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predictive analytics

The predictive analytics process and techniques

Predictive analytics requires a high level of expertise with statistical methods and the ability to build predictive data models. As a result, it’s typically the domain of data scientists, statisticians and other skilled data analysts. They’re supported by data engineers, who help to gather relevant data and prepare it for analysis, and by software developers and business analysts, who help with data visualization, dashboards and reports.

Data scientists use predictive models to look for correlations between different data elements in website clickstream data, patient health records and other types of data sets. Once the data collection has occurred, a statistical model is formulated, trained and modified as needed to produce accurate results. The model is then run against the selected data to generate predictions. Full data sets are analyzed in some applications, but in others, analytics teams use data sampling to streamline the process. The data modeling is validated or revised as additional information becomes available.

The predictive analytics process
The predictive analytics process begins by understanding the business and preparing the data. A statistical model is then created, evaluated and deployed to handle the data and derive predictions.

The predictive analytics process isn’t always linear, and correlations often present themselves where data scientists aren’t looking. For that reason, some enterprises are filling data scientist positions by hiring people who have academic backgrounds in physics and other hard science disciplines. In keeping with the scientific method, these workers are comfortable going where the data leads them. Even if companies follow the more conventional path of hiring data scientists trained in math, statistics and computer science, having an open mind about data exploration is a key attribute for effective predictive analytics.

Once predictive modeling produces actionable results, the analytics team can share them with business executives, usually with the aid of dashboards and reports that present the information and highlight future business opportunities based on the findings. Functional models can also be built into operational applications and data products to provide real-time analytics capabilities, such as a recommendation engine on an online retail website that points customers to particular products based on their browsing activity and purchase choices.

Beyond data modeling, other techniques used by data scientists and experts engaging in predictive analytics may include:

  • text analytics software to mine text-based content, such as Microsoft Word documents, email and social media posts;
  • classification models that organize data into preset categories to make it easier to find and retrieve; and
  • deep neural networking, which can emulate human learning and automate predictive analytics.

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