Top trends and research for senior BI, DS & Risk Assessment professionals

The research papers are aimed at informing the senior data science, analytics, business intelligence, risk assessment professionals of the latest developments and trends in the industry.

All resources are free to download:

AI is Making BI Obsolete, and Machine Learning is Leading the Way

BI can help you start making sense of your data, but it still expects you to do the heavy lifting when it comes to finding insights.

Building predictive models that can cut down your decision time and offer better insights is a must, but achieving them sounds impossible. 

So, why are we still hung up on BI?

This whitepaper covers: 
1. How data science is helping analytics take a leap forward
2. The importance of focusing on the models you use to analyze your data
3. Use cases that you can implement in your organization


Start Small and Scale Smart: Do You Need a Data Science Team, Platform, or Service?

To guide your company towards success, you need a clear idea of what’s around the corner. You need to know what the future holds. And that means data science-backed predictive models. 

More data science doesn’t always mean more data scientists, though. In fact, hiring more data geniuses can only do so much if they don’t have the skills, tools, and data they need to help your organization scale and thrive. 

In this guide, we discuss:

  1. The benefits and pitfalls of different approaches to implementing and growing your data science strategy
  2. How each of these can benefit your business, and where the limitations lie
  3. How to allocate your budget efficiently and effectively when you’re starting out or looking to scale

How Can You Enhance Your Risk Models?

Risk and fraud officers around the world are beginning to realize that their risk models simply aren’t up to scratch. The risk landscape is changing, new datasets are emerging — and your machine learning models need to evolve and rise to the challenge, too. 

In this in-depth guide, we reveal the real reasons your machine learning risk models are falling short, what you can do to fix them — and exactly how to tackle the problem.

You’ll discover: 

  1. The three vital steps to enhancing your risk models
  2. Where to look for the data you don’t have in-house
  3. How to streamline your connections to external data

The research papers available on this page were developed by Explorium – a data science platform that helps data experts and business leaders eliminate the barrier to acquire the right data for better decision-making.