Data-led Transformation: Discovering business value
Creating meaningful business change with data and AI
We know how to separate the signal from the noise. Data, analytics and AI are opening the door to new possibilities for how organizations can grow and differentiate themselves against competition at an accelerated pace. As the C-suite looks for ways to improve utilization and efficiency, unlock new revenue streams and have the ability (and agility) to create new business models, all paths to value share a common denominator… data.
More than half (52%) of the organizations with future-ready operations are already using data and analytics at scale. Reinventing how data and AI initiatives are executed against business strategy results in an organization that can realize a return on investment at speed and ultimately build the case to move from AI pilots to enterprise-wide business transformation.
With Data-led Transformation, data at its core becomes the ultimate competitive asset and differentiator, and by scaling AI with cloud, organizations can reposition their offerings, extend capabilities and improve data and AI maturity to create new sources of value and sustainable growth.
Every organization faces unique challenges and priorities when it comes to delivering value. Data-led Transformation leverages modern platforms, AI, and a strong data foundation to give you a deeper view, helping determine the right path to value.
Improve the maturity of your data and AI capabilities to deliver core business value by monetizing business strategy and maximizing efficiencies.
Build an ecosystem of partners to extend your capabilities and offerings and, thus, create new opportunities for engagement and a better customer experience.
Leverage data and AI to extend into new domains, reinvent your offerings, and evolve with the confidence you can scale to meet changing demand and priorities.
A global fast-food chain embarked on an aggressive growth plan with the objective of serving more customers at a better value, more often, and with a more personalized experience. To achieve this, they’re undertaking a significant data and analytics transformation to meet continued business demand and the rapid pace of technology change. Through an MVP rollout in 120 stores, we built an AI program that looked at data per store, per protein type, per day, etc. to create models that could predict demand, looking at external variables like weather, holidays, and traffic patterns. By defining and investing in the 10-20% of data elements that were critical (>2,000+ down to 100 CDEs), they were able to reduce scope, time and effort by focusing only on data that drove value for their organization. Over 300 billion rows of data were processed to generate 440,000 forecasting models using AI and ML capabilities to provide real-time insights and improve speed to service for the business.
of executives felt confident they had the necessary information and insights to make informed business decisions during the pandemic.
of executives plan to accelerate digital transformation, including an emphasis on moving to cloud (26% increase compared to pre-covid).
revenue growth when digital operating models are used to drive agility at speed.
of organizations lack an enterprise data strategy to fully capitalize on their data assets.
Six critical capabilities are required to achieve business reinvention from a technical, cultural, and adoption standpoint.
Identify data that is not only critical for business change, but also unlocks the greatest value for the organization.
Enable a trusted approach to ensure that critical data is managed, maintained, and governed centrally so it can be responsibly leveraged to develop differentiating capabilities.
Build a secure data foundation and cloud architecture that can harness data for a deeper view into the organization to meet current needs flexibly to scale for the future.
Apply principles of a product-based operating model to structure the right team and processes, facilitate collaboration, and accelerate execution on business priorities.
Industrialize data and AI across the organization to instill a data-driven culture and analytics mindset for better decision-making and, ultimately, business adoption of strategic priorities.
Measure execution, experience, and the impact on business outcomes constantly and consistently.
What are the top 3 skills required for a chief data officer to be successful? What are the greatest challenges the Chief Data Officer faces in delivering on data vision of the organization? Where are the biggest gaps between the CDO’s current primary role and an optimized role?
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