Free guide

How to Safeguard
your Predictive Analytics
against the most costly pitfalls

Avoid the mistakes that every enthusiastic stakeholder makes.

Topics Covered in This Free PDF Guide:

  • One rule of thumb
  • Inputs that make or break it
  • The unwanted temptation
  • When things look too good to be true
  • When the rules of the game change
  • The pervasive and sticky phenomenon
  • Three safeguards against Data-Snooping Bias
  • Refining your model without crippling it

Why are these safeguards so important?

Here at Insightee, we believe that before people even consider touching their data for any quantitative analysis, it is very important to have certain approach and mindset .

Companies are investing huge sums in various advanced analytics technologies. They spend even more on the people who claim to be mastering them. What we see repeatedly in companies is that whenever there is a project or initiative around predictive analytics, machine learning, data mining or similar advanced data processing, the stakeholders put too much hope in a technology and too little attention to the methods and principles of data analytics.

In this way, readers can avoid the costly pitfalls of data analysis, which often equate to several months of project budget plus time when their models could be live and delivering ROI.

Success in predictive analytics depends not only on your intent, data, tools and people. How you handle the data will determine the outcome of the project. Our safeguards are basic principles that every stakeholder should know and that all project participants must respect. They are based on our 20+ years of experience in projects in companies around the world that were often very, very expensive indeed.

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Absolutely. Just sharing some free knowledge that we hope you’ll find useful. Keep us in mind next time you have data analytics questions!

Thousands of professionals had to learn these lessons by spending their budget.