Difference Between Data Analysis and Interpretation: An Overview (2023)

We know that data is in the raw form of information. Raw information has its own journey before it is transformed into insightful data. These pieces of information can be difficult to understand in their 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 might have thought that these terms are synonymous with each other. It's not like this. These two are completely different processes and also follow a chronology in the data science lifecycle.

In this article, we'll look at data analysis, data interpretation, the types of data analysis, what methods are available for data interpretation, and why data analysis and interpretation is important.

AnalytixLabs, a training and enablement solutions company, is India's leading artificial intelligence and data science institute. The institute is led by a team of alumni from IIM, IIT, ISB and McKinsey and has been in existence for ten years. Offering a wide range of data analysis courses including in-depth project work, enabling individuals to become fit for professional roles in AI, data science and data engineering. With decades of experience providing thorough, hands-on, personalized learning, AnalytixLabs is able 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 using statistical techniques to describe, present and evaluate data. It helps to generate meaningful insights, draw conclusions and support the decision-making process. This process of sorting and summarizing data is also used to obtain answers to questions to test whether 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.

there are a fewData analysis toolsaccessible. Some of them are:

• Python
• R
• SAS
• Apache SparkGenericName
• Quadro
• Power BI
• QlikViewGenericName
• Microsoft Excel
• MESSER
• RapidMinerGenericName
• resolving
• OpenRefine
• NodeXL
• io

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 foundation stagejust look at past dates and say what happened in the past. It captures and summarizes the past using measures of central tendency, measures of dispersion and visualizations using panels. This analysis helps you understand how the data got there and does not make predictions or answers about 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 to why something happened. Diagnostic analytics helps you dig deeper by creating detailed, informative, dynamic, and interactive dashboards to answer that question. It disentangles the root cause of the problem and identifies the source of patterns. It is also useful in detecting anomalies. And the factors that affect the business. It can be applied to determine which factors led to an improvement in sales.

3. Predictive Analytics: What is likely to happen?

After identifying the root cause of the problem and understanding the causal relationship between the variables, would you like to know if the event is likely to reoccur? That's what predictive analytics is all about. It predicts the probability of an event, predicts any measurable value, assesses risk, and segments customers into groups. Since it predicts the occurrence of an event, it uses probability. Along with previous summarization and root cause analysis, the models use statistics and machine learning algorithms to predict future outcomes.

4. Prescriptive Analytics: How Can It Happen?

Prescriptive analytics is results-oriented. It combines what and why insights with what is likely to happen to help take action to maximize key business metrics. It dictates the best course of action, strategies. Prescriptive analytics does not predict a single standalone event, but rather a collection of future events using simulation and optimization. It is heavily used in finance, social media, marketing, and transportation. Its uses range from recommending products or films to suggesting strategies to be employed to maximize returns and minimize risks.

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

This advanced type of analysis aims to mimic a human brain to perform tasks like a human being. It combines technologies such as artificial intelligence, semantics, machine learning and deep learning algorithms. It even learns and generates data from the already available data, recovering hidden features and patterns. Cognitive analysis of real-time data is widely usedImage classification and segmentation, object recognition, machine 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 the data being processed and analyzed. It allows us to draw well-founded and meaningful conclusions and implications, deduce meaning between variable relationships, and explain patterns in the data.

Explaining numerical 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 for understanding and interpreting data:

1. Quantitative and
2. qualitative

(Video) PRESENTATION, ANALYSIS AND INTERPRETATION OF DATA

Quantitative methods

Quantitative data interpretation technique is applicable to measurable or numerical data type. Numeric data is of two types:

• Discreet:countable, finite sets. For example: the number of ice creams
• Continuously: not countable. E.g. height, weight, time, speed, humidity, temperature

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

There are two most commonly used quantitative data analysis methods:

• Descriptive statistics:This area of ​​statistics focuses on the description of data, its characteristics. It consists of two categories: measures of central tendency (mean, median, mode) and measures of dispersion, or variability, which indicate how much dispersion there is in the data or the data varies.
• Inference stats:This branch of statistics generalizes or infers what the larger data is, its characteristics 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 further divided according to their characteristics:

• Nominal:Attributes have no precedence or order. E.g. region, gender, classes at school
• Ordinal:Attributes are sorted or sorted in an order. Example: notes
• Binary:It only has two categories. Either yes or no, class 1 or 0.

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

Text data is therefore first encoded and converted to numerical data. Depending on the requirement, different coding approaches are available. Text data is categorized into labels that are used for modeling and interpretation.

A detailed comparison between the two data interpretation methods can be found in this blogHow to understand your company's quantitative and qualitative data.

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. This 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. He draws statistical conclusions, derives connections and implications.

For example, the retailer's business objective is to recommend products to customers based on historical data collected. We began to understand the characteristics of current and former customers. This is a data analysis as it just tells you what the data looks like. Once we start examining and positing customers based on their similar characteristics, this is the interpretation of the data. Here assumptions are made, e.g. B. that customers who buy products from brand X also tend to buy products from another similar brand. Here we think beyond the data and look at the underlying reasoning behind the actual impact data.

The following are the reasons why data analysis and interpretation is important:

1.Informed decision making:

Data analysis and interpretation is crucial to making informed decisions, relying on the data, applying methodical analysis techniques and not intuition or guesswork. This requires implementing a very systematic and structured data collection process.

2.Identification of trends and forecasts needs:

Data analysis provides insights that can predict and define trends that can have a positive impact at an industry level. When many people started watching webseries and movies on online platforms. Producers started creating and releasing more OTT content and this trend grew and changed the dynamics of the entertainment industry.

3.Cost benefit:

One of the most important goals for any business, along with maximum return, 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 using response modeling, uplift response modeling, churn modeling, churn uplift modeling, risk modeling, and fraud detection.

4.Insights claros:

These processes allow organizations to proactively design their performance and processes. It allows companies to learn how customers see them and their limitations and take actionable actions to improve their performance.

You may also like to read:

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:

common questions- Common questions

Questions 1. What are the two most commonly used quantitative data analysis methods?

Resp.The two most commonly used quantitative data analysis methods are:

• Descriptive Statistics and
• inferential statistics

They also like to learn something new.Fundamentals of statistics for data science and analysis

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

Resp.After gathering the necessary data, the three steps to interpreting the data are:

1. Work the insights:Results are observations related to the data and summarize the most important aspects of the data. It is based on the second stage of development of conclusions.
2. Develop conclusions:Conclusions help with reasoning and explain why the data are the way they are.
3. Develop recommendations:Based on the insights and conclusions, we need to develop actionable and result-oriented approaches and gather all necessary additional data.

Questions 3. Which comes first, analysis or interpretation?

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

Questions 4. What techniques exist for analyzing data?

Resp.The techniques available for data analysis are:

• Linear Regression
• logistic regression
• cluster analysis
• Varianzanalyse
• cohort analysis
• time series analysis
• sentiment analysis
• Monte Carlo Simulation
• Support vector machines

Questions 5. What types of data analysis are there?

Resp.The 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 analysis

final thoughts

Data isn't just limited to business applications; Our daily lives are filled with dates, when to get up (here dates are dates), what we eat (food is another piece of information), what stocks to invest in are all dates. In each of these activities, we see what information is available, evaluate it, and make decisions accordingly.

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

To learn practical and career-rewarding skills, explore our offeringData Analysis Courses! Our team of experts will be happy to help you understand your career prospects.

You may also like to read:

1.Difference between data and information? Data x Information

(Video) Data Analysis and Interpretation

2.

3.What is the Salary of a Data Analyst in India?