Business Data Analytics Simplified

June 27, 2022

Introduction to business data analytics 

Business data analytics is the process of applying statistical methods to large volumes of structured and unstructured data to make sense of it. The goal is to understand patterns in the data, identify opportunities for improvement, and make decisions based on that understanding.

Businesses can use business data analytics to improve their performance across various areas, including customer service, sales, marketing, supply chain management, and more.

What are the processes involved in business data analytics

Business data analytics is the process of analyzing and interpreting data, which is often a large amount of unstructured data. This process aims to determine useful information about the business or industry in question.

The first step in business data analytics is gathering and organizing the data. In many cases, this involves identifying the types of data that need to be collected, where it will be stored, and how quickly it needs to be processed and analyzed. Once this process has been completed, the next step is establishing a set of rules for interpreting and analyzing the information collected from each element of the dataset. This stage can take place as part of an analysis phase or as an ongoing process that allows for continuous improvement over time.

Once these steps have been completed, it's time for some analysis! This can involve using advanced algorithms or programming languages to extract insights from raw data sets that may not be easily understandable by humans alone."

Here is the breakdown of the steps:

Data collection and storage

Data collection happens before you start working with the data. It's when you're gathering information about your business or customer so that you can begin to understand what you're dealing with. 

Data cleansing is a process that takes place after the data has been collected, but before it's prepared for use by your modelling process. This can include removing duplicates, grouping similar records together, and filtering out any information that doesn't have a purpose in your particular model. It's important to do this because otherwise, you might end up with inaccurate or useless information.

Data preparation is often a separate step from data cleansing, although they may be done together. This involves preparing all of your raw data into a format that makes sense for your modelling process. This might involve adding missing values or cleaning up inconsistent formats. For example, if you're preparing information about sales revenue per product category, you may want to combine these figures into one overall report so that they're easier to understand by customers.

Data transformation is the final part of the data preparation process and often involves transforming raw data into something more usable before it reaches your modelling system itself. This could involve taking raw numbers like prices per unit sold and turning them into percentages or other measures that are more useful for modelling purposes (such as sales per customer).

Data modelling uses mathematical equations to describe relationships between variables in order to predict future outcomes based on past trends identified through previous models built on similar data sets."

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Benefits of business data analytics

Business data analytics is a way to use data to improve your business operations. It helps you make better decisions, develop new products and services, and increase revenue.

The benefits of business data analytics include:

-Better decision-making: Analytics allows you to analyze large amounts of data in order to make better decisions about how best to use it. You can use analytics to see what customers are buying and when they are buying it, so you can understand what they want and how much they want it. Analytics also help businesses know what products are selling well and which ones aren't selling at all.

-New products and services: Analytics can help you create new products or services by using insights about what customers want based on their purchasing habits. For example, if one type of product isn't selling well in your store, then maybe you should put more stock of another type on the shelves. Or maybe you could try selling organic food at more locations than just one store!

-Increased revenue: Businesses that use analytics will often see an increase in revenue because they have better information about their customers' needs and wants than before.

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Business data analytics is a lot like other forms of analytics as it helps you make better decisions. It's not just about numbers and graphs. Business data analytics is all about using data to understand the business and help you make more informed decisions than just "what do the numbers say?"

It's more than just predictive modelling and big data. Business data analytics includes techniques like text mining, which allows you to find things in your data by looking for patterns in it. It's not always about making money—but it can be! Some businesses use business data analytics to gain insights into their customers' behaviour and needs, which helps them develop products or make decisions about how they do business to improve their bottom line.

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