Research

Sanford Health Becomes Data-Driven

By Robert Morison, Jun 26, 2019

Available to Research & Advisory Network Clients Only

Sanford Health is a major health system with over 49,000 employees, 187,000 health plan members, and $6.1B in annual revenue. The organization has been pursuing a growth strategy for the last two decades. Milestones include: merging with a series of regional health providers, incorporating the North Dakota Public Employees Retirement System (NDPRS) members into its health plan, and most recently the 2019 merger with Good Samaritan Society, the largest not-for-profit provider of senior housing and services in the United States.

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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.

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Building a Storytelling Culture Inside Data and Analytics

By Ruth Milligan, May 22, 2019

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Let’s start with why storytelling matters in data and analytics. Brad Lemons, the SVP of Customer Insights and Analytics at Nationwide Insurance, is known to say to his team, “If you can’t sell your insights, they are worthless. Storytelling is not an option, it is a requirement.” Likewise, Scott Berinato argues that storytelling is one of six “musts” for a strong data science organization. But it persists nonetheless as an unresolved competence gap with only a few shining examples.

Storytelling reveals data insights and analytics science. After completing the rigorous problem-solving and data analysis for a business challenge, it is the best chance of synthesizing the insight to advance key business objectives.

Storytelling is an art, not a science. Analytics professionals tend to be scientists, not artists. The innate ability to understand how people hear and listen is not usually a fluency among the scientific set of analytics practitioners. It demands use of emotion, using the senses so that people can remember and repeat what was shared. It is no less rigorous than science, however, in that a strong story requires rounds of iteration and feedback to ensure it supports the key insights.

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Analytics Fluency – How Optum Is Boosting Six Critical Competencies

By Alex Barclay, May 08, 2019

Available to Research & Advisory Network Clients Only

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.

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Inquiry Response: Agile For Analytics

By IIA Expert, Apr 22, 2019

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Inquiry:

We use an Agile-like methodology for analytics projects and are always looking for ways to improve our execution and speed. Do you have any advice?

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Portland 2019 Analytics Symposium Video: Zachery Anderson

By Zachery Anderson, Apr 17, 2019

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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.

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Portland 2019 Analytics Symposium Video: Michael Li

By Michael Li, Apr 17, 2019

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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.

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Portland 2019 Analytics Symposium Video: Matt Levinson

By Matt Levinson, Apr 17, 2019

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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.

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Building The Analytics Factory at Deere

As a 180-year-old company with 65,000 employees in 30 countries, Deere is the stark opposite of a digital native. Incorporating analytics into different parts of the company has required significant transformation on both the technical and people sides. But all changes have been grounded in the company’s foundational values.

Transformation has required partnerships between the analytics function and other stakeholders, including IT, manufacturing, sales, legal, and more. Partnerships and flexibility have been necessary in reworking traditional processes to become faster and more iterative, and in revising governance and decision making.

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Portland 2019 Analytics Symposium Video: Jesse Anderson

By Jesse Anderson, Apr 17, 2019

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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.

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