Big Data important growth indicator to McKinsey Asia

18 March 2013

McKinsey & Company has laid out an ambitious growth plan in Asia. One of the key drivers to realize this growth lies in services in the area of 'Big Data'. This is what Philippe Mauchard, global head of the 'Solutions' practice of the strategy advisory firm, said in the Asian newspaper The Business Times.

In Singapore McKinsey & Company is busy helping the government gain insights from the deluge of data to serve customers and citizens better. Yet the high demand for 'Big Data' not only stems from the public sector. He points out that McKinsey has been approached by "many large companies in the region" to support them with solving the challenge of mining information from the wealth of data that's available to them. "There is also an important emerging group of mid-tier companies in South-east Asia, especially Singapore, that increasingly need to leverage sophisticated data and analytics based solutions in order to sustain their growth" says Mauchard.

McKinsey - Big Data

As an example he mentions the telecommunication sector: "Consider, for instance, the fact that Asia is already the leading region for the generation of personal location data simply because so many mobile phones are in use in the region. China alone has more mobile phones in use than in any other country in the world".

McKinsey is helping such customers with "finding the crucial 5 per cent of data which is commercially valuable" says.  Next to the use of strategic analyses also deep analytics and IT skills are used.

About Philippe Mauchard

Philippe Mauchard joined McKinsey in 1995 from Mercer Management Consulting. He currently is a partner at the Brussels office and heads McKinsey Solutions, a unit of ~100 dedicated people that leads the firm’s services on the cutting edge between business intelligence and advanced IT technologies.


Machine Learning – Insights that can transform a business

02 November 2018

In recent years, small and major enterprises alike have started to focus more on understanding how they can apply Machine Learning to enhance their business operations. In a guest article from First Consulting, the firm explains how, based on concepts that were developed a couple of decades ago, practical applications now range from allowing IT'ers to filter spam messages out of email traffic, to delivering safe self-driving cars on roads. Machine Learning technology is, subsequently, being applied within a business context at an accelerating rate. 

First Consulting has implemented machine learning applications for many of its clients, including the use of large quantities of data generated by Internet of Things (IoT) sensors and the creation of real-time price prediction models. New technologies allow businesses to make their processes transparent and more efficient with Machine Learning offering unprecedented capabilities in this regard and, thus, in creating value for organisations.

The power of Machine Learning: revealing buried insights

Machine Learning is a method of data analysis in which a computing application derives predictive insights from data. It uses algorithms to analyse data sets to look for patterns and/or correlations which result in useful insights. Among the advantages of Machine Learning is that the predictive insights generated are continuously and automatically updated by the latest data collected. These updated insights can be used as input for the algorithmic decision logic to further improve the prediction accuracy. Depending on the chosen Machine Learning method (for example dimensionality reduction, meta learning or reinforcement learning), varying the inputs to the algorithmic logic can reveal hidden insights.

Machine Learning in a business context

To make effective use of Machine Learning, a clear definition of the business problem is required before commencing. In addition, the methodology (and required software) should only be applied effectively if two key conditions are met:

  • the problem is so complex that a human cannot manually detect the relationships between model input and predicted output; and
  • relevant data is available in both sufficient quantity and quality.

Layered analysis and implementation approach for Machine Learning

Problems which can be solved with simple “if-then” business rules are better candidates for simpler solutions, although these can also be solved through Machine Learning. Insufficient or poor-quality data will result in the algorithms deriving misleading insights into the business problem in question; but where such data is available, it is important to follow a layered analysis and implementation approach for Machine Learning.

Machine Learning applications can be useful in any industry. Common business applications include predictive maintenance, customer segmentation and fraud detection. For example: the case of an insurance company that wants its customers to pay on time. Using billing data already collected through day-to-day operations, the company can derive predictive insights which can help the detection of specific customers that are likely to be unable to pay their bills in the future. In this way, customers can proactively be approached on payments settling, a much more effective strategy compared to reactively trying to deal with non-payment issues.

Machine Learning in action: First Consulting and Hortilux

Hortilux is a family-owned market leader in the production, installation, and maintenance of greenhouse lighting systems. Hortilux produces several products that monitor plants’ growth conditions, and the company was seeking to make use of the data collected. First Consulting implemented a scalable cloud architecture that could handle large data transfers from these sensors. This, in turn, allowed for the creation of a crop-growth prediction model. The model output is visualised for the customer in a web-based application, which was also developed by First Consulting. Machine Learning algorithms were applied to allow the accuracy of this prediction model to improve over time as more data is generated, allowing for better calibration of their products and, thus, increased value for the customer.

Related: Internet of Things cultivation boosts greenhouse horticulture.