Data and analytics leaders

Resources for data science-driven data and analytics leaders.

  • 6 Steps to Jumpstart Machine Learning Using the Resources You Already Have

    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.

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  • Where’s the ROI? Demonstrating the Value of Data

    Where’s the ROI? Demonstrating the Value of Data

    Leaders must find a way to prove, tangibly, that embracing data and investing in better ways to use it has true value and real ROI.

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  • Using External Data to Future-Proof Your Organization and Ensure Success Today and Tomorrow

    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.

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  • 3 Use Cases Data Leaders Must Implement Today

    3 Use Cases Data Leaders Must Implement Today

    From understanding your own business to better predicting uncertain conditions around you, these are just a few ways data leaders can use external data for better business decisions.

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  • Why Data Marketplaces Are the Future of the Data Economy

    Why Data Marketplaces Are the Future of the Data Economy

    Data marketplaces make the lives of data scientists looking for machine learning datasets much easier. Read how.

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  • How Our COVID-19 Signals Give Businesses Better Decision-Making Capabilities

    How Our COVID-19 Signals Give Businesses Better Decision-Making Capabilities

    With ML models rendered useless, we built an entirely new set of COVID-19 signals in our platform that let organizations understand their risk derived from the current pandemic.

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  • How Data and Analytics Leaders Can Avoid Roadblocks to Data Acquisition

    How Data and Analytics Leaders Can Avoid Roadblocks to Data Acquisition

    Data acquisition is a long and time-consuming process that many data and analytics leaders don't have time for. How can you streamline it for a major impact?

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  • How to Connect to the Data Ecosystem

    How to Connect to the Data Ecosystem

    In this article, we explain where external data comes from, the key challenges, and how to connect and utilize it with data science solutions.

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  • Data Bias and What it Means for Your Machine Learning Models

    Data Bias and What it Means for Your Machine Learning Models

    Let’s take a look at some of the most prevalent types of bias, the data mistakes that cause them – and how to prevent this from happening in your own models.

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  • COVID-19 Broke Your Risk Models. This is How External Data Can Fix Them.

    COVID-19 Broke Your Risk Models. This is How External Data Can Fix Them.

    For lenders, pre-COVID-19 data is no longer useful in a post-COVID-19 world. Here's how we're giving our customers external data to ensure their risk models work in our new reality.

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  • Tomorrow Comes Today – Using External Data to Cut Through the Unpredictability of Crises

    Tomorrow Comes Today – Using External Data to Cut Through the Unpredictability of Crises

    It’s time to look outside of our own silos to external data and make sure that despite the crises we face, we can give our organizations the power to make it through. The post Tomorrow Comes Today –

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  • Explorium: Product Overview1:03

    Explorium: Product Overview

    Explorium is the future of data science. We enable you to automatically connect to thousands of data sources, distill the best features, and deploy the best models.

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  • How SMBs Can Boost Their Bottom Line With External Data

    How SMBs Can Boost Their Bottom Line With External Data

    There are some insights that can only come from the data you produce or collect in-house. Historical sales figures, for example. Foot traffic through your store. The post How SMBs Can Boost Their...

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  • Data Scientists and Augmented Data Discovery: A Match Made in Heaven

    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.

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

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

    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.

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  • AI Across the Funnel: The Use Cases You’re Missing Out On

    AI Across the Funnel: The Use Cases You’re Missing Out On

    Building core operations around ML models has helped many companies thrive across many other verticals. Still, others wait to implement at a large scale. Why?

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  • 2020: The Year Data Dominates Data Science

    2020: The Year Data Dominates Data Science

    From data economy growth to KPI-driven DS teams, our data science predictions for 2020 are all about the data.

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  • GlassesUSA.com Uses Explorium to Grow Revenue by Double Digits

    GlassesUSA.com Uses Explorium to Grow Revenue by Double Digits

    GlassesUSA.com increased both conversion rate and order value with Explorium, which lead to a 15-20% increase in the per-session value for their affected segments.

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  • Explorium secures $19M funding to automate data science and machine learning-driven insights

    Explorium secures $19M funding to automate data science and machine learning-driven insights

    Part ML platform, part data marketplace, Explorium promises to automate data and feature discovery, and build and deploy models for your analytics and application needs.

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  • The Complete Guide For Data Acquisition

    The Complete Guide For Data Acquisition

    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.

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