International Institute for Analytics

Southwest Airlines recently launched an Enterprise Data Science Center with the objectives of expanding data science capability, deploying it broadly across the company, and creating competitive advantage. Design of the Center relied upon a series of strategic and tactical conversations with IIA Experts on analytics organization structures. Today, Southwest’s Center features disciplined delivery processes performed by data scientists in clearly defined roles who engage with the business in flexible ways. IIA’s Robert Morison collaborated with Southwest’s Justin Bundick, Director of the Enterprise Data Science Center, to capture the story.

Inquiry:

I manage our analytics team, and it has always in the marketing function. Now other business functions are interested in analytics too. How can I configure analytics to service the growing enterprise-wide needs?

Not long ago, the role of Data Scientist was what most companies wanted to discuss with me in terms of roles they needed to understand and add to their organizations. Then, the role of Data Engineer became a big topic of discussion. In the past year, there has been a massive increase of attention being paid to yet another role that is still new enough that its title hasn’t been standardized. This role is referred to by a range of names from Analytics Translator, to Analytics Catalyst, to Analytics Liaison, and more.

Optum has launched a number of initiatives to boost analytics fluency, especially among its business leaders and team members. The goal is to equip individuals in business units, operations and other key parts of Optum with the knowledge and skills needed to effectively engage, employ and capitalize on analytics. While our efforts are a work in progress, we view analytics fluency as a critical prerequisite to “competing on analytics” and key to our mission of transforming health care. The next sections provide an overview of Optum and the challenges we’re addressing in health care, while subsequent sections describe the motivation for and our experience with fluency-building initiatives to date.

It’s No Game to Find and Keep Your Data Scientists - EA Battles The Market Forces for Talent

In 2013/14, EA’s voluntary turnover among data scientists was 21-22%. It is now 8%, with consistent improvements. These improvements occurred without major changes in compensation and without disproportionate change in investment in the analytics platform, which are common data scientist complaints.

Employment and Training in The Era of AI

As AI replaces some jobs and changes others, it raises questions of, “What is the role for humans in the AI world?” It is most useful to see humans and AI working together, taking advantage of the strengths of each.

The training and learning tracks will vary by role. Foundational learning will be required in all technical roles including basic software engineering, data wrangling, predictive analytics, and data visualization. Data scientists will require additional training in advanced machine learning; data engineers will require more immersion in distributed computing.

The demand for data scientists and analysts is estimated at 140,000 to 190,000. But the demand for data-savvy managers is even greater at 1.5 million. It is unlikely universities will be able to meet this demand. Universities tend to be more theoretical and less focused on practical application. Private training will be needed to fill the gaps.

Nike Gets Up and Running With Machine Learning and AI

Embarking on an AI journey starts with executive leadership and strategic vision. It requires alignment of the culture and capabilities. At Nike, the key elements have been business leaders wanting to be data driven, demanding deeper information, and being committed to enabling the organization.

The first step in getting up and running at Nike was unification of data science activities. For example, previous efforts were in silos by product group, such as a running app. This resulted in consumers having multiple Nike digital IDs. Having one ID per person was essential. Also important was unification of reporting so everyone at Nike was looking at the same numbers.

Creating A Data Engineering Culture

Data scientists get the glory, but when they experience success it is due to a data engineering culture. In a data engineering culture, the value and importance of data engineering are recognized throughout the organization.

Agile for the Data Science Team

Agile is a methodology and a way of working, originally created to improve the speed and results of software development. It emphasizes individuals and interactions over processes and tools; working software over documentation; and collaboration with customers and responsiveness to change. Benefits include greater predictability, adaptability, transparency, and accountability. Agile is known for working in “scrums”—which are teams with well-defined roles. Scrums engage in “sprints,” which are specified work in a set time period.

Dataops and the Impact It Will Have on How We Do Analytics

What do high-performing technology organizations do differently to transform their organizations and deliver astounding business results? This is what Gene Kim has studied for 20 years. His research took him to the DevOps movement.