In this video, we are going to learn the three types of business analytics, namely descriptive, predictive, and prescriptive analytics. But before we proceed to these different types, let's first define what business analytics is. It is a data management solution that uses various tools to analyze and transform data into useful information, identify and anticipate trends and results, make smarter data-driven business decisions, and communicate these results to organizational decision makers through data visualization. In short, business analytics focuses on providing actionable recommendations based on insights generated from collected data.
There is a great deal of confusion between the terms business analytics and business intelligence. Many experts agree that the terms should be used interchangeably as these terms are extremely connected. How are they connected? When we say business intelligence, It uses historical and current data to understand what happened in the past and what is happening now.
Business analytics, on the other hand, builds on the foundation of business intelligence and attempts to make educated predictions and to make data-driven decisions. In this video, we will use the term business analytics. Business analytics uses next-generation technology. In smaller organizations, they may be limited to spreadsheet applications like Microsoft Excel, and Google Sheets.
In larger ones, business analytics isn't surprise wide, and it includes a wide variety of applications such as the following. Increasingly powerful microprocessors, particularly graphics processing units or GPUs. Advanced digital storage capacity and access speed that allow organizations to be able to store and analyze huge amounts of data.
Rapidly increasing transmission speed in computer networks, particularly the internet. for the decision makers to collaborate on difficult, time-sensitive decisions regardless of their locations, machine learning so systems would think and act with less human intervention, and deep learning for systems to learn to think using structures modeled on the human brain. A standard workflow for the business analytics process is as follows.
It starts with identifying the problem, pain point, or opportunity. A huge amount of data is required to come up with a recommended action. These data originate from internal sources such as structured data in relational databases and external sources such as unstructured data from Internet of Things or IoT devices, spreadsheets, or social media. Wherever data comes from, all of the data needs to get pooled and centralized for access and it is usually stored in a data lake. The structured and unstructured data from many sources are combined, sorted, processed, and analyzed.
Organizations clean these data into data marts and data warehouses, which both handle structured data through a process called extract, transform, and load, or ETL. After the data has been cleaned, descriptive analytics, predictive analytics, and prescriptive analytics can then be performed. All these types of analytics produce results.
which must be communicated to decision makers in the organization. Organizations use presentation tools to display the results of analysis to users in visual formats such as charts, figures, and tables. These presentation tools are used for data visualization. Data visualization is a graphic representation of quantitative information.
It makes the results more attractive and can help break down the numbers and models so that the human eye can easily grasp what is being presented. Dashboards are the most common business analytics presentation tool. A dashboard is user-friendly, it is supported by graphics, and it enables managers to examine reports and detailed data.
The results of the business analytics process will almost always lead to new unanswered questions. This new opportunity prompts a new process of data collection and data cleaning. The cleaned data will be used for different types of analytics and the results will be presented using data visualization.
At this point, let's talk about the different types of analytics. The first on the list is descriptive. Descriptive analytics summarizes what has happened in the past and enables decision makers to learn from past behaviors. It interprets historical data to identify trends and patterns using data aggregation and data mining techniques.
Data aggregation is the process that consists of gathering and collecting the data which is then presented in a summarized format. This means data needs to be collected, centralized, cleaned, and then filtered to remove any inaccuracies or redundancies. When we say data mining, it is the process of searching for valuable business information in massive datasets to uncover patterns, trends, and other truths about data that aren't initially visible.
Descriptive analytics is used for affinity analysis, also known as market basket analysis. For example, a convenience store collects data and performs affinity analysis to discover meaningful correlations between beer and diapers. In the store, a chart in a report shows an unexpected trend of these two different products being purchased at the same time. Additional information revealed that this was caused by young men.
From this information, Cross-selling, upselling, sales promotions, loyalty programs, and discounts might be offered. The store design, specifically the physical location of products, might also be changed. Aside from charts, descriptive analytics reports can also be presented using a table. For example, this is how information is viewed in an online analytical processing or all-up system. All-up system can be used to perform complex analytical queries without negatively affecting transactional systems.
To demonstrate slicing, dicing, drilling down, and rolling up, which are common operations in an all-up system, let's use the pivot table. Pivot table sorts, counts, and totals the data stored in a spreadsheet and creates a second table that summarizes the data. To slice and dice is to break a body of information down into smaller parts or to examine it from different viewpoints so that you can understand it better. To slice means to cut, and to dice means to cut into very small parts.
Drill down is a capability that takes the user from a more general view of the data to a more specific one at the click of a mouse. It allows the user to go deeper into more specific layers of the analyzed data or information. On the other hand, roll up performs a combination of data to produce greater summarization or to show lesser details. Results of descriptive analytics can be used in making data-driven decisions. Let's look at this last example for descriptive analytics in this video.
Movie ticket selling companies can capture data about customers, ticket sales, and show times. They can investigate the total sales for different genres of movies, for example, comedy, drama, and horror. These and other analysis help set ticket prices, offer discounts for certain movies or show times, and assign show times of the same movie in different theaters. With enough data and enough processing of descriptive analytics, business analytics tools can start to build predictive models.
This leads us to the next type called predictive analytics. Predictive analytics examines recent and historical data to detect patterns and predict future outcomes and trends. It provides estimates about the likelihood of a future outcome. It forecasts what might happen in the future based on probabilities.
There are many tools and statistical procedures that are used in predictive analytics, and some of them are data mining and linear regression. In predictive analytics, data mining search for valuable information from a large database and predicts trends and behaviors. Linear regression analysis, on the other hand, is used to predict the value of a variable based on the value of another variable. Along with sophisticated predictive modeling, Machine learning and deep learning techniques are used to predict what is likely to happen in the future.
The mentioned tools and statistical procedures are used in real-life applications of predictive analytics. Here are some of them. Data from past promotional mailings are used to identify those prospects who are most likely to respond favorably to future mailings. By tracking players'spending, casinos can learn which customers they make the most money from. They can offer greater incentives to these big spenders to keep them coming back.
Passed data is used to detect fraudulent credit card transactions. If your card is stolen and used fraudulently, then the usage often varies noticeably from your established pattern. Data mining tools can discern this difference and alert your credit card company.
Based on your browsing history, websites predict which ads you will click so they can instantly choose which ad to show you. The last but not the least type of business analytics is called prescriptive analytics. Prescriptive analytics answers the question, what action should we take?
The state-of-the-art type of analytics builds on findings from descriptive and predictive analytics and uses highly advanced tools and techniques to assess the consequences of possible decisions and determine the best course of action in a scenario. Prescriptive analytics attempts to quantify the effect of future decisions in order to advise on possible outcomes before the decisions are actually made. Organizations use a variety of business analytics tools and statistical procedures to perform prescriptive analytics, including optimization, which is defined as a systematically developed process to reach the best solution under defined constraints and assumptions. Simulation, which is a model that mimics the operation of an existing or proposed system, providing evidence for decision-making by being able to test different scenarios or process changes.
And Decision Tree, which is a tree-like model that acts as a decision support tool, visually displaying decisions and their potential outcomes, consequences, and costs. Here are examples where prescriptive analytics is applied. Some companies use prescriptive analytics to optimize production, scheduling, and inventory along the supply chain to ensure they deliver the right products at the right time to the right customer in the most efficient time.
During every trip, a driverless car makes multiple decisions about what to do based on predictions of future outcomes. It must anticipate what might be coming in regard to vehicular traffic, pedestrians, bicyclists, and other objects on the road. The car must analyze the impact of a possible decision before it actually makes that decision. Prescriptive analytics enables companies with critical operations such as in the oil and gas industry.
to analyze a variety of structured and unstructured data to optimize their operations. Another prescriptive analytics application optimizes the scheduling and equipment necessary to pump oil out of the ground. In a nutshell, descriptive analytics interprets historical data to identify trends and patterns.
Predictive analytics is the use of statistics to forecast future outcomes. And prescriptive analytics is the application of techniques to determine which outcome will yield the best result in a given scenario. Deciding which method to employ is dependent on the business situation at hand.
We come to the end of this video lesson. I hope I have given some light to your knowledge about business analytics and its three different types. If you find this helpful, please like and leave a comment. Please consider subscribing to my channel too. Thank you for your time.