Date post: | 12-Apr-2017 |
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Data ProjectJOSEPH MAN
Data is never Simple
It is not just about storing dataIt is not just about presenting reports
It is not just about gathering information
Source: Keith Gordon, Principles of Data Management: Facilitating Information Sharing
Data is not just about Technology
Source: Jerrard B. Gaertner, Identifying and Overcoming Project Management Challenges in Big Data and Predictive Analytics;Mark Troester , Analytics Infrastructure: 15 Considerations
Multiple Disciplines are Involved
Source: Jerrard B. Gaertner, Identifying and Overcoming Project Management Challenges in Big Data and Predictive Analytics
Multiple Data Sources are needed
ETL Process
• Knowledge on multiple disciplines are required• Processes should be understand for each source of data collection• Gaps of meta data should be well studied and addressed• Data modelling and data cleansing are essential• Require good understanding of Data & Process
Multiple Parties are servedReports
ForecastingPrediction
• Demands on reporting could be huge• Visualization facilitates decision making• Interpretation and intervention drives business
improvement• Machine Learning for Prediction Marketing
Data Project is like a Spiral
Source: MIT Online Course, Big Data and Social Analytics
Data projects are different
from Waterfall,
Agile, Iterative.
3V’s and 5R’s need to be Considered
Relevancy The data are those needed?Recency Are the data out-dated?Range Are there enough coverage and granularity?Robustness Does the noise grows faster than the signal?Reliability Does the data collection accurate and reliable?
Source: MIT Online Course, Big Data and Social Analytics
Security and Privacy should always be Addressed
Source: Jerrard B. Gaertner, Identifying and Overcoming Project Management Challenges in Big Data and Predictive Analytics;MIT Online Course, Big Data and Social Analytics
Project Risks could be Challenging
Concentration, Conversion
(ETL) and Data Quality Risks
Few Security and Privacy
Tools
Staff Lack Familiarity and
Training
Architectural Complexity
Lack of Proven Reliability and
3rd Party Certification
Unrealistic Expectations and Pressure to Produce
Difficult to Test in
Conventional Ways
Source: Jerrard B. Gaertner, Identifying and Overcoming Project Management Challenges in Big Data and Predictive Analytics
Despite, the Company can be hugely Benefit from Big Data
Data marts, data bases,
BI, forecasting
Ad hoc reporting, standardized reporting,
fixed reporting
Prescriptive & Predictive Analytics
Forecasting
Optimization & Prediction
Extrapolation
What, When,
How, WhyWhat,When,HowWhat
,When
Source: Jerrard B. Gaertner, Identifying and Overcoming Project Management Challenges in Big Data and Predictive Analytics
Executive Summary Data is never simple
Data is not just about technology Multiple disciples are involved Multiple data sources are needed Multiple parties are served
Data Project is like a spiral 3V’s and 5R’s need to be considered Security and Privacy should always be addressed Project risks could be challenging
Despite, the company can be hugely benefit from Big Data
Q&A
Appendix - Analytics Infrastructure Considerations Vision & Strategy Drive analytic success via common vision
shared by all constituents Support multiple forms of analytics for
maximum value Analytics = Art + Science. IT should focus
on the science to enable analytic art
People It’s not just “Business” and “IT” Get the right people: Don’t skip on training Leverage Center of Excellence (COE)
principles
Process Understand the analytics lifecycle Ultimately it’s about improving the business
process Strike the right balance between control
and user flexibility
Technology Leverage Enterprise Architecture principles
to ensure proper design Think big! Big data & big analytics: High
Performance Analytics is key Integrate BI Platform into the overall IT
infrastructure Data / Information Design data strategy that results in information as a strategic asset Leverage comprehensive Information Management approach Step up to data preparation: Free up the scarce analytic resources
Source: Mark Troester , Analytics Infrastructure: 15 Considerations
Appendix – An Example of Andorra
Source: MIT Online Course, Big Data and Social Analytics
Appendix – Big Data Project
Source: MIT Online Course, Big Data and Social Analytics
Appendix – User Defined Reports, Pros and Cons
Pros IT can provide platforms while not involved in operations Business and operations can have reports in a timely manner
Cons Massive amount of reports would be created but not maintained
and controlled Reports invalid after database change or system enhancement in
future projects
Appendix – Authentication and Entitlement Authentication could link up to the enterprise infrastructure, e.g. Active
Directory Entitlement can be controlled at report level to allow access of certain data by
particular user group only. Further access control can be explore on database tables or columns for user
defined reports
Appendix – Data volume growth exponentially
Source: Jerrard B. Gaertner, Identifying and Overcoming Project Management Challenges in Big Data and Predictive Analytics
Appendix – Pillars of Analytics Success
TANGIBLES1. Quality, relevant data sources (internal and external)2. Sufficient hardware/processing capacity (on site or cloud)3. Appropriate software/analytic tools (local, SAAS, hybrid)
OPERATIONAL BUSINESS PROCESSES4. Appropriate research & operational processes5. Effective data and data quality management6. Defining and implementing proper metrics
MEASUREMENT AND CONTROL7. Proper risk management8. Security & privacy best practices
Source: Jerrard B. Gaertner, Identifying and Overcoming Project Management Challenges in Big Data and Predictive Analytics
Appendix – Pillars of Analytics Success
HUMAN RESOURCES AND SKILLS9. Adequate business process knowledge10. Properly trained and skilled staff11. Access to third party expertise and resources, if needed
ORGANIZATION AND CULTURE12. Suitable (adaptable) organization structure and exceptional change management13. Effective reporting structures and internal communications (includes cross functional)14. Nurturing creative inquiry and celebrating insight
MOST IMPORTANT OF ALL15. Asking the Right Question!Source: Jerrard B. Gaertner, Identifying and Overcoming Project Management Challenges in Big Data and Predictive Analytics