International Institute for Analytics

We co-founded the International Institute for Analytics in 2010. Since it’s now 2020, our sophisticated math skills tell us that IIA has been around for about a decade—although our first full year of operation was in 2011. We thought it might be interesting to reflect on the state of the field that IIA addresses and how it has changed over time.

Inquiry:

We’re a consumer products multinational, and we need to expand our supply chain analytics efforts. What should we be thinking about?

Response:

Each year, the International Institute for Analytics ends the year with a look at the latest analytics trends and the most pressing analytics challenges currently facing organizations. Our predictions are based upon our day-to-day work supporting and advising analytics leaders and organizations. We take advantage of the breadth of expertise and cross-industry perspectives we encounter every day from our clients, partners, and members of the IIA expert network. This is our 10th annual look toward the upcoming year, and our annual Predictions and Priority research brief and the associated webinar have become among IIA’s most popular content of the year. This year, we’ve stuck with our approach of augmenting each of our predictions with a specific priority for leaders to focus on as they attempt to address that prediction. As a result, each priority provides specific guidance as to how to best prepare for, and adapt to, its corresponding prediction.

Open-source software (OSS) is inevitable. For most corporations it’s not a question of “will we use OSS?” The right question is “how much OSS do we already have in production?” More than 90% of IT organizations have OSS in their mission-critical systems. Many commercial software products — from vendors such as IBM and Microsoft — also contain open source software.
Herein we explore these major OSS subjects:

1. OSS history and concepts
2. Quick tour of OSS analytics
3. What does OSS really cost?
4. Positives and negatives of OSS analytics
5. Guidance

Inquiry:

We just formed a Center of Excellence (CoE) for our data science community. We’re finding ourselves challenged by the translator role. Should we up-skill current members, bring in external people from the business, or not have anyone at all?

Inquiry:

We’re charged with expanding our customer data program. We have an initial framework for the platform itself, but now we need to think about our product offerings and the skills set required so we can build out the program. Do you have any advice?

Inquiry:

Our advanced analytics team has been tasked with looking at the state of our data program with an eye toward arriving at a new future-state framework. Currently, our biggest challenge is that our internal customers’ processes are not set up to consume what we provide. Do you have any advice?

Inquiry:

We’ve just started a formal data analytics organization. From a process management perspective, we’re struggling because our data scientists are used to working within their individual silos until they are ready to show their work. We’re having trouble getting them to estimate timelines, commit to deadlines, and collaborate.

Inquiry:

We’re in the beginning stages of a global process transformation with focus on using robotic process automation (RPA). We’re using a vendor to pilot initial use cases, but eventually we’ll own our own capabilities. Do you have any guidance for us?

To set the stage, the analytics organizational structure I’m presenting below pertains to an analytics organization between 60 and 120 people; this is the size that seems to be a sweet spot for an effective and efficient team (large enough to have specialized skill sets, but small enough to effectively demonstrate the benefits of the team). Moreover, I’m presenting such an organizational design in consideration of an analytics effort at an established, traditional corporation, not a digital native. Digital natives will break down differently with more need for data science and data management. With 60 to 120 people, I prefer a centralized organization with P&L Analytics/Ad Hoc Analysis dotted-lined to their business partners.