These four areas of value creation are based on specific use cases that are being explored or have been deployed in businesses today.
Example; A European power distribution company was able to reduce its cash costs by 30 percent over five years by changing its maintenance patterns based on remote analysis of 20 variables to determine the overall health of power transformers. And the impact will vary by industry and by scale of implementation.
Moving on to insights from data, what challenges are you seeing when working with companies?
While details of implementation for each company will be unique, our research and experience suggests that excellence in four areas is crucial to gaining value from big data analytics as a complement to strategy:
- A solid anchor to business value
- A pragmatic approach to IT
- Attracting scarce talent
- Providing insights to the front line.
Decision making is one of the keys in making real changes: Exactly how can advanced analytics and artificial intelligence enhance the decision making process?
After decades of false starts, artificial intelligence is on the verge of a breakthrough, with the latest progress propelled by machine learning/AI. Looking at case studies of digital natives and responses from our survey, we find early evidence that AI implemented at scale delivers attractive returns.
For example, AI allows businesses to provide better forecasts for their supply chain and design better offerings. AI-based approaches to demand forecasting are expected to reduce forecasting errors by 30 to 50 percent from conventional approaches. Lost sales due to product unavailability can be reduced by up to 65 percent.
The German online retailer Otto uses an AI application that is 90 percent accurate in forecasting what the company will sell over the next 30 days.
How is McKinsey helping to drive big data and advanced analytics?
We meet our clients anywhere they are in their journey to become data-driven, providing everything from specific expertise on discrete issues to holistic transformations spanning strategy design, build, implementation, capability building, and ongoing support.
Our iterative, end-to-end approach starts with the identification of opportunities and culminates in broad adoption of new ways of working, all while ensuring that the underpinning technology and organizational model are optimized for each client's specific needs.
Many organisations fail in the execution of analytics programs because they don't build the skills and culture needed to embed new analytics capabilities into their business processes. To ensure that our clients are successful-and will continue to thrive long after our work together is complete-we go beyond the delivery of new models to help them build the capabilities they need to sustain their analytics advantage over the long term.
How we work
- Identify sources of new business value: Every analytics project starts with identifying specific opportunities for analytics-driven revenue growth and performance improvements. We then develop a road map based on a broad range of potential solutions.
- Expand the data ecosystem: We work with clients to build extensive data ecosystems. We assess the sources of data that are available both inside and outside a client's organization, and we enable the creation of new data using affordable technologies such as the Internet of Things (IoT) and smart sensors.
- Build models for trusted insight: Working within integrated client-service teams, our data scientists select the best models and approaches (ranging from basic forecasting to advanced machine learning) and then customize and improve them for the specific client situation, applying deep functional and industry knowledge.
- Integrate user: friendly tools We ensure that the tools we develop allow users at all levels to intuitively connect with data to make new discoveries. Starting with the delivery of mobile visualization techniques and robust self-service environments, we help clients create cultures of curiosity that foster innovation.
- Manage adoption: We help clients understand how these new tools work so they can use them consistently. We collaborate up front, follow up with communication on model performance, and heavily invest in training people across the organisation. By working to ensure that they have the right data-governance strategies in place, we help foster trust in the quality of the data and the resulting insights.
- Create technologies and infrastructure: Our team of software engineers, data engineers, scientists, visualization experts, and consultants can work either within a client's existing environment or on Nerve, our cloud-based platform. Nerve delivers capabilities and solutions in a highly secure and encrypted environment affordably and effectively
- Optimise organisation and governance: We help clients build the IT architecture, data governance, and organizational capabilities to capture the potential of big data and advanced analytics, and we work to ensure that analytics is adopted seamlessly across the overall organisation.