Madrid 2019: Unleashing the potential of advanced analytics in infrastructure

April 2019

la versión en Español

Impacting nearly every major economic sector, the infrastructure industry’s output is a strong force in Iberia’s long-term growth and stability as a global hub. Yet it also faces unprecedented challenges in the form of energy transitions, evolving mobility, and growing project size and complexity. Amid these headwinds, infrastructure lags other industries in critical ways – construction productivity is low, and the sector has historically under-invested in digital tools and processes.

With so much at stake, the infrastructure industry is making moves to revolutionize its lagging productivity. Technology investment is rising in pursuit of this goal, and advanced analytics is poised to be one of the biggest drivers of change. Established industries who have embraced advanced analytics, such as retail, banking, insurance, and healthcare, show that aggressive first-movers create competitive advantages. Those who wish to keep up must be fast-followers with excellent implementation skills.

In this peer-to-peer roundtable discussion, senior Iberian infrastructure leaders discussed the main challenges in implementing advanced analytics; the impact of data-driven approaches in several use cases; and best practices for leaders leveraging analytics in their operations and organizational structures. Key insights included:

  1. Analytics are no longer optional in infrastructure. The industry faces different trigger points that require leaders to consider analytics as part of the way forward. These trigger points include regulatory changes affecting data considerations, lagging business performance, new levels of public scrutiny, and rapidly expanding technology capabilities. Roundtable participants agreed that starting an analytics journey is now of critical importance, with the caveat that a clear purpose in mind is key. For example, companies should pursue data-driven approaches to specific business questions or operational challenges, rather than “analytics for analytics’ sake.”
  2. Initiate the journey with existing data and continuously iterate to incorporate additional and complementary sources. The infrastructure industry generates significant amounts of data in multiple formats that lose value by not being processed in a timely manner. While instituting new data collection processes is crucial, there is a lot of value to be generated by starting to gather insights from existing available data, learning from them, and continuously analyzing new, relevant data to gain a better understanding of reality. The goal should not be to process all company or project data before designing an analytics model (“do not boil the ocean”), but instead quickly scan through and leverage what is already available and then build on top of it.
  3. Analytics do not replace people in infrastructure, but rather expand their capabilities with new tools. Some industry leaders fear that the rise of advanced analytics might lead to the elimination of jobs. While the effects of big data on labor and skills are complex, roundtable participants felt that this fear is often exaggerated. Rather than replacing people, analytics will more likely empower people to do their jobs more effectively by providing new and insightful ways of looking at particular projects and at the business as a whole. One additional implication of this is that advanced analytics will open up the need for new skill sets and talent in existing employee pools, and infrastructure leaders will need to compete with other industries for those.
  4. Infrastructure players are already piloting analytics in promising projects. Albeit at a pilot scale, infrastructure players are already pursuing analytics through three main avenues: i) analyzing existing data to improve operations (e.g., predictive maintenance); ii) investing in technology start-ups to expand their range of use cases; and iii) hiring new types of employees, such as data scientists or programmers. The challenge players face is around how to scale up these efforts while maintaining the focus and excellence in their day-to-day activities, and integrating accordingly.
  5. Develop a cross-functional business, technology, and analytics team to ensure success in the transformation. Building an analytics team with three key roles—business translators, data scientists, and data engineers—will gear the company toward success. However, it is critical that personnel across these three frontiers are in close coordination, such that all three frontiers are simultaneously addressed. Participants cited several examples where digitizing efforts failed due to a lack of balance in involvement across these three roles.
  6. Address the data transformation with a mentality geared toward nimble, agile execution. Leaders suggested prioritizing use-cases in the organization based on value and feasibility in order to quickly achieve success and build momentum. They also discussed deploying a roll-out methodology that allows teams to gradually develop their skills and capabilities, allowing the scale-up of projects over time.
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