, especially for large corporations. An analyst could become overly preoccupied with irrelevant data that might seem valuable but is not since data interpretation is a very delicate procedure and no chances should be made.

Data interpretation is the backbone of businesses that rely on data analysis, and it is a crucial ability for every data scientist to be able to draw conclusions from the analytics' findings.

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