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March: Best of the Web

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 new analytics architectures, an outline of how legacy companies can use AI-driven platforms, upcoming data science trends, how to apply product thinking to analytics, and why there needs to be a paradigm shift within the roles of data executives. There is also an entertaining article exploring the use of analytics to create the best march madness bracket. 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.

Emerging Architectures for Modern Data Infrastructure (Future / a16z)

This article provides a detailed overview of emerging data and analytics system architectures from the perspectives of a16z, a leading venture capital firm, and interviews they conducted with experts and practitioners. The article includes a reference data infrastructure architecture based on these discussions, emerging trends that companies should watch, and it explores three common blueprints (modern business intelligence, multimodal data processing, and AI/ML).

How Legacy Companies Can Pivot to a Platform Model (Harvard Business Review)

Facebook, Amazon, and Google have created powerful, AI-driven platforms of huge value. Can legacy companies learn from and duplicate their success? This article by IIA Co-founder Tom Davenport outlines six things legacy companies must be able to do if they want to leverage a platform business model and provides examples of companies that have successfully done so.

5 Data Science Trends in the Next 5 Years (Open Data Science)

This article explores five broad data science trends to watch over the next half-decade including: 1.) Better naming conventions; 2.) Sustainable applications outside of technology industries; 3.) Data-centric modeling; 4.) Decision science expertise and 5.) Data science creator economy

Sorry But You Are Not a Product Manager (Medium)

IIA has identified the ability to apply "product thinking" to analytics development as a key for advancing analytics maturity. This article outlines six characteristics of good product management that are applicable to analytics development.

The Missing Analytics Executive (Benn Stancil)

"There exists a treadmill at the top." The story of an ambitious data scientist following a promotion to management only to find the role underwhelming and disconnected is all too common, CAO Benn Stancil claims. In this article, Stancil argues for a paradigm shift regarding the roles of analytics leaders, for the benefit of the company and the employee.

For additional insights on the importance of modern architectures, product thinking, and leadership, download IIA's 5 Differentiators to Advance Analytics Maturity Whitepaper (or the full research brief for IIA clients) or 5 Differentiators for Advancing Analytics Maturity eBook.

​​Featured Articles on Analytics Strategy

How Netflix Built Its Real-Time Data Infrastructure (VentureBeat)

How has Netflix built the infrastructure that enables the analysis of Netflix user and operational data to serve subscribers better? This article breaks down this journey in four evolving phases that occurred over five years.

The Gap Between Data Science and the Organization (Towards Data Science)

There exists a gap between the idea of being data-driven and the practice of it - 87% of data science projects never make it to production. This article explains why this gap exists and how we can we work to close it.

Featured Articles on Analytics Leadership and Talent

Effectively Scoping and Communicating Data Science to Decision Makers (Towards Data Science)

In this article, data scientist and former salesperson Nathaniel DiRenzo details his process on how to effectively contextualize and communicate results to decision makers from a value proposition perspective.

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 Data Can Make Better Managers (Harvard Business Review)

The best leaders are not afraid of unfamiliar technologies, instead they adopt an opportunity-oriented mindset and understand the applications of new tech. This article addresses three examples for which Computational Leadership Science (CLS) provides short-term and long-term value for leaders who want to take a data-driven approach to their everyday decisions.

Why Machine Learning Engineers are Replacing Data Scientists (Medium)

As advances in machine learning and artificial intelligence have made their way into the world of business, the position of "machine learning engineer" has seen a massive increase in demand. This article explains the difference between ML engineers and data scientists, and why many data scientists are looking to make the switch to ML engineering.

Why Do Chief Data Officers Have Such Short Tenures? (Harvard Business Review)

IIA Co-Founder Tom Davenport co-authored this HBR article exploring why CDOs have such high turnover rates. In the article the authors, along with Guy Peri, a 6-year tenured CDAO, explain how companies can better define the role and avoid short tenures.

Data-Driven Diversity (Harvard Business Review)

Many companies have implemented initiatives to educate their employees about diversity, equity, and inclusion (DEI). While training is important, this article advocates for a metrics-based approach that can identify problems, establish baselines, and measure progress while also outlining the key considerations for any diversity action plan.

For additional insights on the building a data-driven culture, download IIA’s Dimensions of a Data-Driven Culture eBook (or the full research brief for IIA clients).

Featured Articles on Data and Analytics Technology

Data Literacy Deep Dive: An Introduction to AI, ML and Prediction Literacy (VentureBeat)

Data, AI, ML and prediction literacy are fundamental skills in a world where your personal data, and the preferences and biases hidden in that data, are being used to influence your behaviors, beliefs, and decisions. This article is great resource to begin understanding the ins and outs of AI and ML.

AI Can Change How You Measure — and How You Manage (MIT Sloan Review)

Data-driven leaders are now using AI to surface new KPIs and increase alignment through what David Kiron calls "predictive alignment" — organizations that obtain substantial financial benefits from AI are 10 times more likely to change their KPIs because of AI than other organizations. This article discusses research from MIT Sloan that explains how predictive alignment works and how it can help create new holistic KPIs for your business.

Featured Articles with Analytics Uses and Case Studies

This App Can Diagnose Rare Diseases from a Child's Face (Wired)

Machine learning is driving business differentiation — and medical diagnoses. Read more about how unique ML algorithms are identifying rare diseases in children from photos.

Featured Articles on AI

The 10 Most Innovative Companies in Artificial Intelligence of 2022 (Fast Company)

Fast Company created this interesting list of AI companies as part of the annual Most Innovative Companies issue. Featured companies and applications include LivePerson (conversational AI), Grammarly (content creation), LinkSquares (contracting), immunai (drug development) and Darktrace (cyber security).

What Business Executives Need to Know About AI (VentureBeat)

For many business leaders, AI is still a mystery. They want to apply AI to their business, but the details surrounding its components, implementation, integration and ultimate purpose are not clear. This article provides a good overview of AI areas that executives should understand including development approaches, common challenges, and design principles that lead to successful model deployment.

How Levi’s Uses AI to Accelerate its Design Process and Digital Transformation (VentureBeat)

As ubiquitous as machine learning is in the enterprise sphere, a legacy company such as Levi's might not be the first brand that comes to mind when you think of AI capabilities. In this article, Levi's design coordinator is interviewed about his personal work with machine learning algorithms and how legacy companies might utilize AI and ML with old and unused data.

How Data Can Make Better Managers (Harvard Business Review)

The best leaders are not afraid of unfamiliar technologies, instead they adopt an opportunity-oriented mindset and understand the applications of new tech. This article addresses three examples for which Computational Leadership Science (CLS) provides short-term and long-term value for leaders who want to take a data-driven approach to their everyday decisions.

Entertaining Articles Featuring Analytics

2022 March Madness Predictions (FiveThirtyEight)

This is a great data tool for filling out your tournament brackets. Predictions are based on a composite of six computer power rankings. Read the 'How This Works' to learn how the model works.

About IIA

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. IIA’s family of analytics assessments provide actionable, diagnostic insights for organizations looking to maximize their analytics performance.