International Institute for Analytics

In this webinar, the third in a series on Racial Equality in Analytics, Tamara DaCosta, HR Director, Reporting and Analytics HQ at Turner Construction Company, Diya Wynn, Sr Practice Manager, Emerging Technologies & Intelligent Platforms at Amazon Web Services, and Robert Cathey, Chief Data Officer/VP, Global Data Analytics Solutions, VSP Global will share their experiences as Black professionals who are leaders and practitioners in analytics. In addition to their experiences, they will share their thoughts on what companies need to do to improve the representation of Black analytics professionals through recruitment, retention and focused career development.

After spending several years answering almost daily emails and telephone calls from business managers asking for staffing help and aiding fellow academics with their analytics teaching needs, Dr. Jennifer Priestley of Kennesaw State University and Dr. Robert McGrath of the University of New Hampshire have captured those learnings in a recently released book entitled: Closing the Analytics Talent Gap: An Executive’s Guide to Working with Universities. The book builds a bridge between university analytics programs and business organizations. It promotes a dialog that enables executives to learn how universities can help them find strategically important personnel and universities to learn how they can develop and educate this personnel.

Analytics leaders consistently struggle to get their budgets approved. At IIA, we’ve seen this struggle among our research clients since our founding in 2010. Core to this struggle has been the difficulty analytics leaders have incredibly assigning value to the outcomes of their proposed investments when submitting budgets for executive approval. Unlike IT investments where costs are known as are the expected returns of those costs, analytics functions operate on making bets, the outcomes of which are much harder to estimate. Credibly quantifying impact is challenging.

Inquiry:

We’re planning a microservices architecture to improve our digital customer experience. We’re moving towards a digital experience that it is curated and customized for a particular journey. What should we be thinking about?

Data Governance 1.0 (DG 1.0), rooted in responding to the financial regulations that arose after the 2008 financial crisis, has failed us. The data around data governance tells a very sad story.

“Through 2022, only 20% of organizations investing in information governance will succeed in scaling governance for digital business.” – Gartner

Unfortunately, this has been the same story for a long time and data governance has rarely succeeded in enabling data strategies that aim to drive digital business transformation or competitive advantage with analytics. It’s more often something looked upon as a necessary hedge against legal or compliance issues, which means it’s often looked upon as a burden. The pain of ineffective data governance on analytics has been obvious to those in the data and analytics community for a long time, and now the challenges of COVID-19 and the digital business transformation it has accelerated have made that pain more acute and more visible to a lot more people across the business.

Inquiry:

We’re working on MLOps for the first time. There’s been some machine learning (ML) work done in the business, but on an ad hoc basis. How do we start to evolve as a company such that we can begin to manage our ML work in more of an enterprise portfolio way?

Inquiry:

We are working to improve data management across the enterprise. How can we ensure data integrity while still allowing flexibility? We have a central team with analysts in the business units too.

Buy or build? Cloud or on-prem? Packaged analytics software of custom Python? These are false choices. No business can afford to think of these choices in a binary way. The challenges in terms of business opportunities, talent constraints, compute trade-offs and data availability are highly situational, and each company needs to architect its stack for its needs. Ken Sanford shares his deep experience, as well as some provocative and effective frameworks for making the best choices for your firm, informed by your needs, not by sales pitches and feverish hype.

Inquiry:

We’re creating a roadmap for our data science organization. What small steps should we take now in structuring and hiring to get us to where we want to be in five years? Specifically, we’re considering both a centralized and federated structure, and if we should focus on generalist or specialist data scientists.

Tax agencies manage the essential flow of revenue to governments. That core mission has expanded even as their resources remain constrained. And they face the challenges of any major financial services organization – serve customers, operate efficiently, prevent fraud, and adjust to changing economic and social conditions. To explore how data and analytics can assist agencies in meeting those challenges and forwarding their missions, IIA spoke with Shaun Barry, Director of Government Fraud & Security Intelligence Solutions at SAS Institute, and Deborah Pianko, Principal, Government Fraud & Security Intelligence Solutions at SAS Institute.