Steps to Data Blending for Predictive Analytics

Published on 30 May 2022

Data Blending, Predictive Analytics

In recent years, the amount and diversity of data have expanded substantially, and so have the expectations for analytics. A conventional dashboard representing last month's events is no longer acceptable for decision-makers.

In order to stay ahead of the competition and enhance their company's bottom line, data analysts must comprehend what is likely to occur in the future so that the business may be better prepared to implement significant changes.

By examining current and historical data, predictive analytics enables firms to obtain a competitive advantage by better understanding and predicting the possibility of future occurrences.

The most difficult component of predictive analytics is preparing the appropriate data for analysis. Here, data mixing may be useful.

What is Data Blending?

What began as a means to an end for a data analyst who worked with a single data source has evolved to the need of combining various data sources.

Throughout this growth, data blending has enabled analysts in the line of business to access and mix data from numerous sources to offer deeper knowledge that enables better business decisions.

Analysts utilize data blending to create an analytic dataset in order to answer a particular business issue or capitalize on an opportunity, gaining insight into consumer preferences, marketing campaign outcomes, financial operations, site and merchandise optimization, and much more.

What is Predictive Analytics' Data Blending?

To better predictive analysis/models/results, you must ensure that you are dealing with the appropriate data. However, you must include more data sources and formats than ever before to make the best-educated judgments.

Data blending for predictive analytics enables analysts to devote more effort to model development, review, and deployment, and less time to data preparation.

Analysts attempting predictive analytics contend with:

  • Accessing the appropriate kinds and systems of data; 
  • Preparing and cleaning data; 
  • Joining various datasets; 
  • Providing a repeatable method for future analysis; and 
  • Relying on others to provide the dataset they need.

Alteryx's Predictive Analytics Enables Cost Reductions in Marketing

Southern States Cooperative depends heavily on Alteryx's predictive analytics solutions to enhance its marketing operations.

Deeper Insights

The organization was able to access and combine all pertinent marketing and consumer data from numerous sources and conduct predictive and spatial analytics to identify high-potential prospects for targeted mailings.

Hours vs. Weeks

By decreasing time-to-insight from weeks to just hours and boosting the number of insights obtained, the firm was able to increase campaign response rates by almost 200 per cent on average.

Effortless Workflow

Across the organization, dozens of analytic apps were deployed, all of which were created without coding, and perform functions such as data extraction, purification, exploration, and modelling in a unified process.

Download Alteryx's whitepaper to learn more about Steps to Data Blending for Predictive Analytics only on Whitepapers Online.


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