What it is and why it matters
From elections to sporting events to the stock market, you can find countless opinions on what the future will bring. But without supporting data, any opinion is nothing more than an educated guess.
How do you go from a guess to a prediction? By using data to inform decisions about the future.
Predictive analytics allows you to discover, analyze and act on data. It’s about learning from the past to uncover trends and predict likely outcomes. But that’s not all. Predictive analytics gives you a framework to analyze data over time, leading to more refined outcomes and corrective actions.
And using data to create a coherent view of the future has never been more important. The convergence of big data, time series data, social media, sensor data and mobile devices gives you the potential to add more fuel to your predictive analytics engine. Now it’s up to you to collect, manage and analyze this information – and position your organization for success.
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How does predictive analytics work?
Predictive analytics combines techniques from statistics, data mining and machine learning to find meaning from large amounts of data and help you foretell the future. Whether you’re in marketing, compliance, customer service, operations or any other business unit, your data can show where you are – and predict where you’re going.
How do organizations approach predictive analytics? The key stages for an analytical life cycle include:
- Analytical data preparation – Access, aggregate, clean and prepare your data for optimal results.
- Visualization and exploration – Explore all data to identify relevant variables, trends and relationships.
- Statistical analysis – Use everything from simple descriptive statistics to complex Bayesian analysis to quantify uncertainty, make inferences and drive decisions.
- Predictive modeling – Build the predictive model using statistical, data mining or text mining algorithms, including the critical capability of transforming and selecting key variables.
- Model deployment – Apply the new champion model, once validated and approved, to new data.
- Model management and monitoring – Continously examine model performance to make sure it's up-to-date and delivering valid results.
Predictive analytics in action
Marketing – From telecommunications to education to gaming and beyond, organizations need to forecast customer responses or purchases. Predictive models enable businesses to discover and attract the most profitable customers, helping maximize value from their marketing budget.
Risk – Credit scores assess a buyer’s likelihood to default on purchases like cars, homes or insurance. Credit scores are numbers generated by a predictive model that incorporates all data relevant to creditworthiness. Predictive analytics has other risk-related uses as well, including claims, collections, fraud and security.
Operations – To make an organization more efficient, you need to understand future needs and anticipate demand. Manufacturers need to manage inventory and factory resources. Airlines must decide how many tickets to sell at each price for a flight. Hotels try to predict the number of guests they can expect on a given night. Predictive analytics is at the heart of all of these operational decisions.
Analytics has really helped us in markets where we're trying to predict where the demand's going. Our historical patterns are changing. We need to use analytics to bring multiple variants in, aside from (historical) supply and demand patterns, to understand and predict demand.
Chief Information Officer, The Dow Chemical Company