Want to champion AI? Start with your data
How can you 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.
When AI models are trained on flawed data, the resulting decisions are fundamentally compromised. That’s why your company must not only review the quality of its existing data sets but also examine the robustness of the processes it uses to collect and administer information.
“High-quality, consistent, and well-governed data is the essential foundation for building reliable and scalable AI models that can deliver real business value,” says Ainhoa Alonso, an IESE Executive MBA holder who is now Chief Data and AI Officer at PagoNxt, a payments firm.
Five steps to put your data plan on track
Here are some steps that managers like you can take to align your organization with a strong data strategy:
1. Create a data-driven culture
Cultivating a corporate culture that recognizes the importance of reliable data is critically important, says Ferrándiz. That means making sure data is accessible on a company-wide basis and making sure employees are properly incentivized to use it. Only then can firms plan investments with confidence and take the bold decisions needed to execute a long-term digital strategy.
2. Educate and empower staff
Companies need to move beyond pilots and embed AI into business processes and work practices, according to a team including IESE Professor Evgeny Káganer at MIT’s Center for Information Systems Research. Creating AI-ready teams means providing opportunities and resources for reskilling. Breaking down silos between departments and ensuring data sources are seamlessly linked is also key.
3. Deploy the right systems
Take care to put in place the right modular, interoperable platforms and data ecosystems to enable a free flow of intelligence across the organization, urges Káganer. Choosing the right architecture is a strategic decision that will shape the flow and reliability of data.
4. Establish consistent definitions
Make sure to put in place and maintain a common semantic framework that standardizes data definitions across all business units, says Alonso of PagoNxt. This will enable consistency, interoperability, and a shared understanding of key business and operational concepts across the company.
5. Strengthen data governance and quality
Reinforce the data governance framework to guarantee accuracy, lineage, and trust in the information that powers decision-making and AI models, says Alonso. Governance and ownership of data administration should be in the hands of Chief Data Officer, with a clear mandate to implement rules and oversee levels of access, urges Ferrándiz.
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.
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 Artificial Intelligence for Executives focused program, held at our Munich, Madrid, New York and Barcelona campuses, will help you sharpen your skills and learn how to become a more effective leader in the age of AI.
