International Institute for Analytics

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.

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.

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.

GE aspires to be an algorithmic business, but recognizes this transition will not occur overnight. It will occur in stages as the company develops new capabilities and implements multiple emerging technologies. This transition requires building solid foundational systems and encouraging broad experimentation and innovation using new analytics technologies.

Beyond getting experience with next-generation technologies, transitioning to an algorithmic business requires cultivating an enterprise-wide data culture and changing how people work throughout the company, particularly on the front line.

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.

The Black Box: Interpretability, Reproducibility, and Responsibility

Historically, a model produced a result that was interpreted by a person who made a decision. In recent years, as the amount of data and number of decisions have grown, agency has been taken from humans and given to machines, which make decisions in a black box. Black boxes raise issues around explainability (or interpretability)—being able to explain how a decision was made—and reproducibility —being able to use the same data and model to make an identical decision.

The reality is that being able to explain complex decisions is extremely difficult, and may not be necessary. And, being able to reproduce decisions is also very challenging, as data, tools, software, models, and environments change. Any single change can have a ripple effect that changes everything. The real issues are trust, reliability, and repeatability, particularly in high-stakes decisions. Building trust starts with IT policies, governance, and infrastructure, to enable preserving history and allow for understanding and reproducing decisions. This is the key to gaining trust and scaling analytics.

Information Economy Mapping

Every organization has a naturally occurring information economy. The rules of other economies hold: there is supply and demand, supplier and buyer power, competitive alternatives, infrastructure, regulation, taxation, and more. Two important rules of thumb: demand always finds a way to get its needs met, and there are legitimate, necessary restrictions on freedom.

Roughly 80% of organizations have a Soviet-style, state-controlled information economy. In the other 20% it is a laissez-faire, demand-style information economy. In every organization it is important to know where you are and where you want to go. The right answer is always something other than a command economy or an unfettered laissez faire economy. It is analytics professionals’ job to figure out the optimal state by balancing those requirements and brokering solutions that are palatable to all.

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.