What Is Data Analysis?

There has been a rave about data analysis in recent times. This is because of the benefits that come with data analysis. Due to that, we have decided to discuss what data analysis is, and the benefits that come with it. If you are interested in finding out, you should read through this guide.

What Is Data Analysis?

Though numerous groups, organizations, and professionals have diverse ways to approach data analysis, most of them can be distilled into a one-size-fits-all definition. Data analysis is the act of cleaning, changing, and processing raw data, and taking out 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.

An easy example of data analysis can be seen whenever we take 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 rare 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 Data Analysis?

To grow your business even to grow in your life, sometimes all you need to do is Analysis!

If your business is not growing, then you have to look back and acknowledge your mistakes and make a plan again without repeating those mistakes. And even if your business is growing, then you have to look forward to making the business grow more. All you need to do is analyze your business data and business processes.

What tools do data analysts use?

During the process of data analysis, analysts often use a wide variety of tools to make their work more accurate and efficient. Some of the most common tools in the data analytics industry include:

• Microsoft Excel
• Google Sheets
• SQL
• Tableau
• R or Python
• SAS
• Microsoft Power BI
• Jupyter Notebooks

Data analyst tasks and responsibilities

A data analyst is a person whose job is to gather and interpret data in order to solve a specific problem. The role includes plenty of time spent with data but entails communicating findings too.

Here’s what many data analysts do on a day-to-day basis:

• Gather data: Analysts often collect data themselves. This could include conducting surveys, tracking visitor characteristics on a company website, or buying datasets from data collection specialists.
• Clean data: Raw data might contain duplicates, errors, or outliers. Cleaning the data means maintaining the quality of data in a spreadsheet or through a programming language so that your interpretations won’t be wrong or skewed.
• Model data: This entails creating and designing the structures of a database. You might choose what types of data to store and collect, establish how data categories are related to each other, and work through how the data actually appears.
• Interpret data: Interpreting data will involve finding patterns or trends in data that will help you answer the question at hand.
• Present: Communicating the results of your findings will be a key part of your job. You do this by putting together visualizations like charts and graphs, writing reports, and presenting information to interested parties.

Types of Data Analysis: Techniques and Methods

There are several types of Data Analysis techniques that exist based on business and technology. However, the major Data Analysis methods are:

• Text Analysis
• Statistical Analysis
• Diagnostic Analysis
• Predictive Analysis
• Prescriptive Analysis

Text Analysis

Text Analysis is also addressed as Data Mining. It is one of the methods of data analysis to discover a pattern in large data sets using databases or data mining tools. It is used to transform raw data into business information. Business Intelligence tools are present in the market and are used to take strategic business decisions. Overall it offers a way to extract and examine data and derive patterns and finally interpretation of the data.

Statistical Analysis

Statistical Analysis shows “What happen?” by using past data in the form of dashboards. Statistical Analysis includes the collection, Analysis, interpretation, presentation, and modeling of data. It analyses a set of data or a sample of data. There are two categories of this type of Analysis – Descriptive Analysis and Inferential Analysis.

Descriptive Analysis

analyses complete data or a sample of summarized numerical data. It shows mean and deviation for continuous data whereas percentage and frequency for categorical data.

Inferential Analysis

analyses sample from complete data. In this type of Analysis, you can find different conclusions from the same data by selecting different samples.

Diagnostic Analysis

Diagnostic Analysis shows “Why did it happen?” by finding the cause from the insight found in Statistical Analysis. This Analysis is useful to identify behavior patterns of data. If a new problem arrives in your business process, then you can look into this Analysis to find similar patterns to that problem. And it may have chances to use similar prescriptions for the new problems.

Predictive Analysis

Predictive Analysis shows “what is likely to happen” by using previous data. The simplest data analysis example is like if last year I bought two dresses based on my savings and if this year my salary is increasing double then I can buy four dresses. But of course, it’s not easy like this because you have to think about other circumstances like the chances of the prices of clothes is increased this year or maybe instead of dresses you want to buy a new bike, or you need to buy a house!

So here, this Analysis makes predictions about future outcomes based on current or past data. Forecasting is just an estimate. Its accuracy is based on how much detailed information you have and how much you dig into it.

Prescriptive Analysis

Prescriptive Analysis combines the insight from all previous Analyses to determine which action to take in a current problem or decision. Most data-driven companies are utilizing Prescriptive Analysis because predictive and descriptive Analyses are not enough to improve data performance. Based on current situations and problems, they analyze the data and make decisions.

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

Benefits of Data Analysis

Data has the potential to give a lot of value to organizations, but the analytics component is necessary to uncover that power. As the importance of data analytics grows in the corporate world, it becomes increasingly important for firms to understand how to use it. Let’s see what some ways to do so are.

Informed & improved decision making

When big data joins forces with artificial intelligence, machine learning, and data mining, companies are better equipped to make accurate predictions. For example, predictive analytics can suggest what could happen in response to changes to the business, and prescriptive analytics can indicate how the company should react to these changes. Additionally, enterprises can use data analytics tools to determine the success of changes and visualize the results, so decision-makers know whether to roll the changes out across the business.

More effective marketing

What makes organizations stand out is the unique approach they choose to market their products. By using data analytics, companies can pinpoint precisely what customers are looking for. Data enables businesses to do in-depth analyses of client trends, which companies can then utilize to develop successful, focused, and targeted marketing.

Better & personalized customer experience

By sharing their data, customers expect companies to know them, form relevant interactions, and provide a seamless experience across all touchpoints. Being able to react in real time and make the customer feel valued is only possible through advanced analytics. Data allows for interactions to be tailored to the customer’s personality, thus helping to understand their attitudes in order to deliver personalization in a multi-channel service environment.

Streamline operations

Data analytics may also enable enterprises to identify possible ways to optimize processes and increase revenues. It assists in identifying potential issues, thus preventing them from happening. This allows enterprises to see which activities have produced the best overall outcomes under different circumstances.

Mitigate fraud

Adequate data analytics capabilities will provide the highest levels of fraud protection and overall security for your firm. Using statistical, network, path, and data methodologies for predictive fraud will ensure that real-time threat detection methods, automated warnings, and mitigation drive fast reactions.

If you’re unsure which path you want to go in IT, learning data analytics is an excellent place to start. Thanks to data analytics, the world’s most successful companies are constantly growing.

Conclusion

In conclusion, data analysis can help you improve daily processes and increase your team’s productivity while boosting public satisfaction. By using data analysis to uncover problems that lead to delays and productivity loss, you can take concrete steps to create solutions.