Predictive analytics infers to the use of data, machine learning, and statistical algorithms to decipher the probability of future outcomes based on historical data. By analysing both the current and historical data patterns, predictive analytics determine whether certain outcomes will emerge again. Importantly, these trends help businesses and investors to adjust by prudently utilising their resources to maximise future possible events. In some instances, predictive analytics help mitigates risks and eliminate operational inefficiencies.
Understanding Predictive Analytics
Basically, predictive analytics is a technological model that makes predictions on specific unknowns in the future. To draw any conclusions relies on different methods such as data mining, machine learning, artificial intelligence, statistics, and modeling. Data mining deals with large sets of data to highlight any patterns.
Predictive models apply to various applications. They include:
- Creation of video games
- Weather forecasts. Read our detailed weather prediction guide.
- Customer service
- Translation of voice-to-text in mobile phone messaging
- Developing investment portfolios
*A common trait across all these applications is their use of descriptive statistical models of available data to make future predictions.
Types of Predictive Analytics Models
Perhaps, it’s imperative to start by providing a foundation on the types of data analytics. There exist only 4 data analytics models.
- Descriptive, which solves the question, “What happened?”
- Predictive, solves the query, “What might happen in the future?”
- Prescriptive, answers the question, “What should we do?”
- Diagnostic, solving the question, “Why did all this happen?”
Predictive analytics is an everyday occurrence. From the weather forecasts to the advancement of medical treatment procedures.
Here are some of the commonest forms of predictive analytics.
It’s the most popular method of statistical analysis. If one wants to determine patterns in large sets of data, especially among inputs with linear relationships, regression is the go-to method. Regression works by providing a formula that represents the relationship among the inputs in the dataset. For instance, you can determine how securities can be impacted by price and other factors.
What do you think influences a person’s decisions?
It’s an interesting question. However, using the decision tree can be a refreshing approach due to its literal and critical paradigm. As the name suggests, it epitomizes a tree structure with individual branches and leaves. The branches represent the choices available whereas leaves represent a single but specific decision.
A key advantage of the decision trees is their ability to break down intricate data sets into simple and distinct variables. The foundation of this method manifests by breaking down data into various sections each representing a variable. Despite the prevailing notion of data analysis being difficult, decision trees are quite easy to dissect. Ideally, apply the method when you need to make a decision within a short time.
A form of predictive analytics developed as a way to mimic the human brain. Normally, the method deals with complex data relations using pattern recognition and artificial intelligence. Using neural networks aims to crack sets of big data posing hurdles such as being overwhelming. You can also use it when you find data without any correlation between the inputs and outputs. In most instances, neural networks apply in predictive analytics modeling rather than drawing up explanations.
Having drawn conclusions using the regression and decision trees, neural networks confirm the validity of your findings.
Predictive Analytics in Business
Optimise marketing campaigns
Ever asked how Netflix figured out you would love the show you binge-watched last time? In fact, after the show, you went scrapping for similar shows. It’s not by accident. Businesses collect big data to determine customer behaviour, responses, and purchase decisions. Objective interpretation of such data then helps to build cross-sell opportunities. In short, predictive analytics allows businesses to attract, retain and acquire customers.
A credit score evaluates a client’s likelihood of default. It’s one of the earliest forms of application of predictive analytics risks. The total credit score shows the result of an integrated predictive model pertaining to a person’s creditworthiness. So, next time you visit the bank and the manager turns down your request for credit, don’t frown.
In the current competitive business environment, most companies use predictive models in forecasting inventory and managing available resources. With the Qatar World Cup due in November, airlines use predictive analytics to set ticket prices in order to maximise profits. Also, hotels predict surges of guests depending on bed occupancy and expected seasonal traffic to increase their revenues. At one point, we’ve all been victims of such variations in ticketing or pricing. That’s the power of predictive analytics in improving efficiency and productivity.
Did you know?
The sentencing of the world’s most sensational social media scammer Hushpuppi happens in September 2022 at the US Central District Court in California. A man who pled guilty to money laundering and scamming innocent people of their hard-earned cash to fund his lavish lifestyle will hopefully serve as an example of the vanity of fraud. Good riddance!
Predictive analytics can aid in improving pattern detection as a way of preventing criminal behaviour. Cybersecurity poses a big challenge to the world as more people embrace technological advancements. Proper deployment of predictive analytics can assess networks in real-time to spot any abnormalities that may facilitate fraud activities, or increase vulnerabilities. Activities such as theft of identity, false credit applications, and insurance claims are some of the risks that can be fished out through predictive analytics.
Some people, due to genetic, social, or environmental factors are more prone to developing some conditions such as asthma, and diabetes than others. At the point of care, read NHS in the UK, predictive analytics support clinical decisions that enhance the feasibility of medical systems at the point of care.
Predictive Analytics vs. Machine Learning
Machine learning and Predictive Analytics are often used interchangeably in the business world. However, there is a big difference between the two concepts. Machine learning is a subset of artificial intelligence dealing with the construction of algorithms that can learn from and make predictions on data. Predictive analytics, on the other hand, is a branch of statistics that deals with the use of historical and current data to make predictions about future events.
So, what’s the difference between machine learning and predictive analytics? Machine learning is mainly concerned with the construction of algorithms that at its core involve learning, while predictive analytics focuses on the analysis of data. Predictive analytics mainly apply predictive modeling, which is a process of creating a model that can be used to make predictions about future events.
Criticising Predictive Analytics
In some quarters, predictive analytics is limited or banned because of perceived inequities in its output. Of concern are outcomes that lead to statistical discrimination against racial or ethnic minorities in some areas such as credit access, mortgage, and criminal behaviour.
Accuracy notwithstanding, the inclusion of a person’s race in predictive analytics raises both moral and ethical questions. This highlights the need to ensure big data doesn’t become a cause of social, political, and economic divisions.
Pillars of Data Analytics
In a flash, there are three pillars to data analytics.
- Needs of the entity seeking to use the models.
- Technology and data used to understand the concept
- Actions and insights drawn from the analysis.
For more information about predictive analytics, use these resource guides to further boost your understanding. It’s a huge topic with a massive impact on the business and social world.