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Predictive analytics is the use of data and other tech tools like artificial intelligence (AI) and machine learning (ML) to predict future outcomes. Predictive analytics uses historical data to discover patterns and trends that predict future occurrences.

Currently, many industries are actively using predictive analytics, including manufacturing, healthcare, finance, education, retail, cybersecurity, and agriculture. For example, predictive analytics can be used for everything from predicting business revenue to machine downtime.

As data science evolves, new methods of using data are taking hold. Now, organizations can use data proactively through the use of predictive analytics.

Read more: What is Predictive Analytics?

For organizations ready to take advantage of predictive analytics, there are several best practices to follow for success. These include identifying objectives, testing predictive models, and making continuous improvements.

The first step organizations should take is to define objectives for using predictive analytics. This involves outlining what the organization wants to predict, which will inform how predictive models are developed. These objectives should align with overarching business goals.

For example, if one business goal is to reduce operating expenses, predictive analytics models could predict unnecessary costs such as downtime.

Organizations must also define the key metrics theyll use to ensure the success of their 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 total expenses or operational expense ratio (OER). Organizations should stick to measuring only the KPIs that align with their predictive analytics and business objectives.

A 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, organizations must have access to both historical data and real-time data, as well as both structured and unstructured data.

To build a data set, organizations should extract data from all relevant sources, clean the data in preparation for analysis, and place that data inside a data warehouse. Or, data virtualization tools can aggregate data from disparate sources into one location.

For more information, also see: Four Pillars of a Successful Data Strategy: Making Better Business Decisions

Before using predictive analytics models to predict outcomes, they must be thoroughly tested or validated. Otherwise, predictions may be inaccurate and result in poor business decisions.

Organizations should 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. Organizations should document what occurs with insights and whos responsible for employing them.

Some questions to consider include:

Data changes over time and predictive models should follow suit. Organizations must monitor predictive model performance and make continuous improvements for the best results. This ensures models remain useful and accurate.

There are various ways organizations can improve their predictive models. For example, they can add more data to the models 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. Examples include:

For moreinformation, also see: Best Data Analytics Tools

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.

Predictive analytics takes data analysis a step further. While basic data analysis can show us what happened and what to do about it, predictive analytics shows us what could happen and how we can intervene.

Predictive analytics offers a wide range of benefits across industries, from manufacturing to cybersecurity.

The average automotive manufacturer stands to lose $22,000 per minute during unplanned production downtime. Fortunately, through predictive analytics, manufacturers can make unplanned downtime a thing of the past.

Predictive analytics models can use historical data to find patterns that result in machine breakdowns, required maintenance, etc. Manufacturers can then mitigate risks before they result in costly downtime.

The healthcare industry can benefit from predictive analytics in many ways. For example, predictive models can be used to determine a patients risk factors for diseases such as diabetes and heart disease. As a result, physicians can provide better preventative care.

Retailers must be privy to what customers want to drive revenue. Thats why many retailers are turning to predictive analytics to improve product availability.

For example, predictive models can predict which products that will be in higher demand during certain seasons. Retailers can then ensure they have adequate inventory to deliver on customer needs.

Cyber attacks can be seriously damaging to any organization. According to research by IBM, the average data breach costs $9.44 million on average. Predictive analytics can support organizations in minimizing and even preventing damage.

For example, predictive models can pinpoint trends that indicate potential risks. Organizations can then improve security in these areas to prevent attacks and data loss.

For moreinformation, also see:Data Mining Techniques

As mentioned earlier, the predictive analytics market is expected to grow quickly in the next five years. But what does the future look like?

Predictive analytics will continue to gain in popularity. And as technology such as machine learning and AI become more widely accessible, more organizations, both large and small, will be able to take advantage of predictive analytics.

Predictive analytics will lead the charge in pioneering the use of other forms of data analytics, such as prescriptive analytics. This method not only predicts outcomes but instructs organizations on the actions they should take in relation to the outcomes.

What comes next? For now, organizations must develop data analytics strategies that fit their goals and make room for new, forward-looking analytics methods as they evolve.

Also see: What Is Descriptive Analytics?

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Predictive Analytics Best Practices | eWEEK - eWeek