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The Role of External Data in ML and BI Success. And, How to Easily Access it.
Access to external or alternative data can give significant uplift to your predictive and ML models. External data is becoming a significant competitive advantage for companies across FinTech, Insurance, CPG, Retail, or eCommerce. By connecting to broader data ecosystems, companies are improving their analytics, machine learning, and decision-making. Source: Intelligent Data Summit: Integration Developer News.
Ai4 Panel- Banking, Risk Management, & AI
Understanding and managing risk is crucial for banks to come out on top. As banking becomes more data driven, AI is playing a powerful role in determining the methods and extent to which banks comprehend and handle their risk across a variety of areas and functions, including credit risk, operational risk, market risk and liquidity […]
External Data for Consumer Goods: The Definitive Playbook
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.
10 Questions to Ask Before Buying External (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.
The Business Value and Benefits of External Data Platforms
External data platforms enable organizations to automatically discover and use thousands of relevant external data signals to improve analytics and machine learning programs.
How Data Scientists Can Get a Seat at the Strategy Table
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.
Using External Data to Future-Proof Your Organization and Ensure Success Today and Tomorrow
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.
Part Three – Making Sense of Deployment: Feature Engineering, Training, Testing, and Monitoring
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.
Part Two – Making Sense of Data Prep: ETL, Wrangling, and Data Enrichment
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.
Data Scientists and Augmented Data Discovery: A Match Made in Heaven
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.
Mitigating Risk With External Data, A Guide For CROs
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.
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.
The Definitive Guide to External Data for Fintech
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.
The Guide to External Data for Better User Experiences in Financial Services
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?
Optimize Your Analytics – Why You Need a Data Acquisition Strategy
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
The Most Common Errors in ML Projects and How to Avoid Them
In this whitepaper, we cover some of the most common errors in ML initiatives, and best practices to avoid them
Taking Control of Your Data: An Essential Guide for Marketers
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.
6 Steps to Jumpstart Machine Learning Using the Resources You Already Have
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.