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Archive: 2013 (Analytics Network)

Tuesday, 10 Dec 2013
Michael Mortenson

Morning All,

I am currently involved in helping set up the 4th SCOR (Student Conference on Operational Research) conference ( to be held at the University of Nottinham in the first May bank holiday of next year (2nd-4th May 2014). The conference is aimed at PhD students and young practitioners at the start of their careers and covers a wide range of topics and streams.

As one of which I am chairing a Big Data & Analytics stream and would welcome abstracts from anyone interested in presenting on a wide range of topics across the analytics specturm including:

  • Big Data applications
  • Predictive analytics
  • Machine learning
  • Data science
  • High-performance computing
  • Text analytics & natural language processing
  • Any presentations exploring theoretical or practical examples of how OR/MS and analytics can be linked

As a student conference the rates are being kept particualrly low, provisionally estimated at £125 per person, including 2 nights accommodation, all meals, social events, printed materials and the opportunity to be published in the conference proceedings.

For members of the Analytics Network we are aiming to deliver a live video stream of the event and/or a downloadable video recording of the presentations. More details to follow.

In the meanwhile for anyone interested please check out the conference website ( or am happy to give out any information or answer any questions by email (

Hope to see you there!



Friday, 29 Nov 2013
Sayara Beg

Analytics Network Sponsored Christmas Meetup

Venue: Dirty Dicks Pub, 202 Bishopsgate Speaker: Chair & James Proctor of CNX Search Date: Thursday, 05 December 2013 at 18:00 - 21:00

An opportunity to meet like minded professionals, working in the field of Data Science, exchange contact details and establish long term professional networking relationships for business development opportunities.

There will be nibbles and a drink available, as a well as stimulating conversations with an exciting exchange of ideas.  Register to attend here:

This event has been sponsored by CNX Search, a Data and Analytics Recruitment firm

CNX search provides world-class staffing solutions for customers who have invested in analytics software and want to ensure they get the most from their chosen solutions.  By focusing on Business Intelligence, Data Warehousing, Predictive Analytics and Data Science, CNX positions itself firmly in the most innovative and commercially important area of Information Technology, aligning ourselves as partners to organisations who want to ensure that whether you are in business looking for a specialist, ora professional searching for your next career move, you have a partner that you can rely on to facilitate these changes quickly, professionaly and accurately.

Sunday, 24 Nov 2013
Sayara Beg
Friday, 15 Nov 2013
Sayara Beg

In 2007, Professor Sir David King outlined seven principles for scientific researchers to follow, aimed at preventing corrupt practices and establishing trust between science and society.

They are:

  1. Act with skill and care in all scientific work. Maintain up to date skills and assist their development in others.
  2. Take steps to prevent corrupt practices and professional misconduct. Declare conflicts of interest.
  3. Be alert to the ways in which research derives from and affects the work of other people, and respect the rights and reputations of others.
  4. Ensure that your work is lawful and justified.
  5. Minimise and justify any adverse effect your work may have on people, animals and the natural environment.
  6. Seek to discuss the issues that science raises for society. Listen to the aspirations and concerns of others.
  7. Do not knowingly mislead, or allow others to be misled, about scientific matters. Present and review scientific evidence, theory or interpretation honestly and accurately.

How effectively can these principles be applied to Data Scientists in the field of Advance Analytics? 

Should it fall to The Operational Research Society, as a professional body, to advocate these principles?  To train its members and ensure they remain committed to them, and to maintain its application as advances are made in the field, through continous professional development courses (CPDs)?

Friday, 4 Oct 2013
Gavin Blackett

The BBC's Bottom Line programme discussed Big Data on Radio 4 this week.

The programme is available on iPlayer at the moment (but possibly only in the UK) 

Monday, 26 Aug 2013
Michael Mortenson

Interesting post from Vincent Granville about the relative popularity of various analytics-related terms on Google searches.


Of particular interest to this group I thought was this one:

Google search popularity

The orange line represents "six sigma", the blue "data mining", the green "big data" and the red "operations research".

The increase in "big data" does seem to have some correlation with declines in "six sigma" and "data mining", although the decline seems to have begun before "big data" rises in popularity. "Operations research" seeems less effected although there does seem to be steady decline over the period.

I am always a little wary of these because we could easily say that "operations research" is less of a catch-all: management science, operational research, optimisation, etc. may take away some of the search 'juice'.

However, do we think there is something in the declines of certain quantitative methods and rise of big data? Is the emergence of Hadoop et al a threat to OR's popularity? Or is this just surface level and a poor indication of what is actually going on in business?

Monday, 29 Jul 2013
Sayara Beg

What is the difference between the 'recent' concept of Graph Algorithms compared to the well known Operational Research solution of the 'Travelling Salesman' problem. 

Is there a difference, or are they fundamentally the same approach to a common problem, with slightly different perspectives?


Friday, 5 Jul 2013
Sayara Beg

It could be argued that cleansing data, transforming it and loading it in to structured database tables is a certain way to eliminate all the interesting discoveries that could possibly be made.

I believe, the real data science takes place whilst analysing the dirty, uncleansed and unstructured data.  Why, because, as the saying goes, 'the devil is in the details'.

Performing any form of scientific experiment using big data is a huge task and can be at a high risk of misinterpretation and misrepresentation if noise and bias is not accurately identified and clearly labeled.  The smallest error or oversight at the beginning of any scientific data experiment can snowball into a completely useless set of analytical results, which could become uncomfortable to unravel or expose to the audience, once realised. 

If we did cleanse the data using pre-defined rules & logic, then transformed the data and subsequently stored it all in structured tables, have we not sanitised the data so much, that any result we produce will be considerably 'biased by default' to rules and logic applied pre-analysis to render the results as 'dismissible with no real research value'?

I say, you should apply all the data science analytical methods you want to, before you extract, transform and load, because, to quote Jeff Jonas "all errors such as misspellings and numeric transpositions are valuable regardless of whether these errors have been generated by accident or are professionally fabricated lies created by sophisticated criminals"  In other words, the devil is in the detail.


Sayara (Chair)

Thursday, 27 Jun 2013
Sayara Beg

Despite the excitement around data science, big data and analytics, the ambiguity of these terms has led to poor communication between data scientists and organisations seeking their help.

In this report, authors Harlan Harris, Sean Murphy and Marck Valsma examine their survey of serveral hundred data science practioners in mid-2012, when they asked respondents how they viewed their skill, careers and experiences with prospective employers.  The results were striking.

Based on the survey data, the authors found that data scientists today can be clustered into four subgroups, each with a different mix of skillsets.  Their purpose is to identify a new, more precise vocabulary for data science roles, teams and their career paths.


(special thanks to Nigel Philips for finding this report)

Tuesday, 25 Jun 2013
Sayara Beg

Bright North have just released their latest BI Analytics whitepaper which can be downloaded at

They are looking for feedback on the whitepaper.

What are your thoughts?

Monday, 24 Jun 2013
Sayara Beg

Marketing is a profession in which Big Data and analytics are extensively used. are hosting a webinar in association with IBM on Wednesday 17 July. The IBM speaker is Colin Linsky, Worldwide predictive Analytics Retail leader, and his webinar will cover:

  • Real life examples of applying analytics
  • How to gain actionable insight from disparate data sources
  • The power of predictive analytics
  • How to use analytics to improve marketing ROI
  • How to benefit from predictive analytics without programming or advanced statistical knowledge

If you're interested in registering, or know someone who might, here's the link

Sunday, 23 Jun 2013
Sayara Beg

Data Science

The term could be broken down in to the following processes and steps, in this order:

  1. How to frame the question that needs to be answered?
  2. How to identify the data and the sources of that data, to analyse and to answer the question?
  3. How to locate and capture the data required for analysis?
  4. How to cleanse the data, mash-up the data or conduct 'data-munging', once the data has been located and collected? 
  5. How to identify the right technology to capture and cleanse the data for analysis?
  6. How to choose the right selection of scientific methods to answer the given question and the order in which those scientific methods should be applied [machine learning algorithims included]?
  7. How to identify the appropriate technology to support the scientific analysis to be conducted
  8. How to correctly match the relevant data attributes from the source data, to the relevant parameters of each scientific method being used?
  9. How to interpret and relate the analytical results produced from the various scientific methods, back to the question being asked?
  10. How to visually represent the analytical results to the audience, in order to deliver the answer the original question and what is the best technology to use?

If these ten steps were to define the Laws of Data Science, what governance and standards should be put in place, to support these steps?

Look forward to your comments and suggestions.


Chair of the Analytics Network

Friday, 21 Jun 2013
Gavin Blackett

The aim of this stream is bringing together scholars and practitioners interested in Business Analytics Optimisation to address current developments and challenges to organize, store, manage, protect, analyse, mine and optimise Big Data, and to benefit society, industry, academia, and government.

Major topics include but not limited to: analytics techniques in Practice; managing Big Data,; architectures for massively parallel processing; Big Data mining tools and techniques; machine learning algorithms for Big Data, algorithms and systems for Big Data search, visualization analytics for Big Data, software / algorithms to support any aspect of Big Data computation, large-scale modelling for social media, and optimisation modelling for Big Data.

Early bird discounts ends 30th June.  Click here for more details

Tuesday, 18 Jun 2013
Sayara Beg

Much has been said about how being a 'Data Scientist' is the sexiest job title ever and that it's the hottest job demand out there, with very little supply to fill the jobs.  So where are these jobs and what are the employer's looking for.

Post here if you have a job for a Data Scientist and lets us know what you skills and experience you are looking for in that role. 

Don't forget to include location of role and how to apply or contact you for further details.


Chair of the Analytics Network

Monday, 17 Jun 2013
Gavin Blackett

I was thinking about Sanjit Atwal's session at the  Developments in Analytics and Big Data – Adding Value seminar on 12th June,  As you recall, Sanjit is CEO of Squawka who deliver 2nd screen analytics in near real-time to football fans whils they are watching a game.  The technology used by Squawka and the analysis and insight it gave to supporters was impressive but it raised a series of questions about the potential implications for organisations:

  • will users want near real-time analytics in their work environment if they are used to it in their personal life?
  • does this present a great opportunity or great challenge to meet those user expectations?  A lot of organisations are still struggling to reach base level in reporting (think of the month end financial reporting process)
  • is (near) real-time reporting really required in a business context?  Obviously there are operational situations where real-time is required but does the majority of analytics actually require real-time?


Friday, 14 Jun 2013
Sayara Beg

Welcome to the Analytics Network 2013

As the new Chair of the Analytics Network, I am excited about what the future holds for our member 'Networkers'.

After a wonderful event on the 12th June 2013 at the fabulous location 'The Institute of Engineering and Technology', the Analytics Network committee and I are working hard to put together an impressive programme of events and socials over the coming months to keep you busy and involved.

Firstly, pencil in your diary the date: October 17th 2013; 4pm to 8pm.  This will be the next event, held at the London South Bank University.  We are particularly excited about this event, as we have decided to be bold and ambitious, and attempt to stream it live, so our pre-registered distant 'Networkers' can join in to.  Look out for more details.

To boldly go.....


Chair of the Analytics Network

Wednesday, 17 Apr 2013
Gavin Blackett

What do we know about data visualisation?  Read HBR's thoughts here

Monday, 8 Apr 2013
Gavin Blackett

Developments in Analytics and Big Data – Adding Value

Over the last 75 years, O.R. professionals have developed mature methodologies to analyse and use data that can add significant value in big data analytics. The aim of this event is to show how developments in Analytics are leading to increased competitive advantage in these challenging times.

Confirmed Speakers:

John Hopes Lead Partner, Business Modelling, Ernst & Young.

Gearóid Madden Accenture Analytics Innovation Centre

Colin Shearer SPSS – data mining pioneer and developer of Clementine software.

Detlef Nauck, BT - Chief Research Scientist and BT’s leading expert in data analytics.

Fintan Galvin Founder and CEO, i01

Sanjit Atwal - CEO, Squawka

Aaron Sugarman Head of Commercial Analytics, TUI


Wednesday, 12 June 2013 from 9am to 5.30pm

Institution of Engineering & Technology, Savoy Place, London, WC2R 0BL

Fee £75+VAT until 1 May then £150+VAT

Includes buffet lunch


For more about this event, and to book online, visit

Friday, 15 Feb 2013
Gavin Blackett

The Analytics Network will be officially launched at an event on Wednesday 6 March at The Brewery Chiswell Street London EC1Y 4SD starting at 6.15pm. We are delighted that Jacqui Taylor, CEO and founder of Flying Binary has agreed to be our guest speaker on the topic of how people who work in the Analytics field can benefit from the skills of those who work in O.R. – and vice versa.

John Hopes, chair of the Analytics Network, will talk about its aims, and will invite discussion on what the network can do for the analytics community.

Numbers will have to be limited, so to ensure you’re there for the launch, please click here to reserve a place.

Please note that this is a FREE event.

Tea/coffee will be served on arrival, with a glass of wine available to facilitate discussions after the talk.

Thursday, 14 Feb 2013
Gavin Blackett

... is Data Scientist - at least according to Thomas Davenport and DJ Patil writing in Harvard Business Review.

Read the full article here:


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