How Predictive Analytics Works

1. Data Collection and Preparation 

The process begins with gathering relevant data from different sources. Advanced analytics platforms use built-in data connectors, which make it easier to import data from databases, spreadsheets, and cloud services.

Once collected, these software tools clean and prepare the data. They automatically detect inconsistencies, handle missing values, and suggest data transformations—restructuring or formatting to make it easier to analyze.

2. Exploratory Data Analysis

Next, data scientists and analysts use data-collecting platforms to visually explore the data. These platforms often provide interactive features like dashboards, charts, graphs, and pivot tables, enabling users to uncover patterns, correlations, and anomalies within the data.

This step informs initial hypotheses and helps decide which modeling techniques are the most appropriate. For example, let’s pretend you’re analyzing your retail company’s sales data. Your analysts might use a scatter plot to visualize the relationship between advertising spend and revenue. 

They notice a positive correlation but also observe that the relationship isn’t linear—it plateaus at higher spending levels. They believe there is a point where spending more on advertising gives less benefit. They choose to use non-linear models or decision trees to show this relationship.

3. Choosing Statistical Models

This phase is where analytics platforms' more advanced capabilities come into play. Many business intelligence (BI) software and services use AI (artificial intelligence) to help with statistical modeling, which involves using mathematical modeling techniques to represent relationships within data and predict future outcomes. 

There are various types of predictive analytics models used to solve different kinds of problems: 

• Regression analysis models examining relationships between variables (e.g., employee tenure and output)
• Classification models categorizing data (e.g., predicting customer churn)
• Time series models analyzing data over time to forecast future values (e.g., stock prices or seasonal trends)
• Clustering models grouping similar data points (e.g., market segmentation)

4. Prediction Generation 

Once a model is built and validated, analytics platforms efficiently generate predictions on new data. Most allow you to schedule regular updates to your machine-learning models, ensuring predictions remain current as new data comes in and is updated in real-time.

5. Visualization and Interpretation

The second-last step is presenting the results in a way that’s easy for decision-makers to understand. Most platforms tend to have interactive dashboards that display both historical data and future predictions, allowing you to explore different scenarios and understand the factors driving the predictions.

6. Deployment 

Once your predictive model is ready, it must be put into action. This means setting up the models to receive new data regularly, making predictions quickly, and sharing those predictions with the right people or systems.

For example, if you’ve built a model to predict customer behavior, deployment might involve:

• Connecting the model to your company’s customer database

• Setting it up to make predictions every evening

• Sending those predictions to your sales team’s software each morning 

The goal is to make your mode’s predictions easily available and usable in day-to-day operations. Invariably, this involves IT teams integrating the model securely into your existing company systems.

The Benefits of Predictive Analytics for Your Business

At its core, predictive analytics unveils trends before they emerge, enabling sharp, data-driven decisions. The trifecta of rich historical data, cutting-edge statistical algorithms, and sophisticated machine learning techniques empowers you to forecast outcomes with uncanny precision. 

Such foresight lets you optimize operations, reduce risks, and identify new opportunities before they fully materialize. From predicting customer behavior to anticipating equipment failures, predictive analytics empowers businesses to proactively anticipate and respond to market dynamics, gaining a competitive edge.. 

Not only does it enhance efficiency and profitability, but it also facilitates more personalized customer experiences and targeted marketing efforts. As data continues to gro

Challenges and Considerations Around Predictive Analytics

Ongoing research, collaboration, and public discourse are necessary to ensure AI analytics systems are developed and deployed responsibly, maximizing their benefits while minimizing potential harm.

Data Quality

Inaccurate, incomplete, or biased datasets can lead to flawed models and unreliable predictions. Ensuring data integrity through rigorous collection methods, thorough cleaning processes, and continuous validation is essential for building trustworthy AI systems.

Ethical Implications

Biases present in training data can perpetuate or exacerbate societal inequalities, leading to unfair outcomes in areas like hiring, lending, or criminal justice. 

Privacy concerns are a significant consideration due to the vast amounts of personal data often needed to train effective models. This necessitates robust data protection measures, transparent data usage policies, and the implementation of supervised machine learning.

Model Interpretability

As models become more complex, understanding how they arrive at specific conclusions becomes increasingly difficult. This "black box" problem can hinder trust in AI systems, especially in high-stakes domains like healthcare or finance. Developing techniques to explain model decisions, such as SHAP (Shapley Additive explanations) values or LIME (Local Interpretable Model-agnostic Explanations), is vital for accountability and regulatory compliance.

Get Predictive Analytics with MicroStrategy

Now that you understand the value of predictive analytics, the next step is finding the right platform to help you harness its power—and gain a competitive advantage. Your organization needs a reliable solution. It should have tools that help team members of all skill levels analyze data effectively.As a data director, product professional, or IT leader, you need the right solution for creating predictive analytics models. Our MicroStrategy ONE platform expertly organizes complex source data using relevant business concepts and definitions, ensuring AI accurately interprets your data within your specific business context. Contact us to find out more.