Predictive analytics is the use of data and other tech tools like artificial intelligence (AI) and machine learning (ML) to predict future outcomes, it also uses historical data to discover patterns and trends that predict future occurrences.
Many industries are actively using predictive analytics, including manufacturing, healthcare, finance and education, as these predictive analytics can be used for everything from predicting business revenue to machine downtime.
So, what are the steps to introducing predictive analytics to your business?
- The first step you should take is to define objectives for using predictive analytics which should align with your overarching business goals. This involves outlining what the organization wants to predict, which will inform how predictive models are developed. For example, if one business goal is to reduce operating expenses, predictive analytics models could predict unnecessary costs such as downtime.
- You must also define the key metrics used to ensure the success of the data initiatives. These are the key performance indicators (KPIs) that show progress toward predictive analytics objectives.
For the example above, KPIs for reducing operating expenses may include an operational expense ratio (OER). To avoid conflicting data you should stick to measuring only the KPIs that align with your predictive analytics and business objectives.
- High-quality prediction requires high-quality data. The data sets used for predictive analytics must be accurate, large, and relevant to the objectives. For the best results, you must have access to both historical data and real-time data, as well as both structured and unstructured data. To build a data set, extract data from all relevant sources, clean the data in preparation for analysis, and place that data inside a data warehouse.
- Before using predictive analytics models to predict outcomes, they must be thoroughly tested and validated to ensure the predictions are accurate. Run tests using sample data sets to determine the accuracy of predictions first. Once a predictive model is proven to be accurate, it can then be put to use.
- After testing and deploying predictive models, insights that are uncovered must be put to proper use. Ensure you document what occurs with insights and who’s responsible for employing them.
Some questions to consider include:
- Who should insights be shared with?
- What actions should be taken?
- Are there insights that require immediate action?
- Are there insights that should be revisited at a later time?
- Data changes over time and predictive models should always follow suit. Monitor the predictive model performance and make continuous improvements for the best results. This ensures models remain useful and accurate. There are various ways to improve predictive models, add more data to the model’s data set or re-tune, re-train, and re-test the model to determine areas that are in need of improvement.
- The last step is to actually implement the software. There are a number of predictive analytics software tools that can be deployed, dependent on your business need.
There are three predictive analytics models that are most commonly used:
Classification: Classification models categorize data to show relationships within a dataset. These models are used to answer questions with binary outputs like “yes or no.”
Clustering: Clustering models group data based on attributes without human intervention.
Time series: Time series models work to analyze data points that are collected over specific time periods, such as per hour or daily.
Once these models are planned, predictive analytics is quite simple. First, data is collected based on the type of prediction an organization wants to make. Then, one of these statistical models is developed and trained to predict outcomes using the collected data.
Once the model generates any kind of prediction, it can then be used to inform decisions. Through automation, some predictive models can even be instructed to perform actions based on predictions.