"and bob's your uncle." - navigating the sea of cloud offerings to business...
TRANSCRIPT
... and Bob's your uncle.
Navigating the sea of Cloud offerings for business opportunities
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Last of three talks TODAY
Cloud Management, Services, Application and FINTECH Theatre
13:05 - 13:30
Software Architecture, DevOps and FINTECH Theatre
14:05 - 14:30
Understanding CX:How Customer focussed are you,
actually?
It's no one's fault, it's in the process!:
IT service management in the Cloud era
"... and Bob's your uncle.”Navigating the sea of Cloud
offerings for business opportunities
Innovations and Showcase theatre
15:35 – 16:00
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This is you.
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This is you at CEE, CSEE & DCW.
25,000 expected visitors over two days.Source: CloserStill Media
You’re a hard worker:
16 h over two days1560 contacts per hour 26 contacts per minute.
2 s to decide: hot lead or not?
Hello, you!
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That is impossible.
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A bit of shout out:
Which are your strategies?
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CloudWATCH Cloud landscape clustering app
It provides usable results within seconds.
It is based on unbiased sample data.
The data is publically available.
It uses proven statistical procedures.
The results are reproducible.
The app is publicly available, and free to use.
https://tethys.oerc.ox.ac.uk:8443/cluster/index.xhtml
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1-slide explanation of the app
38 responses, scoring the importance of NIST Cloud characteristics for them selves
Out of these, your business opportunities lie with this cluster!
(Had you provided your scores…)
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How!?
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1. Data taking / scoring
“How important is … for your work/service/project?”
For 13 Cloud computing characteristics
1. Data taking / scoring
12Essential
Common
NIST SP800-145
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2. Interactive analysis
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3. Access underlying data
2-step process:• Submit scores• Offline verification
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A bit of statisticsPrincipal Component Analysis (Karl Pierson 1901, Harold Hotelling 1930)
Multi-variance analysisEmphasises variance and patterns in dataData transformation for dimension reduction in analysis
Hierarchical clustering using Euclidian distanceForm clusters of “similar” respondents, starting with 1-member clusters“Similarity” (i.e., distance) calculated using Euclidian distance functionDistance between clusters uses “weighed pair-group centroid”
performs well with large variance in cluster sizes
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A visual guide to PCA: 2D example
http://setosa.io/ev/principal-component-analysis/
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A visual guide to PCA: 3D example
http://setosa.io/ev/principal-component-analysis/
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A visual guide to PCA: 13D Cloud landscape
13-dimensional Biplot
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A visual guide to PCA: What is significant?Scree plot of ”eigenvalues” of each of the 13 NIST characteristics
Kaiser-Guttman criterion:Characteristics with eigenvalues of 1.0 or greater are significant, and the residual can be discarded.
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A visual guide to PCA: Data projectionEmphasized
Dampened
13-dimensional scorings
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A visual guide to PCA: Data projectionEmphasized
Dampened
Image credits: scribol.com
13-dimensional scorings
… and how they look like in 5 dimensions
Data projection
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Hierarchical clustering visualised
Distance
Many small clustersFewer, larger clusters
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Can I have a go?
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Our analysis vs. your needs
1. Is a full 13-characteristics analysis always required?2. Is the Kaiser-Guttman criterion always a good idea?3. How does the data projection influence the clustering?4. Does the chosen clustering distance threshold yield insights?5. Are there quality criteria for clusters?
We let you choose for yourself!https://tethys.oerc.ox.ac.uk:8443/cluster/index.xhtml
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Using the tool: What you can control1. Choose characteristics
for analysis.
2. Control significancethreshold.
3. Configure data projection
4. Control cluster size
5. Advanced clustering options coming soon
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A call to action
We lack sample data – so we need your help.Please submit your response!
The more data, the better!Please ask your network to add their scores, too!
What you will get out of itFree analyses, and as many as you like!Free dissemination – anyone using the tool will see your name!Free media coverage – listing in cloudwatchhub.eu directory
Thank you!
Michel Drescher, Cloud Standards SpecialistOxford e-Research Centre, University of Oxford