DavidFor a business to stay above the level in this digital age, the business needs to engage in conscious data analysis. Data analysis not only guides the business on the right decisions but also helps them to channel their resources in the best way.
It might interest you to know that despite the ample information or data at our disposal, only less than 5% are collected and analyzed properly in our everyday decision-making in our businesses and organizations.
So in this article, we are going to comprehensively explain the meaning of data analysis, and how you can use it to advance your business decisions, we will also explore the importance, the types and the techniques.
What is Data Analysis?
Data analysis can simply be defined as the systematic way of collecting, modelling, and analyzing data to extract useful information that aids decision-making. The process of data analysis uses analytical and logical reasoning to gain information from the data.
Given the easy accessibility of data in this age, understanding how to analyze and extract true meaning from our business’s digital insights is one of the primary drivers of success.
Why is Data analysis important?
The importance of data analysis in business today can not be overemphasized, because making conscious data analysis choices are the only way to be truly confident in business decisions. Some successful businesses may be created on intuition, but almost all successful business choices are data-based.
Data analysis helps in market research, product research, positioning, customer reviews, sentiment analysis, or any other issue for which data exists, and accurate data analysis will provide information that organizations need to make the right choices. In general, data analysis is used in business to help organizations make better and right decisions.
In a simple example, an organization that makes use of data analysis on their customers gets the best information on all aspects of their behaviour, you can understand which channels they use to communicate with you, their demographics, interests, habits, purchasing behaviours, and more.
Being aware of these customers’ behaviours helps your marketing strategies allow you to identify new potential customers, and avoid wasting resources on targeting the wrong people or sending the wrong message. You can also track customer satisfaction by analyzing your client’s reviews or your customer service department’s performance.
In summary, you need data analysis to be able to understand where to invest your capital, detect growth opportunities, predict your incomes, or tackle uncommon situations before they become problems.
Types of data analysis
Now that we have understood what data analysis is and its relevance to any business to be successful, we will now look at the various types. There are 5 types of data analysis, which are as follows;
- Descriptive analysis (what happened)
Just like the name, the descriptive analysis answers the question of what happened, it is the first phase of the analytic process. It arranges, rearranges, and interprets raw data into useful information for your business.
Descriptive analysis alone is not enough to answer the question of why something happened or even predict the future outcome, but it serves as a prelude to subsequent analysis.
- Investigative analysis(how to examine data relationships)
Mainly applied in data mining, the investigative analysis examines the relationships between data and variables. The investigative analysis enables you to find connections and generate hypotheses and possible solutions for specific problems.
- Diagnostic analysis (why it happened)
When you have done the descriptive analysis and discovered what happened, and the investigation has established the relationships, the diagnostic analysis answers the question of why it happened. It helps you to find out the reason something happened and to know the best possible way to tackle it.
The diagnostic analysis can be said to be the most crucial of all the analyses as it is designed to provide direct and practical answers to certain questions, and also helps you to be able to pinpoint the exact ways of tackling the issue or challenge.
- Predictive analysis (what will happen)
This analysis enables you to predict the future with past data. It uses the results of the previously mentioned descriptive, exploratory, and diagnostic analysis, and also artificial intelligence to predict the outcome of an action.
Predictive analysis helps you discover vital development initiatives that will not help your decision-making process, but also give you a headstart in your business.
For example, if each time the government increased workers’ salaries, there is a corresponding increase in foodstuffs sales. It means that if salaries are increased this year, there is likely going to be an increase in foodstuffs sales too. This is a common analysis, but predictive analytics can be used in wider angles like sales forecasting and risk assessment.
- Prescriptive analysis (way out).
The prescriptive analysis is crucial in the analytic process, it combines all the information obtained from the previous analysis, and tries to provide possible solutions and ways out. It is all about using the information in your hands to develop a practical business strategy.
Let’s now look at the various data analysis methods;
Let me quickly show you some common data analysis methods, there include but are not limited to;
- Cluster analysis: this is simply the grouping of similar data together. In a business sense, the grouping of customers may be based on demographics, purchasing behaviours, monetary value, or any other factor that might be relevant to your company.
- Regression analysis: this analysis makes use of historical events, it compares how the change in one variable affects another variable.
- Text analysis: This is the process of simplifying or breaking down large sets of textual data and arranging them into more manageable data.
Data analysis techniques
- Articulate your needs.
Knowing why you want to gather or carry out the data analysis is what is done in the phase, you articulate your needs for the data, You have to decide which type of data analysis you want to do! In this phase, you have to decide what to analyze and how to measure it, you have to understand why you are investigating and what measures you have to use to do this Analysis.
- Collect your data.
After articulating your need for the data, you will get a clear idea about what things you have to measure and what your findings should be. Now it’s time to collect your data based on requirements. Once you collect your data, remember that the collected data must be processed or organized for Analysis. As you collect data from various sources, you must keep a log with a collection date and source of the data.
- Data cleaning.
Data collection often involves different sources, which most times leaves you with some irrelevant information that can be overwhelming to deal with. Therefore, it should be cleaned. The data which is collected may contain duplicate records, white spaces or errors. The data should be cleaned and error-free. This phase must be done before Analysis because based on data cleaning, your output of Analysis will be closer to your expected outcome.
- Analyze your data.
Once the data is collected, cleaned, and processed, it is ready for Analysis. As you manipulate data, you may find you have the exact information you need, or you might need to collect more data. During this phase, you can use data analysis tools and software which will help you to understand, interpret, and derive conclusions based on the requirements and the area you are working on.
- Data interpretation.
After analyzing your data, the next stage is the interpretation of your results. You can choose the way to express, arrange, represent or communicate your data analysis, either you can use or leave it in simple words or maybe a table or chart. Then use the results of your data analysis process to answer your questions, and also decide your best course of action.
- Data visualization.
This is the physical or systematic representation of all your data, data visualization is very common in our everyday life, the graphs and charts are all forms of data visualization. By presenting the data graphically, it is just aimed at making it easily comprehensible for the brain.
Data visualization, therefore, helps to discover or unfound unknown information and trends, by consciously observing relationships and juxtaposing data sets, it is now much easier for you to find useful information that will advance your business.
Summary:
- We started by elaborately defining for you what data analysis is.
- Then we went through why data analysis is vital to any business.
- We discussed the types, the methods and the techniques of data analysis.
We wish to still reiterate that any business that makes use of data analysis can not be compared to one built on intuition. Actions taken to run your business must be backed with reasons. The internet has made data collection easy and very accessible too. I hope you find this article helpful.
You can find out: All You Need to Know About Data Analytics Certification
James steller
Mitrobe Staff
mitrobe
