Author Archives: Mark Hunter

Sorry Technology – Big Data Just Made Your Job Even Harder

Wednesday September 4th, 2013

In my last blog I discussed how “Big Data” was making the analytic professional’s job even harder.  This time round I’ve got Technology in my cross-hairs! 

Let’s be honest, Technology professionals have historically had a hard time understanding the field of analytics.  They would normally approach this as if they were building an operational application and send a Business Analyst to dutifully gather some business requirements and the conversation would go something like this:

Technology: “Hello, I’ve just been assigned to your project – what’s your business requirement?”

Analytics guy: “I need to build a model to predict something (churn, cross-sale, probability of default, expected customer value etc.)”

Technology: “Ok – I’m here to help(!) Please tell me the data that you require”

Analytics guy: “I don’t know, that’s the whole point of building a model – to find out what data will help predict this outcome”

Technology: “But we can’t start development until you write down all of your data requirements”

Analytics guy: “In that case I need all the data, for ever!”

This Technology nightmare just got worse, not only does ‘all the data, for ever’ now include multi-structured data like web-logs and text sources but the requirement just got exponentially tougher:

“I need all analytics on all the data, for ever” where ‘all analytics’ includes ‘Big Data’ Analytics like Graph Analysis and Text Mining. 

No single technology can deliver on this requirement (as you would have heard on the latest Teradata webinar – Hadoop and the Data Warehouse: When to Use Which), so Technology need to assemble the components in such a way that offers the analytical community a seamless, integrated environment to work with. 

The integration between these disparate technologies will be a key enabler to allow organisations to create insights rapidly.

The other challenge is how to ensure that the organisation’s ability to act on these insights does not become the bottleneck.  As Jack Welch said “An organisation’s ability to learn, and translate that learning into action rapidly, is the ultimate competitive advantage

So the final piece of the puzzle for Technology to solve is the deployment of analytics into business processes and customer interaction points.  If an organisation can solve this as well as ‘all analytics on all the data, for ever’ they will create a real source of competitive advantage which will be difficult for their competitors to recreate.

Mark Hunter is a Financial Services Industry Consultant with Teradata Australia & New Zealand. Mark has 15 years of banking experience gained in the UK and Asia. He has extensive experience in developing analytical capability to drive data-led decisions. He has worked across the entire customer lifecycle with specialist knowledge in Marketing and Risk. You can also follow Mark on twitter @Mark_Hunter_Mel

The Top 10 Requirements to be a Data Scientist

Wednesday July 17th, 2013

I recently had a conversation about the difficulty in hiring analytic professionals (or data scientists if you are based in California!) and it’s not surprising given the skills and behaviours being sought (this list was created by looking at relevant Job Descriptions on LinkedIn):

  1. Analytical skill-set
    1. Mathematics / statistics (including experimental design)
    2. Domain knowledge (i.e. Industry specific processes where analytic are applied)
    3. Technology / data
  2. Communication skills (story-telling)
  3. Curiosity (willingness to challenge the status quo)
  4. Collaboration
  5. Commercial acumen
  6. Customer-centric
  7. Problem-solving skills
  8. Proactive
  9. Strategic
  10. Willingness to spend lots of time justifying your existence in the organisation

Ok, so I added #10 based on my experience to bring it up to a round number!

So when you throw “Big Data” into the mix, is it any wonder there is a skills shortage? (McKinsey is May 2011 predicted a shortage in the United States alone of 140k to 190k by 2018, as well as 1.5m managers and analysts with the know-how to use the analysis of big data to make effective decisions).  

The good news is that, when building any team, every team member is unique and will have relative strengths in different areas – no one person has to be an expert in all aspects.  The team can be constructed to complement and leverage these different skill-sets. 

However, the worrying trend that I’ve seen recently is the ‘Dumbing Down’ of Analytics.  Organisations faced with this challenge believe it is the analytical skill-set requirement which can be relaxed – I totally disagree.

 “Say you is data analyst because use of Google Analytics like say you is doctor because watch House.” @BigDataBorat

If I was going to have a medical procedure I would happily trade-off the communication skills of the doctor to ensure they had the required medical skills!!

“Big Data”

So how does the advent of “Big Data” impact this challenge?

First up, I am not going to attempt to (re)define “Big Data”.  Doug Laney (@Doug_Laney) from Gartner penned the famous Three ‘Vs’ back in 2001 and I’ve seen plenty of others distort and misuse this definition to fit their particular narrative (perhaps the vendor sales equivalent should be the Three ‘B’s – Blah Blah Blah!! ©Mark Hunter 2013!)

I believe “Big Data” will cause the greatest disruption in the ‘Analytic Skill-set’ requirement – in particular the mathematics / statistics and technology components.

Mathematics / Statistics

Traditionally analytics was based on ‘relational’ data, where tools like SAS & SQL have been prevalent; however the move is now towards ‘non-relational’ analytics.  Examples include Graph Analysis (used for Network Analysis), Path Analysis (used to understand path across disparate time-based events, like path to purchase across multi-channel interactions) and Text Analytics (which isn’t new, but is becoming more mature).  The good news here is that analyst professionals should be able to brush up on these techniques pretty quickly as many of these concepts are covered at university.


The “Big Data” technology landscape is getting pretty complex, but I believe Teradata is leading the way in its vision for the logical data warehouse.  We recognise that Hadoop is an important component of the logical data warehouse as it delivers a commercially viable solution to the challenge of ‘keeping all data forever’.

Teradata’s Unified Data Architecture includes the Teradata Data Warehouse, Hadoop and the Teradata Aster discovery platform.  The good news for the analytic community is the level of integration between the components and the creation of the patented SQL/MR within the Teradata Aster platform.

The Teradata Data Warehouse and Teradata Aster both support Apache HCatalog, making it easier to share and reuse data stored in Hadoop.  But the real ‘secret sauce’ is the creation of SQL/MR within the Teradata Aster platform.  This brings Map:Reduce programming to the SQL-savvy analyst which is a game-changer as I don’t really want to be add hard-core JAVA programming to the list of requirements!!

So rather than ‘Dumbing Down’ the analytic skill-set, let’s look at adding incremental ‘non-relational’ analytical techniques and simplifying the extended technology landscape. 

Everything should be made as simple as possible… but not simpler” Albert Einstein.

Mark Hunter is a Financial Services Industry Consultant with Teradata Australia & New Zealand. Mark has 15 years of banking experience gained in the UK and Asia. He has extensive experience in developing analytical capability to drive data-led decisions. He has worked across the entire customer lifecycle with specialist knowledge in Marketing and Risk. You can also follow Mark on twitter @Mark_Hunter_Mel

How Many Data Tests Is Your Organisation Running?

Monday January 21st, 2013

I first saw Dr Manu Sharma (Principle Data Scientist @ LinkedIn) speak at Teradata Partners in October 2011 when he gave a presentation on Data Science at LinkedIn.  I recently read that Manu drew a crowd of 1200 at TiE Mumbai which just shows how popular both Data Science & LinkedIn are.  Across both talks, Manu reinforced a few concepts that I’ve subscribed to in my career, namely:

  1. Data gathering, statistics and modelling need to be blended with curiosity and intuition to solve business problems.
  2. Good business analysts (now commonly called data scientists or ninjas!) should drive the business (assuming the organisation wishes to be data-led).
  3. More data is better than less data & raw data is better than processed data.
  4. Simple models are always better than complex models.
  5. It’s better to fail fast, iterate and test…test…test.


At any point in time there are 100’s of A/B tests running on your LinkedIn page, which Manu describes as one of the key techniques leveraged to move data up the value chain towards wisdom.  It is interesting that LinkedIn leverages A/B testing to this extent whilst other more complex organisations (with significantly more products & channels) run far fewer tests.

So, let me leave you with a couple of questions:

  1. How many tests are you running across your organisation?
  2. Are you serious about being data-led (or just paying lip service to the data science trend)?

Mark Hunter is a Financial Services Industry Consultant with Teradata Australia & New Zealand.  Mark has 15 years of banking experience gained in the UK and Asia. He has extensive experience in developing analytical capability to drive data-led decisions. He has worked across the entire customer lifecycle with specialist knowledge in Marketing and Risk.

Mark spent 11 years in the UK working for Barclays Bank & Royal Bank of Scotland in various roles. He then relocated to Beijing to head up the Analytics function for the RBS Bank of China co-operation and latterly took up the role of Regional Head of Decision Management for RBS Retail and Commercial businesses, based in Hong Kong.

Join and follow Teradata Australia and New Zealand via Twitter @TeradataANZ and the TeradataANZ Linkedin Group.

The Confusion of Real-Time Predictive Analytics

Wednesday November 21st, 2012

Following on from the recent Curt Monash blog ‘Real-time confusion’, I too have a bug to bear with the recent ‘real-time’ hype – my particular area of annoyance relates to real-time predictive analytics.

I get very concerned when I hear claims that a new technology can build predictive models in seconds or minutes as I would never want to deploy a predictive model that was built in such a timeframe.

There is governance required for developing well-designed and well-built predictive models.  There are invaluable steps to work through before you even get to running the predictive model, for example understanding your data, dealing with outliers, developing a sampling methodology, defining your objective variable, working with continuous variables (of which there are many approaches!) and of course applying that rare commodity – ‘business understanding’.

There should also be a review of the model and a business call on the way the model should be deployed, for example if this is a risk scorecard – what should the cut-off be placed?  If it’s a marketing selection model – how much of the population should be contacted?  This in itself requires a fairly in-depth piece of analysis to help make an informed decision.

Let me state (for the record!) that I do value the predictive model step running quickly, I would love to be able to build more models, better models, more granular models but the real-time hype is over the top!

Infonomics – the practice of information economics

Friday August 3rd, 2012

I recently read a paper by Doug Laney from Gartner which started to challenge companies to treat information as another category of intangible financial asset. I’ve often heard companies talk about their data as ‘a source of competitive advantage’, yet there is a reluctance to try to measure the true value of this asset (maybe this falls into the ‘too hard’ category!).

Accounting policy is such that public companies are not currently required to split out the value of information assets, and I loved Doug’s example of the gap between Facebook’s recent $100B market valuation (now $70B) versus its book value of under $7B – the gap possibly representing the markets view of Facebook’s ability to monetise their information assets. In the past I’ve seen this represented as Goodwill on the Balance Sheet…

I totally agree with Doug when he argues that, regardless of accounting policies, attempting to value information assets offers a range of benefits to organisations. Perhaps the most fundamental benefit is ensuring that investment is right-sized to the expected value of the asset.

I would recommend starting down the path of valuing your information assets, then challenging the organisation to ensure that they are leveraging these assets for optimal return…

Mark Hunter 

Do More With Your Data – Build Business Value

Monday May 14th, 2012

I’m really looking forward to this week as we will be hosting Teradata Universe 2012 ( in both Sydney and Melbourne. This year we are privileged to welcome some excellent speakers from around the world and it’s a great opportunity for our Australian users to benefit from their thought-leadership.

We will have presentations from Barnes & Noble, eBay, MGM Resorts, Morgan Stanley Smith Barney, Qantas, SAS and The MITRE Corporation as well as hearing from Scott Gnau, Stephen Brobst and Marc Fabbo from Teradata.

There is also the opportunity to arrange 1:1 meetings with our speakers, which – from experience – is a fantastic opportunity to share experiences and understand how leading organisations are leveraging their data, fitting the theme of Universe this year – ‘Build Business Value’. I enjoy the challenge of working out how to take these leading practices and evolve them into a different environment.
Please get involved this year and tweet your comments via the hashtag #TDU12. See you at Universe!

More information can be found here

Fact Based Decision-Making: A Source of Competitive Differentiation

Friday March 9th, 2012

The global finance sector is experiencing some difficulties, as evidenced by recent financial results and announcements by the banks to raise interest rates and cut jobs.


I believe in the current climate it is even more important to increase the competency around fact-based decision-making. The Banks have a responsibility to shareholders to outperform the market irrespective of conditions.


I recently read a blog by Baker Street Publishing in which the author attempts to define a ‘good decision’. The definition discussed (which I like) comes from Chris Argyris of Harvard – ‘an informed choice based on valid information’.


For me, the informed choice for Banks comes from using advanced analytics to predict and course-correct as opposed to using reporting to simply describe the market conditions. It is the optimisation of fact-based decisions in different market conditions that delivers a lasting competitive differentiation.


Advanced analytics is not something which is easily ‘commoditised’ (despite many vendors’ claims!). A commodity is used to describe a class of goods or a service for which there is demand, but is supplied without qualitative differentiation. I believe advanced analytics is actually the opposite; they can be a source of differentiation. If you hear anyone talking about ‘commodity analytics’, please direct them to this blog!


The final component discussed in the blog is ‘internal commitment’; this is where I believe many organisations face the biggest challenge. I don’t believe advanced analytics, and its ability to provide sustainable competitive advantage, is understood well enough. Perhaps we should take the lead from the blog and all study the movie ‘Moneyball’!


Mark Hunter

Was this a sales pitch?

Monday December 12th, 2011

I had the opportunity to attend (and jointly present at) the Teradata Partners event in San Diego in October and was blown away. This event is the main global Teradata User Group and the emphasis is definitely on the ‘User Group’!


Having spent most of my career working in banks I have attended a few vendor events in my time and have been disappointed (and sometimes annoyed) when vendors try the hard selling approach.


The Teradata Partners event was completely different; it is organised and run by Teradata customers and there’s a couple of details which I believe make it unique:

  • The event was not introduced by Teradata; instead the President of the Partners Steering Committee – Erin Redshaw from Loblaw (one of the largest Canadian supermarkets) did an excellent job of facilitating the event.
  • Most sessions were led by customers (or in my case jointly presented by a customer & Teradata), with the emphasis on knowledge sharing. I was really impressed by the candour of the presenters, openly sharing their challenges, thought processes and results – hoping that their peers learn from their experiences.
  • Evaluation forms were circulated for each session and one question stood out: ‘Was this a sales pitch?’ This was really impressive – sales pitches (either from Teradata or from customers) were outlawed; I can only imagine presenters scoring badly on this question would not be invited to speak at future events!
  • Another important aspect of the event was the opportunity for customers to interact directly with their global peers on a 1:1 basis. Teradata actively encourages this type of interaction and is happy to facilitate introductions across their global client base.

Next year the event will be held in Washington D.C., I definitely recommend you all attend. 


Mark Hunter

Simplicity is the Ultimate Sophistication

Wednesday July 27th, 2011

I finally made it to Lake Mountain at the weekend to give my 3 year-old her first taste of tobogganing. It was a really pleasant set-up consisting of a couple of small snow runs (Village Run & Koala Creek) next to the Visitor Centre which had all the basics (hot food & coffee!).

It reminded me how much I enjoy simplicity – I am always impressed with people who can simplify a problem and provide an articulate response. This really is a skill that is important at home (my 3 year-old is going through the ‘why?’ phase!) and has benefits in a corporate environment.

How many times have we been told ‘it’s too complicated’? I believe this is sometimes a convenient hiding-place; we should try to understand if the complexity is naturally occurring (and perhaps inevitable) or if the complexity is self-generated and unnecessary.

Doing business today is complex enough; however some organisations magnify the inherent complexity by adding unnecessary layers of management, confused accountability, poor communications and sheer lack of focus. There are often too many people engaged in the search for a ‘silver bullet’, when they should be focussing on getting the basics right.

For some reason, this focus on getting the basics right has become unfashionable, so I would like to draw upon a quote from Leonardo da Vinci in an attempt to bring it back into vogue – ‘Simplicity is the ultimate sophistication’. I couldn’t say it better than this, so I won’t try – simple!

Mark Hunter 

Components of the lucrative Chinese Credit Card Market

Tuesday March 29th, 2011

The Chinese credit card market is particularly close to my heart, having spent almost 3 years in Beijing helping to build a successful credit card portfolio.  I read a Lafferty World Cards Intelligence report last month which reported that the Chinese credit card market moved into profitability for the first time in 2010.  The $1 profit per card is low compared to other markets, but is significant as it shows credit cards to be a profitable venture for Banks in China, a country with a savings culture.

The number of credit cards issued now stands at 230 million and by 2015 is forecast to be the largest credit card market on the planet.  There will, of course, be winners and losers within the many credit card providers in China and I personally believe analytics will decide!

One of my favourite books – ‘Competing on Analytics’ by Thomas H Davenport and Jeanne G Harris – discusses three key components of analytical capability:

  1. Organisation;
  2. Human; and
  3. Technology

Many credit card providers have invested heavily in technology, building scale across the organisation from back to front office.  However I believe the organisation and human components will decide the winners from the losers – I’ll give two examples:

  1. Establishing a fact-based culture – this is an extremely difficult goal in China where the decision-making process can be opaque and is usually multi-faceted.  In my experience, fact does win out occasionally but this is not guaranteed!
  2. Choosing a distinctive capability – many of the credit card providers are involved in a ‘me too’ strategy – there is very little differentiation in the market and pricing is strictly regulated.  I have yet to see an organisation try to lead on analytical capability, therefore the business of creating and acting on analytical insights is not central to the company’s strategy and is often overlooked.

Unfortunately, many executives have fallen into the trap of just obtaining the data and analytical software they need, thinking that analytics are synonymous with technology – once they realise that the other two components need to be addressed I think we’ll see some pretty exciting times ahead…

Mark Hunter