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How to Lead in a Data-Rich World

Top executives share three key insights for harnessing information

  • October 2019

“I have 193,000 unopened emails in my inbox,” said a leader of a prominent data company, holding aloft his mobile phone in front of a group of bemused and sympathetic peers at Egon Zehnder’s Oct. 9 dinner for leaders from the tech, ecommerce, consumer and health industries.

He’s not the only one facing an overwhelming inbox. We are inundated with data in our personal and professional lives—from our consumer spending patterns to our health habits. Businesses have the mammoth challenge of weeding out the data that matters and using it to drive more informed and speedier decisions to better serve their customers. However, many companies are not set up to leverage data effectively. Nearly 70 percent of companies say they have not created data-driven organizations, despite major investments in systems, according to a recent survey of 64 C-level technology and business executives from American Express, Ford Motor, General Electric, General Motors and Johnson & Johnson among others. We asked leaders at our dinner how they are tackling data challenges and how companies can get benefit from this information. What follows are three key learnings from our discussion.

Data drives the need for global companies to localize.

While the volume of data can be a challenge in itself, some leaders in attendance said their biggest challenge is how to galvanize the organization around it. The single biggest blocker for us is the ability to move with speed and agility, said Achal Agarwal, president of Kimberly-Clark Asia Pacific. He noted that the availability of data on consumers forces Kimberly-Clark to act locally. “Tell me, how can a regional office truly add value to what is happening in China?” Agarwal asked. His view, shared by many in attendance, is that it is imperative for strong and capable leaders to be on the ground. The role of regional and global executives is to ensure that local leaders have all of the resources they need to be more agile and responsive to customers.

Localization and the ability to glean insights at a granular level helps to drive better decision-making—provided you have the talent capable of interpreting it. “The most important early hire is a data scientist,” said Dan Neary, vice president of Facebook Asia Pacific. “Someone who can help translate the right data into insights and actions.” Other executives in attendance believed this is easier said than done, as there is tremendous competition for this type of talent.

Look at the right measures of success, especially when driving culture change.

The telecommunications industry has always had a stranglehold on data—and yet it is seen as a laggard inextracting value from it. Success today is not about data availability, but rather talent, culture and what is measured. “If we are always going to only measure success based on the rate of return on capital, we are going to have a hard time reinventing ourselves,” said a CEO in telecommunications company. Some organizations are successfully looking beyond just return on capital to talent attraction and retention. Bloomberg, for example, has been actively entering new business models and successfully bringing in disruptive talent. Enter a Bloomberg office, and it’s an inspiring destination in itself—which, says Parry Ravindranathan, president and managing director-international of Bloomberg Media, is critical for millennials.

Don’t over-rely on data; marry it with the discipline of good judgment.

Companies with data at the core of their business model reflected that they often have to pull back teams that tend toward “analysis paralysis.” “There is so much data that data teams risk doing analysis without knowing what use it will create,”
Gunjan Soni, the CEO of fashion ecommerce company Zalora said. “A much better approach is to be clear on what business decisions need to be made and only look for data that enables making those decisions.”

One of the biggest benefits for companies with data at their core is harnessing the sophistication of self-learning algorithms. “These algorithms are our big focus, and they are helping make decisions like onsite pricing faster and without manual
intervention. We would like to try it in other functions as well,” Soni said.

There is a view that within a few years, it’s likely that even data scientists will become a commodity as learning algorithms replace the need for them. Ultimately, it could well be the ability to marry good judgement, insights and intuition with
the science of data that will be the most sought after talent capability in the future.

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