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.
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Table of Contents
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:
- Apache spark
- BI de energia
- Microsoft Excel
There are five types of data analysis:
- descriptive analysis
- diagnostic analysis
- predictive analytics
- Prescriptive Analytics
- 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:
- quantitative, e.g
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 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:
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.
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.
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.
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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:
|data analysis||Data interpretation|
|Sinn||Data analysis is the process of discovering patterns and trends in data.||Data interpretation is the process of assigning meaning to data. It's about explaining the patterns and trends discovered in the data.|
|Chronology||Data analysis comes first, followed by data interpretation.||Data interpretation is the next step after data analysis.|
|types/methods||The five types of data analysis are descriptive analysis, diagnostic analysis, predictive analysis, prescriptive analysis, and cognitive analysis.||Data interpretation methods are quantitative methods and qualitative methods.|
|Because it is necessary?||In short, compress the data into an understandable and usable form for advanced analysis and forecasting.||Data interpretation is necessary as numbers do not speak for themselves. You have to intervene manually to understand what the numbers are saying.|
|example||For example, the top 5 teams in terms of win rate are Real Madrid, Barcelona, Atlético Madrid, Valencia and Athletic Bilbao.||An example of an interpretation implies that 95% of the population falls within the range of 136.54 to 143.45.|
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:
- 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.
- Develop conclusions:Conclusions help with reasoning and explain why the data is the way it is.
- 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
- cohort analysis
- time series analysis
- sentiment analysis
- 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
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.
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What is the difference between data interpretation and Data Analysis? ›
Data analysis is the process of uncovering patterns and trends in the data. Data interpretation is the process of assigning meaning to the data. It involves explaining those discovered patterns and trends in the data. Data analysis comes first, followed by data interpretation.What is data analytics and interpretation? ›
Data interpretation refers to the process of using diverse analytical methods to review data and arrive at relevant conclusions. The interpretation of data helps researchers to categorize, manipulate, and summarize the information in order to answer critical questions.Which of the following best differentiates Data Analysis from interpretation? ›
Data analysis involves conceptualization but interpretation does not.What is an interpretation and analysis? ›
The process by which sense and meaning are made of the data gathered in qualitative research, and by which the emergent knowledge is applied to clients' problems. This data often takes the form of records of group discussions and interviews, but is not limited to this.How do you do Data Analysis and interpretation? ›
- Analyse. Examine each component of the data in order to draw conclusions. ...
- Interpret. Explain what these findings mean in the given context. ...
- Present. Select, organise and group ideas and evidence in a logical way.
In data analytics and data science, there are four main types of data analysis: Descriptive, diagnostic, predictive, and prescriptive. In this post, we'll explain each of the four different types of data analysis and consider why they're useful.What is the objective of Data Analysis and data interpretation? ›
Data analysis is defined as a process of cleaning, transforming, and modeling data to discover useful information for business decision-making. The purpose of Data Analysis is to extract useful information from data and taking the decision based upon the data analysis.What is data interpretation with example? ›
Data interpretation is the process of reviewing data through some predefined processes which will help assign some meaning to the data and arrive at a relevant conclusion. It involves taking the result of data analysis, making inferences on the relations studied, and using them to conclude.What is the importance of analysis and interpretation of data? ›
Scientists analyze and interpret data to look for meaning that can serve as evidence. Often scientists seek to determine whether variables are related and how much they are related.What are the four stages of analysis and interpretation? ›
There are four steps to data interpretation: 1) assemble the information you'll need, 2) develop findings, 3) develop conclusions, and 4) develop recommendations. The following sections describe each step. The sections on findings, conclusions, and recommendations suggest questions you should answer at each step.
What is an interpretation answer? ›
Interpretation is the act of explaining, reframing, or otherwise showing your own understanding of something. A person who translates one language into another is called an interpreter because they are explaining what a person is saying to someone who doesn't understand.What is interpretation mean? ›
The mean is the sum of all the data points divided by the number of the data points itself. To calculate mean, one must simply add all the values together. Then the individual must divide the resulting sum by the number of values itself. Consequently, the result that arrives is the mean or average score.Is the first steps in data analysis and interpretation? ›
- Step One: Ask The Right Questions. So you're ready to get started. ...
- Step Two: Data Collection. This brings us to the next step: data collection. ...
- Step Three: Data Cleaning. ...
- Step Four: Analyzing The Data. ...
- Step Five: Interpreting The Results.
- STEP 1: DEFINE QUESTIONS & GOALS.
- STEP 2: COLLECT DATA.
- STEP 3: DATA WRANGLING.
- STEP 4: DETERMINE ANALYSIS.
- STEP 5: INTERPRET RESULTS.
Chapter 8: DATA ANALYSIS, INTERPRETATION, AND PRESENTATION - INTERACTION DESIGN: beyond human-computer interaction, 3rd Edition [Book]What are the main points of Data Analysis? ›
Data analysis is a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making.What are the 4 types of interpretation? ›
- Consecutive Interpreting. With consecutive interpretation, speakers will talk for up to five minutes or longer before taking a break to allow interpretation to occur. ...
- Simultaneous Interpreting. ...
- Whisper Interpreting. ...
- Escort/Travel Interpreting. ...
- Over-the-phone interpreting.
The three basic interpretation modes are simultaneous interpretation (SI), consecutive interpretation, and whispered interpretation. However, modern linguists suggest that there are more than simultaneous interpretation, consecutive interpretation, and whispered interpretation to interpretation modes.How do you write an interpretation? ›
The Interpretive Analysis Essay should have an introduction, body, and a conclusion. The writer must consistently quote and paraphrase the literary work in the introduction, body, and conclusion to help them in their analysis and in determining the possible meanings.Why is interpretation important? ›
Interpretation enables effective communication between people all across the world. They serve as cultural defenders, knowledge carriers, and enable better business communication in the global market.
What are the 3 main steps of interpreting? ›
The theoretical model of the interpreting process in ITT consists of three stages: 1) comprehension, 2) deverbalization, and 3) reformulation, which claims that language reformulation starts only after source language comprehension has been completed.What are the 5 types of analysis? ›
At different stages of business analytics, a huge amount of data is processed and depending on the requirement of the type of analysis, there are 5 types of analytics – Descriptive, Diagnostic, Predictive, Prescriptive and cognitive analytics.What is the process of data analysis? ›
Data Analysis is a process of collecting, transforming, cleaning, and modeling data with the goal of discovering the required information. The results so obtained are communicated, suggesting conclusions, and supporting decision-making.What are the two types of interpretation? ›
There are two modes of interpreting: simultaneous, and consecutive. Simultaneous interpreting requires interpreters to listen and speak (or sign) at the same time someone is speaking (or signing).What is interpret in your own words? ›
To interpret is to give or provide the meaning of something, or to construe or understand something in a particular way.What is good interpretation? ›
Good interpretations have coherence, correspondence, and completeness. Interpreting art is an endeavor that is both individual and communal. The admissibility of an interpretation is determined by a community of interpreters and the community is self-correcting.What is interpretation in research? ›
"Interpretation refers to the process of making sense of numerical data that has been collected, analysed and presented".What type of word is interpretation? ›
interpretation. / (ɪnˌtɜːprɪˈteɪʃən) / noun. the act or process of interpreting or explaining; elucidation.What is the difference between interpretation and definition? ›
Definitions, in one sense of the term, are roughly descriptions or explana- tions of the meaning of words. But interpretations are in a way much the same sort of thing, but there is a difference. Interpretations are without any of that sense of a fixed sense of final determination that we expect from definitions.What exactly is data analytics? ›
Data analytics helps individuals and organizations make sense of data. Data analysts typically analyze raw data for insights and trends. They use various tools and techniques to help organizations make decisions and succeed.
What are the 4 types of data analytics? ›
There are four types of analytics, Descriptive, Diagnostic, Predictive, and Prescriptive.What are the 5 types of data analytics? ›
At different stages of business analytics, a huge amount of data is processed and depending on the requirement of the type of analysis, there are 5 types of analytics – Descriptive, Diagnostic, Predictive, Prescriptive and cognitive analytics.What are 3 types of data analytics and its definitions? ›
There are three types of analytics that businesses use to drive their decision making; descriptive analytics, which tell us what has already happened; predictive analytics, which show us what could happen, and finally, prescriptive analytics, which inform us what should happen in the future.What is data analysis in simple words? ›
Data Analysis is the process of systematically applying statistical and/or logical techniques to describe and illustrate, condense and recap, and evaluate data.What is the importance of data analysis? ›
Why Is Data Analytics Important? Data Analysis is essential as it helps businesses understand their customers better, improves sales, improves customer targeting, reduces costs, and allows for the creation of better problem-solving strategies.What is data analytics Short answer? ›
Data analytics (DA) is the process of examining data sets in order to find trends and draw conclusions about the information they contain. Increasingly, data analytics is done with the aid of specialized systems and software.What are the 7 data analysis process? ›
7 Steps of Data Analysis
Define the business objective. Source and collect data. Process and clean the data. Perform exploratory data analysis (EDA).
There are three tiers of data analysis: reporting, insights, and prediction.What are the 3 data analysis steps? ›
These steps and many others fall into three stages of the data analysis process: evaluate, clean, and summarize.What are the 4 steps of data analytics? ›
- Descriptive analytics.
- Diagnostic analytics.
- Predictive analytics.
- Prescriptive analytics.
What are the 8 stages of data analysis? ›
data analysis process follows certain phases such as business problem statement, understanding and acquiring the data, extract data from various sources, applying data quality for data cleaning, feature selection by doing exploratory data analysis, outliers identification and removal, transforming the data, creating ...What are the six stages of data analysis? ›
Data analytics involves mainly six important phases that are carried out in a cycle - Data discovery, Data preparation, Planning of data models, the building of data models, communication of results, and operationalization.What are the two main types of data analytics? ›
Descriptive (business intelligence and data mining) Prescriptive (optimization and simulation)What are the 7 types of data? ›