Harvard Business Review ($) - May 1, 2021
Getting AI to Scale
Don’t try to change everything at once, but do begin with something important.
This article suggests that disruption stems from 11 sources of macro change. To stay agile, organizations should pay attention to the early signals of change in these areas. The author advises that a cross-functional team be assigned individually to each source and meet periodically (online or offline) to share what they are finding. In this way, the team can identifying trends as well as consider what decisions might be impacted in the organization.
11 Sources of Disruption
A quantitative futurist advises leaders to pay attention to 11 sources of disruption.
McKinsey & Company - Apr 26, 2021
The strategy-analytics revolution
It’s time to bring advanced analytics into the strategy room—here’s why.
Wired ($) - Apr 26, 2021
This Researcher Says AI Is Neither Artificial nor Intelligent
Kate Crawford, who holds positions at USC and Microsoft, says in a new book that even experts working on the technology misunderstand AI.
McKinsey & Company - Apr 23, 2021
A smarter way to digitize maintenance and reliability
In heavy-equipment maintenance, digital and analytics tools are struggling to live up to their promise. Here’s how to help them pull their weight.
Wired ($) - Apr 23, 2021
Now for AI’s Latest Trick: Writing Computer Code
Programs such as GPT-3 can compose convincing text. Some people are using the tool to automate software development and hunt for bugs.
Medium ($) - Apr 22, 2021
Why We are Building an Information Company Powered by Editorial Algorithms
Applied XL raises $1.5M to develop high precision AI systems that distill insights from big data, giving professionals and investors access to reliable life sciences information.
BCG - Apr 27, 2021
The Moment of Truth in Every Digital Journey
Many companies that pursue a digital transformation get stuck. Digital initiatives that have broad potential never emerge from individual business units (BUs) or functional areas. Why? Because the companies haven’t developed the necessary capabilities to scale digital throughout the enterprise and make digital the normal way of doing business.
Medium ($) - Apr 28, 2021
How Netflix uses Data Analytics: A Case Study
The contribution of big data and analytics in the success of Netflix.
This article is about the online shopping platform, Shopee, and the way that the organization is aligned to integrate data-driven insights into new product features and offerings. The firm currently has market-facing business owners, internal engineers (including data science teams), and product managers – with the product managers bridging the gap between the other two. Historically, the business owners and product managers have worked together to design the future products. However, the data science insights (coming out of the engineering department) are beginning to inform these decisions. The article hints at the need for an enhanced two-way dialog on the product roadmap, as well as the need for product managers to have data science fluency.
Forbes ($) - Apr 20, 2021
The Future Of Work Now: Product Managers At Shopee
Shopee’s growth and success have been driven by its mobile-centric strategy, enabled by its data, analytics and AI capabilities. Over 95% of Shopee orders occur through the mobile app, and the company hyper-localizes its content and e-commerce processes within each of the markets they operate in, and integrates social interaction with e-commerce for a social shopping experience.
Medium ($) - Apr 20, 2021
Data is the basis for most digital decision-making but how reliable is data?
Data is the basis for many operations, but it doesn’t mean data is always reliable. Things can get complicated when you don’t know which data source is reliable and which is not. But we must use data all the time. Sometimes it is possible to increase the accuracy, but the more meaningful solution is to build a software layer to correct data before using it.
Medium ($) - Apr 17, 2021
Operationalization: the art and science of making metrics
Essential psychology for all data professionals
HBR.org ($) - Apr 21, 2021
How One Company Worked to Root Out Bias from Performance Reviews
About two years ago, a midsize U.S. law firm reached out to the Center for WorkLife Law to learn how bias was surfacing in their performance evaluations. The firm’s D&I director had spot-checked a sample of supervisor evaluations for bias and identified several red flags. They decided they wanted to go a step further and take a data-driven approach. (Music to our ears!)
This article focuses on the idea of “relevance” and how we need to know enough about our customers that we can deliver our product or service in the way that each specific customer wants it to be delivered. The author contends that the democratization (commoditization) of AI allows for this type of understanding of our clients, so it should be a level field to compete on the experience that we deliver. I’m not sure that this level of AI is that common, but I do like the idea of leveraging data to strive for this high bar of “relevance”.
Forbes ($) - Apr 12, 2021
How Digital Underdogs Can Fight Back With Relevance
Ten years ago, when I only waited ten minutes to get a cab, I was thrilled. Flash forward to today, if I order an Uber that’s ten minutes away, I’ll immediately cancel and request another. This is a textbook example of how consumer expectations constantly evolve due to advancements in technology.
McKinsey & Company - Apr 7, 2021
Calculating complexity: Maximizing the value of customization
New tools that help pinpoint complexity’s cost—and where it comes from—can help companies make better tradeoffs in managing product portfolios.
Medium ($) - Apr 5, 2021
How To Tell a Compelling Story With Data
3 Steps To Turn Any Data Analysis Into a Memorable Story
Harvard Business Review ($) - Mar 5, 2021
Data Is Great — But It’s Not a Replacement for Talking to Customers
Many companies rely too much on big data and analytics in their hunt for strategic insights. They’d do better if they actually went out and talked to their customers instead, as Toyota and Adobe do, because data is too rooted in what managers already think their customers are interested in.
BCG - Mar 29, 2021
Delivering on the Promise of First-Party Data
It’s no secret that first-party data—proprietary information that a company collects directly from the customer, with consent—is critical for improving relationships with customers and driving better business results. And such data will become even more essential for digital marketing success as increasing numbers of companies stop using third-party cookies to track website visitors. Moreover, BCG research has found that data-driven marketing can double revenue and increase cost savings by 1.6 times.
Harvard Business Review ($) - Mar 18, 2021
AI Should Augment Human Intelligence, Not Replace It
Will smart machines really replace human workers? Probably not. People and AI both bring different abilities and strengths to the table. The real question is: how can human intelligence work with artificial intelligence to produce augmented intelligence. Chess Grandmaster Garry Kasparov offers some unique insight here. After losing to IBM’s Deep Blue, he began to experiment how a computer helper changed players’ competitive advantage in high-level chess games. What he discovered was that having the best players and the best program was less a predictor of success than having a really good process. Put simply, “Weak human + machine + better process was superior to a strong computer alone and, more remarkably, superior to a strong human + machine + inferior process.” As leaders look at how to incorporate AI into their organizations, they’ll have to manage expectations as AI is introduced, invest in bringing teams together and perfecting processes, and refine their own leadership abilities.
Harvard Business Review ($) - Mar 8, 2021
4 Ways to Democratize Data Science in Your Organization
Where do your data efforts lie? Many organizations leave data to a team of data scientists and focus their efforts where there is lots of data. While that approach may make sense on paper, using your data more strategically and more broadly across your organization — by using data to inform big swing decisions and by getting everyone involved – your company has a higher chance for a successful data science transformation.
To make data science more strategic and democratic in your company, take the following steps. First, focus on problems or opportunities with the highest level of strategic benefit. Second, develop “citizen data scientists” across the organization. Third, reprioritize data science efforts and reassign your data scientists. Finally, develop and communicate a broad vision of data science.
All Time Favorites
McKinsey & Company - Jun 1, 2002
HBR.org - Feb 7, 2012
HBR.org - Nov 29, 2013