Big data trap

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Francis Piéraut

Don't fall in the big data trap key principles

Data & Analytics

Big Data trap
francis@qmining.com
@fraka6

Data/Big Data Knowledge Action
People care about Knowledge/actions not data

Agenda
● Big data dilemma
● When are we doing Big Data?
● Maturity/Evolution steps
● The big data trap
● Optimal design = real time data-mining
● Increase your chances of success

The Big Data Dilemma

Big Data =
Data + IO bounded (disk)
CPU
<100%Data
IO bounded

QA
BI
Maturity
Barriers of entry Levels
Just another barrier of entry

Trap = no KPI
● No KPI -> batch processing -> big data
● KPI -> real time -> no big data complexity

Optimal design = real-time data-mining
● Events -> everything is an event
● + Rule -> create signal from events
● + KPIs -> selection of signals (top level)
● + Incident = signal static/dynamic thresholds
● + Root causes analysis
○ Bayesian inference (ratio signal)
○ Signal correlation (std signal)
○ Rule filtering (domain specific)

Increase chances of success
● Data driven culture
● Data quality culture (Avoid logs)
● Reach Analytics/BI level
● KISS

Recap
● Big Data = Small Data + IO bound
● Big data->Data->Analytics->Mining->Predictive
○ Data Quality = BIGGEST PROBLEM
○ Big Data = another barrier of entry
● Big data trap = no KPI
● KISS = real time data mining

hum...
Questions?
francis@qmining.com

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怎样办理伦敦大学城市学院毕业证（CITY毕业证书）成绩单学校原版复制

怎样办理伦敦大学城市学院毕业证（CITY毕业证书）成绩单学校原版复制

怎样办理伦敦大学城市学院毕业证（CITY毕业证书）成绩单学校原版复制

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Context 1. Housing Agent collected resale prices on HDB apartments in Singapore. Objective 2. To predict resale prices in to advise his potential clients. Strategies 3. Explore & Clean data for analysis. 4. Perform K-Means Clustering, in Orange, to find possible segments in the customer data. 5. Tune the model to improve its performance. 6. Visualise the findings, share conclusions, and give insight-driven recommendations. Author: Anthony mok Date: 18 Nov 2023 Email: xxiaohao@yahoo.com

Predicting HDB Resale Prices - Conducting Linear Regression Analysis With Orange

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Predicting HDB Resale Prices - Conducting Linear Regression Analysis With Orange

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Big data trap

1. Big Data trap francis@qmining.com @fraka6

2. Data/Big Data Knowledge Action People care about Knowledge/actions not data

3. Agenda ● Big data dilemma ● When are we doing Big Data? ● Maturity/Evolution steps ● The big data trap ● Optimal design = real time data-mining ● Increase your chances of success

4. The Big Data Dilemma

5. Big Data = Data + IO bounded (disk) CPU <100%Data IO bounded

6. QA BI Maturity Barriers of entry Levels Just another barrier of entry

7. Trap = no KPI ● No KPI -> batch processing -> big data ● KPI -> real time -> no big data complexity

8. Optimal design = real-time data-mining ● Events -> everything is an event ● + Rule -> create signal from events ● + KPIs -> selection of signals (top level) ● + Incident = signal static/dynamic thresholds ● + Root causes analysis ○ Bayesian inference (ratio signal) ○ Signal correlation (std signal) ○ Rule filtering (domain specific)

9. Increase chances of success ● Data driven culture ● Data quality culture (Avoid logs) ● Reach Analytics/BI level ● KISS

10. Recap ● Big Data = Small Data + IO bound ● Big data->Data->Analytics->Mining->Predictive ○ Data Quality = BIGGEST PROBLEM ○ Big Data = another barrier of entry ● Big data trap = no KPI ● KISS = real time data mining

11. hum... Questions? francis@qmining.com