Prepared Minds

Don’t try to change everything at once, but do begin with something important.

A quantitative futurist advises leaders to pay attention to 11 sources of disruption.

It’s time to bring advanced analytics into the strategy room—here’s why.

Kate Crawford, who holds positions at USC and Microsoft, says in a new book that even experts working on the technology misunderstand AI. 

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.

Programs such as GPT-3 can compose convincing text. Some people are using the tool to automate software development and hunt for bugs. 

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.

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.

The contribution of big data and analytics in the success of Netflix.

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. 

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.

Essential psychology for all data professionals

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!)

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.

New tools that help pinpoint complexity’s cost—and where it comes from—can help companies make better tradeoffs in managing product portfolios.

3 Steps To Turn Any Data Analysis Into a Memorable Story

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.

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.

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.

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.