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Interview With Martin Willcox, Senior Director In Teradata's Go-To Market Organization

For those of you who are following dataIQ's "Big Data 100" list, the name of Martin Willcox will sound familiar. In the same list, Martin was listed as one of the most influential people in UK data-driven business.
Martin has 21 years of experience in the IT industry, worked for 5 organisations and was formerly the Data Warehouse Manager at Co-operative Retail in the UK and later the Senior Data Architect at Co-operative Group. Currently he is working as a Senior Director in Teradata’s Go-To Market organization. Prior to taking-up his current appointment, Martin led Teradata’s International Big Data CoE – a team of Data Scientists, Technology and Architecture Consultants tasked with assisting Teradata customers throughout Europe, the Middle East, Africa and Asia to realise value from their Big Data assets.
Martin Willcox
He is also a blogger for Teradata and an often presenter at many international conferences. As Martin joins as one of the keynotes on Data Innovation Summit 2017 we had a privilege to make a short interview with him regarding data-driven innovation and analytics in general. 

Q: Martin, I would like to start this interview with the topic of IOT, as I noticed that you are blogging and presenting on many conferences about the same topic. Can you share with us briefly your thoughts on IOT and “Analytics of Things”?

Martin: I spent a lot of last year helping Teradata customers with their IoT initiatives. A lot of organisations are – rightly! - very excited about the possibilities of the “Analytics of Things”, but underestimate the scope of the data management challenges that IoT data sources often present. Unfortunately, even machine-generated data are often incomplete, inconsistent and inaccurate! Sometimes the machines are even better at generating bad data – and quickly! - than are the Carbon-based lifeforms!

Q:From your perspective what are the biggest or the most common hinders, or challenges, organisations face when it comes to data-driven innovation? 

Martin: There is often a tension in the data and analytics space between “doing it right” and “doing it quickly”. Innovating means taking risks – and being prepared to fail. So, it becomes really important to move quickly – “to fail fast and learn quick”. Equally, data and analytics only deliver ROI when we use them to change the way we do business and that often means integrating them into a high-value – maybe even mission-critical – business process. Balancing the instincts and interests of the “hackers” and the “engineers” becomes critical. It’s not that one way is “right” and the other is “wrong” – more that it increasingly becomes very important to understand where you are in the process and which approach is most relevant right now. At least in my experience, organisational and cultural factors are as important in becoming a “data-driven organisation” as are data and technology.

Q:For those organisations that just started their journey in data-driven Innovation , what would be your recommendation for speeding up the process of turning data into insight and action?

Martin:It sounds obvious, but start with the business problem - or the opportunity - that you are trying to address. It’s very easy to get lost in the technology – or in the beauty of the algorithms – and to end-up solving the wrong problem, or to generate “insight” that isn’t really news to anyone in the frontline of the business. It’s not that analytics and technology aren’t important – just the reverse. But they are tools. And tools make great servants, but poor masters. For most organisations, the fastest way to get faster is to stop wasting time-and-energy on the things that don’t matter.

Q: How big part technology plays in this?

Martin: In Silicon Valley, the joke is that there are three types of companies: those that are already data companies; those that will soon be data companies; and those that will soon be out of business. And you can’t manage, integrate and analyse data at Web and IoT scale without great technologies. 

Q:Considering the amount of data, the technology available, the competence and the new/old processes in place, in which industries do you see the biggest impact of data-driven innovation?

Martin:Because I am lucky enough to work across a lot of industries and a lot of geographies, I get asked this question – or variations on it, like “is industry X more-or-less advanced than industry Y” - a lot. And I’d love to have a neat answer. But I think that the reality is that the world is increasingly flat – and that anywhere where you have competitive markets, or significant external pressures, you will find companies and organisations innovating with data and analytics. In the last 12 months, some of the most interesting projects that I personally have seen at Teradata have involved US Restaurant chains, European Manufacturers, Asian Telecommunication operators, European Retailers and an Asian Government. If there is a pattern in that little lot, I haven’t figured out what it is yet.  Q: Looking at data science and data-driven innovation from technology point of view, what can we expect in the next 12 months. 

Martin: That’s a tough question to give a short answer to. There is clearly some very exciting progress being made in Artificial Intelligence and Machine Learning, but “Ockham’s Razor” applies to analytics just the same way it does to everything else - and often the “best” modelling technique is the simplest one that is sufficiently accurate for the problem you are trying to solve. The explosion of IoT data sources means that there will be no let-up in the requirement to store and analyse large volumes of complex, multi-structured data – and I think it also implies that the industry will have to get better at managing and exploiting time-series data. And I think we’ll see more-and-more organisations move data and workloads to the Cloud – but also to understand the constraints and the issues that, at least in the short-term, will prevent them from moving some other data there – so that so-called Hybrid Cloud architectures will become the norm.

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Any facts, figures or references stated here are made by the author & don't reflect the endorsement of iU at all times unless otherwise drafted by official staff at iU. This article was first published here on 1st April 2017.

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