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Interview: Big Data for the telecom industry

T.C. Seow | June 17, 2014
In an exclusive email interview, the CTO for Telecommunications Solutions at Pivotal Inc. talks about what telecom companies are doing to harness the power of Big Data, and what they should be doing to move quickly ahead.

Paul Davey, Pivotal
Photo: Paul Davey

Paul Davey is general manager and CTO, Telecommunications Solutions, at Pivotal Inc. He has global leadership responsibility for delivering the Pivotal strategy into the global telecom industry. Previously with the Vodafone Group, Davey joined EMC in June 2012. He is in charge of driving Pivotal's product and solution portfolio development to meet current and emerging customer needs to deliver innovative business solutions to Pivotal's customers. He has 25 years of experience driving business growth IT and Networks through the practical application of emerging technology to clearly defined customer business needs.

Q: The buzz around Big Data seems to have confused rather than clarified the air around the use of massive amounts of data to generate insight into business decision making. At what stage are we in truly harnessing Big Data, and what fundamental factors (hardware requirements, clean data, skills, etc.) must be in place even before one talks about it?

Paul Davey: It is clear that, in 2014, "Big Data" is at the very top of the hype-cycle. Emerging technology is capable of delivering new business insights faster and cheaper than ever. Globally, we are instrumenting our everyday lives with billions of low-cost sensors. Our ability to extract new intelligence from previously unconnected events, and turn that intelligence into new value propositions is well covered in academic and marketing literature. What is not well documented is how to transition the business from established BI dashboards and decision-making structures to a new dynamic high speed business model.

If the literature is to be believed, Big Data changes everything, including a need to assign value to data assets on the enterprise balance sheet. The role of the CIO shifts from being the custodian of corporate knowledge and records to being the source of new raw-materials for the next generation of data driven business value.

Market speed is a brutal master. Go too fast and you are over exposed to untested business theory and missing something vital to the business; go too slow and you are scrambling to catch-up. So what do we need and when? Here's what I think should be considered:

  • Clarity of business purpose. If what the business wants from Big Data is faster answers to the same questions then Big Data is probably not going to help. If the business wants new insights then we need to be clear about what and why and when.
  • Agile technology. The shift to cloud platforms that scale out at better-than-linear cost. If we are to believe the hype we will need to have access to external cloud services as we move workloads in and out of the enterprise.
  • A clear migration plan. Hadoop and Cloud Foundry, as open source components of this new Big Data world, promise much in terms of cost and agility. Is the HA, back-up, security, of my Hadoop instance sufficiently well developed for my workloads? Which workloads can I move now, what evidence is there that the workload I want to move will actually deliver the promise?
  • Data governance. This new world of Big Data means I am going to have to allow different people to access all or parts of my data, with tools and in ways we not previously seen. Do we have a good enough discipline in the organisation to know who is doing what with our information? Where is it? Either "on-premise" or "off-premise".
  • End User Devices. Today we are assuming that many employees will use their own devices. Do we have sufficient control over the use of our data on devices we don't own? If not, how will we control the use of our data asset, both through devices and software controls as well as through policy and employment contracts.
  • Are we tracking provenance? If we are mashing up data from different sources, what are we doing to track data provenance and make sure we are comparing relevant data-sets when we mash up data from different sources?
  • What rights do we have? As we source streams of intelligence from outside the business, what structures do we have in place to manage access and use of data as a resource in line with policy and commercial agreements?
  • Is this a free-for-all? Who will have access to what and why? Do they have the skills to extract the value? Everyone is talking about data scientists… but nobody seems to be able to find them. Is this a CIO problem or an R&D problem? If data is the new raw material for the business where should these resources be owned and controlled?

 

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