Here is a roundup of interesting sites, resources and articles from around the web, curated by IIA. This month’s edition includes great articles on machine learning, modern data analytics platforms, building a data-driven culture and data literacy. Follow us on Twitter (@iianalytics) and LinkedIn to receive daily updates on IIA content and curated content as it becomes available.
Featured Article from Analytics Leadership Consortium Newsletter
Each month, IIA’s Analytics Leadership Consortium (ALC) publishes a newsletter featuring reviews of timely and relevant 3rd party articles. Here is one of the articles highlighted in a previous newsletter.
IIA’s article summary: Data literacy, visualization literacy, tech literacy, the idea of all employees being literate and able to communicate in these forms is a hot topic for many organizations. While this article is on the older side, the challenges and opportunities are just as relevant today and it is exactly why Valerie Logan of The Data Lodge was invited to speak about data literacy at the ALC Fall Virtual Summit and host a recent webinar (7 Ways to Build the Case for Data Literacy)
This article explores the importance of visualizations and highlights the fact that audiences will have varying degrees of skill to accurately interpret the message. Be sure to keep all audiences in mind when designing visualizations.
One way to accomplish reaching a broader audience is by adding chart explainers; give your audience the tools to understand what to look for, provide guidance on how to interpret the chart and other tips to aid the consumer.
An interesting experiment would be to gather a diverse group of representatives from different functions and share some of the graphical examples included in the article. Do people understand them? What do they like about them? Dislike? How could they be improved? Make it an open discussion so as a visualization designer, you can learn what resonates and where extra support may be needed.
How to Win with Machine Learning (Harvard Business Review)
To build a competitive advantage with machine learning predictions you must answer three questions: 1.) Do you have enough training data? 2.) How fast are your feedback loops? and 3.) How good are your predictions?
How to Set AI Goals (O’Reilly Radar)
Identifying AI opportunities and setting appropriate goals are critical to AI success, and yet can be difficult to do in practice. This article provides some reasons for this including a lack of AI literacy, maturity, and many other factors.
Building a Successful Modern Data Analytics Platform in the Cloud (ML-Guy / Medium)
You must let go of monolithic thinking and design to benefit from modern cloud architectures. This article discusses steps to build a scalable, flexible, and cost-effective data analytics platform on AWS.
All-in-one platforms built from open-source software make it easy to perform certain workflows but make it hard to explore and grow beyond those boundaries. This article explores why Best-in Breed is a better choice.
The 5 Data Consolidation Patterns (The Startup / Medium)
This is a basic overview of five different data technologies: 1.) Data Lakes, 2.) Data Hubs, 3.) Data Virtualization/Data Federation, 4.) Data Warehouse, and 5.) Operational Data Stores. Outlines how the data warehouse is a permanent anchor fixture, and the others serve as source layers or augmentation layers — related or linked information.
We Got It Wrong – Data Isn't About Decision Making (The Startup / Medium)
This great article explores the difference between decision making and behaviors in human and cultural context. It provides some interesting insights on why struggle to adopt data-driven decision making and advanced analytics.
An overview of key findings from Alation's first State of Data Culture Report. One interesting findings from the report is an interesting knowledge disconnect between expertise and self-evaluation - companies with low capabilities and expertise actually rate themselves high.
Data Scientists Are from Mars, and Businesspeople Are from Venus (The Startup / Medium)
A good compare and contrast of data scientists and businesspeople using the classic relationship model.
Top Technologies To Achieve Security And Privacy Of Sensitive Data In AI Models (Analytics India Magazine)
A good review of techniques for preserving privacy and security of data for AI models including differential privacy, secure multi-party computation, federated learning, homomorphic encryption and Blockchain.
Enterprise AI/Machine Learning: Lessons Learned (Toward Data Science)
Some useful, real-world insights from perspectives gained from helping enterprises accelerate their AI/ML journey.
The State of AI 2020 (Towards Data Science)
This article provides a solid, in depth overview of the past, present, and future of AI.
Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) is developing a system that can decipher a lost language without knowing its relation to other languages.
This article provides an overview of a better way to find approximate solutions to the notorious 'traveling salesperson' optimization problem, often used to test the limits of efficient computation.
Analytics Case Studies
Even bourbon, one of the most popular spirits in the world, is being impacted by data and analytics. This article reviews how some Kentucky distilleries are adding data analytics, automation, IoT sensors, and RFID tags to streamline production.
How Amazon Automated Work and Put Its People to Better Use (Harvard Business Review)
Amazon is working to automate office work with its "Hands off the Wheel" program. The goal is to automate tasks so that the company can assign people to build new products — to do more with the people on staff, rather than doing the same with fewer people.
The Importance of Good Data (Walmart Global Tech / Medium)
A good overview on the importance of good data from the perspectives of Walmart Tech's Bill Groves.
Improving batteries has always been hampered by slow experimentation and discovery processes. This article explores how machine learning is speeding it up by orders of magnitude.
Matt Truck, VC at FirstMark, published his annual data and AI landscape. He notes that "to succeed, every modern company will need to be not just a software company, but also a data company" and provides a compelling overview of why this is true.
This is a good overview and comparison for the machine learning services from Amazon Web Services, Microsoft Azure and Google Cloud Platform.
These videos provide an overview of IIA Expert Jesse Anderson's new book Data Teams: A Unified Management Model for Successful Data-Focused Teams.
This interesting report from Data & Society explores the hybrid sociotechnical nature of repair work required to integrate an AI system into clinical health care.
This market research report from Alation has some interesting insights including that 1.) data quality remains the soft underbelly of data culture with decision makers often questioning data, 2.) COVID19 is accelerating the move toward data culture and 3.) the existence of a data culture disconnect and what organizations are doing to create a data culture.
Featured News and Information Sites
Articles on Artificial Intelligence from Bloomberg News.
Data & Society studies the social implications of data-centric technologies & automation. They produce original research on topics including AI and automation, the impact of technology on labor and health, and online disinformation.
Analytics India Magazine is dedicated to championing and promoting the analytics ecosystem in India.
Analytics in Sports
A New Way to Classify NBA Players Using Analytics (Towards Data Science)
An interesting article on how data and analytics are expanding the traditional player roles (guard, forward, center) into clusters based on a player's style and game impact.
If Billy Beane Is Done With Baseball, He’s Left An Indelible Mark (FiveThirtyEight)
Bill Beane's impact on sports analytics has been significant. This article summarizes the impact of "Moneyball" on the Oakland A's and baseball as a whole.
IIA is the industry’s leading source of insights and advisory services for companies transitioning to data-driven decision-making and advanced analytics. IIA continuously seeks out insights, information and experts to elevate our client’s and our community’s analytics expertise through two service lines. IIA's Research and Advisory Network (RAN) provides clients with access to the world's largest analytics-focused expert network; a resource designed to accelerate analytics teams' progress against their projects and initiatives. IIA’s Analytics Leadership Consortium is a closed network of analytics executives from diverse industries who meet to share and discuss best practices, as well as discover and develop analytics innovation, all for the purpose of improving the business impact of analytics at their firms.