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Fragmented or comprehensive – the state of the ‘Data Profession’

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Fragmented or comprehensive – the state of the ‘Data Profession’

  |   Blog

I’m continually surprised by the responsibilities and titles of new data roles emerging within the ‘data profession’.  Admittedly this is a fairly nebulous concept and I suspect amongst practitioners there are a variety of opinions as to what the composition of this space looks like.  However there are within this area certain trends that I suspect most practitioners would agree on.  Data is being taken more seriously by organisations than ever before with comparable growth in terms of dedicated ‘data people’, investment and technology.


As part of this industry growth most organisations with mature Information Management capabilities possess Data professionals working on both the technical side of the spectrum (data modellers, architects, metadata specialists and so on) and on the softer, business side (data quality, data governance and data management professionals).  However does this maturity necessarily translate to effectiveness and efficiency?  Has this growth in resources and profile led to healthy development for the profession or fragmentation?


Our experience has been that many organisations believe hiring data professionals across the spectrum is the most important requirement for a cohesive approach.  More often than not however we come across the same problems that have dogged the industry since its earliest days.  Technical experts unable to convey the business benefits of their proposed solutions whilst those at the ‘data coalface’ continue to exhibit a shortfall in technical appreciation and understanding.  This often leads to incoherent Data Strategies and many organisations’ data functions being viewed as academically rather than business driven.  Worse still sometimes data fiefdoms emerge with rival tribes battling for control of an organisation’s data landscape.  Hardly productive for a discipline that still has some way to go to achieve its Public Relations ambitions.


So what is the solution?  Appoint a benevolent data dictator in the form of a Chief Data Officer to unite the tribes?  Clearly divide up responsibilities and requirements into separate expertise areas?  Rarely does a data problem area however simply consist of modelling conundrums, quality defects or accessibility issues – usually there are a range of compounding issues signalling a need to get the experts working together.  A coherent, well sponsored and widely agreed Data Strategy is clearly a must.  However one thing we’re often surprised about is how little time is spent by each end of the spectrum understanding the other’s day to day problem set.  Data Management, Governance and Quality teams too infrequently engage the Modelling, Architecture and Metadata experts with queries and requirements and vice versa.  If we are all experts working in the ’data space’ what are we afraid of?


So what does this future look like?  Are these barriers likely to dissipate and are there any ways to avoid these situations?  Certainly on the tools front the technology is becoming more accessible and intuitive than ever before.  There are an array of adaptors for instance in most cleansing, modelling, reporting & visualisation tools meaning loading data is itself no longer a hugely significant requirement.  The social media generation also have far more of an appreciation of both sides of the data spectrum than before.  However this has also encouraged a somewhat ubiquitous view of data – it should just work with minimal effort.  There is a risk that less and less time will be dedicated to getting the fundamentals right.


There will always be many ways to skin a cat.  In our view in order to become less cyclical in nature and a trusted advisor within organisations the most successful data professionals of the future will possess wider skillsets, deeper technical understanding and also the softer skills necessary to clearly articulate problems and solutions.  Whilst specialists will always exist to as efficiently as possible deliver proven solutions simply referring data problems to them will diminish as an acceptable solution.  A cohesive Data Strategy should always originate and be owned by the business, however it also has to be inclusive if it is to succeed.

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Moth class sailing, three boats

Building Analytics of the Moth Nationals using Tableau Public edition

  |   Events

At the recent Moth UK National championships we decided to have a go at presenting various facts about the event and the class using Tableau Public .  This is a great viral marketing idea by Tableau and we’ve certainly had fun learning this limited, lightweight version of their popular Business Intelligence software.


The Moth class is one of the leading extreme singlehanded racing classes within modern sailing, with rules that let designers go wild and test sailor’s skills to the limit.  Constructed from carbon fibre the boats weigh around 30kgs in total and run on hydrofoils.  These are effectively aeroplane wings under the water which generate lift allowing the boats to fly above the water at speeds of up to 32 knots (36mph).


James has competed in the class for a number of years, finishing 26th at the last world championship in Italy.  At this year’s UK National Championships we decided to visualise some key pieces of information.  Here James talks through some of the findings and whether they yielded any insights.

 Whilst relaxing ashore after another 3 hard races during this years National Championships the usual musings cropped up during conversations with other competitors.  “He / She went well in that race, they are relatively light”.  “That type of hydrofoil seems to dominate in less than 15 knots of breeze”.  “Sail X seems to be the best when its windy”.  These are the kind of questions that are often asked at Moth events but rarely answered with a great deal of rigour.  A great deal of science goes into the design of the boats using CFD software & other tools, analysing data on results & performance however usually receives little beyond anecdotal attention.


Given we had some time on our hands around racing and I was keen to learn more about using Tableau Public collecting some data for analysis seemed like a good idea.  So after racing in true ‘lean’ fashion one evening an entry list was passed around in the bar for competitors to, as honestly as possible, fill out their weights in KGs.  The hunger for data then set in and we started working out what other data points we could easily capture about the event.  Soon we had peak and average windspeeds for the races, results, equipment breakdowns and other stats.  All free, easily collectable and potentially quite insightful information if presented and analysed in the correct fashion.  So what did we find out about this unique class?


Firstly there doesn’t appear to be a significant correlation between age and results.  Does this mean experience can offset youth and fitness to a degree?  Perhaps older sailors have bigger budgets and are able to afford the latest, faster and more reliable equipment?  Lots of food for thought, however with an age range of 14 to 55 years of age the Moth class has to be at the more ‘age inclusive’ end of the sailing spectrum despite its extreme nature.


Its been a common view outside of the class that you have to be lightweight to compete.  Within the class there is also a common perception that the heavier sailors perform better in windier conditions due to having more leverage when hiking (hanging off the side…) thus consistently keeping more power in the sail. Conversely in lighter wind conditions heavier sailors (myself included…) will often complain that its difficult to match the pace of lighter sailors, partly due to lighter sailors helping the boat to rise onto the hydrofoils earlier.  Overall when compared against performance there doesn’t appear to be a significant correlation during the week between results and weight.  The top 25 overall featured an impressive range of 89 KGs down to 67 KGs.  Perhaps due to the wide range of conditions (races held in winds ranging from 16-28 knot averages) both the heavier ‘dogs’ and the lighter had their day during the event?


Looking at the three windiest races (4-6) there is a slight concentration between 73 and 86 KGs within the top 25 finishers which would go someway to supporting the hypothesis that heavier sailors perform better in these conditions.  Looking at the three lightest races (3,10 & 11) however there’s less of a correlation.  The sub 70 KG sailors certainly didn’t ‘walk away with it’ and there is still a cluster between 80 and 70 KGs in the top 25.  Perhaps technique or equipment are more important factors here?  Its possible to look at equipment using the summary statistics tab to try and shed some more light on this.


Looking at equipment breakdowns and average results for the top 25 overall in the 3 windiest and lightest races there are some interesting results.  The Exocets seem fast both in the windy and the light conditions, the Mach2 performed slightly better in the windier races and the Ninja (a boat normally famed for light wind results) performed better in the windier races.  The largest performance difference for the rocket was due to retirements in the windy races.  Sail choice is slightly harder to judge due to competitors changing sails depending on conditions, however from the data we have the North seems to be a more consistent sail than the 16.  Whether this is because those with more ability use it or it is a performance advantages for those competitors is debatable.


Boat design

Average result in 3 Lightest races Average result in 3 Windiest races
Ninja 21.4 17.2
Rocket 12 37
Exocet 10.56 10.59
Mach2 19.57 17.87
Sail design
KA MSL16 14.21 17.38
North GM1 8.4 6.27


There’s a clear correlation, unsurprisingly, between consistency (range of results) and overall results.  There are also however some notable exceptions for the week worth highlighting.  Mike Cooke & Ben Paton proved to be 2 anomalies in this area – despite being 2 out of the 5 most inconsistent competitors in the regatta they bucked the trend and still both managed to finish in the top 12 overall – quite a feat.  Ollie Holden also possibly earned the title of Mr Consistent with an overall result of 25, no retirements and a spread of just 9 points between his worst and best results – very impressive.


Overall its been quite interesting visualising this data in Tableau Public – it has the odd frustrating feature but generally it’s a good tool to use.  Tableau’s viral marketing approach seems to be yielding benefits as its spreading like wildfire particularly within journalism.  There were some new insights that I believe we gleaned from the data, it was also however useful for backing up existing understanding using hard facts and figures.

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  |   Blog

We are thrilled to announce the launch of our corporate website for Data to Value Ltd.an innovative, London-based Data Consultancy. As Founding Partners our drive is to help our clients draw as much value from their data assets as possible.  For some time we have discussed at length how despite tools, approaches and techniques evolving considerably within the Data Management space the same problems that were cropping up 20 years ago still preoccupy many organisation’s data initiatives today. It seems that many firms despite enhancing their tools and approaches forget or continue to struggle with the fundamentals. In certain areas technical advances (such as storage developments and Big Data) have even created new problems.


So how do firms go about tackling the elephant that is the Data Management space? We believe the most feasible approach is to break down your vision into well-defined, achievable and tangible components that all stakeholders can understand. We don’t believe success in Data Management automatically translates into requirements for elaborate, organisation-wide programmes of work. Mature Data Management is as much cultural as it is technological, operational or organisational. We also believe that effective Data Management doesn’t have to cost the earth and great progress can be made as part of Business As Usual.


We hope you enjoy the website. We plan to share our thoughts, insights and musings as much as possible via our Twitter feed, Blogs and White Papers on the website. Please feel free to subscribe to the site for access to exclusive materials and White Papers. Whether you are an organisation struggling within the  Management space or a practitioner please do drop us a line.




James & Nigel


Founding Partners – Data to Value Ltd.

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