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May: 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 how to frame data science problems, how to create a digitally literate company from the ground up, and how to have a mindset that’s conducive to success in data science. There is also an entertaining article on how the top 600 soccer clubs are ranked by ESPN. 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 subscribed here) features timely and relevant third-party articles. Here are the articles highlighted in the “Article of the Week” from the February Normal Distribution emails.

What Does Career Progression Look Like for a Data Scientist (Towards Data Science)

So, what does career progression look like for the "sexiest job of the 21st century"? This article details how data scientists’ roles evolve through the corporate ladder across four different areas:

  • Problem solving
  • Communication
  • Managing Relationships
  • Work planning and delegation

Framing Data Science Problems the Right Way from the Start (MIT Sloan Review)

The failure rate of data initiatives is estimated to be over 80% - why is that? This article makes the case that because initial data science problems are poorly defined, they lead to a massively inflated project failure rate. Furthermore, it describes how you can better define your data problems and find more success in your data projects.

Democratizing Transformation (Harvard Business Review)

Harvard Business Review has a great article series on creating a digitally literate organization. This is the first piece in the series and covers the five stages of digital transformation, from the traditional stage, where digital and technology are the province of the IT department, through to the platform stage, where a comprehensive software foundation enables the rapid deployment of AI applications.

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 separate ROIs for different sub-teams.

The Mindset of a Successful Data Scientist (Towards Data Science)

IIA has written about the six must-have mindsets of a successful analytics leader. This thoughtful article also discusses the often-overlooked mental aspects of a being successful data scientist. Specifically, the author discusses having a goal-oriented mindset when selecting models, how to navigate relationships with stakeholders, and learning how to fail.

Featured Articles on Analytics Strategy

Estimating the Total Costs of Your Cloud Analytics Platform (InformationWeek)

In order to facilitate an enterprise data analytics process, sometimes tens of software products are needed. When purveying the data analytics software landscape, the task of estimating the cost of the various stacks can be daunting. Thankfully, this InformationWeek article succinctly provides various estimates on the most popular software stacks including the Azure stack, AWS stack, Google stack, and more.

Don’t Let a Misguided AI Strategy Sabotage Your Brand Experience (Forbes)

AI is starting to become a mainstay in businesses all around the world. But are they trading efficiency for effectiveness? This Forbes article details the shortcomings of AI-centered self-service technology, how it's degrading some brands' images, and how to change a brand-eroding AI strategy into a brand-building AI strategy.

Lean Data Science (Locally Optimistic)

7 out of 10 executives whose companies had made investments in artificial intelligence (AI) said they had seen minimal or no impact from them, according to a 2019 MIT SMR-BCG Artificial Intelligence Global Executive Study and Research Report. This article postulates that the cause of this disfunction is that AI teams have focused too much on technological methods and not enough on how to deliver traditional business value. The author goes into great detail on how to apply a lean approach to data science, describing:

  • How to measure business outcomes, not model performance
  • How to ship early and often
  • And how to embrace failure

The Next Great Digital Advantage (Harvard Business Review)

The third article in HBR's great series on the digitally literate organization covers the concept of competing on product-in-use data through datagraphs. "We’ve all seen the signs in front of McDonald’s announcing 'Over X Billion Served' and have watched the number rise over the years. But tracking how many burgers are sold every day, month, or year is a relic of the past. Today ask: Do we know where each consumer buys her burgers? At what time? What does she drink with it? What does she do before or after buying a burger? How can we satisfy more of her needs so that she keeps coming back? Datagraphs capture this information, helping to reshape competition in every sector. Leaders must invest in upgrading their data architecture to enable a real-time, comprehensive view of how consumers interact with their products and services so that they can develop unique ways to solve customer problems."

Your Data Initiatives Can’t Just be for Data Scientists (Harvard Business Review)

"Without buy-in from your company’s rank and file, even the cleverest AI-derived model will sit idle and 'data-driven decision-making' will just go around in circles. Companies need to start seeing regular people as part of their data strategy. Data teams must work with regular people every day, develop a feel for their problems and opportunities, and embrace their hopes and fears surrounding data, then focus on equipping people with the tools they need to formulate and solve their own problems." In addition to making the case for incorporating non-technical people into your data projects, this article also gives examples of roles laymen can take during each step of a project.

Featured Articles on Analytics Leadership and Talent

Top 3 Data Science Courses Every Data Scientist Should Consider (Towards AI)

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.

10 Books Every Data Practitioner Should Read (Medium)

AI Consultants, QuantumBlack, have a book club that put together this thorough reading list with books covering topics from everyday algorithmic thinking to gender data gaps.

Helping Nontechnical Execs Select Analytics Solutions (VentureBeat)

Analytics investments can be some of the costliest investments for an organization, and unsurprisingly this means more and more people are roped into the purchasing decision. As more nontechnical individuals are brought into the purchasing decision, the need to effectively explain and convince new analytics solutions to lay people rises. This article breaks down how to see a purchasing decision from various perspectives and be more persuasive to non-technical decision makers.

5 Ways to Address Regulations Around AI-enabled Hiring and Employment (VentureBeat)

Fears of bias in AI hiring practices have long been documented, but the first piece of legislation on the matter was not passed until extremely recently. With more legislation expected to be passed around the world, this article details 5 tips for employers looking to adopt AI HR practices with the future restrictions in mind.

Featured Articles on Data and Analytics Technology

Is Data Mesh Fool’s Gold? Creating a Business-centric Data Strategy (Data Science Central)

The author of this article argues that many large organizations are wrongly spending time and money building out data meshes that aren't supported by underlying holes in fundamental business aspects, such as alignment on value creation. The article follows up with a data mesh primer and discusses the difficulties of data meshes.

Featured Articles with Analytics Uses and Case Studies

Deep Learning Poised to ‘Blow Up’ Famed Fluid Equations (Quantum Magazine)

For centuries, mathematicians have tried to prove that Euler’s fluid equations can produce nonsensical answers. A new approach to machine learning has researchers betting that “blowup” is near.

Featured Articles on AI

The Power of Natural Language Processing (Harvard Business Review)

Consensus on AI would say that computers outperform humans on data-driven decision making but are still worse than humans at qualitative tasks. That could change soon with a natural language processing (NLP) tool. This article explores the rapid advancements in NLP tech and how companies can best utilize it.

AI on the Front Lines (MIT Sloan Review)

"End users often resist adopting AI tools to guide decision-making because they see few benefits for themselves, and the new tools may even require additional work and result in a loss of autonomy." This MIT article details the woes of typical end user-AI conflict and explains 3 roots of end user resistance and 3 ways to encourage front-line adoption.

When is AI Actually Explainable? (Medium)

If you've felt like you're not up to speed on Explainable AI (aka XAI), you're in luck. This all-encompassing article covers:

  • Exactly what XAI is
  • Why people are interested in XAI
  • The different types of XAI
  • What's on the horizon for XAI

AI Adoption in the Enterprise 2022 (O’Reilly)

O'Reilly just released the results of their 2022 AI adoption survey with some surprisingly similar results to 2021's survey. Has AI adoption stagnated? You can read the report here and what O'Reilly thinks about the current crossroads we are at in enterprise AI adoption.

Entertaining Articles Featuring Analytics

Global Club Soccer Rankings (FiveThirtyEight)

FiveThirtyEight has compiled a list of the top 640 soccer clubs based on a complex analytical rating system that has been developed for ESPN. The list is updated after every match a team plays. You can find out how the system works here:

AI Sommelier Generates Wine Reviews Without Ever Opening a Bottle (Scientific American)

"A new algorithm writes wine and beer reviews that sound like they were penned by human critics. Is that a good thing?" This article explores how researchers are using AI to write evocative reviews and why they're doing it.

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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.