Here is a roundup of interesting sites, resources, and articles from around the web, curated by IIA. August’s edition features several interesting articles on creating an effective lifecycle for analytics applications. IIA’s perspectives on this topic are featured in its Analytics Application Lifecycle Framework eBook. There are also interesting articles on building a data literacy program, quantum computing, creating a data science career ladder and the rise of the data strategist role. 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.
10 Ways Enterprises Can Use the Edge
IIA’s article summary:
This article does a nice job explaining edge computing and the benefits it provides users when it comes to low latency, high throughput, and real-time analysis. John Deere is the first example in a series of 10 use cases that highlight the power of edge computing whether it’s precision farming, asset performance analytics for offshore drilling rigs, or next-gen gaming. View the full article here.
- If you’re not already using edge computing, examine some potential use cases that could benefit your organization.
- Be prepared to evaluate costs for the infrastructure required to support edge computing e.g., “the best placement of edge data centers, specifically their proximity to mobile sites.”
- Continue to search for edge computing use cases to become knowledgeable on the ever-evolving technology and inspire additional research and work within your own organization.
Featured Articles on Analytics Strategy
Legacy Companies Need to Become More Data-Driven — Fast (Harvard Business Review)
Five tactics to become more data-driven: 1. Know your business, and prioritize which data is most important to your firm, 2. Link technology investments to high-value business objectives, 3. Centralize data infrastructure, decentralize customer management, 4. Educate C-Suite executives on the business value of machine learning and AI and 5. Start small and demonstrate measurable business outcomes, while recognizing that transformational change often takes decades.
How to Build Data Literacy in Your Company (MIT Management Sloan School)
This article provides a great overview of data literacy and why it is important. It also outlines eight important elements of an effective data literacy program.
The Rise of the Data Strategist (Toward Data Science)
The need to translate business problems to analytical solutions and insights is driving the creation of data strategy and data translator roles that incorporate product thinking to the development of analytics solutions.
The Data Science Management Process (MIT Sloan Review)
This article outlines five core tasks for managing enterprise data science: 1.) Drive collaboration, 2.) Develop human capital, 3.) Ensure data quality, 4.) Manage project portfolio and 5.) Ensure business impact.
What is the Data Science Life Cycle? (Mihail Eric Blog)
Good, in-depth framework for building successful data science and analytics solutions.
Good insights on the differences between strategy (the act of making an integrated set of choices, which positions the organization to win) and planning (the act of laying out projects with timelines, deliverables, budgets, and responsibilities).
Featured Articles on Analytics Leadership and Talent
IIA Expert Adam McElhinney provides 5 useful tips for creating data science and analytics career paths, includes a practical example of the career framework developed at Uptake.
This article provides a good overview of tools and best practices that can help improve the effectiveness of your data science team.
Featured Articles on Data and Analytics Technology
Data Documentation Woes? Here’s a Framework (Toward Data Science)
Data documentation can yield great value, but it is difficult to institutionalize good practices. This article has some good insights on building a documentation-first culture.
Why Unstructured Data is the Future of Data Management (VentureBeat)
Good interview with Krishna Subramanian, President and COO of Komprise, on the growing need to manage unstructured data for regulatory, analytic, and decision-making purposes.
How Does a Quantum Computer Work? (Scientific America)
Good 8-min video overview of how a quantum computer actually works.
Featured Articles with Analytics Uses and Case Studies
The Pursuit of AI-Driven Wealth Management (MIT Sloan Review)
Tom Davenport and Randy Bean explore recent the use of AI in wealth management by Wealthfront, Vanguard and Morgan Stanley.
The US healthcare system suffers from a fundamental misalignment of incentives among its three participants (providers, insurers, and pharma/devices). Pending regulations focused on transparency will open up new analytics opportunities in healthcare.
A good overview on applying AI and ML to call center activities that improve customer experience.
Good insights on how leading retailers are leveraging AI-driven chatbots to improve customer service and engagement.
Fraud Detection with Graph Analytics (Toward Data Science)
Lina Faik from Dataiku provides a great overview on how to use graph analytics for fraud detection.
Featured Articles on AI
Self-Driving Cars are Self-Driving Bullets (The Mobilist)
The quest to create self-driving automobiles continues to prove difficult and this article from Clive Thompson provides a good summary of the challenges that remain.
AI has Become a Design Problem (VentureBeat)
AI faces numerous issues on the road to commercialization including understanding how the models actually work, controlling the data that feeds AI, and addressing growing distrust in those that wield this technology. While engineering solutions can address many of these issues, human-centric design is equally as essential.
AI For Business: Myths and Realities (Forbes)
4 AI myths and some essential best practices for AI success including data literacy and AI literacy.
Featured Resources and Blog Sites
This is a great resource for COVID19 forecast data and includes a nice visualization tool.
A comprehensive list of 19 AI books to read in 2020. Includes synopsis of each book and links to more information.
Open Data Science features articles on data science topics including platforms, Data Ops, AI, ML, visualization and more. They also hold conferences and events.
Alistair Croll will serve as master of ceremonies for IIA's 2021 Fall Symposium. His new book, with co-author Emily Ross, explores how innovative companies "subvert the system" by finding and exploiting loopholes in how we share, discover, and act.
Featured News and Information Sites
Datafloq covers big data, blockchain, artificial intelligence and other emerging technologies from a European perspective.
Inside Big Data is a news outlet that distills news, strategies, products and services in the world of Big Data. They publish a regular 'In the News' column and a quarterly 'Impact 50' list of the most important big data companies that are very useful.
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.