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 the importance of an effective data strategy; the key mindsets that differentiate successful analytics leaders; why you should not force data scientists to do scrum; and how the role of a CDO changes in a data mesh world. There is also a great set of articles covering the key analytics and AI events of 2021 and predictions for 2022. Follow us on Twitter (@iianalytics) 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 with here) features timely and relevant third-party articles. Here are the articles highlighted in the “Article of the Week” from the January Normal Distribution emails.
IIA CEO Jack Phillips authored an editorial this week in industry news source Inside Big Data entitled “Leading and Organizing Analytics for Success. Here’s What the Emerging Leader of Analytics Will Look Like.” In the editorial, Phillips argues that while analytics used to be everyone’s job, it is time to identify a group of leadership qualities that are specific to the skills, background, and behaviors of data and analytics leaders who are the newest members of many enterprise executive teams. Based on IIA interviews with analytics leaders at organizations such as John Deere, Cigna, CVS Health, Phillips has identified six core categories for analytics leaders: personal/character; leadership; organizational; business; quantitative; and technological.
For additional insights, read the blog from Jack Phillips and Jason Larson:
The Role of the CDO in the Data Mesh World (Toward Data Science)
Data Mesh is a federated architecture where data is made available from logical domains rather than from a centralized store. CDO's attempting to follow a traditional model of data governance espoused by many CDO's will result in blurred lines, grey areas, gaps and unnecessary friction between parties. Instead, CDO's must become strategic, transformational thinkers driving the data agenda forward; facilitators rather than doers; and champions of a data culture.
Why Do You Need a Data Strategy? (Toward Data Science)
Most organizations don't yet have a strategy in place on how to extract the right value from data to establish a competitive advantage. This article provides a good overview on aligning your data strategy to your business strategy and the three key components of a successful data strategy (data value creation, data foundation and execution plan).
For additional insights on how to create an effective data strategy download IIA’s Creating a Data Strategy.
10 Reasons to Combine Digital Twins and Synthetic Data (VentureBeat)
Synthetic data and digital twins are complementary approaches for leveraging real-world data to improve AI and product design. This article outlines 10 areas where combining these approaches can deliver value including personalized medicine, supply chain management, and customer experience.
Featured Articles on Analytics Strategy
Don't Make Data Scientists Do Scrum (Toward Data Science)
Scrum has become very popular over the last decade and many organizations are trying to apply it to data science work. This article explores the use of scrum in data science work and provides some suggestions on other ways to achieve the desired outcomes of agile (individuals and interactions over processes and tools) in data science work.
The lack of tangible results from AI has less to do with the technology and more to do with how people interact with the technologies to achieve intended objectives. This Forbes article outlines three reasons why AI initiatives fail (lack of leadership buy-in, inadequate user training, and fear of human obsolescence) and how well-designed user education can prevent AI failure.
Data Science in 2022 and Beyond (Toward Data Science)
Advances in low-code/no-code tools will enable more businesses to leverage the power of data science. Organizations will still need to upskill their workforce in three key areas to take advantage of these tools: 1) mindset (training employees to think more like data scientists); 2) Skillset (no-code/low-code tool experts); and 3) Dataset (applying tools to build specific business solutions).
This ZDNet article features a roundup of 2022 analytics and AI predictions from a variety of articles. Topics include supply chain challenges, continued talent shortages, growing interest in data mesh/data fabric architectures, and increased use of no-code/low-code.
Getting results from data science and machine learning strategies remains elusive for many enterprises. This article outlines some key areas to consider in your 2022 roadmap including Adaptive ML, collaborative workflow support, MLOps, better privacy policies, and better focus on use cases and metrics.
Featured Articles on Analytics Leadership and Talent
This interesting KD Nuggets article by Eric Siegel (@predictanalytic) summarizes results from a recent KD Nuggets poll and explores why a lack of prudent leadership leads to the pervasive failure of ML projects.
The Secret Ingredient of Thriving Companies? Human Magic. (Harvard Business Review)
IIA has identified innovation-oriented cultures as a key differentiator for advancing analytics maturity. The traditional corporate approach to motivating people has been a combination of financial carrots and sticks designed to mobilize everyone around a plan designed by a few smart people at the top. However, multiple studies confirm that for work involving cognitive or creative skills, traditional financial rewards don't motivate employees or drive performance. This HBR article outlines six ingredients for unleashing creativity with innovation-oriented cultures.
For additional insights on the importance of innovation-oriented cultures download IIA’s 5 Differentiators to Advance Analytics Maturity.
Featured Articles on Data and Analytics Technology
Top Data Science Cheat Sheets (Medium)
This article features a useful roundup of data science cheat sheets. Topics include Python, R, SQL, data visualization tools, machine learning, and deep learning.
Among the ten technology themes for 2022 from Fast Company include continued changes in remote work, the transformation of healthcare, and the growing importance of cybersecurity.
Featured Articles with Analytics Uses and Case Studies
The Anatomy of a Data Science Use Case (Toward Data Science)
This article provides a good overview of why well-defined data science use cases are needed and recommends they include 4 components: 1) Business Problem; 2) As-Is Scenario; 3) To-Be Scenario; and 4) Technical Solution.
Catching Up Fast by Driving Value From AI (MIT Sloan Management Review)
In this article, IIA co-founder Tom Davenport (@tdav) and Randy Bean (@randybeannvp) profile how Scotiabank was able to catch up in AI by leveraging a pragmatic strategy of focusing on business value, improving existing operations, and facilitating closer relationships with customers.
How AI Could Prevent the Development of New Illicit Drugs (Scientific American)
Synthetic opioids are increasingly driving opioid-related deaths in the United States, which reached more than 75,000 this year. This Scientific American article explores how law enforcement and forensic chemists are using a deep learning algorithm called DarkNPS to identify potential designer drugs that may not yet even exist, with the goal of helping law enforcement and regulators keep pace with this rapidly growing problem.
How Marketers Can Address Data Challenges to Drive Growth (MIT Sloan Management Review)
Today’s marketers are more responsible than ever for leveraging their company’s data and analytics capabilities to shape a better customer experience. The Marketing Analytics Canvas outlined in this article is a useful framework for planning and implementing marketing analytics solutions.
Featured Articles on AI
Top 5 Techniques for Explainable AI (Toward Data Science)
Widespread adoption of AI will require stakeholders to understand how AI predictions are generated. This article outlines ways to make AI explainable using five techniques: 1) data visualization; 2) logistic regression models; 3) decision tree models; 4) neural network models; and 5) SHAP.
AI-at-Scale Hinges on Gaining a ‘Social License’ (MIT Sloan Management Review)
The public has a variety of concerns about AI, including the algorithmic institutionalization of income, gender, racial, and geographic prejudices; privacy concerns; and political issues. Winning a social license to deploy AI will require organizations to adhere to the principles of responsible AI design; ensure that all stakeholders perceive the benefits of using AI as greater than the costs; and demonstrate that they can be trusted and will be accountable.
10 AI Predictions For 2022 (Forbes)
Among the ten Forbes Magazine AI predictions for 2022 are a growing interest in language AI, increased valuations for climate AI startups, and an end to U.S. and Chinese AI collaboration.
2021 was an eventful year for AI. This VentureBeat article reviews some of the biggest AI trends from 2021 including multimodal models, large language models, increasing pressure to commercialize AI, increased compute power, and growing regulation, while it also looks ahead to 2022.
Interesting Sports and Science Articles
DeepMind has created a system called Player of Games, which can perform well at both perfect information games (e.g., the Chinese board game Go and chess) as well as imperfect information games (e.g., poker). This technology has applications for real-world use cases like traffic planning, contract negotiations, and customer service.
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.