Every business uses various kinds of data analytics in the modern world to make better decisions. They are descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics. Each assists uniquely. Descriptive analytics describes what has happened. Diagnostic analytics explains why this happened. Descriptive analytics tells what has happened. Prescriptive analytics recommends a course of action. These serve in different sectors such as healthcare, finance, marketing, etc. This paper elaborates on each class in layperson’s terms and use cases. In detail, we also discuss data science types of analytics, business analytics types, and analytics techniques.
Types of Data Analytics
Four keys types of analytics, the first step to becoming a master of data. Each type has its job. They assist users in comprehending past information, detecting issues, predicting future trends, and creating sound plans. Now, let us take them all apart one by one.
Descriptive Analytics
Descriptive analytics is the first step in the analytics journey. It also allows you to understand what happened previously. Using charts, tables, or numbers, it summarizes data. Such analytics are commonplace across every business. It enables tracking of how performance changes over time. Descriptive Analytics: Descriptive analytics answers business questions. It uses revenue reports, customer counts, or monthly sales data to demonstrate trends. This allows teams to see if things are getting better or not. They can figure out what products were hit or what months were slow.
- Most companies use tools such as Excel, Power BI, or Google Data Studio to do this. These tools convert unstructured data into analytic graphs. This helps managers and team leaders understand the health of the business. They can identify high-performance areas and weaknesses quickly.
- One limitation of predictive analytics One limitation of predictive analytics is that it doesn’t tell you why Oone is a limitation of predictive analytics. It does not make future predictions. It only shows facts. That’s also why it’s often followed by diagnostic analytics.
- In other words, descriptive analytics describes: “What happened?”
- It is an essential foundation for other kinds of analytics. Other methods can’t work without this. Students also use descriptive analytics in schools. Those are all wrinkly, individual practices that they test month after month, checking if they have made progress with their test scores. It is useful to follow up on the progression.
- Again, this type is also helpful in sports. After a game, coaches check player statistics to see who excelled. Descriptive analytics is used in hospitals to check patients’ records and trends. Descriptive analytics reflects tthe past through forms and charts.
Diagnostic Analytics
Now that we know what happened, the next question is: Why did it happen? This is where diagnostic analytics comes into play. It goes deeper into the data. It is here that the reason of the results is discovered. It finds trends or relationships between different data sets. Diagnostic analytics aids in detecting reasons for declines or increases in data. For example, if sales dropped in May, this analytics helps check whether it happened due to bad weather, low marketing, supply, etc. It does this by drilling down the data.
- Analysts use data mining and correlations to do this. Data mining finds patterns. Correlation measures how one thing impacts another. Less of one thing might be bad for business. Helperueux: If two things are connected, it is easy to troubleshoot.
- Suppose a cellphone company notices an uptick in complaints. Diagnostic analytics comes in to confirm the root cause. Network issues or billing errors could be the reason for this. And once the cause is known, the company can repair the problem.
- Diagnostic analytics is also quite useful in sectors including healthcare and banking. If a hospital sees more patients returning after surgery, they look into whether it’s a proportion of short stays or poor aftercare using diagnostic analytics.
- This type assists companies in making more intelligent decisions. It provides good explanations, not just hunches. When the root cause is identified, enterprises can rectify the issues swiftly.
- In schools, diagnostic analytics gives teachers insight into why students fail. Is it bad teaching, hard topics, or that they don’t study? It helps them enhance learning approaches further.
- Diagnostic analytics is basically a doctor. It detects (symptoms, problems), locates the problem, and provides a remedy. Otherwise, any remedy would be a hit-or-miss affair.
Predictive Analytics
After a company learns what has happened and why it has happened, they want to see what will happen next. Here is where the predictive analytics kicks in. It utilizes previous data to forecast future events. It does so through models, statistics, and machine learning.
- Marketing employs predictive analytics extensively. Companies use it to determine what products customers might purchase next. Websites present products to you based on your previous decisions. That is predictive analytics at work.
- It also helps in banking. Banks use it to determine whether someone will default on a loan payment. They rely on the person’s previous payment history and additional data. This helps them reduce risk.
- Predictive analytics is also used in weather forecasting. It analyzes historical weather trends to predict tomorrow’s conditions. In healthcare, it allows doctors to determine if a person is likely to develop a particular disease based on their symptoms and prior health.
- This analytics works when we provide clean and big data. The results get better with an increase in data. It uses algorithms for pattern recognition. Once it has identified these patterns, the algorithm uses them to predict what will happen next.
- Predictive analytics contributes to how we plan. If a store was aware that it was likely to be raining, it may carry more umbrellas. If a school anticipates more exam failures, it may arrange extra support.
- However, it doesn’t always deliver a perfect score. But it provides a fair sense of what might occur. Companies leverage it to minimize losses and maximize profits.
- So the short answer is that predictive analytics does answer: “What might happen?
- This makes you prepared to deal with risks or opportunities in the future. It better aligns future planning and present action.
Prescriptive Analytics
Now, we have come to the final class – prescriptive analytics. With knowledge of the past, the reasons, and the chances for the future, the next question is: “What should we do now?” Prescriptive analytics provides the answer to that question.
- It describes what is likely to happen and what to do next. It offers better decision-making counsel. It employs sophisticated tools like artificial intelligence, simulation, and optimization models.
- So, say, an airline company. It wants to know how to price tickets right. Prescriptive analytics is then applied using customer demand, weather, and fuel costs to recommend the optimal price. It means more profit and fewer empty seats.
- For instance, in healthcare, prescriptive analytics helps to determine which treatment a patient should follow. It diagnoses symptoms, and past history and compares various treatment options. Then it gives the best plan.
- This is the type of analytics we need in supply chain management. It demonstrates the quickest and cheapest way to dispatch goods. Thus, it helps to shorten delivery time and save money.
- Prescriptive analytics is the GPS of the future. It displays the route and suggests the optimal one considering the traffic, distance, and road conditions. It enables businesses to make effective decisions, not merely informed ones.
- To do prescriptive analytics well, companies have to be well managed. They have to maintain the quality of data and use good tools. It also requires experts who can interpret the results accurately.
- It requires time and investment, but the payoff is solid. It aids in better control and long-term planning.
- In simple terms, prescriptive analytics provides actionable steps based on a comprehensive study of data. It translates data into actionable insights.”
Data Science: Categories of Analytics
Data science entails all four types of analytics (descriptive, diagnostic, predictive, and prescriptive). Data scientists use them to extract meaningful insights from raw data.
Analytics is applied across all phases of data science—from gathering data, cleaning it, analyzing it, and, finally, providing solutions. Each type aids at a different phase.
- Descriptive analytics aids in the creation of reports and dashboards.
- Diagnostic analytics assist in figuring out the patterns in complex data.
- Predictive analytics aids in the creation of machine learning models .
- Prescriptive analytics allows us to simulate several options before settling on the right one.
For instance, data analytics techniques in data science consist of clustering and regression of decision trees and neural networks. These techniques are applied collectively through the four types of analytics. For instance, predictive analytics typically relies on regression.
Skills You Must Have To Sustain
It’s Not Only Coding. It’s also about asking the right questions and selecting the correct kind of analytics. For example, if a company wants to know the reason behind the drop in their app users, they use diagnostic analytics.
The next step may be applying predictive analytics to see whether additional users might churn. Then, prescripted analytics makes recommendations for how to prevent that loss.
Thus, every type of data analytics acts as a piece of the puzzle. Together they use it to solve real-world problems.
Types of Business Analytics and Examples
All four types of data analytics fall under business analytics types. Each company uses at least one category type each day. Everyone uses analytics to scale up faster and smarter, from small shops to big banks.
- Descriptive analytics show historical sales in marketing. Diagnostic analytics determines why some ads did not work. Predictive analytics attempts to figure out which ad might work next. Prescriptive analytics is the best marketing guide.
- Such finance analytics are used to detect fraud, set budgets, and manage spending. In HR, it’s in hiring better people, lowering leave days, and keeping workers happy.
- Applications of data analytics in business are:
- A shop analyzing previous data to determine what to replenish.
- A bank whose loan approvals fell last month.
- A mobile app that predicts with what users you are going to remove it.
Example: A delivery company optimizing for best routes based on real-time traffic.
Real-world examples of how powerful analytics can be. Any business can grow faster with good data and the right tools. Well, analytics is no longer a special task . It is now embedded in daily work across disciplines.
Relevance to ACCA Syllabus
ACCA is increasingly leveraging data analytics in areas such as performance management, audit, and financial reporting. This means ACCA students should gain an understanding of relevant business decision-making of how descriptive, diagnostic, predictive, and prescriptive analytics affect business decision-making. These analytics assist budget analysis, fraud detection, risk assessment, and future planning —sine qua non for strategic business leaders and performance management papers.
Types of Data Analytics ACCA Questions
Q1. Which data analytics type tells you why something happened in financial performance?
A) Descriptive Analytics
B) Diagnostic Analytics
C) Predictive Analytics
D) Prescriptive Analytics
Ans: B) Diagnostic Analytics
Q2: An example of this would be a report showing last quarter! ‘s sales figures and gross profit margins:
A) Predictive Analytics
B) Prescriptive Analytics
C) Descriptive Analytics
D) Diagnostic Analytics
Ans: C) Descriptive Analytics
Q3: What kind of analytics will assist ACCA professionals in predicting their budgets?
A) Diagnostic Analytics
B) Descriptive Analytics
C) Predictive Analytics
D) Text Analytics
Ans: C) Predictive Analytics
Q4: What type of analytics gives recommendations for optimizing business strategies?
A) Predictive Analytics
B) Prescriptive Analytics
C) Diagnostic Analytics
D) Descriptive Analytics
Ans: B) Prescriptive Analytics
Q5: In performance management, what analytics is most useful for analyzing cost variances and their reasons?
A) Descriptive Analytics
B) Prescriptive Analytics
C) Diagnostic Analytics
D) Predictive Analytics
Ans: C) Diagnostic Analytics
Relevance to US CMA Syllabus
In the US CMA syllabus, data analytics is relevant under Part 1: Financial Planning, Performance and Analytics. CMAs should be able to analyze the data trends, diagnose business problems, and assist in forecasting using various analytics. Managerial accounting should focus on understanding data-driven decision-making.
Types of Data Analytics CMA Questions
Q1: What type of analytics is used to predict future performance based on trends?
A) Diagnostic Analytics
B) Prescriptive Analytics
C) Predictive Analytics
D) Descriptive Analytics
Ans: C) Predictive Analytics
Q2: Why does CMA study historical data? This is an example of:
A) Diagnostic Analytics
B) Descriptive Analytics
C) Prescriptive Analytics
D) Predictive Analytics
Ans: B) Descriptive Analytics
Q3: What data analytics can be used to advise a CMA on cost control?
A) Descriptive Analytics
B) Predictive Analytics
C) Diagnostic Analytics
D) Prescriptive Analytics
Ans: D) Prescriptive Analytics
Q4: What type of analytics allows us to find the reason behind budget deviations?
A) Descriptive Analytics
B) Diagnostic Analytics
C) Predictive Analytics
D) Prescriptive Analytics
Ans: B) Diagnostic Analytics
Q5: When a CMA makes decisions about how much of what inventory to reorder for next month using this data, they are using:
A) Predictive Analytics
B) Prescriptive Analytics
C) Diagnostic Analytics
D) Descriptive Analytics
Ans: A) Predictive Analytics
Relevance to US CPA Syllabus
Data analytics is a featured component of the Business Environment and Concepts (BEC) and Audit & Attestation (AUD) US CPA tail of exams. CPAs apply analytics to risk assessment audits, analyze financial statements/facts, and collaborate with internal control tests. It assists with predicting financial outcomes and in identifying frauds as well.
Types of Data Analytics US CPA Questions
Q1: A CPA uses analytics that tests audit samples to reduce detection risk. Which type is this?
A) Predictive Analytics
B) Descriptive Analytics
C) Diagnostic Analytics
D) Prescriptive Analytics
Ans: B) Descriptive Analytics
Q2: If a CPA is determining the reason for unexpected changes in revenue, they are using:
A) Prescriptive Analytics
B) Descriptive Analytics
C) Diagnostic Analytics
D) Predictive Analytics
Ans: C) Diagnostic Analytics
Q3: What kind of analytics help a CPA project future cash flow?
A) Descriptive Analytics
B) Prescriptive Analytics
C) Diagnostic Analytics
D) Predictive Analytics
Ans: D) Predictive Analytics
Q4: Which type of analytics allows CPAs to recommend actions to enhance internal controls?
A) Descriptive Analytics
B) Diagnostic Analytics
C) Prescriptive Analytics
D) Predictive Analytics
Ans: C) Prescriptive Analytics
Q5: An expense trend over time on a dashboard is an example of:
A) Predictive Analytics
B) Descriptive Analytics
C) Diagnostic Analytics
D) Prescriptive Analytics
Ans: B) Descriptive Analytics
Relevance to CFA Syllabus
Typically, you have alluded to types of data analytics with respect to the CFA Syllabus.
For CFA candidates, types of data analytics are advanced in portfolio management, equity investments, and analysis of financial reporting. Data analytics helps in the value assessment of the asset, risk management, and predicting returns. Clarifying these concepts is the key feature of modern finance, which offers a productive investment decision through predictive models and big data comprehension.
Types of Data Analytics US CFA Questions
Q1: You have acted as a sentence paraphraser. This is:
A) Prescriptive Analytics
B) Descriptive Analytics
C) Predictive Analytics
D) Diagnostic Analytics
Ans: C) Predictive Analytics
Q2: What is the CFA professional evaluating to analyze the performance of bonds?
A) Descriptive Analytics
B) Prescriptive Analytics
C) Predictive Analytics
D) Diagnostic Analytics
Ans: A) Descriptive Analytics
Q3: Which analytics type can help explain fund-level return movements?
A) Predictive Analytics
B) Descriptive Analytics
C) Diagnostic Analytics
D) Prescriptive Analytics
Ans: C) Diagnostic Analytics
Q4: Optimization models are prescribed by a CFA. This involves:
A) Prescriptive Analytics
B) Descriptive Analytics
C) Diagnostic Analytics
D) Predictive Analytics
Ans: A) Prescriptive Analytics
Q5: The investment dashboard that tracks risk-adjusted returns for the month makes use of:
A) Predictive Analytics
B) Descriptive Analytics
C) Prescriptive Analytics
D) Diagnostic Analytics
Ans: B) Descriptive Analytics
