Data analytics, in Layman’s terms, is the practice of evaluating information to answer queries and identify trends accordingly. It may also be used to extract insights to get the necessary data to create a strategy to make your business’s operations more impactful.
Data Analytics is usually done in four different ways, categorized as the following —
- Descriptive: It answers the question of “what has happened?”
- Diagnostic: It answers the query, “Why did it happen?
- Prescriptive: This answers the question of “what can you do next?”
- Predictive: It focuses on “what may happen in the future?”
In this article, we’ll focus on only one of the types of data analytics – Predictive Analysis, and tell you how it works. Let’s keep reading to know more about it.
Predictive Analysis: A Fundamental Introduction
Predictive analytics involves leveraging information to find or foresee potential future trends and occurrences, drawing from past data to anticipate scenarios for strategic decision-making.
These predictions span from immediate projections like machinery breakdowns to more long-term forecasts such as a company’s finances over the upcoming year.
This type of analysis is executed through manual methods or machine-learning algorithms, both relying on historical data to make educated guesses about the future.
Regression analysis, a key application in predictive analytics, establishes connections between variables, generating mathematical equations that help forecast outcomes if certain variables change.
Jan Hammond (a Professor at Harvard Business School) emphasizes that regression analysis uncovers the structure of relationships between variables and offers insights into how well the data aligns with these relationships.
These insights prove invaluable for examining historical patterns and devising forecasts.
Using predictive analytics empowers better decision-making and the creation of data-driven strategies. Numerous real-world instances showcase its effectiveness, motivating businesses to implement it for their own benefit.
How Does It Work: The Follow-Ups
Making predictive analysis work to make the most out of your business can be difficult. That’s why you need to talk to an expert before you begin investing in it. Nonetheless, if you are technologically capable and want to go DIY, the following tips might be beneficial for you.
1: Understanding the Basics
At its core, predictive analysis involves using historical data, statistical algorithms, and machine learning techniques to identify patterns and predict future events or trends.
Imagine it as a weather forecast for your business—utilizing the previous weather patterns to anticipate what’s coming.
2: Data Collection and Preprocessing
The process begins with gathering relevant data, often from various sources such as databases, sensors, social media, or customer interactions.
This data undergoes preprocessing. It involves cleaning, organizing, and transforming it into a usable format. This step is crucial because the accuracy of predictions heavily relies on the quality of the data you currently have in your hands.
3: Exploratory Data Analysis (EDA)
Next comes exploratory data analysis, where analysts explore and visualize the information to understand relationships, trends, and anomalies. Graphs, charts, and statistical measures help in uncovering patterns that might be hidden within the data.
4: Feature Selection and Engineering
Not all data is equally important for making predictions.
Feature selection involves identifying the most relevant variables (features) that will contribute significantly to the predictive model. Engineers can also come up with new features derived from existing ones to enhance the model’s accuracy.
5: Model Building
The heart of predictive analysis lies in building models.
This involves choosing an appropriate algorithm or a combination of algorithms depending on the nature of the problem and data. Regression, decision trees, neural networks, and ensemble methods are some common techniques used to create predictive models.
6: Training and Validation
The selected model is then trained using historical data, where it learns the underlying patterns and relationships between variables.
Validation is crucial to ensure the model’s accuracy and generalizability. This involves testing the model on a separate dataset to assess its performance.
7: Prediction and Evaluation
Once the model is deemed reliable, it’s ready for predictions. New data is fed into the model, and it generates forecasts or classifications based on the patterns it learned during training.
The predictions are then evaluated using metrics such as accuracy, precision, recall, or area under the curve, depending on the type of prediction.
8: Iteration and Improvement
Predictive analysis isn’t a one-time process. It’s iterative.
Analysts tend to constantly fine-tune models by incorporating new data, adjusting algorithms, or tweaking features to improve accuracy and adapt to changing patterns.
Examples of Predictive Analysis
Predictive analysis is currently being used in five areas of business, including – entertainment and hospitality, finance, marketing, manufacturing, and healthcare. Let’s learn more about it.
1: Finance — To Forecast Your Business’ Future Cash Flow
All companies must maintain regular financial records, and predictive analytics is crucial for foreseeing your organization’s future condition.
By analyzing previous financial statements and industry data, you can estimate future sales, revenue, and costs, enabling informed decision-making based on these projections.
2: Healthcare — Early Detection of Healthcare Issues
The Wyss Institute (Harvard University) collaborated with the KeepSmilin4Abbie Foundation to create AbbieSense, a wearable device for predicting anaphylactic allergic reactions.
This sensor identifies early physiological signs signaling an impending reaction much faster than human detection.
Using algorithms, it not only forecasts the overall severity of the reaction but also notifies the caregivers while autonomously administering life-saving epinephrine when needed.
This rapid predictive capability of the tool has the potential to be life-saving, surpassing the speed of manual detection.
3: Manufacturing — To Prevent Malfunction
Predictive analytics isn’t just about foreseeing likely scenarios; it’s also about preventing undesirable events. Take manufacturing, for example.
Algorithms trained on past data can accurately anticipate machinery breakdowns. When these predictions indicate an imminent malfunction, the system alerts an employee who can halt operations, potentially saving the company significant costs in repairs and damaged goods.
Some algorithms even suggest solutions to prevent future breakdowns and enhance efficiency, showcasing how various analytical methods work together to tackle issues.
4: Marketing — Targeting Behavioural Attributes
In marketing, vast consumer data is used to tailor content, ads, and strategies for reaching potential customers effectively.
By analyzing past behaviors, predictive analytics is employed to anticipate future trends. This helps in forecasting sales patterns and aligning campaigns with optimal timing.
Moreover, studying historical data assists in estimating the probability of a lead progressing through the purchasing process.
For instance, using statistical models like linear regression, marketers can predict a lead’s conversion likelihood based on their interaction with content. Armed with this information, targeted advertising can be strategically timed throughout a customer’s journey.
5: Entertainment — Taking Care of the Staff Needs
An extensive case study in Business Analytics involves Caesars Entertainment utilising predictive analytics to determine the required staffing levels for their venues, such as hotels and casinos, at specific times.
In industries like entertainment and hospitality, the flow of customers relies on numerous variables, impacting the optimal number of staff needed at any given time.
Being overstaffed incurs unnecessary expenses while understaffing can lead to poor customer experiences, overburdened employees, and costly errors.
To forecast the volume of hotel check-ins on a particular day, a team devised a multiple regression model that factored in various variables.
This model empowered Caesars to staff their hotels and casinos effectively, steering clear of overstaffing as much as possible.
The Bottom Line
Predictive analysis isn’t magical—it’s a meticulous process driven by data and algorithms. It empowers businesses to make informed decisions, anticipate trends, and mitigate risks.
As technology advances and data becomes more abundant, the potential of predictive analysis continues to expand, reshaping the way we approach the future.
By understanding the basic workings of predictive analysis, we can appreciate its significance in guiding strategic decisions and unlocking opportunities in a more data-driven world.