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.

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.

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.

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.

If you have ever wondered how data governance, which is such a critical part of the data environment, got such a bad reputation as ineffective, time consuming and a thing to be avoided, this paper will bring you up to speed. It will help you understand the context in which you might be implementing a new data governance approach. That ideal new approach is covered in the paper “Data Governance 2.0: Enabling Digitization & Analytics.”

If you have ever participated in a sports team, academic team or a club, or volunteered with a group of individuals, then you experienced the power of coming together to work on a common goal. A Community of Practice (CoP) is a lot like a team-based activity, and this paper discusses how to establish a CoP within your organization and why it’s an important activity to consider as an analytics leader.

When to Consider a Catalyst:

• Is your organization or team realizing your full potential from your analytics efforts?

• Are analytics models making it to production and adding quantifiable value?

• Is leadership trusting the results from the investment you’ve made in Data Scientists, Data Wranglers and Data Engineers?

• Are internal and external customers delighted in the solutions presented, or for that matter, is it even being measured?

Complying with the Long-Duration Targeted Improvements standard presents major challenges to the business processes and information systems of insurance companies. At the same time, it offers them the opportunity to make rapid progress in modernizing those processes and systems to deliver management insight of unprecedented value. To explore the two sides of the LDTI coin, IIA interviewed Naxine Chang, FSA, MAAA, North America Insurance Strategy Head at SAS Institute.

The world of money laundering and other financial crimes – and they do span the globe – continues to reshape rapidly. The amount of money laundered is estimated at between 2 and 5 percent of global GDP. The midpoint of that range has over $3 trillion in illicit funds moving annually through the financial services industry. That’s several million dollars a minute. If the money laundering “industry” were a country, it would have the fourth or fifth largest GDP in the world…Today, continuous and sometimes radical improvement has become a business imperative. Institutions must rethink and accelerate their processes, become both more efficient and more nimble, and react faster to the changing schemes of financial crime.

Project prioritization is one of those activities that seems simple and straightforward on the surface, but scratch at it just a little and hidden complexities are quickly revealed. While there is general agreement that “good” prioritization contributes to the overall effectiveness of an analytics function, rarely is any effort taken to define “good” and map out an agreed-upon approach to get there. The obvious outcome of a prioritization process that lacks intentionality is general chaos — analytics teams are overwhelmed and expressing a need for more resources, business leaders are frustrated that their needs aren’t being met, the loudest voice often gets his or her way, and the enterprise isn’t optimizing the return on its investment in analytical talent.