Difference between data analysis and interpretation: an overview (2023)

As is well known, data is available to us in the form of raw information. Raw information has a journey of its own before becoming insightful data. This information can be difficult to understand in its raw form and cannot be fed directly into algorithms. It goes through a series of steps.

The two most important steps on this ladder are data analysis and interpretation. Some of us may have thought that these terms are synonymous. You are not. These two are completely different processes and also follow a timeline in the data science lifecycle.

In this article, we examine data analysis, data interpretation, types of data analysis, what methods are available for data interpretation, and why data analysis and interpretation are important.

AnalytixLabs, an enablement and training solutions company, is India's premier data science and AI institute. Led by a team of IIM, IIT, ISB and McKinsey alumni, the Institute is in its tenth year. Offers a wide range of data analysis courses, including in-depth project work, enabling individuals to prepare for professional roles in artificial intelligence, data science and data engineering. With decades of experience delivering meticulous, hands-on, and personalized learning, AnalytixLabs has the expertise to turn aspiring professionals into "industry-ready" professionals.

What is data analysis?

Data analysis is “described as the process of bringing order, structure and meaning to collected data”. Data analysis aims to discover patterns or regularities by observing, examining, organizing, transforming and modeling the collected data.

It is a methodical approach to applying statistical techniques to describe, present and analyze data. This helps to gain meaningful insights, draw conclusions and support the decision-making process. This process of sorting and summarizing data is also used to get answers to questions to check if the hypothesis is valid. Exploratory data analysis is a big part of data analysis. It is about understanding and discovering the relationships between the variables present in the data.

SeveralData Analysis Toolsaccessible. Some of them are:

• Python
• R
• S.A.S.
• Apache spark
• Quadro
• BI de energia
• QlikViewGenericName
• Microsoft Excel
• make
• RapidMinerGenericName
• Solving
• OpenRefine
• NodoXL
• she

There are five types of data analysis:

1. descriptive analysis
2. diagnostic analysis
3. predictive analytics
4. Prescriptive Analytics
5. Cognitive Analysis

1.Descriptive analysis: what happened?

As the name suggests, descriptive analysis describes the data🇧🇷 The founding phaseit just looks at the previous data and says what happened in the past. Capture and summarize the past using measures of central tendency, measures of dispersion, and viewing by panels. This analysis helps you understand how the data is there and does not make predictions or answers as to why something happened. It is useful for generating reports, tracking key performance indicators (KPIs), sales leads, and revenue reports.

2.Diagnostic analysis: why did this happen?

Once you've determined what happened, the next logical step in the process is to find the answer as to why something happened. Diagnostic analytics help you dig deeper by creating detailed, informative, dynamic, and interactive dashboards to answer that question. It unravels the root cause of the problem and identifies the source of patterns. It is also useful for detecting anomalies. And the factors that affect the business. It can be applied to determine what factors led to an improvement in sales.

3. Predictive Analytics: What is Likely to Happen?

Once you find the root cause of the problem and understand the causal relationship between the variables, it would be good to know if the event is likely to happen again. Predictive analytics is all about that. Predict the likelihood of an event, forecast any measurable quantity, assess risk, and segment customers into groups. Because it predicts the occurrence of an event, it uses probability. In addition to the above summary and root cause analysis, the models use statistics and machine learning algorithms to predict future outcomes.

4. Prescriptive Analysis: How is it implemented?

Prescriptive analytics is result-oriented. Combine insights into what happened and why with what is likely to happen to help with actions to maximize key business metrics. Prescribes the best course of action, strategies. Prescriptive analytics does not predict a single independent event, but rather a collection of future events through simulation and optimization. It has many uses in finance, social media, marketing, and transportation. Its uses range from recommending products or films to suggesting what strategies to use to get the maximum return and minimize risk.

5.Cognitive Analytics: Mimicking the human brain to perform tasks

This advanced type of analysis aims to mimic a human brain to perform tasks like a human. It combines technologies such as artificial intelligence, semantics, machine learning and deep learning algorithms. It even learns and generates data from data already available and retrieves hidden features and patterns. Cognitive analysis of real-time data is widely used inImage classification and segmentation, object recognition, automatic translation, virtual assistants and chatbots.

What is data interpretation?

Once the data has been analyzed, the next progressive step is to interpret the data.

Data interpretation is the process of assigning meaning to processed and analyzed data. It allows us to draw well-founded and meaningful conclusions and implications, infer meaning between variable relationships, and explain patterns in the data.

Explaining numeric data points and categorical data points would require different methods; therefore, the different nature of the data requires different data interpretation techniques.

There are two main techniques to understand and interpret data:

1. quantitative, e.g
2. qualitative

(Video) Data Analysis and Interpretation

Quantitative methods

The quantitative data interpretation technique is applicable to measurable or numeric data types. There are two types of numeric data:

• Discreet:Countable and finite sets. For example: the number of ice creams
• continually: countless. For example: height, weight, time, speed, humidity, temperature

Numerical data is relatively easier to analyze using statistical modeling methods, including measures of central tendency and variability. These can be represented visually by charts such as bar charts, pie charts, line charts, line charts. Tables are also used to present complex information divided into categories.

The two most commonly used quantitative data analysis methods are:

• Descriptive Statistics:This area of ​​statistics focuses on describing data and their properties. It consists of two categories: measures of central tendency (mean, median, mode) and measures of spread, or variability, which indicate how spread out the data is, or how much the data varies.
• Inference Statistics:This branch of statistics generalizes, or infers, what larger data is like, based on the sample drawn from that larger data.

qualitative methods

Qualitative methods are implemented to analyze textual and descriptive data, referred to as categorical data. Text data is generally unstructured. Qualitative data are divided according to their characteristics:

• Nominal:Attributes have no sorting or order. E.g.: region, gender, classes in school
• Ordinal:Attributes are sorted or ordered in an order. eg: degree
• Sense:It only has two categories. Yes or no, class 1 or 0.

Unlike numeric data, categorical data cannot be analyzed directly because it is not statistical data and machines only understand the language of numbers.

Therefore, text data is first encoded and converted into numeric data. Depending on the requirement, different coding approaches are available. Text data is sorted into labels used for modeling and interpretation.

For a detailed comparison between the two data interpretation methods, see this blog atHow to understand quantitative and qualitative data in your company.

Importance of data analysis and interpretation

Data analysis aims to bring order and structure to data by manipulating it, summarizing it and reducing it to an interpretable form. It helps to discover patterns in the data. Data interpretation aims to perform and apply processes that assign meaning to these patterns discovered through data analysis. Draw statistical conclusions, derive connections and implications.

For example, the retailer's business goal is to recommend products to customers based on previously collected data. We began to understand the characteristics of current and former customers. This is data analysis as it only tells you what the data looks like. Once we start examining and naming customers based on similar characteristics, this is the data interpretation. Here assumptions are made, e.g. B. that customers who buy products from brand X are also likely to buy products from another similar brand. Here we think beyond the data and examine the underlying reasoning behind the data for real implications.

Data analysis and interpretation is important for the following reasons:

1.Informed decision-making:

Analyzing and interpreting data is essential to making informed, data-driven decisions and applying methodical analysis techniques instead of intuition or guesswork. This requires the implementation of a very systematic and structured data collection process.

2.Recognizing trends and predicting needs:

Data analysis provides insights that can predict and identify trends that can positively impact the industry. When many people started watching web series and movies on online platforms. Producers started creating and releasing more OTT content and this trend caught on and changed the dynamics of the entertainment industry.

3.Rentable:

One of the most important goals for any business, along with maximum profitability, is cost reduction. Data-driven informed decisions not only help improve business metrics, but also reduce costs, which is another way to generate revenue. Predictive data analytics helps achieve this goal through the use of response models, positive response models, churn models, churn growth models, risk models, and fraud detection.

4.Clear information:

These processes enable organizations to look ahead to their performance and processes. It enables organizations to understand how customers see them and their limitations, and take practical steps to improve their performance.

You might also be interested in reading:

What is the difference between data analysis and interpretation?

Data analysis and interpretation turns collected data into story points to generate insights. There are differences between the two processes which are as follows:

frequently asked questions- Frequently Asked Questions

Question 1. What are the two most widely used quantitative data analysis methods?

answerThe two most commonly used quantitative data analysis methods are:

• Descriptive statistics and
• inferential statistics

You are also welcome to inform yourselfStatistical foundations for data science and analytics

Question 2. What are the 3 steps in data interpretation?

answerAfter collecting the required data, the three steps to interpreting the data are:

1. Develop Discoveries:The results are observations about the data and summarize the most important aspects of the data. It is based on the second stage of developing conclusions.
2. Develop conclusions:Conclusions help with reasoning and explain why the data is the way it is.
3. Develop recommendations:Based on the results and conclusions, we should form result-oriented practical approaches and collect additional data if necessary.

Question 3. Which comes first, analysis or interpretation?

answerThe hierarchy is analysis followed by interpretation. The dictionary meaning of the word interprets "to explain the meaning of (information or action)". To explain the meaning of a data set or chart, or its nuances, we must first analyze it; only then can we interpret it.

Question 4. What are data analysis techniques?

answerThe techniques available to analyze the data are:

• Lineare Regression
• logistic regression
• Clusteranalyse
• Variationsanalyse
• cohort analysis
• time series analysis
• sentiment analysis
• Monte-Carlo-Simulation
• Support vector machines

Question 5. What types of data analysis are there?

answerThe types of data analysis are:

• descriptive analysis
• diagnostic analysis
• predictive analytics
• Prescriptive Analytics
• Cognitive Analysis

For better understanding you can also refer to this blog: Different types of data and business analytics

final thoughts

Data is not just limited to business applications; Our daily life is full of data about when to get up (there is time), what to eat (food is given another), what stocks to invest in, everything is given. In each of these activities, we check what information is available, evaluate it and make decisions accordingly.

The only difference here is the scope and impact of these decisions. But the process of how we make those decisions remains the same. In this post, we explain the processes of data analysis, what is data interpretation, their respective types, their importance to business, and the difference between data analysis and interpretation. Feel free to leave your questions and thoughts in the comments section below.

To learn practical and career-enhancing skills, explore our offeringData Analysis Courses🇧🇷 Our team of experts is happy to help you understand your career prospects.

You might also be interested in reading:

1.What is the difference between data and information? Data versus information

(Video) What is Analysis and interpretation of Data - Research methodology

2.

3.What is the salary of a data analyst in India?