It's time to take feature generation - a subset of feature engineering - from an art to a science by opening up additional data sources to achieve breakthroughs in predictive models.
This complete guide breaks down data acquisition into six steps, including data provider due diligence and data provider tests to uplift your model's accuracy.
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.
Data science automation has historically focused on hyperparameter tuning and model optimization but now it’s time to see how new tools can empower data scientists to use more and better data.
Feature selection is a key step in building powerful and interpretable machine learning models, but it’s also one of the easiest to get wrong.
How can you earn yourself a seat at the table when decisions are being made? It starts with making yourself accessible and invaluable. Read how you can get started now.
ML has gone from buzzword to business necessity, and implementing it is quickly becoming mandatory. Here are 6 easy steps to follow to get going with the resources you have.
Marketers and Data Science: Tapping Into The Data You Need to Build a Smarter Marketing Organization
In these increasingly uncertain times, marketing leaders who start thinking data science-driven will not only stay ahead of the pack but also keep their organizations afloat.
If the current economic crisis has shown the business world anything, it’s that no amount of data analysis can prepare you for the event of having the financial market flipping upside down.
Your organization’s risk management strategies are going to need a major overhaul. Insights from your historical data simply won’t be enough to help you assess the risks that are coming your way.
Making your training data better is much easier than you think, and you can use several easy strategies for quick wins.
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.
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.
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.
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.
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.
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.
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.
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.
Are your KYC processes streamlined enough to get the answers you need, fast? Or are valuable customers dropping off before you get the chance to onboard them?
In this whitepaper, we cover some of the most common errors in ML initiatives, and best practices to avoid them
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
"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."
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.
External data platforms enable organizations to automatically discover and use thousands of relevant external data signals to improve analytics and machine learning programs.
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.
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.
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.
External data has the power to boost your analytics and machine learning models - driving more value and ROI. By adding important context not found in your internal data you uncover competitive advantages. External data
Modernizing data architectures is top of mind for many IT, data, and analytics leaders. Providing users with the right data, at the right time, and in the right form is a requirement to improve an
Explorium research survey If you’d like to know how you compare with other organizations in the pursuit of the most relevant external (3rd-party) data, then don’t miss our latest research. We surveyed data leaders from
Many B2B marketers and sales teams recognize the value of targeting small and medium-sized businesses (SMBs). The sheer volume of SMBs makes it worth your efforts; the US Small Business Administration estimates there are over
There are many options available when it comes to B2B datasets and providers. The challenge is finding the right data sources for your business, who have relevant, accurate, compliant, and regularly updated data.