After all the analysis confirmed with the proper
analyzable data set, we would then come to the step 3: - Case Management to
manage and execute the follow up actions identified from the insights produced;
step 4: - Analytics Review to review the effectiveness on the identified
Analytics Rules and suggest improvements; step 5: - Optimization on executing
the improvements to make the process be more efficient and effective.
These three steps are critical to the Analytics
Cycle as:
A. Case Management is to manage and execute the follow-up actions from the
analysis insights which are always help to continuously improve and contribute
the actual business.
B. Analytics Review is to monitoring the progress and the performance of
every single step on case execution; and to identify the best way to resolve
the issues when applying the theoretical analytics ideas into the real
business.
C. Optimization is to execute and test the suggestion identified and, more
importantly, to help on smoothing the case execution and to improve its’
effectiveness and efficiency.
These three steps are highly dependent with each
other and might require to move back to step 1 or 2 if further data needed or fine-tuning
the analytics rules when executing. On one hand, step 4 is mainly relies on the data produced
from step 3 and, on the other hand, step 4 and 5 would potentially speed up and also help to improve the case execution process in step 3 and, more importantly, to improve the entire analytics project become more efficient and effective.
Example on the e-Discovery process,
after step 1 – ETL on loading all the acquired data into the system and step 2 –
Analytics Rules to identify the right screening criteria, included, but not
limited to, keywords list, time period filtering, etc. We would then move forward to review and tag
the hit items with proper step 3 – Case Management process; and could analyse
the effectiveness and the performance of the works with step 4 – Analytics
Review, such as reviewing the periodic progress report to understand the efficiency of the
reviewers for management purpose, to identify the keywords’ performance, etc. On applying step 5 – Optimization, one idea is that if a keyword always produces non-relevant hit, we could then leverage the sampling approaches rather than
full-review on the related hit population which would potentially speed up the review
process. If you are interested in how we could improve the e-Discovery process, please be patient and I would share more possible analytics on e-Discovery process in the future e-Discovery related post.
In conclusion, here comes the end of the Analytics
Cycle overview. This is only one of the
general flows on how Data Analytics and the Big Data approach could be
applied. But again, it always requires
times and money investments to produce profitable and adorable result. In the
future data analytics post, I would try to share some of the real-life analytics examples. Hope all of you found this
useful and I would be always happy to discuss if you are interested.
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