Before making any important decisions we all want to find out what’s at stake, the pros and cons, and the possible outcomes. Similarly, any organization wants to make decisions based on information or data. This is where data analysis or data analytics plays an important role.
Data analysis is the collection, analysis, and use of data to tell stories using charts and visualizations so that businesses can make better decisions. It is the practice of working with data to extract meaningful information to take informed decisions. It is the process of cleaning, transforming, and modeling the data. This can be considered a creative expression of numbers.
Data analysis tools make it easier for users to process and manipulate data, analyze the relationships and correlations between data sets, and help to identify patterns and trends for interpretation.
Importance of Data Analysis:
Decision-making becomes easier as it can be done based on facts and not guesswork.
Reduce cost and save money and resources on implementing the wrong strategies.
Also, predictive analysis can help in business improvements.
Targeted business and happy customers, understanding demographics, interests, habits,
behaviors, and more will drive success to the marketing strategies in long term.
Data Analysis Process:
Identify the business requirement or the problem statement. First of all, we have to think about why we want to do this data analysis. We have to identify which type of analysis we want to do.
Collect the raw data from a variety of sources to help answer the problem statement. Once the data is collected it must be processed or organized for Analysis. We must keep a log with a collection date and source of the data.
Clean the data as the collected data may not be useful or irrelevant. The data cleaning process includes removing errors and duplicates, identifying the inaccuracy of data, and reviewing the quality of data. This phase must be done before the Analysis because based on data cleaning, the output of the Analysis will be closer to the expected results.
After data is collected, cleaned, and processed it is ready for Analysis. During this phase we can use various data analysis techniques and tools to understand, interpret, and derive conclusions based on the requirements (finding trends, correlations, and variations).
After analyzing the data, Interpret the results to see the answers to the problem statement. We can choose either simple words or charts or tables to communicate the results of our data analysis.
Data visualization appears in the form of charts and graphs. Data shown graphically is easier to understand and process. Data visualization is often used to discover unknown facts and trends.
Types of Data Analysis:
Descriptive Analysis answers the question “What happened?”
It does this by ordering, manipulating, and interpreting raw data from various sources to turn it into valuable insights in a meaningful way. After descriptive analysis, the data is organized and ready to perform further investigation. This type of analysis helps describe or summarize quantitative data by presenting statistics.
It provides the with a view of key indicators and measures in business.
Descriptive analytics assesses historical data for finding trends, customer patterns, areas of improvement, etc.
Diagnostic Analysis answers the question “Why it happened?”
Diagnostic analytics deep dives into the data to find all complexity and root causes of a problem. This Analysis is useful to identify behavior patterns of data. If a new problem arrives in the business process, then we can look into this Analysis to find similar patterns of that problem. It identifies and responds to anomalies within the data.
This can find the cause of symptoms, mitigation, and solutions.
If we know why something happened, we will be able to pinpoint the exact ways of tackling the issue or challenge.
Predictive Analysis answers the question “what is likely to happen”
Predictive analytics is prediction and forecasting based on historical or past data. It analyzes current and historic data to make predictions about the future or any unknown events. Predictive analytics is used to forecast all sorts of future outcomes, and while it can never be one hundred percent accurate, it does eliminate much of the guesswork. It is also helpful in developing an informed projection of how things may unfold in particular areas of the business and enables businesses to plan.
Prescriptive Analysis answers the question "What do I need to do?"
Prescriptive analytics looks at what has happened, why it happened, and what might happen in order to determine what should be done next. Prescriptive analytics aims to anticipate problems in advance and recommend solutions and it predicts what will happen with a high probability in the future. It helps take action before an issue occurs. Prescriptive analytics is the most complex type of analysis, involving algorithms, machine learning, statistical methods, and computational modeling procedures.
Cognitive Analysis answers the question "How it will perform?"
Cognitive Analytics is designed to handle immeasurable, unpredictable, and chaotic behavior within a business process. Cognitive Analytics uses multiple analytical techniques to analyze large data sets and give structure to the unstructured data.
With the right type of analysis, organizations can use their data to make smarter decisions, invest wisely, improve processes, and increase the success rate.