Becoming Customer Adaptive – a brief look at what the analysts are saying.

Becoming Customer Adaptive – a brief look at what the analysts are saying.

Becoming Customer Adaptive – a brief look at what the analysts are saying.

A few weeks ago I spent time at the Ovum Industry Congress.  The topic of the congress focused on “Becoming Customer Adaptive”.  Before I get into some of the insights that I garnered from the event, here is a view of what said title means:

Excerpt taken from the Ovum agenda

“Power has shifted to the digitally connected customer, and business models designed for a more predictable world are fast becoming redundant or uncompetitive. This is driving the need for organizations – irrespective of industry or sector – to become customer-adaptive. But getting on the right path is no easy matter, especially with so many “digital-this” and “transformative-that” messages vying for attention.

A customer-adaptive enterprise is one that is able to remain persistently relevant to its customers or the communities it hopes to serve. It does this by being able to spot, capture and use the signals of change that are present in the oceans of data that surround us.

Ironically, disruptive technologies, particularly mobile, social, Big Data and cloud computing, provide the means to tap into the collective capabilities of the organization, and to apply these in the best interests of its customers and communities.

A customer-adaptive enterprise requires visionary and ethical leadership to build the long-term trust so essential for developing enduring relationships, and of course it needs an engaged, motivated, and well prepared workforce to make it happen.”


That’s the overall view from Ovum.  There were a few highlights for me:

  1. O2 & Exterion Media (biggest advertiser in the UK) – were showcasing how mining data collected by O2, where Exterion are able to tap into and start targeting adverts based on a group of people and their similar profiles. The premise is that as data is gathered, O2 supply it anonymised (no customer names), they aggregate it based on crowds and start to spot specific trends about customer journeys.  This ensures that they are not following the pattern of one person, but the pattern of people and similar trends amongst those people.  Spooky – kind of – inevitable when you think the mass of data that is collected.  Don’t worry this isn’t minority report where you walk past adverts and they address you by your name
  2. BP Innovation centre –Harini Kulatunga Technology Innovation Manager discussed some of the quite extraordinary things that BP are undertaking to digitise their assets. Just to add some context to this, Harini works in the Digitial Innovation Organisation at BP.  The DIO helps solve business problems with new and emerging digital technologies. Harini assesses the capability of advanced technologies from 3D reality capture for improving geological interpretations to robotics for next generation logistics in field operations.  A few examples (among many others) she gave about the innovation they are currently undertaking are:
    1. Off-shore rigs have to be maintained and checked and one of the most dangerous jobs a person has to undertake is to be lowered down below the platform and survey the quality of pipes looking for degradation etc. The cost to BP for getting a person onto the rig and making sure they do their work in complete safety is approx. £100k.  The innovation they are looking at now is a machine that crawls down and attaches itself to the pipes and starts to scan each section of the pipe relaying digital images and data about the health of those pipes.  The data will give them a real-time view of degradation and other factors, being able to take remedial action quicker than the human operator.  No person needed, less risk of injury / health and safety fears, and over time the investment will no doubt pay for itself.
    2. The other example she gave was around maintenance in some of their remote locations where engineers have to endure extreme temperatures in order to conduct maintenance. Right now, they have very specific maintenance criteria and strict controls around what has been maintained, who maintained them, when they were maintained etc.  All of this paper based!  Her challenge, how to digitise this entire process so that an engineer can locate and maintain a part knowing it’s history without having to default to paper.  It’s simple, give them a device like a tablet, that can provide them with a digitised view of the plant and click on the part, pipe, machine that needs to be maintained and in one instance be able to see the full maintenance history by clicking on the object and tap into all the various notes.  Better still if they want to verify they are fixing the right pipe, they take the tablet and scan the RFID code, that then tells them according to the manifest on the tablet they are fixing the right pipe.  Pretty cool for the engineers not having to flick through reams of paper, this innovation and the use cases no doubt will stretch beyond what they might be using it for in other areas.  The only thing that they are struggling with is how to commercialise this.

Two specific companies and their innovations that stuck out to me in the conference.

Now moving onto the topic of data, as Ovum mentioned that “Ironically, disruptive technologies, particularly mobile, social, Big Data and cloud computing, provide the means to tap into the collective capabilities of the organization, and to apply these in the best interests of its customers and communities.”

A separate work stream centred around data the title “Sensing, Analytics & Big Data” – Tim Jennings the Ovum analyst summed it up in one slide:

  • Exploratory analytics is the beginning of the evolutionary endpoint for BI.
  • Cloud will become the default home for new data.
  • Embedded analytics will provide incremental benefits in a way that is invisible to most users.
  • A rising tide of IT spending will continue lifting enterprise investment in big data.
  • Appliance and cloud will drive the next wave of Hadoop adoption in order to make the platform simpler to implement for the next group of enterprises.
  • SQL still remains supreme for big data analytics, and will become more so as Hadoop adoption reaches a larger cohort of enterprises with more modest internal IT programming skills to early adopters.
  • Machine learning will become a checklist item for data-wrangling and predictive analytics tools.

Contrary to the above a poll was conducted in real-time in the work stream which asked the following questions:

What do you see as the biggest barriers to exploiting big data and analytics in your organisation?


  • Data Security – 5%
  • Data Privacy – 5%
  • Data sovereignty – 20%
  • Data Quality – 30%
  • Data Skills – 40%

Data Quality and Data skills are the two biggest items that are in the forefront of how big data and analytics will be successful in organisations, still a long way to go.

The second poll was focused on the usage of big data and analytics in organisations today, the answers:

  • BI & Analytics are only available via data specialists – 44%
  • Users able to build and run their own queries on pre-defined data sets – 33%
  • Users can explore available data sets and build queries – 0%
  • Users can explore and visualise available data sets and build queries – 13%
  • Users can apply a range of analytics methods to available data sets and visualise the results – 10%

On that note, there was one slide in the two days that made me chuckle – title “make data useful to policy makers” – it consisted of three points and a survey:

  • Today’s analytics tools are easy to use…but so are hand grenades
  • Data is powerful stuff. Use it wisely. (that wasn’t yoda!)
  • Software cannot replace skills

The below points came from a survey undertaken in government – the question: What are the top 3 challenges using analytics in your organisation:

  1. Lack of employees with relevant skills and training
  2. Not enough time
  3. Complicated processes and restrictions

The last and final item that returns back to theme of the conference around customer-adaptive enterprises.  A slide flashed up in one of the final sessions posed the question

“Why obsess about data?”

  • Customer-adaptive enterprises flourish because they can sense and respond appropriately
  • To compete effectively, organisations have to have a single view of their core processes, their supply chain, and their customers
  • The most advanced organisations embed analytics throughout their value chain

Thanks to Ovum for inviting me and I hope this gave you some insights into the conference.  Please share any thoughts you have in the comments below.