Reading list: Applying analytics 

Analytics. It is something every company should be using. Right?

Netflix uses it not only to suggest what viewers should watch, but to decide what projects to fund. Disney is using it to reduce ride wait times and deliver better customer service.

Most organizations can benefit from introducing analytics into their processes. But doing it right requires work. Here are some examples to get you started:

Analytics are providing a competitive advantage. For the first time in four years, the number of companies gaining a competitive advantage from analytics increased. This is in large part due to companies not only deriving insights from data and analytics, but also innovating with analytics. This report, which explores the foundation for these innovation processes, is based on MIT Sloan Management Review’s 2017 Data and Analytics report, and includes responses from 2,602 business executives, managers, and analytics professionals.

Read “Analytics as a source of business innovation” at MIT Sloan Management Review.

Analytics can help with hard decisions. Doctors who perform kidney transplants typically have as little as an hour to decide whether they should accept or reject a donor kidney for their patient. They make those decisions based largely on intuition, but according to MIT Sloan professors Nikos Trichakis and Dimitris Bertsimas, it is impossible for surgeons to know if a better kidney may end up being offered to their patient. To help doctors make more informed decisions, Trichakis and Bertsimas developed an analytics-based predictive model that doctors can use to determine the probability of a better kidney becoming available.

Read “How an analytics-based predictive model can improve kidney transplant survival rates.”

Companies in any industry can learn from how analytics is used in sports. Sports teams at all levels, from high school through professional, are using analytics to construct rosters, develop game plans, and improve athletes’ health and wellness. But analytics was not always accepted in the sports industry, writes MIT Sloan professor Ben Shields in MIT Sloan Management Review. Other industries looking to integrate analytics into their processes can use the three strategies outlined in this article as a framework.

Read “Integrating analytics in your organization: Lessons from the sports industry” from MIT Sloan Management Review.

Startup funding can be smarter. How do many venture capitalists decide where to invest their money? They rely on instinct or prioritize companies from people they know, according to Arturo Moreno, MBA ’17. His company, PreSeries, has created a machine learning-powered platform to introduce analytics into the process. The goal is to make funding startups an objective, data-driven process.

Read “PreSeries wants to make it easier for startups to get funded.”

There’s a human side of analytics. Tracking analytics is one thing. Acting on analytics is something else entirely. And it takes human intuition to figure out what information is important and what is not. Sometimes employees have to present their data findings to people who aren’t familiar with using data or analytics. In those situations, how you present your data is important. As is applying a human touch to the mathematical models that inform analytics. This article explores what two MIT Sloan students learned during a course about doing just that.

Read “Talking to your boss about data.”

Analytics teams aren’t delivering what they are capable of — yet. Analytics teams have the potential to lead business innovation, but most are seen as internal consultants or service providers, writes head of BMO Financial Group’s analytics centre of excellence Lori C. Bieda in MIT Sloan Management Review. In a field with a dearth of talent, a high turnover rate, and difficulty translating analytics findings for managers, this article offers ways management can hold on to talent and, in turn, monetize their data.

Read “Leading analytics teams in changing times” from MIT Sloan Management Review. 

Find a needle in the data haystack. The typical process for drug discovery can take years and cost billions of dollars, according to twoXAR co-founder Andrew M. Radin, MBA ’14. By analyzing large data sets of biomedical information to find unexpected associations between diseases and the drugs that could treat them, though, twoXAR can shave years off that process and save companies money.

Read “Bringing big data to drug discovery.”

People analytics matter. Analytics is now being used in human resources management. “People analytics” can result in better hires and increased profits, and can even help correct workplace biases, according to a blog post from MIT Sloan Executive Education.

Read “Gaining competitive advantage with people analytics” at MIT Sloan Executive Education.