@openDEFRA with #openDATA in #openCULTURE: Valuing #Data #Life and #Internet over #Money

cmcxuw_wkaagcbfThis unconference on Tuesday 28 June in an open space of the Nobel House at Smith Square in Central London was more than a shot in the arm:

  • it gave me hope that my faith in people and the internet is justified for our children’s and grandchildren’s future;
  • while I’ve analysed the long term organisation of man-made evil and its victims for a considerable length of time, I can now pin my hopes on the humanness of ‘geeks’ – in governmental settings:
  • Be bold, be human, be your Self is the motto that  James Arthur Cattell got from the UK Civil Service Leadership Statement;
  • James is a ‘government hacker’ and had facilitated the event most admirably and charmingly;
  • these DEFRA geeks are using social media in very effective and creative ways:

Continue reading

Posted in 3D Metrics, DEFRA, Open data | 1 Comment

Benefits of ‘3D Metric’ Diagrams: Linking Data Silos for Improved Policy Making

16 04 09 Benefits Diagrams-page-001The gap between scientific and business language does not seem to narrow despite computer screens as the common medium:

  • big business data‘ seems to concentrate on ‘non-structured‘ data relating to patterns of consumption, as growth of profit, turnover and productivity are the ‘mantras’ in companies that are expected to sell or be bought up…
    • very big science data‘, however, consists in my view mainly of ‘structured‘ data, i.e. time series:
    • financial or originating from sensors: measurements from instruments;
    • but ‘big business data’ can ALSO be expressed in time series;
    • altogether:
      • better business strategy decisions can be taken
      • better policy can be made
      • and who knows what scientific improvements can be made!?…better

On the Benefits of ‘3dM Diagrams’

 21 layers 3 pollutants Excel  21 layers 3 pollutants 3dM

3dM Diagrams ‘unclutter’ Excel graphs:
e.g. data from the London Air Quality Network

 21 layers 3 pollutants 3dM  21 layers Sorted

3dM Diagrams create ‘visual priorities’ and new insights for ‘optimal slopes’.
This leads to ‘domain experts’ determining novel parameters for automated processing.

3dM Diagrams are generic, i.e. independent of domain and of time scales.
They can include forecasts derived from new algorithms.Thus they allow for:-·       linking ‘data silos’ and comparing different time frames – which leads to

Ø   contrasting short, medium and long term trend periods defined by ‘domain experts’;

·       ‘data experts’ discovering correlations – which leads to

Ø   defining ‘optimal slopes’ for automating ‘big data’ and real time processing;

·       enhancing processes that influence interdependencies – which leads to

Ø   improving policy by making better informed decisions from connected data silos.

3dM Diagrams illustrate for Domain and Data Experts
what Executive Summaries demonstrate for Policy Makers:
Key Findings as the Basis for Executive Decisions


Posted in Data Visualization, Forecasting, Layering Complex Data, Trend Analysis | Tagged | 2 Comments

@techUK What’s the ‘big idea’ behind #BigData?

“Bigger is better” has long been an ‘unconscious [gender] bias’. But I complemented Small is Beautiful with Micro is Powerful and Nano is Wonderful, ever since I saw what my re-visualisation of images could reveal.

Thanks to joining techUK, I could re-visit Big Data in the context of knowledge and understanding, just as Daniel Perklin of M.R.S. Company in Toronto, as he quotes Goethe and Faust in his Big Data Primer where his delightful infographic links to.

Big data[1] has been made topical mainly by ‘big business’ and tends to refer to unstructured data[2], i.e. text-heavy, with a view to analysing consumer data, promising managers to make better business decisions. Continue reading

Posted in 3D Metrics, Big data, Data Profiling | Leave a comment

Say it on Video: a Game Changer for Forecasting, Diagrams and Image Analysis by Unifying #DataAnalytics

Three generic software methods are the outcome of my ‘software-aided thinking’:

  1. 3dM Forecasts‘ – with short-, medium- and long-term trends – which I expressed in this video on Time is Money – Money is Time Series;
    • the prezi underlying the video is here;
  2. 3dM Diagrams‘ – which illustrated in this video using Air Quality data as an example;
    • the underlying prezi is here;
  3. 3dM Images‘ – which I demonstrated in this video to Enhance Machine Vision;

Here are the other two short videos: on ‘3dM Diagrams’

and ‘3dM Images’:

Posted in 3D Metrics, Data Based Science, Data Science, Forecasting, Image Analysis, Innovation, Layering Complex Data, Measurement Based Science, On-screen measuring, Web Science | Tagged , , , | 2 Comments

#DataAnalytics – where #science and #commerce meet: Big Business #DataProfiles as ‘3dM Diagrams’?

15 12 03 Big Data MeetupNice venue, this ‘incubator space’ called Runway East. I had not expected it to be as ‘grand’.

The first person I spoke with was Gabriel Hughes whose company is called Metageni – Creating Data Stories.

I clearly was in my element!

Then I was impressed to see an attractive lady speaking (we are only 12% women in science and technology):

  • Mariam Cook has created PositionDial – an ‘ethical barometer’ for illustrating the gap between what they say and what they do. It can be you, your brand or your product! She told the story of EveryWoman as global INFLUENCER with social clout. It so happened that I next to the co-founder Maxine Benson MBE. What a coincidental privilege!
  • My question is: when will it be appropriate to add ‘Position Profiles‘ in addition to the Dial? After all my ‘3dM Diagrams‘ have this unique look and can sort of ‘anything’ visually comparable.
 Laqn 05 BL0 NO NOX NO2 PM25 PM10 D sortedLaqn 10 BL0 NO NOX NO2 PM25 PM10 W sortedLaqn 21 NO NOX NO2 D sorted 198

Most complementary presenters were:

  • Nick Jefferson is partner in the advisory firm Monticello and spoke beautifully about authenticity resulting from Ethos, Pathos, Logos being united, citing Aristoteles.
  • John Lyons of Brandmovers UK gave another good presentation of a ‘data story’ as a ‘use case’, i.e. the answer to the question: which data feeds which social engagement campaign?

Results of the discussion for me were:

  1. Data ‘Science’ is a rebranding of Data ‘Analytics’;
  2. Lots of people don’t seem to know what they’re looking for when they’re after ‘insights’;
  3. The difference between ‘structured’ (mainly numerical) data and ‘unstructured’ (mainly verbal) data is about as far as distinctions go between
  • tweets as supposedly the most valued ‘pulse of our times’
  • Facebook and YouTube Likes, Followers and Subscribers
  • LinkedIn Connections
  • time series
  • multi-dimensional data
  • images.
Posted in 3D Metrics, Data Profiling, Layering Complex Data | Tagged , , , , | 1 Comment

‘3dM Diagrams’ to See Data Profiles and Measure Data Qualities

This time I used my prototype for a grant application to the Department for Transport. Our hope was to build a ‘Smart Air Quality Portal‘ with their money. But it just contributed to the height of the pile of rejected submissions.

Our screenshots don’t tell a story, but speak more than 1,000 words, as they ‘parallel’ 5, 10 and 21 air pollutants, don’t they?


 Laqn 05 BL0 NO NOX NO2 PM25 PM10 D sortedLaqn 10 BL0 NO NOX NO2 PM25 PM10 W sortedLaqn 21 NO NOX NO2 D sorted 198

We proposed to look at Air Quality data:

  • to correlate data from national weather stations
  • with data from local council pollutants.

But maybe we need PositionDial to show the gap between what they say and what they do?

We can compare data that has hitherto NOT been comparable!

We can now find correlations that we may have suspected, but now can see!

Or do we need Big Financial and Very Big Science data for 3dM Diagrams to become ‘interesting’ for getting more insights and measuring more qualities?

Posted in 3D Metrics, Data Profiling, Layering Complex Data | Tagged , | 1 Comment