Management consulting view on big data

by Sergiy Nesterko on June 25th, 2012

The Economist

The amount of data recorded and analyzed in business, medicine, education and public policy is increasing every day at a rapid rate, to the extent that it is hard to keep pace with it. I am particularly interested in how, and whether, the leaders of organizations and government bodies are responding to and extracting value from the phenomenon.

Particularly interesting is the point of view of top management consulting firms, who are also very interested in the trend. For example, McKinsey Institute published a report on big data a year ago. More recently, there was a recording of a QA session with a senior partner of BCG Philip Evans on big data posted on Schumpeter blog on The Economist about a week ago.

Specifically, Mr. Evans eluded to how the emergence of "big data" may change the course of strategic development of companies. The most recent method has been vertical integration, when companies aim to acquire/develop more entities along the supply chain (i.e., electric power supplier aims to operate not only power plants, but also raw materials, power grids etc) to reduce costs. According to Mr. Evans, during the "big data" era, we will see more of horizontal integration, when instead of operating several entities along the supply chain, a company focuses on one, and grows by scaling the product up to many markets. As per Mr. Evans, an example of this approach is Google.

Additionally, Mr. Evans stated that companies will become fragmented into two camps, the one where there exists a well-defined serializable product or service around which a company can scale up, such as "inferring patterns in large amounts of data", and another where more unique individual skills are needed, such as entrepreneurship, creativity etc.

I found the interview very interesting. We do see successful companies employing horizontal integration (Google, Apple, Amazon). That is, they do focus on a few important products or services, and scale them up to multiple markets. Does this have anything to do with "big data"? It certainly does, as horizontal integration is employed by big players in the big data realm as well, such as EMC. However, horizontal integration is inherent more to the concept of the Internet and the evolution of IT, as is the "big data" phenomenon.

Secondly, I have to disagree with the statement that inferring patterns in large amounts of data is (easily) serializable. This task is an open scientific problem that is a subject of active current research. The only solutions existent at the moment are those belonging to the second camp as defined by Mr. Evans. A task of attempting to design an algorithm to extract a specific answer to a specific question from a dataset in a given format needs to be approached individually by qualified specialists such as statisticians. Such project does involve creativity and a substantial amount of intellectual effort. After an approach is developed, it can be replicated for the specific dataset it has been designed for (say, when more observations have been collected), and not for other datasets, otherwise the results may be unreliable.

More broadly, what does the phenomenon mean for companies? Horizontal integration is implied by the ability to quickly scale up products and services implied by the development of the Internet and the IT, as is big data. So, what is the message of the latter by itself?

Let us not make the matter overly complicated. Buried in the terabytes of "big data" is the ability of companies to be better informed about the market around them and their own internal operations, to optimize activities better, to find out what the competition is up to better, to price their products better than competition, and so on. "Being better informed" is a value generating asset, and companies with large amounts of repeated features (many instances of the same product/service sold, large numbers of employees, many visitors seeing their ads on the Internet) need to realize this. The first ones that do, and those who employ the better methods of extracting interpretable information from the relevant data sources will benefit from the value of being better informed than others.

I couldn't be more excited about the fact that companies, governments, educational institutions and public policy agencies are beginning to realize the value of being better informed by patterns inferred from data, be they massive, big, or not so big. The fact that top management consultants are talking about it means that top executives are demonstrating this interest.

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2 Responses to “Management consulting view on big data”

  1. JohnS says:

    I do not agree with the assessment that structural changes in the dataset necessitate invalidation of pre-established correlations or predictors that are shown to be statistically significant under the existing dataset. the whole point of extrapolation via sampling is to formulate predictors that reliably apply to entire populations. An example of this is election exit polling and the winner forecasting.

  2. Sergiy says:

    John,

    If you are talking about the dataset that has, for example, streaming data, then structural changes in correlations over time do in fact require re-evaluation of which predictors are important and which ones aren't. This can be built into an algorithmic method as is the case with some machine learning methods. This way, no human intervention is necessary. However, human intervention can cause huge predictive performance gains as generic machine learning algorithms may have time window adjustment issues and general low predictive properties if applied blindly.

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