International Institute for Analytics

Enabling AI and ML in an organization requires multiple processes and capabilities and even then, it is hard to do! At Georgian we are constantly engaging with startups that are built on incredibly forward-leaning AI/ML applications, but at the same time we are also deep in conversation with more traditional, larger companies that have amassed broad, unique data sets and are looking to mature their AI/ML capabilities to leverage these assets.

Inquiry:

I’ve put together a list of responsibilities for the data engineer we’d like to find and hire, and I’d appreciate your advice. I lead analytics for enterprise operations—procurement, manufacturing, engineering—and we plan to migrate data and analytics to Azure.

In non-data-driven businesses, data may play a part in decision-making, but it doesn’t drive decisions. Instead, leaders may rely on intuition, mistake conventional wisdom for facts, and use confirmation bias (sifting the data so it agrees with the desired outcome).

On the other hand, data-driven businesses let data guide decisions, outcomes and strategies, even when the data goes against inclination and time-honored assumptions. Having a data-driven culture means that you go where the data and your interpretation of the data lead you, rather than using data selectively to reinforce your positions for other reasons, or ignoring the signals in your data when those signals don’t align with your preferences, expectations or desired outcome.

Inquiry:

We have extensive loyalty club data and perform extensive membership analytics. What should we be thinking about when it comes to data monetization? What types of data governance are necessary for our current state? Beyond membership analytics, what are some common use cases we should consider focusing on next?

Inquiry:

We are focusing on improving our digital user experience and would like to speak with someone who can help us better understand what data is available for us to collect at this point in time.

There are many wrong ways to drive data literacy and only one right way - in context. Data Literacy efforts are only effective when they enable people to actively use and apply their new data literacy which they do when you put these programs in context. Join IIA and Metric Insights together with two deeply experienced leaders in Data Literacy at large companies as we share practical techniques to improve understanding and use data in the context of the business, the roles, and the organization.

Inquiry:

We recently joined enterprise analytics and BI into one team. As we merge into one corporate support function, what should we focus on, especially when it comes to serving business lines and functions that also have embedded analysts?

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.