Netflix recommends the perfect movie for you. Your favorite online shop sends you an email about a product you’ve been eyeing. Later, you get a notification from your bank about fraud detection – thankfully, they flagged it before damage was done. What do all these scenarios have in common? Predictive analytics powers them.
From tech giants like Netflix and Amazon to financial institutions, healthcare providers, and even small local businesses, organizations of all sizes and industries are leveraging the power of predictive analytics. It's not just about big data anymore – it's about smart data that can predict the future.
How does predictive analytics work?
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.
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 models to receive new data regularly, making predictions quickly, and sharing those predictions with the right people or systems. The goal is to make your predictions easily available and usable in day-to-day operations. Invariably, this involves IT teams integrating the model securely into your existing company systems.
Benefits of predictive analytics for your business
At its core, predictive analytics unveil 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 empower 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 experience and targeted marketing efforts.
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.
Summary
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. The 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.
(source: https://www.microstrategy.com/blog/start-predicting-start-profiting-the-power-of-predictive-analytics)
