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Introduction

This report analyzes the impact of new automated predictive analytical capabilities available in analytics solutions that enable non-experts to extrapolate future trends from enterprise data at the click of a button.

Highlights

  • Data sets need to be well curated for prediction models to run properly, highlighting the need for a strong enterprise data culture and good data management tools.

Features and Benefits

  • Analyzes the impact of new automated predictive analytical capabilities available in analytics solutions.
  • Identifies the steps enterprises should take to fully take advantage of self-service predictive analytics capabilities.

Key questions answered

  • How will the introduction and refinement of automatic predictive analytics capabilities in self-service analytics applications impact the analytics market?
  • What should enterprises do to fully benefit from new automated self-service analytics capabilities?

Table of contents

Summary

  • Catalyst
  • Ovum view
  • Key messages

Recommendations

  • Recommendations for enterprises
  • Recommendations for vendors

Expert insights with code-free predictive analytics

  • Automated and visual predictive capabilities, the self-service analytics way
  • Predictions for everyone, not just data scientists

In algorithms we trust, but only if we understand them

  • To take up prediction tools, users need to know how they work
  • Easily adjustable predictive models reflect enterprise reality

Embedding predictive analytics tools into core enterprise applications

  • Predictions are more valuable if they are viewed in the business context

Predictive analytics capabilities rely on well-curated data

  • Good data management is essential to break the garbage in/out cycle
  • To make full advantage of predictive analytics, the enterprise data culture must not be neglected

Appendix

  • Methodology
  • Further reading
  • Author

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