How can today’s leaders win the race against time to mine their companies’ data resources and translate them into higher efficiency and productivity?
That is one of the defining challenges facing executives as they seek to harness Artificial Intelligence (AI) for business growth. While AI promises to transform industries, its power ultimately depends on the quality of the data that fuels it.
“Data is the gasoline of artificial intelligence,” explains IESE Professor Luis Ferrándiz. “If you incorporate into your algorithms data that isn’t good enough, the outputs won’t be good enough either.”
Why data quality matters
Investments in digital tools will fall short if the underlying information is inconsistent, incomplete, or poorly structured. That’s why companies must not only review the quality of their existing data sets but also examine the robustness of their processes for collecting and managing information.
Equally critical is cultivating a corporate culture that recognizes the importance of reliable data, says Ferrándiz. Only then can firms plan investments with confidence and take the bold decisions needed to execute a long-term digital strategy.
Five steps to put your data plan on track
IESE experts Luis Ferrándiz and Quim Cortés, IESE’s Data Director, recommend the following steps for managers seeking to align their organizations around a stronger data strategy:
1. Secure top-management commitment
Data initiatives thrive when driven from the top. Senior leadership must not only endorse but actively champion a company-wide, top-down strategy.
2. Tackle the challenge step by step
Start small by cleaning and structuring existing datasets, then build controls for consistent data collection. This foundation will allow you to use data more effectively to improve operations.
3. Educate and empower staff
Training employees in the value of good data ensures that quality becomes part of the organizational culture. “To know how to use these new tools brings benefits to organizations fundamentally in terms of efficiency and productivity,” says Ferrándiz.
4. Be selective
Not all data is equally valuable. Identify the sources and datasets that offer the greatest potential to generate insights and support the business areas that need them most. “The value lies in how you work the data to transform it into information or insights which are of real use,” says Cortés.
5. Create a sense of urgency
While building a data strategy requires careful planning, companies cannot afford to delay. Ferrándiz warns that firms that move too slowly risk “missing the boat” and losing critical opportunities for profit growth.
From strategy to competitive advantage
For business leaders, the data challenge is both technical and cultural. Winning organizations will be those that invest in robust processes, empower employees to value data, and act decisively before competitors outpace them.
At IESE, teams are already deploying data to help develop opportunities for sales for company programs and to enhance contacts with the school’s more than 60,000 alumni, says Cortés.
In today’s environment, where AI is reshaping competitive advantage, the question is not whether to act, but how quickly you can turn your company’s data into a catalyst for productivity and growth.
Hungry to know more about the AI transformation, or prepare to take your career to the next stage? IESE’s executive programs can help you sharpen your skills and learn how to become a more effective leader. Learn more about polishing your professional profile in a global, transformative environment.
