As a business analyst, you need to understand what Data Analysis is and how you can use it to solve business problems.
Analysing data entails looking through, cleaning, transforming, and modelling it in order to find relevant information, draw conclusions, and help in decision-making.
For instance, let’s imagine you have a ton of sales information for a store. In order to determine which items are selling the best, which ones are popular with particular demographics, or whether there are any particular variables influencing sales, data analysis would require looking through that information.
You can make well-informed decisions regarding what things to carry, how much to charge for them, and who to target as a customer by looking at the data. Check out our Business analyst training online to learn more.
Types of Data Analysis
Various techniques and processes are included in data analysis, depending on the goals and type of data being examined. Here are a few typical categories of data analysis:
1.Descriptive Analysis
One of the basic forms of data analysis is descriptive analysis, which seeks to provide and summarise data in an understandable and instructive way. Descriptive analysis offers insights into the features and patterns within the data by computing different statistics.
Here are some examples of techniques used in descriptive analysis:
Survey Results
Suppose you polled your colleagues to find out what their favourite recreational pursuits were.
Following the gathering of responses, the data can be summarised using descriptive analysis. You could figure out what proportion of colleagues like to read, play sports, watch movies, or go on outdoor activities. You would then get a general idea of what their preferences are.
Sales Data
Let’s say you want to examine your sales data as the manager of a small internet firm. You may determine the most frequently sold products, the average daily sales, and the distribution of sales across various client segments by using descriptive analysis. You can use this information to determine your target market, the most popular products, and the peak sales periods.
Exam Scores
Descriptive analysis is a useful tool for university students to understand how they are doing in various subjects. You could figure out the range of scores, the average score, and the proportion of students who scored more than a particular threshold, for instance. Your strengths and weaknesses would be revealed by this study, allowing you to concentrate on the areas that still need work.
2.Diagnostic AnalysisÂ
Diagnostic analysis is a powerful technique you can use to understand the cause-and-effect relationships within a dataset. It goes beyond descriptive analysis and focuses on investigating patterns and trends to identify the factors that contribute to specific outcomes or behaviours.
In order to help you better understand, read these examples:
Customer Churn
Let’s say you are employed by a telecom company and you would like to know why certain of your customers are churning—leaving the company.
You can investigate a number of variables, including customer satisfaction levels, price, service usage patterns, and client demographics, by performing a diagnostic study. You may find that customers with longer contract durations and poorer customer satisfaction are more likely to churn by examining these parameters and their relationship to churn. This knowledge can assist the business in proactively enhancing customer happiness and lowering attrition.
Product Performance
Diagnostic analysis can also be used to evaluate how well a service or product is performing. Let’s say you are an e-commerce employee and you would like to find out why some products are rated better by customers than others.
Through the evaluation of client feedback, product attributes, costs, and delivery schedules, you can identify trends and pinpoint the primary factors that influence consumer contentment. This study may show that products with competitive pricing, quick delivery, and satisfied customers typically have better ratings. Equipped with this understanding, you can maximise your product offers and marketing tactics to improve total client satisfaction and revenue.
3.Predictive AnalysisÂ
Predictive analysis, as its name suggests, builds models and predicts future events using historical data.
In order to predict future trends, this entails using machine learning to identify patterns and links in historical data. For example, you can forecast future sales by taking into account variables like advertising spend, seasonality, and economic indicators.
To produce precise forecasts, predictive analysis takes into account variables like trends, seasonality, and cyclical patterns. It is extensively utilised in a variety of industries, including finance, weather forecasting, demand forecasting, and stock market analysis.
Conclusion
For you to be successful as a Business Analyst, you need to have a strong foundation of data analysis. You can check out our business analyst online training.
