Data analysis

What is data analysis?

Organizations use data analysis to make informed decisions and improve efficiency within the company. Its main goals are identifying problems, collecting raw data sets, cleaning the data, and analyzing and interpreting information. Because of its numerous benefits, data analysis is used in many industries, such as marketing, e-commerce, business, etc.

To conduct data analysis, you can use various techniques such as descriptive analysis, predictive analysis, or regression analysis.

Why data analysis is important?

Data analysis as a subset of data analytics allows organizations to find areas for improvement, identify trends, avoid unnecessary risks, and convert raw data into insights that can be turned into concrete actions.

Supports making informed decisions

Data analysts analyze data and create understandable visualizations to support decision-making, so organizations don't need to rely on pure guesswork. Thanks to data analysis, companies can make better choices supported by concrete evidence. They can analyze the pros and cons of a solution, optimize business processes, and improve products.

Provides competitive advantage

Data analysis plays a significant role in gaining competitive advantage because it enables you to find market trends, research user behavior, identify customer preferences, and even compare your company's performance against the competition.

Improves understanding of an organization

Data analysis can give you insight into customer desires and help you understand your business. A data analyst can collect data about your business processes and identify areas for improvement. Analyzing data that concern internal processes can also help you optimize them and make them more efficient.

Risk assessment

Performing the data analysis process allows organizations to predict and prevent potential risks. It can prevent them from making harmful business decisions, enable them to protect their reputation, and save costs.

Types of data analysis

There are four different types of data analysis techniques that you can use for your business processes.

Descriptive analysis

The main task of descriptive analysis is to answer the question: What happened? As the name suggests, it describes what has already happened and summarizes the data so that it can be easily understood. For example, you may use descriptive statistical analysis during A/B testing to see which CTA button design performs better.

Diagnostic analysis

Diagnostic analysis asks the question: Why did it happen? It goes beyond descriptive analysis and tries to identify the causes of a phenomenon or behavior. For example, collected data show that the number of purchases in an online store has increased in recent months. After further examination, the team discovered it was most likely due to the approaching holiday season.

Predictive analysis

Predictive analysis answers the question: What will happen? It uses historical data to predict future trends and patterns. For example, you may notice that a product sells more during certain months, so you can predict that this will likely happen next year as well.

Prescriptive analysis

Prescriptive analysis asks the question: What to do next? It summarizes all the insight you've gathered from previous studies and collected data and converts them into guidelines and recommendations. For example, if a particular product sells best during certain months, then marketing during this period should capitalize on this.

Regression analysis

Regression analysis shows the relationship between a dependent variable and one or more independent variables. It can be used to predict unknown variables and is useful for situations like launching a new product and predicting its success or predicting how the business will grow and develop.

What does the data analysis process consist of?

The data analysis process consists of several steps that aid organizations in turning raw data into actionable insights.

1. Identify

First, the organization should decide which aspect of the business to focus on and what it tries to improve. For instance, it should determine whether it will examine business processes, product sales, etc.

2. Collect

Then, it's time for data collection. You can collect data from various internal sources, like CRM software, user interviews, or A/B testing. The type of data you collect is determined by the goals you want to achieve. These data might be the number of products sold, conversion rate, bounce rate, etc.

3. Clean

Cleaning data involves eliminating any duplicate or incorrect data so that it's ready for analysis. It's also a good moment for standardizing data structure and format.

4. Perform initial exploratory data analysis

This step allows you to prepare an initial data analysis and summarize their main characteristics. The exploratory analysis can help analysts validate hypotheses, check for inaccuracies, and select suitable modeling techniques.

5. Analyze

You can use various data analysis tools to draw conclusions from the data. During this step, you will find trends, patterns, correlations, etc.

6. Visualize and interpret

During this stage, you turn the analyzed data into data visualizations that will help you present the analysis' findings to stakeholders. Data visualization will provide you with a clean and understandable presentation. You can also use it to suggest recommendations based on the interpretation of data.

Tools for data analysis

Aside from data analysis techniques, tools can also facilitate this process. These can be software for aggregating and visualizing data or programming languages for computing.

Microsoft Excel

Microsoft Excel is helpful for aggregating data. It has many features that enable you to create various visualizations with graphs and charts. It also has an "analyze data" function that allows a user to quickly analyze data, visualize it, and create natural language queries. These queries are just questions you can ask about the data without the need to construct formulas.

Google Charts

Google Charts offers users a library of different types of charts (e.g., pie charts, table charts, calendar charts, and waterfall charts). Users can import data directly from databases or web pages to create charts and adjust the styles to match a website's look and feel. Moreover, users can also make the charts interactive.

R

R is an open-source programming language with a command-line interface for statistical computations and visualizations. It provides developers with various statistical techniques, such as linear and nonlinear modeling, clustering, or time-series analysis. R can also help data analysts convert unstructured data into structured format. Another advantage is that it can run on all operating systems.

Python

Python is an open-source general-purpose programming language widely used in data science. It offers mathematical libraries containing functions and analytical tools such as Pandas, Numpy, Matplotlib, and SciPy. These libraries allow developers to visualize data, perform structured data operations, and create data frames and linear algebra modules.

Power BI

Power BI is software data analysts can use for data visualization and reporting, data integration, AI-supported predictions, tracking key performance indicators, financial analysis, and monitoring market trends.

Summary

Data analysis will help you improve and optimize business processes. Its main goal is to collect, analyze, visualize, and interpret data to create strategic recommendations. With the amount of generated data growing, companies must continuously improve the data analysis process to stay ahead of the competition.