International Institute for Analytics

The vast majority of people building analytics and data science processes have every intention of being good and ethical. As a result, most potentially unethical and evil processes arise in situations where that wasn’t the intention. The problem is typically that proper focus and governance is not in place to keep analytics and data science processes on the side of good. On top of that, what is good and what is evil isn’t nearly as clear cut as we’d wish it to be.

Information Economies in Organizations

The data warehouse revolution began in 1991 when Bill Inmon published Building the Data Warehouse. Inmon observed, early in that book, that every organization has a naturally occurring information economy, and that most naturally occurring information economies were inefficient, duplicative and prone to produce suboptimal decisions.

This observation of Inmon’s has not gotten anywhere near the credit, or attention, it deserves. A decade’s worth of collective practice in advanced analytics should tell us that everything we know about real-world economies applies to our information economies. There is demand for information by people and functions in an organization, and there is a supply of (some of) that information. There is (some amount) of technical and procedural infrastructure – some kind of market — to bring demand and supply together in an organized way. That “market” infrastructure is often partial, fragile and in some cases ineffective. There are competitive alternatives (like cloud service providers and SaaS vendors), over- and under-regulation (various data governance models), excessive demand-side taxation (cost allocation strategies), failure to invest in infrastructure, and all other elements of economies.

When organizations are planning strategy-driven large-scale advanced analytics programs, they should begin their planning by characterizing their as-is information economy.

We’ve had technical people focused on the ingestion and management of data for decades. But, only recently has data engineering become a critical, widespread role. Why is that? This post will outline a somewhat contrarian view as to why data engineering has become a critical function and how we might expect the role to evolve over time.

Why do some analytics projects succeed while so many fail? According to Gartner analyst Nick Heudecker, as many as 85% of big data projects fail. However, the ROI from the other 15% that succeed is incredibly promising. With such a clearly high barrier to competency in executing big data strategies, there remains significant opportunity for first-mover advantage for enterprises that can crack the code to improving their outcomes.

So, what can organizations do to increase their chances of big data success? Part of the answer lies in creating a data engineering culture. This is the necessary foundation underpinning a big data analytics proficiency and enables companies to outperform the competition.

Analytics experts love data. But just presenting raw data or even insights derived from data isn’t good enough. To create business value from data requires that analytics professionals develop skills at data storytelling. This entails telling persuasive stories, tailored to a specific audience, that combine data, narrative, and visuals effectively.

Why Storytelling?

Human beings love stories. In fact, author Philip Pullman has written, “After nourishment, shelter, and companionship, stories are the thing we need the most in the world.” And scriptwriting expert Robert McKee has said, “Storytelling is the most powerful way to put ideas into the world today.”

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.