Transforming Administrative Data Into Actionable Business Statistics

In case you are an MBA student, you already know that data is the new oil. However, there are some types of valuable data other than the one that is neatly packaged up in a market research report. This is usually found in the business records that your organization gathers on regular basis. In the modern-day business analytics scenario, the possibility of getting valuable insights from administrative data in business decision making is crucial.

One of the most valuable skills in today’s business environment is the ability to timely collect and interpret business data. If you are an MBA student focusing on business statistics, it can only be beneficial to really understand this process to boost your expertise and analytics skills. This guide will explain the methodologies, few examples and share useful resources that will help immensely during your post graduate course in management.

Understanding Administrative Data

Administrative data include any data gathered mainly by government departments, organizations, and companies for administrative purposes. Such data includes tax returns, social security records, as well as the customer transactional data. Some other characteristics of data include:

Customer records: Such records include purchase transactions, customer demography information such as their age and geographical location, as well as past interactions with respect to the services and products.
Sales data: It consists of data daily sales, performance of products and even discount offers on certain products.
Human resources records: These include detailed information that relates to the employees, such as their demographics, performance assessments, payroll information as well as benefits offered to them.
Operational data: These include data on stock balance, production and manufacturing records, supply chain information etc.

Why is Administrative Data Important?

Administrative data is considered one of the most unexplored but valuable sources of information today because of its diversity. As administrative data is nothing but the existing data which has been recorded from a long period of time, it is often cheaper than collecting new data. This data helps in decision making, determining the most efficient and effective business resources, formulation of govt policies etc. In addition, this type of data is used as a framework for conducting future surveys and research in order to improve the existing products and service delivery.

Real-World Examples

Organisations today are continuously identifying how administrative data can be leveraged to enhance business processes and the experience they provide to their consumers.

Customer Churn Prediction: Telecom companies have started monitoring customer service interactions to forecast which customer will consider changing the telecom provider. By doing so, they are also able to come up with their own exclusive and personalized offers which they can use to bait the customers before loosing them.
Inventory Optimization: In retail, mapping sales data with that of inventory data can help make demand predictions and prevent stockouts or overstocking of products. This makes it easy for retailers to manage their stock as per the demand in the market to save huge inventory cost.
Targeted Marketing: Some of the large online retailing companies such as Amazon target customer through recommendations of products that are likely to suit their preferences based on the information they provide and their past purchase behaviour. This leads to recommending items that they are more likely to purchase, thus making shopping more enjoyable and boosting sales for the store.
Fraud Detection: Administrative data also plays a critical role in banking, where it is used for fraud detection. Banks analyse the transaction records as well as customer profiles to spot unusual patterns that could indicate fraudulent activity. By catching these issues early, they can safeguard their customers.

Methodologies for Transforming Administrative Data

Data Cleaning and Preparation:

Identify and correct errors to ensure the data is accurate and complete. Convert data into a consistent format for analysis. Handle missing data by using methods like imputation or deletion.

Integration and Linking:

Merge data sources to combine multiple datasets for comprehensive analysis. Ensure data consistency by verifying that data across sources aligns correctly.

Data Analysis Techniques:

Use descriptive statistics to summarize the main features of the dataset. Apply inferential statistics to draw conclusions and make predictions based on the data. Utilize predictive modelling to use historical data to forecast future trends.

Facts and Figures

Increased Use of Data Analytics: According to a survey by New Vantage Partners, 97.2% of executives report that their companies are investing in big data and AI initiatives.
Growth in Data Jobs: The U.S. Bureau of Labor Statistics projects a 31% growth in data-related jobs from 2019 to 2029, significantly higher than the average for all occupations.

Why To Avail Business Statistics Assignment Help Services

For MBA students in the USA and Australia, helpful study assistance from an industry expert can be a game-changer in mastering business statistics. Our Business Statistics Assignment Help service offers valuable support for not only studying the basic concepts but also gives you the 360-degree overview of current scenario on how business-related data is utilized and researched for making valuable business decisions. Some of the best features of our services include:

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Helpful Resources and Textbooks

Books:

Business Statistics for Contemporary Decision Making by Ken Black
Practical Statistics for Data Scientists by Peter Bruce and Andrew Bruce

Online Courses:

edX’s Business Analytics by Harvard Business School

Jeremy Posted on 20-Jun-2024 11:59:00