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 a blog on the future of privacy leadership, an article asking the question "How Much Data Is Too Much for Organizations to Derive Value?", and a piece on the behavioral aspect to getting the most out of Gen AI. Follow us on Twitter and LinkedIn to receive daily updates on IIA content and curated content as it becomes available.
Featured Articles on Leadership and Culture
Google No Longer Has a Chief Privacy Officer. Should You Follow Suit? (Information Week)
Google's decision to eliminate the Chief Privacy Officer (CPO) role has stirred significant discussion in the tech industry. Instead of having a single CPO, Google is embedding privacy professionals within individual product teams. This restructuring aims to integrate privacy considerations directly into product development processes, potentially enhancing privacy standards across all products.
Done is Better Than Perfect (Towards Data Science)
Perfect is the enemy of good - even in data science. The article emphasizes the importance of timely results and their impact compared to "flawless" work. The author suggests being more pragmatic by considering reversibility, cost, and impact of decisions.
Featured Articles on Analytics and AI Strategy
When Generative AI Meets Product Development (MIT Sloan Review)
This piece discusses how Gen AI is being integrated into product development and gives some examples like Boston's Loft design agency who use AI to generate product ideas and refine designs. Gen AI's usage in current product design is purely for boosting innovation efficiency.
The 6 Disciplines Companies Need to Get the Most Out of Gen AI (Harvard Business Review)
Some observers are beginning to question whether gen AI will produce enough value to exceed its costs. It can, but extracting economic value from gen AI requires several different types of disciplined capabilities. Unfortunately, most companies lack these. The good news is they can develop them. Specifically, companies should invest in behavioral change, controlled experimentation, measurement of business value, data management, human capital development, and systems thinking. Once these capabilities are in place, companies should focus on picking the right projects for gen AI. They should do this by 1) funding the responsible rebels, 2) choosing projects that are quick, practical wins, and 3) linking those projects to the company’s identity.
Featured Articles on Data and Data Storytelling
How Much Data Is Too Much for Organizations to Derive Value? (Information Week)
This article discusses the challenges organizations face in managing and deriving value from large volumes of data. It highlights that while more data can provide deeper insights, it can also lead to data overload, making it difficult to extract actionable information. The key is finding a balance between collecting data and effectively analyzing it to drive business decisions.
Describing Data: A Statology Primer (KD Nuggets)
This primer covers measures of central tendency, measures of dispersion, and the SOCS framework for understanding data distributions.