What Is Assisted Predictive Modeling?

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Click to learn more about author Kartik Patel.

Assisted predictive modeling incorporates complex, sophisticated analytical and forecasting techniques in a self-serve environment where business users can employ tools to guide them through recommended techniques and report formats and ensure that the methods and reports they choose are appropriate to the type of data and information they need.

Users select the data they want to analyze and use the self-serve tools to point them in a clear direction, so they can illustrate patterns and trends and use auto-suggest and recommended analytical techniques to determine the best approach. These tools take the guesswork out of forecasting and business results predictions and make it possible for business users to produce clear, accurate information without the assistance of IT or data scientists.

Is Assisted Predictive Modeling Suitable for Business Users?

In Gartner’s report entitled Augmented Analytics Is the Future of Data and Analytics (ID G00326012), Gartner analysts predict that:

“By 2020, augmented analytics — a paradigm that includes natural-language query and narration, augmented data preparation, automated advanced analytics, and visual-based data discovery capabilities — will be a dominant driver of new purchases of business intelligence, analytics, and Data Science and machine learning platforms and of embedded analytics.”

The reason for this analytics evolution is simple. Every business is operating in a rapidly changing competitive environment and market. Business users must have the tools they need to analyze data, draw conclusions, predict results, and help the organization achieve its goals. No enterprise has the time or the luxury to wait for IT or professional analysts or data scientists to produce reports. There is too much information, and there are too many data sources to make these old analytical and forecasting paradigms work today. What every organization needs is a clear guide to results — one that every business user can leverage without expensive, time-consuming training or special skills.

With the right combination of sophisticated tools and easy-to-use navigation, drag and drop flexibility, and personalized dashboard capabilities, every one of your users can have the power and flexibility of predictive analytics, so they can plan and make decisions, share data, and analyze like a pro!

The right self-serve advanced data discovery and predictive modeling solution should be easy to implement, easy to personalize, and easy to use. It should incorporate complex, sophisticated algorithms and analytical methods into the back end and enable intuitive, integrated, interactive dashboards on the front end, so business users can predict future values with time series forecasting and use simple linear, least-square, and multiple linear regressions for causation and prediction, as well as Bayes classification, associative, decision tree, and K-Nearest Neighbor techniques for classification and prediction. These methods should be applied to produce reports and clearly illustrate results, taking the guesswork and complication out of the process of planning and forecasting.

Assisted predictive modeling allows for planning and forecasting at any point in time and enables business users and enterprises to test assumptions and theories in a risk-free environment so that the planning process is no longer restricted to an annual or quarterly process but rather is dynamic, active, and meaningful.

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