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August 2011

Analytics Guru: Are Telecoms Ready for the Biz Intelligence Explosion?

Analytics Guru: Are Telecoms Ready for the Biz Intelligence Explosion?

Making sweeping generalizations on B/OSS software is a risky business, but I’d like to divide the history of B/OSS software into three decades:

Decade          Software Category in Greatest Demand

1984 to 1993 — Billing Software for Large Incumbents — Custom

1994 to 2003 — Billing/OSS Software — Commercial Off-The-Shelf (COTS)

2004 to Now —  Analytics & Assurance Software — COTS

In the first decade, after the AT&T divestiture, the IXCs and Baby Bells thirsted for custom billing software that was built for them by integrators like Andersen Consulting, American Management Systems and Bellcore.  During the second decade, true off-the-shelf software emerged from firms like Amdocs, CBIS, Kenan, Kingston, Clarity, Micromuse, Siebel Systems, and MetaSolv.

But by 2004, the B/OSS market had matured.  While startup operators certainly needed new billing and operations software, most of the hotly contested turf battles were decided, and the B/OSS market shifted into maintenance/upgrade mode.

We’re now in the decade of analytics, typified by software used to analyze, audit, and assure operations in greater detail.  Service assurance is certainly one big category of software that employs analytics.  Another is customer behavior and marketing analytics, the cousin of CRM.  Still another is the business, fraud, and revenue assurance sector.  Even billing and charging software, these days, includes a heavy analytics component to drive marketing and promotional offers.

So if we’re in an analytics era, it might be a good idea to figure out where this big analytics category may be headed.

To gain that perspective, I spoke to analytics expert John Myers, founding principal and senior strategist for Blue Buffalo Group, a Colorado telecom-focused business intelligence (BI) consultancy.  Many will remember that Myers was associated for five years with Subex, where he consulted with their Americas Sales/Pre-Sales organization.

Dan Baker: For starters, John, what exactly does an analytics consultant do?  And what type of beast is a Blue Buffalo?

John Myers: Sure, Dan.  There are several hardware and software companies who want to apply their hardware and tools in the telecommunications space.  But they have relatively little knowledge on telecom industry requirements.  That is the sort of knowledge I consult with them about.  And as you can imagine, with cloud computing and mobile broadband in high gear, there’s great interest out there.

As for a, or rather THE, Blue Buffalo Group: It’s a name I developed based on the Native North American mythology of the buffalo being the communicator of knowledge and wisdom.  It also doesn’t hurt that Blue Buffalo incorporates my undergraduate (University of Michigan) and graduate studies (University of Colorado) in the name of my business.

John, what’s the best definition of business intelligence?

Well, I think lots of people make the mistake of equating business intelligence with dashboards and/or reports.  These are really two different things.

It is about taking data from your environment, giving it a context, coming back with knowledge associated with that particular context, then actually taking action on that knowledge.

For example, let’s say we have achieved the ultimate in Smart Grid deployment and every single device in my home is connected to the Internet.  OK now, if today is the hottest day in August and my dashboard shows that a brownout is imminent because everyone is running their air conditioners full blast, what do I do next?  Well, maybe I tell all the refrigerators in the city to reduce their cooling temperature by three degrees or turn off the lights in every room in the city not currently occupied with people.

Business intelligence really is taking action on the knowledge, or rules, like the Smart Grid example.  What distinguishes true business intelligence from a dashboard is that the dashboard only “presents“ information or data.

Now to link the “concept“ of business intelligence to telecommunications, look at revenue assurance.  A lot of times you cannot automate a corrective Revenue Assurance or Service Assurance action because of certain operational or regulatory restrictions.  For instance, if an invoice has been issued to a customer, to make a change on the bill you generally need to get the permission of the customer.  This is a result of “cramming,” “slamming,“ etc.  However, before a customer’s invoice is issued and within certain operational constraints, you can use business-intelligence analytics to automate Revenue Assurance or Fraud Management discrepancies to reduce internal costs and improve customer satisfaction.

While we in the telecom industry certainly have made a lot of progress in terms of business analytics relating to Revenue Assurance and Fraud Management systems in the past few years, we still have a long ways to go in terms of adding true business intelligence analytics to those practices.

As the telecom industry has evolved, the number of events that need to be tracked in analytics applications like Revenue Assurance, Fraud Management, and Service Assurance are growing big time.

Yes, that is correct! From the proliferation of GPS-enabled smart phones and IPTV implementations, the telecom industry is at the forefront of the “big-data“ wave.

And it is not only the amount of events that has increased, but the complexity.  It used to be fairly easy to identify an event on a telecom network in a single CDR.  Then when we had long voice calls and we learned how to stitch those long-call duration activities and links together and recognize them as one big event.

Now imagine rather than a long-duration call with a couple of segments, you envision IPTV video content.  We are not talking in single digits or tens of events to stitch together, but rather millions of IP protocol events that make up a single customer-facing event like a mobile TV sporting event or an over-the-top (OTT) IPTV movie presentation.

Incidentally, I have heard that between the hours of 6 p.m. and 12 midnight, 24 percent of all North American traffic that goes across broadband networks is coming from Netflix — not Netflix-like activities, but Netflix itself.

Is Deep Packet Inspection a worthwhile strategy for collecting these events?

With DPI, it really depends on how much data you want to store.  Now in a large data-management environment that can handle “big-data“ requirements, you can track as much data as you want.  But are you really going to make use of that data because it can be expensive to store DPI data?

As long as an operator is charging only a monthly recurring fee for bandwidth, the business case for that doesn’t make sense.  However, as soon as a mobile carrier moves into tiered, mobile data pricing models, that’s a different story and thus a different business case.  If someone runs Netflix through a wirelessly connected tablet or smartphone (as opposed to streaming across Wi-Fi), the wireless carrier is going to love that because sooner or later the user is going to exceed his or her mobile broadband data cap and thus produce greater revenues for the telecom carrier.

It sounds like you will still run up against a storage issue, even if you are a big carrier.

Yes “big-data“ collection and analysis is, pardon the pun, a huge problem.

However, think about the progression we’ve seen over the past two decades.  Voice CDRs were relatively easy to track and store.  Then when SMS came on board, you went from say 20 CDRs per user, per day, to 100 mixed xDRs per day.  Now as you add mobile data to the mix, you’re talking about another 10-100x increase, in terms of the number of events that are going through the device.  A smartphone, by its very nature, is on all the time.  And depending on a carrier’s business model, you need to analyze a majority of that subscriber’s activity from a Revenue Assurance, Fraud Management and Customer Billing perspective.

What’s the answer to “big-data“ analytics issues, John?

I think the answer comes from a couple of directions.  First, we can learn a lot from our past experiences as we moved from landline to mobile, then mobile to SMS, and so forth.  If we bring some of that learning forward, we’ll be better prepared for the huge event record volumes that are here now, and will only continue to grow with, mobile broadband.

Secondly, database technologies have matured greatly in the past few years.  We are now at the point where it is not just possible but feasible from a business-case perspective to store all this data and provide access to it from an analytical platform(s) around a carrier.  Many data-management organizations made possible the kind of scalable analytical data sources that will allow you to do Fraud Management and Revenue Assurance, though it may not be good for traditional operational database management applications like Customer Billing or Customer Relationship Management (CRM).

In no particular order, here are some of the players in this “big-data“ space:

  • AsterData, now part of Teradata — itself one of the leaders in “big-data“
  • Dataupia
  • Greenplum, part of EMC
  • Netezza, part of IBM
  • ParAccel
  • Vertica, part of HP

As you can see from that list, many of the large hardware companies have recently been acquiring players in this high-end, “big-data“ analytics space.

Most, if not all, of these players are leveraging MPP or Massively Parallel Processing or Hadoop architectures to achieve this success.  MPP technology has been around for a relatively long time and Hadoop is one of the new line of technologies spawned by internet groups like Google, etc.

As these new databases come into play, does that mean the RA/fraud software vendors of the world need to re-architect their systems?

It really depends on how robust the underlying data architectures are in those Revenue Assurance, Fraud Management and Service Assurance platforms.  Theoretically, an application that utilizes a standard OLTP database, like Oracle or IBM, can use an MPP data management platform that mimics a standard OLTP structure.

Some “big-data“ solutions emulate a standard OLTP component so that they look and act like an OLTP database.  It really depends on how much encapsulation there is between the “big-data” platform and the Revenue Assurance, Fraud Management/Service Assurance application itself.  They will determine how much re-architecting you have to do.

This article first appeared in Billing and OSS World.

Copyright 2011 Black Swan Telecom Journal

 

About the Expert

John Meyers

John Meyers

John Myers is the founding principal and senior analyst/strategist for the Blue Buffalo Group in Lafayette, Colorado.  He is also responsible for the telecommunications channel of the B-eye-Network where he comments on all aspects of the telecom industry including video content distribution.

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