DataCamp Digest is our monthly roundup aimed at providing the most up-to-date insights and news on all things data science. In this edition, we discuss why it’s hard to become data-driven, the traits of future-proof organizations, how to adopt a product mindset to building dashboards, and more.
Why is it so hard to become a data-driven company? | Harvard Business Review
In 2021, to be a successful company is to be a data-driven company. This is why it’s no surprise that the majority of fortune 1000 organizations are investing in data and artificial intelligence initiatives. Despite this investment, only a few are reporting actual business impact. Find out how data culture and skills is the primary impediment to scaling value with data science, and what to do about it.
How to build data literacy at your company | MIT Sloan
Keeping with the same theme of building a data culture, this article deep dives into building data literacy programs at an organization. From assessing data skills, to building a culture of learning, find out the key components of a successful data literacy program.
The nine traits of future-ready companies | McKinsey
A fun and interactive infographic on the key traits of future-proof organizations. Hint: Data and learning are fundamental pillars for future-proof organizations to accelerate growth.
One of our favorite reads of the month, this article dives into different communication styles and how they can improve or worsen remote work.
Find out how to drive better alignment and impact on data analysis requests by asking one important question.
Dashboards are one of the simpler and most impactful data products to build at an organization. In this article, Shopify Data Scientist Lin Taylor outlines a product thinking approach to maximize impact when developing dashboards for the organization.
As organizations start making the most of their data for analytics and machine learning, they will need to begin leveraging external datasets to refine their models and analytics outputs. Find out more about the importance of external datasets in this article.
Data Observability: Building Data Quality Monitors Using SQL | Barr Moses & Ryan Kearns
In this series of articles, Barr Moses and Ryan Kearns outline how to build data quality monitors in SQL to identify freshness and anomalies (Part 1), and extract the root cause of these data anomalies (Part 2).
Interpretable Machine Learning | Christoph Molnar
In this book, Christoph Molnar details the importance of explainable machine learning, techniques data teams can adopt today, and the future of machine learning interpretability.
Just as it did for software development, open-source will empower organizations to make the most of their data and create transformative solutions, processes, and products with machine learning and data science. Find out all you need to know about open-source in data science in our exclusive new guide.
Data-driven transformation is a long process requiring organizations to go through different data maturity stages. In this webinar, we outline what these data maturity stages are and what steps you can take to grow from one data maturity stage to another.