Big Data

How technology SMEs in B2B space can create new revenue streams by monetizing big data

OLYMPUS DIGITAL CAMERABig data is one of the big themes of Gartner these days, as part of the so-called nexus of forces (see http://www.gartner.com/technology/topics/big-data.jsp).  Harvard Business Review and other major business journals also regularly publish articles about big data and its use in top-tier organizations like Amazon, Google, Walmart, eBay, and FICO Falcon Credit Card Fraud Detection System.  The examples provided are typically large organizations operating in consumer space, with hundreds of thousands or millions of customers and with a need to better understand (lifetime) value, cost, risk, and opportunity for specific segments of their current or prospective customers.  Typical data set sizes are in the range of terabytes or petabytes.

Based on a conversation a month ago or so with a long-time business friend of mine, I have tried to understand to what extent the concept of big data extends to technology SMEs operating in B2B space, and I will use this blog post to articulate my thoughts on the matter.

(Note that I will in this blog post restrict myself to big data as the concept is commonly understood in the business community, not including for example science, government, and intelligence.  I will also restrict myself to problems that at least with a stretch have implications for sales or marketing.  I am specifically excluding internal big data projects without direct objective of monetizing same data or insights based on same.)

Before I start, I should probably say something about what I mean with big data.  As indicated above, it has to do with large data sets, possibly in the range of terabytes or petabytes.  It also has to do with extracting actionable analytical insights by detecting consistent patterns in these data sets.  So, far we are in the realm of large sample traditional statistics and pattern recognition.  What differentiates big data in the new meaning are: velocity and variety / heterogeneity (the last two Vs in Gartner’s 3V model), data input that is driven by external factors, quantitative analysis that goes beyond traditional statistics, and often a less stringent definition of significance than in traditional statistics (for example if one wants to conduct a simple business experiment to test a business hypothesis).

At first sight, it may appear that there are no big data to monetize for a traditional technology SME working in B2B space, with customer data for typically 10-50 customers.  But, once one starts to dig down in all the various technical data repositories created and partially owned by a typical small technology SME or its products as part of normal operations, one typically finds a lot of gefundenes Fressen, for example support requests per customer, usage data, internet of things data, transaction logs, data logs (possibly from hundreds of sensors sampled at sub-second intervals), chat logs, and RFID data.  Such data repositories may be installed on premise in own organization, in the cloud, or on premise in client organizations,

An interesting case is Rolls Royce, which has in place a system for remote monitoring of all its engines sold to its aircraft, helicopter, and shipping customers for subsequent processing in a remote service organization staffed with around 200 engineers (see for example http://www.bigdata-startups.com/BigData-startup/rolls-royce-shifts-higher-gear-big-data/ for more details).

How might such data be monetized?  Generally, it boils down to refined or adjusted customer segmentation; new or adjusted offerings (think Rolls Royce’ service offering); reduced delivery costs; differentiated or adjusted pricing (think traditional standard support fee vs. segment-specific support fee); new or adjusted pricing models (think transaction-based pricing or up time-based pricing); reduced risk (for the SME or for the SME’s clients); and sheer selling of detailed data plus associated professional services (aggregated across customers or on a per customer basis).  (I am using the word ‘adjusted’ here intentionally, as big data is generally a continuous process.)

In general, data owned by an SME are easy to obtain and data stored on premise by a client of the same SME are less easy to obtain.  However, the cloud is an enabling factor here, and by moving an application to the cloud, an application can easily be instrumented / equipped with probes for detailed data gathering and data distribution.

What are then possible next step for big data-enabling your technology SME: i) identify commercial opportunities for big data in your organization; ii) instrument your systems, applications, or organizations for gathering the information; iii) send this not necessarily homogenous information to central storage and manage the information; iv) analyze the information with relevant tools, whether statistical methods, visualization, BI, or simulation software; v) transform analytical insights into business recommendations; vi) implement and evaluate these recommendations; vii) build a business model around this new information; and viii) allocate organizational ownership for (i)-(vii).

You may at this point ask three critical questions:  i) Are we necessarily talking about terabytes or petabytes of data?  ii) What is news here, compared with what good statistics, good BI or good visualization has always been able to deliver?  iii) What has all this to do in a blog about sales effectiveness?  The answers to these questions are from my perspective: No, big data is not about the exact size of certain data sets.  Big data is more a novel way of thinking or framing certain business opportunities than about a radical transformation of current business practices.  Big data will fundamentally change selling in B2B space and create immense business opportunity for forward-leaning sales and marketing organizations, as well as for their clients.

Grim

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