How does predictive analytics software transform your data into actionable insights?

PA is helpful to recognize anomalies and act before an issue occurs. PA reduces unplanned downtime of devices and services and supports more reliable operations or usage of solutions.

Predictive analytics gives you a competitive edge by providing a glimpse into a possible future. This enables you to act faster or prepare action plans for mitigating undesired events. By doing so, you can realize all potentials and negate possible risks. Common use-cases include forecasting demand and stocking products in advance, predictive maintenance to reduce machine downtime, and analyzing schedules to prioritize orders and meet customer deadlines.

Consider an e-commerce company that uses predictive analytics software. The software collects data on customer purchases and browsing habits, processes it to identify patterns, and predicts future buying behaviours. It might reveal that customers who buy product A are likely to buy product B within two weeks. Armed with this insight, the company can launch targeted promotions for product B to these customers, boosting sales and enhancing customer satisfaction.

There are plenty of exercises to be taken on online platforms (with or without certification) in which you can follow a few simple steps to create a basic machine learning model. By my opinion that's when you learn the most basic principles: to do it once. Take an exercise like predicting future basketball games or credit card frauds. Afterwards, you'll understand the basic reasoning behind and you'll have an overarching view which will help you understand everything more clearly when drilling deeper in the topic.

There are a lot of concepts in the market. But I prefer the C's framework : ensure your data is current, complete, clean, consistent, credible and compliant. Get your own set of "cleanup rules" which you'll learn on the job. There's lists available, but more than often you need to bounce your head against the wall for the best lessons

Preparing data is where you spend most of your time when deploying predictive analytics, but it's also the most important part for accurate predictions. Use a suitable ETL workflow to get your data right. Include validity checks in your data flows to exclude outliers or incomplete data entries, which can result from human errors. Initially, understand what good data looks like for your use case. You'll spend significant time upfront preparing your initial data set for the first trial. Fortunately, you need to do this properly only once. Future data loads will be smoother if you invest time in the first data preparation. So, take your time and don't rush this process. It's worth the investment.

Before analysis, data must be cleaned and preprocessed to ensure quality. This step involves: Data Cleaning: Removing or correcting inaccurate, incomplete, or duplicate data. Data Transformation: Converting data into a suitable format or structure for analysis. Feature Engineering: Creating new features or variables that will improve the model’s performance. Data Integration: Combining data from different sources to provide a comprehensive dataset.

The first step is to overcome privacy and security concerns and connect and collect data. It is crucial to get data from a critical mass of devices or users. Then you can prepare data for further processing.

Fortunately, you don't need to be an expert model builder to fully utilize predictive analytics. Most software solutions come with predefined models, so you just need to know which model type to use for each use case. Even if you need to expand your solution, you can use popular Python libraries that have all the common models available. Your task is to get the right data, prepare your data sets, apply different models, and evaluate which one performs the best. With the introduction of GenAI, this becomes even less complex, as you can ask a chatbot which models might fit your use case and have it explain the evaluation statistics.

Make sure to validate your model for performance. Try different models and check with your validation data what the best model is in terms of outcome, accuracy, precision, sensitivity, specificity, roc-curve, etc...

Based on the nature of the data and the problem to be solved, the appropriate predictive model is selected. Common models include: Regression Models: For predicting continuous outcomes. Classification Models: For predicting categorical outcomes. Time Series Models: For forecasting future values based on past data. Clustering Models: For grouping similar data points. Anomaly Detection Models: For identifying unusual patterns that do not conform to expected behavior.

The key to meaningful insights and added value is combining model-generated insights with business context and your own knowledge of the industry and current developments. While machine learning models excel at finding patterns and predicting results, they lack the necessary context and experience to put all the puzzle pieces together. This is where an experienced data analyst comes in, providing the context needed to turn insights into actions.

The predictions and insights generated by the model are presented to stakeholders through reports, dashboards, and alerts. These insights can drive various business actions, such as: Optimizing Operations: Improving efficiency and reducing costs. Enhancing Customer Experience: Personalizing services and improving satisfaction. Identifying Risks: Proactively addressing potential issues. Driving Strategic Decisions: Guiding long-term planning and investments.

The best decisions are made, not only predicted. Make sure to build something where people want to make a decision. Building recommendations in an area of the business where there's no strategic vision to go is just a waste of time. Because how good the model itself will be, there's just not enough management buy-in to support the decision. Choose relevant datasets and business problem to build your model upon.

All types of data analytics are geared toward making better decisions. To make the best use of your data, align your business strategy with your data strategy. Understand what you want to achieve and identify the questions you need to answer to make the best decisions possible. Ensure your systems and processes can produce the data to support these decisions. Prepare, analyze, and present the data with these decisions in mind. This approach will align your business and data strategy, taking your analytics to the next level.

Just as software refines its algorithms with use, analysts and decision-makers must embark on a continuous learning journey. Review whether the decisions you made based on predictive analytics were correct and if the metrics used were as supportive as initially thought. This can be achieved by analyzing the past with traditional descriptive analytics tools and checking how your decisions influenced the outcomes.

Predictive analytics is an ongoing process. Models are continuously monitored and updated with new data to maintain accuracy and relevance. Feedback loops help refine models and improve predictive power over time.

Never forget that technology is just a tool; it won't solve your business problems on its own. To use predictive analytics to its full potential, you need to establish a data-driven culture and increase the data literacy of your workforce. Technology won't guarantee success, but people who know how to use it will.