Businesses have long used data to help them set strategy, make operational decisions and manage risks. Now, advanced analytics take that capability to new levels. They create better, faster and always-on ways of finding the profitable, sustainable growth that has been so elusive in recent years.
The big challenge is not to get leaders excited about the potential; it’s turning that potential into reality. Companies have been investing in this area for some time. But those investments too often disappoint.
A handful of CEOs are cutting through the complexity and finding ways to harness the power of analytics. One of our recent surveys, of global consumer products leaders, confirmed that most are still finding it hard to secure value from analytics. However, 11% of the leaders in our survey say that their company is finally deriving actionable insights from all sources of data; 10% believe they can now deliver insights and analytics at scale. And 33% are confident that they can make and implement decisions quickly.
Standing out from the competition
Many companies have appointed a high-level team to study analytics and set priorities for how to apply them across their organizations. They’ve supported those plans with significant investment. But those plans then fail to deliver.
The minority of leaders who are breaking out of that pattern have found the missing piece in the analytics jigsaw. They are winning with analytics because they have achieved the right balance between the technological element of analytics and what we at EY call the “human element.” They have invested in the operational infrastructure that enables them to produce analytics — new technology and tools, data quality and advanced analytics skillsets. But they’ve also paid full attention to the capabilities that enable people in their organization to actually use those analytics.
Companies often fail to achieve their goals because they focus on the former at the expense of the latter. Really it’s about balancing analytics production with analytics consumption. The former covers the technical capabilities needed to create analytics:
- Infrastructure and tools
- Controls on data quality
- Competence in data science
The latter relates to the human factors needed to make the best use of analytics insights:
- Organization and process design
- Culture and leadership
- Embedded learning and development