Businesses today need every edge and advantage they can get. Thanks to obstacles like rapidly changing markets, economic uncertainty, shifting political landscapes, finicky consumer attitudes, and even global pandemics, businesses today are working with slimmer margins for error.

Companies that want to stay in business and thrive can improve their odds of success by making smart choices while answering the question: “What is data analysis?” And how does an individual or organization make these choices? They collect as much useful, actionable information as possible and then use it to make better-informed decisions!

This strategy is common sense, and it applies to personal life as well as business. No one makes important decisions without first finding out what’s at stake, the pros and cons, and the possible outcomes. Similarly, no company that wants to succeed should make decisions based on bad data. Organizations need information; they need data. This is where data analysis or data analytics enters the picture.

The job of understanding data is currently one of the growing industries in today's day and age, where data is considered as the 'new oil' in the market. Our Data Analytics Program can help you learn how to make sense of data and get trends from them.

Now, before getting into the details about the data analysis methods, let us first answer the question, what is data analysis? 

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What Is Data Analysis?

Although many groups, organizations, and experts have different ways of approaching data analysis, most of them can be distilled into a one-size-fits-all definition. Data analysis is the process of cleaning, changing, and processing raw data and extracting actionable, relevant information that helps businesses make informed decisions. The procedure helps reduce the risks inherent in decision-making by providing useful insights and statistics, often presented in charts, images, tables, and graphs.

A simple example of data analysis can be seen whenever we make a decision in our daily lives by evaluating what has happened in the past or what will happen if we make that decision. Basically, this is the process of analyzing the past or future and making a decision based on that analysis.

It’s not uncommon to hear the term “big data” brought up in discussions about data analysis. Data analysis plays a crucial role in processing big data into useful information. Neophyte data analysts who want to dig deeper by revisiting big data fundamentals should go back to the basic question, “What is data?”

Why is Data Analysis Important?

Here is a list of reasons why data analysis is crucial to doing business today.

  • Better Customer Targeting: You don’t want to waste your business’s precious time, resources, and money putting together advertising campaigns targeted at demographic groups that have little to no interest in the goods and services you offer. Data analysis helps you see where you should be focusing your advertising efforts.
  • You Will Know Your Target Customers Better: Data analysis tracks how well your products and campaigns are performing within your target demographic. Through data analysis, your business can get a better idea of your target audience’s spending habits, disposable income, and most likely areas of interest. This data helps businesses set prices, determine the length of ad campaigns, and even help project the number of goods needed.
  • Reduce Operational Costs: Data analysis shows you which areas in your business need more resources and money, and which areas are not producing and thus should be scaled back or eliminated outright.
  • Better Problem-Solving Methods: Informed decisions are more likely to be successful decisions. Data provides businesses with information. You can see where this progression is leading. Data analysis helps businesses make the right choices and avoid costly pitfalls.
  • You Get More Accurate Data: If you want to make informed decisions, you need data, but there’s more to it. The data in question must be accurate. Data analysis helps businesses acquire relevant, accurate information, suitable for developing future marketing strategies, business plans, and realigning the company’s vision or mission.

What Is the Data Analysis Process?

Answering the question “what is data analysis” is only the first step. Now we will look at how it’s performed. The process of data analysis, or alternately, data analysis steps, involves gathering all the information, processing it, exploring the data, and using it to find patterns and other insights. The process of data analysis consists of:

  • Data Requirement Gathering: Ask yourself why you’re doing this analysis, what type of data you want to use, and what data you plan to analyze.
  • Data Collection: Guided by your identified requirements, it’s time to collect the data from your sources. Sources include case studies, surveys, interviews, questionnaires, direct observation, and focus groups. Make sure to organize the collected data for analysis.
  • Data Cleaning: Not all of the data you collect will be useful, so it’s time to clean it up. This process is where you remove white spaces, duplicate records, and basic errors. Data cleaning is mandatory before sending the information on for analysis.
  • Data Analysis: Here is where you use data analysis software and other tools to help you interpret and understand the data and arrive at conclusions. Data analysis tools include Excel, Python, R, Looker, Rapid Miner, Chartio, Metabase, Redash, and Microsoft Power BI.
  • Data Interpretation: Now that you have your results, you need to interpret them and come up with the best courses of action based on your findings.
  • Data Visualization: Data visualization is a fancy way of saying, “graphically show your information in a way that people can read and understand it.” You can use charts, graphs, maps, bullet points, or a host of other methods. Visualization helps you derive valuable insights by helping you compare datasets and observe relationships.

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What Is the Importance of Data Analysis in Research?

A huge part of a researcher’s job is to sift through data. That is literally the definition of “research.” However, today’s Information Age routinely produces a tidal wave of data, enough to overwhelm even the most dedicated researcher.

Data analysis, therefore, plays a key role in distilling this information into a more accurate and relevant form, making it easier for researchers to do to their job.

Data analysis also provides researchers with a vast selection of different tools, such as descriptive statistics, inferential analysis, and quantitative analysis.

So, to sum it up, data analysis offers researchers better data and better ways to analyze and study said data.

What is Data Analysis: Types of Data Analysis

A half-dozen popular types of data analysis are available today, commonly employed in the worlds of technology and business. They are: 

  • Diagnostic Analysis: Diagnostic analysis answers the question, “Why did this happen?” Using insights gained from statistical analysis (more on that later!), analysts use diagnostic analysis to identify patterns in data. Ideally, the analysts find similar patterns that existed in the past, and consequently, use those solutions to resolve the present challenges hopefully.
  • Predictive Analysis: Predictive analysis answers the question, “What is most likely to happen?” By using patterns found in older data as well as current events, analysts predict future events. While there’s no such thing as 100 percent accurate forecasting, the odds improve if the analysts have plenty of detailed information and the discipline to research it thoroughly.
  • Prescriptive Analysis: Mix all the insights gained from the other data analysis types, and you have prescriptive analysis. Sometimes, an issue can’t be solved solely with one analysis type, and instead requires multiple insights.
  • Statistical Analysis: Statistical analysis answers the question, “What happened?” This analysis covers data collection, analysis, modeling, interpretation, and presentation using dashboards. The statistical analysis breaks down into two sub-categories:
  1. Descriptive: Descriptive analysis works with either complete or selections of summarized numerical data. It illustrates means and deviations in continuous data and percentages and frequencies in categorical data.
  2. Inferential: Inferential analysis works with samples derived from complete data. An analyst can arrive at different conclusions from the same comprehensive data set just by choosing different samplings.
  • Text Analysis: Also called “data mining,” text analysis uses databases and data mining tools to discover patterns residing in large datasets. It transforms raw data into useful business information. Text analysis is arguably the most straightforward and the most direct method of data analysis.

Next, we will get into the depths to understand about the data analysis methods.

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Data Analysis Methods

Some professionals use the terms “data analysis methods” and “data analysis techniques” interchangeably. To further complicate matters, sometimes people throw in the previously discussed “data analysis types” into the fray as well! Our hope here is to establish a distinction between what kinds of data analysis exist, and the various ways it’s used.

Although there are many data analysis methods available, they all fall into one of two primary types: qualitative analysis and quantitative analysis.

  • Qualitative Data Analysis: The qualitative data analysis method derives data via words, symbols, pictures, and observations. This method doesn’t use statistics. The most common qualitative methods include:
    • Content Analysis, for analyzing behavioral and verbal data.
    • Narrative Analysis, for working with data culled from interviews, diaries, surveys.
    • Grounded Theory, for developing causal explanations of a given event by studying and extrapolating from one or more past cases.
  • Quantitative Data Analysis: Statistical data analysis methods collect raw data and process it into numerical data. Quantitative analysis methods include:
  1. Hypothesis Testing, for assessing the truth of a given hypothesis or theory for a data set or demographic.
  2. Mean, or average determines a subject’s overall trend by dividing the sum of a list of numbers by the number of items on the list.
  3. Sample Size Determination uses a small sample taken from a larger group of people and analyzed. The results gained are considered representative of the entire body. 

We can further expand our discussion of data analysis by showing various techniques, broken down by different concepts and tools. 

Top 7 Data Analysis Tools

So, here's a list of the top seven data analysis tools in terms of popularity, learning, and performance.

  • Tableau Public

It is a free data visualization application that links to any data source you can think of whether it's a corporate Data Warehouse, Microsoft Excel, or web-based information. It also generates data visualizations, maps, dashboards, and so on, all with real-time changes that are shown on the web. These may also be shared on social media or with your customer, and you can download the files in several formats. 

However, it truly shines when you have an excellent data source. That's when you realize Tableau's ultimate potential. Tableau's Big Data features make it indispensable. Its approach to data analysis and visualization is considerably better than that of any other data visualization software on the market.

  • R Programming

Well, R is the industry's premier analytics tool, and it's extensively used for statistics and data modeling. It can readily alter data and show it in a variety of formats. It has outperformed SAS in several aspects, including data capacity, performance, and results. 

R may be compiled and run on a broad range of systems, including Windows, UNIX, and macOS. It offers 11,556 packages and lets you explore them by category. Also, R has tools for installing all packages automatically based on user needs, which may be used with Big Data.

  • Python

It's a scripting language that is simple to understand, write, as well as maintain. Furthermore, it's a free open-source tool. Guido van Rossum developed it in the late 1980s and it supports both structured and functional programming methodologies. Python is simple to learn since it is related to Ruby, JavaScript, and PHP. 

Python also contains excellent machine learning packages such as Tensorflow, Theano, Scikitlearn, and Keras. Another useful characteristic of Python is that it can be built on any platform, such as a MongoDB database, SQL browser, or JSON. It also excels at handling text data.

  • Apache Spark

Apache was created in 2009 by the AMP Lab at the University of California, Berkeley. Apache Spark is a large-scale data processing engine that performs applications hundred times quicker when it comes to memory and 10 times faster on disk in Hadoop clusters. 

It is based on data science, and its design makes data science simple. Spark is also popular for developing data pipelines and machine learning models. Spark also contains the MLlib package, which provides a progressive collection of machine algorithms for recurring data science procedures like classification, collaborative filtering, regression, clustering, and so on.

  • SAS

SAS is basically a data manipulation programming ecosystem and language that is a market leader in analytics. The SAS Institute created it in 1966, and it was expanded upon in the 1980s as well as the 1990s. It is simple to use and administer, and it can analyze data from any source. 

In 2011, SAS released a significant collection of solutions for customer intelligence, as well as numerous SAS modules for social media, online, and marketing analytics. These are now often used to profile clients and prospects. It can also forecast their actions and manage and improve communications.

  • Excel

Excel is a popular, basic, and frequently leveraged analytical tool in practically all industries. Whether you are a Sas, R, or Tableau specialist, you will still need to utilize Excel. When analytics on the client's internal data is required, Excel comes in handy. 

It analyzes the hard work of summarizing the data with a preview of pivot tables, which aids in filtering the data according to the client's needs. Excel includes a sophisticated business analytics feature that aids in modeling skills. It has prebuilt tools such as automated relationship recognition, DAX measure generation, and time grouping.

  • RapidMiner

It is an extremely capable comprehensive data analysis tool. It's created by the same house that does predictive analysis as well as other advanced analytics such as machine learning, text analysis, visual analytics, and data mining without the use of programming. 

RapidMiner supports all data source types, including Microsoft SQL, Excel, Access, Oracle, Teradata, Dbase, IBM SPSS, MySQL, Ingres, IBM DB2, Sybase, and others. This tool is quite powerful, as it can provide analytics based on real-world data transformation settings, allowing you to customize the data sets and formats for predictive analysis.

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Artificial Intelligence and Machine Learning

AI is on the rise and has proven a valuable tool in the world of data analysis. Related analysis techniques include:

  • Artificial Neural Networks
  • Decision Trees
  • Evolutionary Programming
  • Fuzzy Logic

Mathematics and Statistics

This is the technique where you find number-crunching data analytics. The techniques include:

  • Descriptive Analysis
  • Dispersion Analysis
  • Discriminant Analysis
  • Factor Analysis
  • Regression Analysis
  • Time Series Analysis

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Graphs and Visualization

We are visually oriented creatures. Images and displays attract our attention and stay in our memory longer. The techniques include:

  •   Charts, which break down into the following types:
  1. Area Chart
  2. Bubble Chart
  3. Column Charts and Bar Charts
  4. Funnel Chart
  5. Gantt Chart
  6. Line Chart
  7. Pie Chart
  8. Radar Chart
  9. Word Cloud Chart
  10. Gauge
  • Frame Diagram
  • Rectangular Tree Diagram
  • Maps, which in turn break down into four distinct types:
  1. Flow Map
  2. Heat Map
  3. Point Map
  4. Regional Map
  • Scatter Plot

Have a look at the video below that will give you a brief understanding of who is a data analyst, the various responsibilities of a data analyst, and the skills required to become a data analyst.

How to Become a Data Analyst

NOw that we have answered the question “what is data analysis”, if you want to pursue a career in data analytics, you should start by first researching what it takes to become a data analyst. You should follow this up by taking selected data analytics courses, such as the Data Analyst Master’s certification training course offered by Simplilearn.

This seven-course Data Analyst Master’s Program is run in collaboration with IBM and will make you an expert in data analysis. You will learn about data analysis tools and techniques, working with SQL databases, the R and Python languages, creating data visualizations, and how to apply statistics and predictive analytics in a commercial environment. 

You can even check out the PG Program in Data Analytics in partnership with Purdue University and in collaboration with IBM. This program provides a hands-on approach with case studies and industry-aligned projects to bring the relevant concepts live. You will get broad exposure to key technologies and skills currently used in data analytics.

According to Forbes, the data analytics profession is exploding. The United States Bureau of Labor Statistics forecasts impressively robust growth for data science jobs skills and predicts that the data science field will grow about 28 percent through 2026. backs up these predictions, reporting that, by the end of 2021, almost 70 percent of business leaders surveyed will look for prospective job candidates that have data skills.

Payscale reports that Data Analysts can earn a yearly average of USD 62,559. Payscale also shows Data Analysts in India making an annual average of ₹456,667.

So, if you want a career that pays handsomely and will always be in demand, then check out Simplilearn and get started on your new, brighter future!

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1. What is the role of data analytics?

Data Analytics is the process of collecting, cleaning, sorting, and processing raw data to extract relevant and valuable information to help businesses. An in-depth understanding of data can improve customer experience, retention, targeting, reducing operational costs, and problem-solving methods.

2. What are the types of data analytics?

Diagnostic Analysis, Predictive Analysis, Prescriptive Analysis, Text Analysis, and Statistical Analysis are the most commonly used data analytics types. Statistical analysis can be further broken down into Descriptive Analytics and Inferential Analysis.

3. What are the analytical tools used in data analytics?

The top 10 data analytical tools are Sequentum Enterprise, Datapine, Looker, KNIME, Lexalytics, SAS Forecasting, RapidMiner, OpenRefine, Talend, and NodeXL. The tools aid different data analysis processes, from data gathering to data sorting and analysis. 

4. What is the career growth in data analytics?

Starting off as a Data Analysis, you can quickly move into Senior Analyst, then Analytics Manager, Director of Analytics, or even Chief Data Officer (CDO).

5. 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. 

6. Who Is Using Data Analytics?

Data Analytics has now been adopted almost across every industry. Regardless of company size or industry popularity, data analytics plays a huge part in helping businesses understand their customer’s needs and then use it to better tweak their products or services. Data Analytics is prominently used across industries such as Healthcare, Travel, Hospitality, and even FMCG products.

About the Author

Karin KelleyKarin Kelley

Karin has spent more than a decade writing about emerging enterprise and cloud technologies. A passionate and lifelong researcher, learner, and writer, Karin is also a big fan of the outdoors, music, literature, and environmental and social sustainability.

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