How to Analyze Data For Your Business in 5 Steps

Data analysis is the process of collecting and examining statistical information and drawing conclusions out of it. While doing this process, professionals check and judge large amounts of information from different sources to find patterns and trends. This turns data, like customer reviews and feedback, into insights that managers can use to make decisions and develop strategies and business plans. You will read more on how to analyze data for business in 5 steps in detail.

To find facts and make smart decisions, businesses collect information from audiences of customers. The process of data analysis includes changing numerical values into accessible insights about different business areas. By learning how to gather and check data properly, you can improve your critical thinking, planning and decision making skills in your workplace.

In this blog, we will explain how to analyze data for business in 5 steps, what is big data analytics, analysis of data and steps and processes of data analysis.

What is Big Data Analytics?

It is the systematic processing and analysis of large amounts of data to extract valuable insights and help data analysts make data informed decisions. This process allows companies to support the growing data derived from diverse sources including the internet. Things like sensors, social media, financial transactions and smart devices are used to derive actionable intelligence through advanced techniques.

Big data analytics use advanced analytics on large structured and unstructured data collections to make valuable business insights. It is used widely across industries like healthcare, education, insurance, AI, retail, and manufacturing. This helps in understanding what is working and what isn’t,this is to improve processes, systems, and profits.

Big data analytics is important because it helps companies hold their data to identify opportunities for improvement. It helps companies reduce costs and develop better, customer-centric products and services. During the COVID-19 pandemic, big data informed health ministries. They informed each nation’s government on how to proceed with vaccinations and come up with solutions for reducing pandemic outbreaks in the future. Big data analytics lend a hand to companies and governments to make sense of data and make better decisions.

How to Analyze Data?

Data professionals usually follow a 5 step process, when they start a new project involving data analysis, the steps are :

1. Identify business questions

2. Collect and store data

3. Prepare and clean data

4. Analyze data

5. Communicate and visualize data

In this blog, coming next we will be taking a closer look at each of the 5 steps on how to analyze data for business. With every new project, it's important to know that data science workflow depends on the task, sticking to the plan and well-defined framework. This will help you plan, apply and make your work better.

1. Identify Business Questions

Many companies spend millions in collecting all kinds of data from different sources, but a lot of companies fail to create value from it. Data is only as good as the questions you ask. It doesn’t matter how much data a company owns or how many data scientists comprise the department. The data only becomes a game-changer once you have pinpoint the right questions.

Here is a list of some example questions :

• What does the company need?

• What type of data is required?

• How can data help solve a problem/ business questions?

• How will we measure results?

• How will the data tasks be shared among the team?

• What technique will we use in the data analysis process?

By the end of this first step, you should have a clear-cut idea of how to proceed in data science workflow. This will help you find the difficulties of the data and achieve your goals. To identify the right business questions its important to improve complications and will save your time and other resources.

1. Collect and Store Data

Now you might have a clear idea of questions, let’s get our hands dirty now. Firstly, you need to collect and store your data in a safe place to analyze it. In this data-driven world, a huge pile of data is generated every second. The three main sources of data are :

• Company Data - It is created by companies in their daily activities. It can be customer data, web events, financial transactions, or survey data. This is usually stored in mutual databases.

• Machine Data - Nowadays, advanced technologies and an increasing number of electronic devices are making data. They range from cameras and smartwatches to satellites and smart houses.

• Open Data - The data which is being used is to create value for economies, governments and companies which are releasing data that can be used freely. This can also be done through an open data portal and application programming interfaces (API).

There are two types of data:

• Quantitative Data - Its information can be counted with numerical values. It’s usually structured in spreadsheets or SQL databases.

• Qualitative Data - The data which is generated in bulk is qualitative. Some of them are text, audio, video, images, or social media data.

Which type of data and techniques will be used depends upon the business questions you wish to answer. Usually, collecting, storing and analyzing qualitative data requires more advanced methods as compared to quantitative data.

1. Clean and Prepare Data

After you have collected and stored your data, the next step is for you to assess its quality. It's important for your data analysis to have good quality data. Your insights will be wrong/ misleading if your information is inaccurate, incomplete or inconsistent. That is why spending your time cleaning and preparing your data is very important.

Rarely raw data comes in ready for analysis, and so it is important to find and correct errors in your data. This process includes fixing errors like -

• Removing duplicate rows, cells, or columns. Especially if you're dealing with large datasets that consume a lot of memory.

• Dealing with white spaces in datasets also called ‘null values’.

• Managing abnormal or unusual and extreme values aka ‘outliers’.

• Bringing data structure and types in line, so that all data is shown in the same way.

Spotting errors and unusuality in data itself is data analysis, usually known as exploratory data analysis.

1. Analyze Data

Now after your data is clean, you're ready to analyze your data. Finding patterns, connections, predictions and insights is one of the most satisfying parts of the data scientist’s work. Different techniques are available depending upon the goals of the analysis and the type of data.

In this booming technological world, new methods and techniques have come to deal with every type of data. They vary from simple linear relapses to advanced techniques from cutting edge fields, like machine learning, natural language processing (NLP), and computer vision.

Below you will read about some of the most famous data analysis methods to dive deeper into your analysis.

Machine Learning :

This category of artificial intelligence (AI) gives us a set of algorithms, which enables machines to learn patterns and trends from the given previous data. Once the algorithms are trained, they are able to make evident predictions with increasing correctness.

There are three types of machine learning, depending on the type of problem you need to solve:

• Supervised learning - It involves teaching a model on a labeled training set of historical data from which it learns the connections between input and output data.

• Unsupervised learning - It deals with identifying the natural structure of the data without giving any dependent variable. It detects common patterns and differentiates the data points. And then based on their attributes and the given information, the machine makes predictions on data.

• Reinforcement learning - It involves an algorithm step by step learning by interacting with an environment. It decides which actions can bring nearer solutions, identifies which one can drive out based on its past experience, and then performs the best action for that step.

Natural Language Processing :

It is a field of machine learning that shows how to give computers the ability to understand human language, both spoken and written. NPL is one of the fastest- growing fields in data science.

Computer Vision :

The major goal of computer vision is to help computers see and understand the content of digital images. Computer vision is important to enable, such as self-driving cars.

1. Visualize and Communicate Results

The last step of analyzing data for business is visualizing and communicating the results of your data analysis. To turn your understanding into decision-making, you should make sure that your audience and key stakeholders understand your work.

In this final step, data visualization is like the dancing queen. In this blog, as it is already mentioned, data visualization is the act of translating the data into a visual context. This can be done using charts, animations, plots, infographics and so on. The main goal is to make it easier for humans to identify trends, outliers and patterns in data.

Final Words on How to Analyze Data for Business

We hope that through this blog about ‘how to analyze data for business in 5 steps’ it will be easier for you to understand and you are ready to start your data analysis career. The role of a business data analyst is multi-dimensional and includes various aspects of business. From acting as a bridge between the IT and business teams to finding profitable growth avenues. One can definitely take their first step towards a rewarding career in this exciting field.

Hello, I am Robert, a technical content writer with a flair for words and a deep love for storytelling with an experience of 5 years.