What is Data Analysis? Learn the Data Analysis Process
What is data analysis?
The collection of data and forming a data set will not give you any insight into the data. Rather, to generate any meaningful result for any dataset, the data needs to be cleaned and analyzed using different mathematical and statistical methods. Data analysis is hence the process of cleaning and analyzing data.
Although many different firms have different ways and methodologies to analyze a dataset, the basic procedure of remains the same. All data analysis processes include data collection, data cleaning, and analysis using reports and visuals. Marking from the very first step of analysis till the last, each step is done keeping the objective/goal in mind.
Importance of Data Analysis
With an increase in almost everything around the world, the need for data and its analysis has grown at a tremendous rate. It is important in almost all fields and industries. Let’s look at some of the reasons as to why it is important in our day-to-day life.
- It lets you see which of the demographic audience is responding the best towards your product or services and helps you target that demographic group of people through advertising and marketing.
- It helps you reduce the operational cost for your business and also helps in increasing the revenue. When you know your target audience you can cut off many operational costs and by targeting advertising and marketing, you can see an increase in revenue as well.
- It helps you understand your target audience better which in turn helps you to set the prices for your products or services or helps you restructure the ad campaigns or accommodate any change with respect to your product or service.
- It gives you an accurate insight into any problem you are facing in any area of your business and helps in developing better problem-solving methods.
- It gives you an insight into the industry, your fellow competitors, or the changes in the preference of your target audience. All this information generated through data analysis helps you understand the potential risk and opportunities that your business can face in the future.
- It gives you insight in a very subtle manner that helps you to make decisions faster and in a better manner as the decisions will now be backed up by data.
- It helps you generate an overall insight into your business which helps you look at the performance of your product or service in every aspect.
Type of data analysis
With different use cases of data analysis, there are different types or methods in which data can be analyzed these days. Below mentioned are some of the broad data analysis methods which are used in today’s time.
1. Text Analysis
This is mostly used for qualitative data collection and incorporates both text and machine learning logic to generate meaning from the collected data set or data sets. Most businesses use this kind of analysis to generate reports that are opinionated in nature.
2. Data mining
This type of analysis is done mostly by the stock market and the like fields and businesses. This data analysis process mostly accounts for detecting any kind of anomaly in data or pattern or relationship of any kind in order to generate reports or results.
3. Statistical Analysis
This uses different ways and methodologies to generate reports and present data. There are two broad ways in which data can be analyzed under this method – one by using the descriptive statistical methods like mean, median, mode or by running a regression on data based on multiple hypotheses and theories.
4. Diagnostic Analysis
This type of analysis is done by forming relationships between the visible data points and the root cause for those numbers. For instance, a sudden fall in revenue numbers and the multiple reasons behind this root cause. This type of data analysis aims to answer the ‘why?’ part of the data set.
5. Predictive Analysis
This type of analysis as the name suggests uses historic data to predict the future and is mostly used to analyze the upcoming change in industry or the market or the customer preference. By using predictive data analysis, one can identify possible risks and opportunities in the future and can have a first-mover advantage over its competitors.
6. Prescriptive Analysis
This type of analysis can be understood as a mix-and-match kind of data analysis where almost all the data is combined and analyzed under different scenarios based on the objective for which we want to analyze our data set.
Process of data analysis
The process of data analysis does not start with just applying various tricks and tactics to generate results out of the data set. Rather, the process begins at a much-much earlier stage. Let’s dive deeper and understand the process of data analysis.
- The very first step begins when you have an objective as to why you want to collect data or what is the problem statement that you want to look at through data.
- Once the objective of data collection is set, the next step is figuring out the way in which you want to collect data and start data collection.
- Even when you have sufficient data, you cannot dive into the process of data collection as it might show errors or will not be accurate enough. So the next step in is data cleaning.
- Once your data is clean you are all set to use different tools and methods for your analysis.
- The process does not end here. Once you have analyzed the data, the next step in your data analysis process will be to interpret the results generated from the analysis.
- After you have an interpretation, figure out the best way to visualize the data for easy understanding of the results. This will be your last step in the data analysis process.
Uses of data analysis
With the increasing number of businesses, the usage of data analysis is also increasing at a tremendous rate. Below mentioned are the few areas where analysis is used on a regular basis.
- It is used extensively for all kinds of market research and acts as a backup for the result of the market research.
- One of the most used cases is in the stock markets where everything relies on data and its analysis for both future predictions and for getting an understanding of the past performance of the stock market.
- Just like stock markets, each and every economy is also being valued on various factors and each factor is backed up by data which is further analyzed to know the performance of that economy as compared to the other economies.
- One of the fast-emerging ways of marketing is social media and every day vast amount of data is being collected and analyzed to know the performance of marketing and figuring out ways to bloom the business.
Quantitative v/s Qualitative data analysis
There are broadly two kinds of data that are being collected around the world – qualitative data and quantitative data. Both kinds of data sets use different ways of analysis as both of them are very different in nature.
Qualitative data as the name suggests focuses more on the quality and is usually collected in the form of long or short texts, images, and videos. When analysis is done for this kind of data sets, the results usually answer the question of ‘why?’ and ‘what?’ for the problem statement.
Quantitative data on the other hand is measurable and are expressed in numeric form. These kinds of data are usually collected in the form of metrics or scores and on analysis answer the question of ‘how much?’ and ‘how often?’ for the objective of data collection.
Now that we have gotten a glimpse of the various uses and the importance of data analysis, we can gain insights from the collected data to boost the growth of the company to new heights. Further, it can help us get a peek into the psyche of the customers to help deepen our understanding of them.
Which is the best tool for data analysis?
There are many tools available for analyzing data. However, there are few which give an extraordinary analysis of data. These tools are – Excel, Tableau, Splunk, Power BI, R, and Python.
What is the main purpose of data analysis?
The main purpose of data analysis is to clear, sort, and filter the data to see if there is any kind of trend in data or to look at some issues and problem-solving.