In this eBook we propose 10 questions that you should ask before starting on your external data acquisition...
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In this eBook we propose 10 questions that you should ask before starting on your external data acquisition journey. We also provide reference material to help you find the answers to those questions.
The right data is a competitive edge. Over the past several years fintech, hedge funds, and investment companies have started to augment their conventional data sources with alternative data.
External data platforms enable organizations to automatically discover and use thousands of relevant external data signals to improve analytics and machine learning programs.
Explore our resource Rethinking Data Acquisition with Explorium. Without the right technology - and the right data - many analytics and ML projects never get off the ground.
What is your external data acquisition strategy? If you’d like to know how you compare with other organizations in the pursuit of the most relevant 3rd-party data, then don’t miss our latest research.
It’s no secret that external data can transform organizations’ data science and advanced analytics, but finding it is easier said than done. See how a data acquisition strategy helps
In this whitepaper, we cover some of the most common errors in ML initiatives, and best practices to avoid them
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Do you have enough data to get the insights you need? And if not, how can you fill the gaps? In part one of this series, we dive deep into auditing, discovery, and acquisition.
In part two of our series, we cover ETL, data wrangling, and data enrichment so you can ensure your data is ready to give you the insights you need.
Part three of our series gets technical with an in-depth look at the best ways to split your data for training, different testing methodologies, feature engineering, and monitoring.
Creating a data science team is about so much more than tracking down the right job titles or developing the right algorithms. Find out the key roles you need to build a rock star data science team.
The current credit scoring model is outdated and in need of an upgrade. Read how to go about building a smarter, more accurate credit scoring model - and the data you need to do so.
How can marketers leverage all their data for better predictive insights? It’s all about knowing what you need, how it can help, and the right platforms and tools that can help achieve your goals.
In our interconnected, globalized world, it’s harder and harder to track the weak spots in your supply chain. That’s why we created this handy guide to understanding and mitigating supply chain risk.
Why are we still hung up on BI? It’s time to embrace a paradigm that empowers us to make smarter, better predictions using real data with machine learning.
It’s no secret that while most organizations understand the importance of machine learning, most initiatives never make it off the ground. Follow this guide to guarantee you make it to production.
There’s no one way to start using data science. This guide walks you through the pros and cons of each approach and discusses how to allocate your budget efficiently.