What is essential is invisible to data: Transformation Analytics, organizations, and people

Once again, technology is at the center of the political, social, and economic scene, monopolizing the media on a global scale. Artificial intelligence and its virtually worldwide access, due to new available tools such as Chat GPT, make these advances accessible to the general population. These new tools create structural changes, as we can see through headlines that read “Elon Musk, Bill Gates, Steve Wozniak and over 1,000 leaders sign a petition to stop advances in artificial intelligence,” or “Italy becomes the first country in banning AI from Chat GPT.”

For organizations, there’s no escaping these trends. The Human Capital Analytics study carried out by Mercer in 2020 concluded the following:

• 78% of organizations agree that analytics are changing the nature of competitive advantage.
• 66% of global companies are investing over $5 million in software analytics.
• 12% of global businesses described their analytic development as “in the lead.”
• 10% of organizations agree that change management is an obstacle for the success of analytic strategies.

Though these technologies are evidently here to stay, there is still much work to be done regarding organizations’ adoption of them. This is why now is a good time to reflect on how we, as people, can adapt to these innovations. It’s not the organizations that need to change, but the people that make them up.

In the 80s, Steve Jobs spoke of computers and the impact they would have on people’s lives. “Computers are like a bicycle for our minds,” he said. When I heard this quote once again just recently, it made me reflect on the potential these changes offer our organizations. Jobs’ quote resonated with me, eventually leading me to recall Greg McKeown’s Essentialism. In his book, McKeown urges the reader to rid themselves of whatever is not essential to them and to eliminate any possible wastes of time, setting the focus on their objectives by reducing and simplifying tasks that are unaligned with them. 

This might lead you to ask: what does this have to do with data analytics?

When analyzing these trends, advanced analytics allow us to fast forward significantly when compared to descriptive analytics. While descriptive tools offer us a picture based on the past and the present, these new tools broaden our view and add a new dimension: behavioral propensity, allowing us to look into the future. When we combine an advanced quantitative approach with a qualitative one, we are given the chance to enhance behavioral propensity and increase the possibility of creating future scenarios. This approach with the focus on transformation is what we call transformation analytics.

Transformation analytics offers us a glance into the future that allows us to prioritize and set our efforts on the key variables that accelerate our organization’s transformation towards where we aim to be. Regardless of what our objective might be (adopting a change, accelerating people performance within an organization, working on the potential for transformation in the future, understanding upcoming rotation, digital maturity), transformation analytics can bring about this new dimension of analysis. 

So, what will our goal be?

The key—our added value—will reside in the questions that bring data analytics to life: what the crucial, central, profound points are that we desire to work on. In other words, it will become increasingly important for organizations to discover the issues they wish to resolve through these tools with growing expertise in order to comprehend their potential in its entirety. The more essential it is and the more precise our questions and hypotheses are, the higher the chances that these tools generate the differential we’re seeking. In this sense, asking Chat GPT questions that go from general to specific in order to experiment this concept serves as a pilot test. 

Artificial Intelligence will offer “expertise” and technical point of view. This will allow us to focus on the more strategic aspects of the organization, generating questions that require an answer, and the hypotheses that feed these tools in order to discover the trends that will allow us to prioritize and focus our efforts on the more critical aspects of the organization. 

As The Little Prince once said in Antoine de Saint-Exupéry’s story, what is essential is invisible to the eyes. If we fail to understand the root questions that drive transformation and move people, and without a clear hypothesis of what we wish to change, artificial intelligence is just another piece of data within our ecosystem. THIS IS OUR VALUE.

Our role in organizations is increasingly strategic, more profound, and is closely related to discovering these key questions that help us change our ecosystems. We can leave the rest to AI.

By Guido Olomudzski, Chief Customer Office - OLIVIA Brasil