This article explains what is data analysis, its types, process, methods, techniques, tools, benefits, and barriers. Find out why it is a critical concept.
Data analysis is popularly defined as a process of cleaning, transforming, and modeling data to discover useful information for decision making. Data analysis is used in different business, science, and social science domains. In today’s business world, decisions are required to be made more scientific to help the business of rate more effectively, and here, data analysis plays a crucial role.
Whenever we take any decision, whether, in our day to day life or our business, we tend to think about what happened last time and what will happen if you choose a particular decision, it is very much like analyzing our past or future and making decisions accordingly. For that purpose, we gather memories or data related to our history and then keeping the goals and dreams in our minds; we make decisions accordingly. It is nothing but data analysis.
In this article, you will learn about the importance of data analysis, its types, processes, and techniques.
Why is Data Analysis critical?
To grow in business or even in life, sometimes all you need to do is analyze.
Suppose if a particular business is not growing, then the owner or the person concerned with decision making should look back and acknowledge the mistakes made by the company and make a plan again without repeating those mistakes. Even if the business of a particular company is growing, then the data analyst should take data and make decisions to make the business grow even more. It is where data analysis plays a crucial role.
Data analysis is more than just presenting numbers and figures to the management. It requires an in-depth approach to record, analyze, and to check the data and present the findings in an easily understandable format.
To make the right decision, the data analyst should be able to-
- Predict customer trends and behaviors
- Analyze, interpret and deliver data in meaningful ways
- Increase business productivity
- Drive effective decision-making
Data Analysis Tools
Data analysis tools generally make it easier for the data analysts to collect and organize the data and submit it in an easily understandable format to the management. Nowadays, data analytics software is widely used to provide meaningful analysis to a broad set of data. It helps in saving time and also making better decisions. Some of the accessible data analysis software are-
- Microsoft HDInsight
- Splice Machine
Types of Data Analysis
Text analysis is also called data mining. The text analysis is a method to discover a pattern in given large data sets with the help of a data basis or data mining tools. The data isn’t transformed into business information and is then presented to the management. The management takes the help of business intelligence tools that are present in the market and seek advice to make useful strategic decisions regarding business growth. In simple language, text analysis of a simple way to extract and examine data and derive patterns and finally interpret data.
Statistical analysis, in terms of business intelligence (BI), involves accumulating, followed by examining every data sample in a set of items from which samples can be drawn. An example, in statistical analysis, is a representative selection drawn from a total population. After the examination of data is done, it is presented and model in the way which is easily understandable by the management.
The statistical analysis shows “what has happened in the past.”
Diagnostic analysis generally answers the question “why did it happen?” by finding the cause from the inside found by statistical analysis. Diagnostic analysis is essential to identify behavior patterns of data. Suppose a new problem comes into the business process. The management can look into the diagnostic report to find similar patterns of that problem, and hence, there might be some changes to you similar solutions for the new challenge.
Predictive analysis has the answer to the question, “What is likely to happen?” Predictive analysis is used to make predictions about unknown future events. Many techniques are used from data mining, statistics, modeling, machine learning, and artificial intelligence to analyze the current in the past data and to make predictions. Relationships among many factors are captured to assess risk with a particular set of conditions to assign a score, or weightage. This type of analysis allows an organization for the company to become proactive and look forward to anticipating the outcomes and behaviors based on the data and not on a bunch of assumptions.
The normative analysis combines the data from all the previous studies to determine which action to be taken in a current problem or decision. Most data-driven companies are digitalizing this analysis because the predictive and descriptive analysis is not enough to improve data performance. In an authoritative analysis, data from a variety of both descriptive and predictive sources is gathered for its models and is applied to the process of decision making. The existing conditions and possible diseases to determine how it should impact the future are also included in prescriptive analysis.
Data analysis process
The data analysis process merely is gathering information by using a particular application or tool which allows you to explore the data and find a pattern in it. Several phases can be differentiated based on the function, as described below. The phases are iterative, which means feedback from later phases may result in additional work in earlier phases.
Data requirement gathering
The first phase is the data requirement gathering. Before initiating the data analysis, one needs to be clear exactly why the data analyst wants to do a particular analysis. We also need to decide what kind of data analysis he should do you and what type of data he has to analyze. The analyst has to understand why the data is being investigated and what are the measures he has to use to do the analysis.
After requirement gathering, things will be cleared off what has to be measured and what has to be found. Now the analyst has to collect data from the given source. After receiving the data, the collected data must be appropriately organized for the analysis. Since data can be obtained from different sources, a log must be maintained where collection date and the source of data is duly mentioned.
Once the required data is collected and it is appropriately organized, there may be some data that are not useful for a relevant to the aim of analysis; hence these data should be removed. Some data may also contain duplicate records, whitespaces, or even errors. The data should be clean and error-free. A variety of analytical techniques are used to identify the common mistakes like inaccuracy of data, duplication, etc.
What’s the data is collected organized and thoroughly cleaned; it is ready for analysis. A variety of techniques called exploratory data analysis can be used by analysts to understand the message containing the data. These days, you will get the exact information you need, or will no weather more data has to be collected.
After the data is analyzed, it is time to interpret the results. The way to express a communicate the information is up to the analyst. It is his choice whether to use simple words or take the help of a table of charts. The main purpose of the data interpretation phase is to present the easily understandable data to the management.
Data visualization has become very common in our day to day life. They often appear in the form of graphs, charts, or tables. The data is generally shown graphically so that it becomes easier for the human brain to understand and process. In business, the management team may not be able to interpret raw data interpretation, and hence data visualization in such cases is constructive. The data visualization is often credited to discover unknown facts on trends. Meaning full information can be found by observing relationships and comparing the data sets. Hence, to analyze data correctly, the management prefers to take the help of data visualization. Complex data is made more accessible, understandable, and usable to the administration, and hence decision making becomes more comfortable.
Techniques for analyzing Quantitative data
Famous author Jonathan Koomey has recommended a series of best practices for understanding quantitative data. These include:
- Check raw data for anomalies before performing an analysis;
- Re-perform essential calculations, such as verifying columns of data that are formula-driven;
- It is advisable to confirm main totals are the sum of subtotals;
- You should check relationships between numbers that should be related in a predictable way, such as ratios over time;
- Normalize names to make comparisons more comfortable, such as analyzing amounts per person or relative to GDP or as an index value corresponding to a base year;
- Break problems into parts by analyzing factors that led to the results, such as DuPont analysis of return on equity
One popular method for precise data analysis is regression analysis. When a business management team has to make predictions for forecast future trends, regression studies are preferred as they are excellent tools in such cases. Recreations measure the relationship between a dependent variable and an independent variable. A dependent variable, mathematical terms, is defined as a variable that is desired to be measured whereas, and the independent variable is the data that is used to predict the dependent variable. In data analysis, there can be only one dependent variable, but there can be a nearly limitless number of independent ones. Uncovering areas in the operations that can be optimized by highlighting trends and relationships between two factors can also be held by regression.
Another popular method is hypothesis testing. It is also known as T testing. This analysis method lets the business management compare the data they have against the hypothesis and assumptions they have made about their future operations. It also helps the team forecast how specific decisions made can impact the organization.
Barriers to useful Data Analysis
Barriers to useful analysis may be present among the analyst performing the data analysis or among the business management team. There are many challenges to data analysis, like distinguishing facts from opinion, cognitive biases, and even innumeracy.
Confusing facts and opinion. In data analysis obtaining relevant facts are required to answer questions, supporter confusion, or even test hypothesis on making predictions for the future. By definition, facts are impossible to deny or disprove, which means that any analyst involved in particular data analysis should be able to agree upon the fact. When mixing facts and opinions, there is always a huge possibility that the opinion is entirely wrong.
Cognitive biases. A variety of cognitive biases can affect data analysis adversely. For example, confirmation bias is a tendency to search or interpret and information in a way that confirms once perception. So in some reserves of data analysis, some kind of individuals may Discovery information that does not support their views. The analyst should be trained specially to be aware of all types of these devices and how to overcome them.
Innumeracy. Data analysts are generally sound with a variety of numerical techniques. But the management team of the company may not be so good in those numerical on mathematical methods, and hence it can become misleading or even confusing to understand. Thus the numerical techniques should be used in a way that is understandable for the management team.
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