Read below for a roundup of interesting sites, resources, and articles from around the web, curated and contextualized by unbiased analytics experts at IIA. Highlights include articles about solving the roadblock between data insights and persuading decision makers, how to develop a data culture, how to better adopt the agile framework, and tips on how to measure the ROI of your data team. There is also an entertaining article on how analytics are used to gain a competitive advantage in high-speed sailboat racing. Follow us on Twitter and LinkedIn to receive daily updates on IIA content and curated content as it becomes available.
“Article of the Week” from IIA’s Normal Distribution
Each week, IIA’s Normal Distribution email (sent to anyone that has filled out a form on our website or subscribe here) features timely and relevant third-party articles. Here are the articles highlighted in the “Article of the Week” from the March Normal Distribution emails.
Data Science and the Art of Persuasion (Harvard Business Review)
Despite heavy investments in analytics, many companies have been disappointed in the results. This article argues that the inability of analytics teams to communicate insights properly is a core cause of this disappointment and outlines six key talents (project management, data wrangling, data analysis, subject expertise, design, and storytelling) and four steps for achieving success:
1. Define talents, not team members
2. Hire to create a portfolio of necessary talents
3. Expose team members to talents they don’t have, and;
4. Structure projects around talents.
IIA CAO Bill Franks' recent webinar Creating and Delivering an Effective Data-Driven Presentation is another excellent resource on how to distill and present analytical insights. You can read his blog here and watch the webinar recording here.
6 Essential Steps to Building a Great Data Culture (Towards Data Science)
"Companies that only put money and effort into hiring good data talents but pay no attention to building a data culture in the rest of the organization usually struggle with keeping those good data talents." To run a successful analytics program you don't just need talented data scientists, you need people to ask them the right questions. This article details 6 steps your company must take to develop a great data culture:
1. Start with no-code options
2. Provide on-the-job learning opportunities
3. Make sure everyone has access to the databases
4. Have all your data in one place
5. Have a dedicated group to answer data-related questions
6. Start linking queries to numbers
For more help with forming a productive data driven culture, read IIA's Dimensions of a Data Driven Culture eBook
Agile Doesn't Work Without Psychological Safety (Harvard Business Review)
Over the last 20 years, the agile movement has gained astonishing momentum, but approximately half of organizations that undertake agile transformations fail in their attempts. The author in this HBR article evaluated several agile teams and conducted a series of interviews with leading agile experts and found five ways to increase psychological safety to foster a collaborative, successful agile team.
How to Measure the ROI of Your Data Team? (Towards Data Science)
Despite data teams constantly trawling the analytics of - well, everything - we constantly hear of the difficulties of measuring the ROI of the data analysts themselves. This article discusses why it's important to measure the ROI of data teams, why it's so hard, and breaks down in detail how to measure the ROI of data teams.
The article breaks the process of measuring ROI into two key stages: 1. Cleaning the data and 2. Operationalizing the data. The author also details 5 KPIs for creating clean data and explains why you need different ROIs for different sub-teams.
Featured Articles on Analytics Strategy
How Data Is Humanizing Customer Experiences (MIT Sloan)
Data-driven personalization is coming to businesses across all industries. This article provides a good overview of how personalization is impacting insurance, healthcare and retail.
A Method for Measuring Analytical Work (Substack)
"One of the great ironies of the analytics industry is its utter inability to measure itself". In this article, Mode CAO Benn Stancil explores why analysts seem to have trouble measuring their own success and suggests that the sole metric to base analytical success should be the amount of time spent on a decision.
Your Data Literacy Depends on Understanding the Types of Data and How They’re Captured (Harvard Business Review)
Data literacy is an increasingly important skill for the everyday citizen. This article breaks down 5 concepts non-technical people need to understand:
1. Data generation
2. The look and feel of data to analysts
3. Statistics intuition and common statistical pitfalls
4. Machine learning and AI
5. Data ethics
Featured Articles on Analytics Leadership and Talent
Why Data Scientists and Engineers Quit Their Jobs (Towards Data Science)
Everyone is struggling to keep their data and analytics talent. This article provides useful details on the economic, technical, and environmental reasons why data scientists quit their jobs.
In this article, Tom Davenport addresses the overarching organizational problem that "technology changes rapidly, but organizations change much more slowly". Davenport argues that there needs to be a new organizational approach to digital transformation, particularly towards AI, while also acknowledging that adopting too many new technologies at once can also be detrimental.
Data Science Lessons We're Not Learning Fast Enough (Towards Data Science)
In this article, Professor Fabrizio Fantini breaks down 3 fundamental mistakes he sees both new and established data scientists making:
1. Assuming near-perfect data
2. Misunderstanding what drives your industry
3. Attempting to predict rather than drive outcomes
Featured Articles on Data and Analytics Technology
Dashboards are Dead (Towards Data Science)
Dashboards brought widespread analytics usage through a convenient self-service tool, but many have overlooked the flaws of these systems. The author of this article, Taylor Brownlow, breaks down the issues of dashboards from a data scientist perspective and explains why data notebooks are the self-service tool of the future.
Featured Articles on Analytics Uses and Case Studies
DeepMind’s New AI Helps Restore Damaged Ancient Texts (Wired)
This Wired article dives into the amazing work Google's #AI DeepMind is doing in the academic history field. The AI technology is able to decode ancient texts that were previously indecipherable by humans.
AI is Helping Treat Healthcare as if it’s a Supply Chain Problem (MIT Technology Review)
"Why is it that Coca-Cola can deliver ice-cold cola to some of the most remote places in the world but we can't do something similar in health care?" Some groups in the healthcare industry have begun iterating on extant AI technologies within the supply chain industry, providing insights to on where to set up new clinics, how to allocate equipment and staff, and what spending to prioritize. Currently these processes are only being used in poorer countries, but the article states that the same tools could be used in the US as well.
Featured Articles on AI
The Data Scientist of the Future, According to Microsoft (Towards Data Science)
With the massive investment into automated machine learning products like Microsoft's Azure AutoML, it's predicted that there will be further ambiguity between the Data Scientist role of the past and the Data Analyst role of the future. While historically, analysts could only be reactionary and display past trends, analysts can now build features to be ingested by AutoML tooling to produce insight into the future.
MIT Sloan Management Review and BCG have assembled an international panel of AI experts to examine topical questions related to the future of responsible AI (RAI). This month's question was, "Should RAI be a top management agenda item at organizations across industries and geographies?". Industry experts overwhelmingly agreed and discuss why focusing on AI in an increasingly competitive marketplace is paramount to the survival of most companies.
Chipotle recently announced they are testing "Chippy", a custom robot designed to make tortilla chips, with plans to roll the robot out to southern California locations in the near future. While making chips might not be the most exciting development in widespread robotics and automation incorporation, the new partnership between Chipotle and Miso Robotics reflects a large industry’s eagerness to embrace automation technologies.
Entertaining Articles Featuring Analytics
This is an interesting article on how researchers set the record for fastest speed run by a robot. Machine learning techniques, particularly reinforced learning, were used to allow the robot to "learn" through trial-and-error the fastest way to run.
From computer designed hydrofoil catamarans capable of reaching speeds over 50 kts to IoT enabled data streams for fans, SailGP has transformed sailboat racing with data and analytics. Good example of both analytics disruption and innovation.
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