Leadership Matters

Perspectives on the key issues impacting senior leaders and their organizations
December 8, 2021

The Elements of Data-Driven Transformation in Latin American Companies

Earlier in 2021, we published an article based on interviews with data and analytics leaders at 18 Latin American companies that have risen to the challenge of becoming a data-driven organization. Our research debunks a few myths: that internal resistance is an obstacle to technology transformation, that the process of change must be prohibitively expensive, and that quick results aren’t possible.

To discuss our research, we recently hosted a roundtable discussion with a group of CDOs on some of the ways in which Latin American companies are surmounting these myths and embracing data-driven transformation. Our lively virtual conversation examined how, with the right attention to data and culture, you can see results quickly. Here’s more insight from that conversation.

The cultural characteristics that affect data transformation

Our hypothesis at the beginning of our research was that we would uncover some fairly straightforward organizational culture traits that naturally lead to successful digital readiness initiatives such as AI. We expected to see that inherently “data-driven” cultures would naturally be better at these types of efforts. Yet our study found little correlation between those cultural traits and returns on digital readiness investments.

As we dug deeper in our interviews with Latin American digital and AI leaders, some interesting patterns bubbled to the surface. Digital-native companies tend to have more flexible, learning-oriented cultures; on the other hand, long-standing legacy companies, such as banks, tend to prioritize safety, order and risk-aversion. Yet, our research found that more long-standing companies can produce returns that are just as solid as digital natives.

So what distinguished those organizations that captured value on AI from those that did not? First and foremost was a set of leaders and capabilities across the organization that made the most of the culture that was already established. Rather than seeking to “transform” an already established culture, the top leaders captured the existing culture and worked within it to drive the greatest returns.

Establishing a data-centric, data-literate culture

Creating a data-driven culture from the ground up is a challenge. Take as one example a pharmaceutical company full of talented people with PhDs in mathematics and biology, but who do not have the analytical knowledge for AI. Educating them on the use of analytics and data — and the place for AI in their business — is essential so they will buy into the right mindset. When you educate the organization, you can move from a push model of initiatives being driven by the chief data officer to a pull model where people across the organization come up with use cases on their own.

Another successful strategy we saw in many companies was identifying pockets of signature initiatives that could drive the most value, rather than trying to holistically change the enterprise culture into a data-ready culture in one fell swoop. These organizations embarked on focused endeavors with a high probability of success, and greater satisfaction around a new way of doing things. Consider it a sort of incubator within the larger enterprise culture.

Within that subculture, you can then create different incentives and organizing principles for digital transformation initiatives, and then start to build some initial momentum. As the organization sees the success and value created, it's much easier to scale the subculture than it is to shift an enterprise culture. In other words, start small and then scale and multiply the subculture slowly across the enterprise.

Balancing the pressure for fast results with the uncompromising need for high-quality data

Companies are on the hook to demonstrate the fast results from technology efforts, but at the same time, there must be a focus on data quality. That doesn’t necessarily mean you need 100% data accuracy for a specific use case, but you need to set a standard of acceptability, particularly to prove to the board that these new directions are viable.

It can feel like a bit of a paradox for leaders — seeking fast results yet demanding data integrity, which calls for two specific teams within a company. Yet our experience and research has shown that organizations must build up both elements to successfully scale up analytics.

Once the value of the technology becomes evident, companies can begin to bring in more data from other departments and sources (even externally) to extend use cases and derive more value from efforts.

What Latin American companies need to get started

Some specific opportunities make digital transformation highly achievable for companies across a spectrum of industries in Latin America. Investing in automated data processing, for instance, can take the onus off a company’s data analyst efforts so they can focus on extracting intelligence from data.

In terms of people talent, HR organizations in Latin America can systematize what they’ve learned and ensure they are passed down to new generations of executives. Building a strong partnership with universities is one way for companies to cultivate talent right from the source.

Still, the roundtable discussion made it clear that the people and organizations successful with data analytics generally started with what they had. You’ve got to put points up on the board quickly. From there, it’s a steady journey.