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What’s now and next in analytics, AI, and automation

Source | www.mckinsey.com

Innovations in digitization, analytics, artificial intelligence, and automation are creating performance and productivity opportunities for business and the economy, even as they reshape employment and the future of work.

Rapid technological advances in digitization and data and analyticshave been reshaping the business landscape, supercharging performance, and enabling the emergence of new business innovations and new forms of competition. At the same time, the technology itself continues to evolve, bringing new waves of advances in robotics, analytics, and artificial intelligence (AI), and especially machine learning. Together they amount to a step change in technical capabilities that could have profound implications for business, for the economy, and more broadly, for society.

Table of contents

  1. The opportunity available now
  2. The next wave of opportunity
  3. What about employment and work?
  4. What should leaders do?

The opportunity available now

Some companies are gaining a competitive edge with their use of data and analytics, which can enable faster and larger-scale evidence-based decision making, insight generation, and process optimization. But there is room to catch up and to excel. Harnessing digitization’s potential is similarly uneven.

Data and analytics are transformational, yet many companies are capturing only a fraction of their value

Data and analytics have been changing the basis of competition in the years since our first report on big data in 2011. Leading companies are using their capabilities not only to improve their core operations but also to launch entirely new business models. The network effects of digital platforms are creating a winner-take-most dynamic in some markets. Yet while the volume of available data has grown exponentially in recent years, most companies are capturing only a fraction of the potential value in terms of revenue and profit gains.

Effective data and analytics transformations have several components:

  • Asking fundamental questions to shape the strategic vision: What will data and analytics be used for? How will the insights drive value? Which data sets are most useful for the insights needed?
  • Solving for the problems in the way data is generated, collected, and organized. Many incumbents struggle to switch from legacy data systems to a more nimble and flexible architecture that can get the most out of big data and analytics. They may also need to digitize their operations more fully in order to capture more data from their customer interactions, supply chains, equipment, and internal processes.

 

Read On….

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