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Business Intelligence & Analytics (GMobile (business values…
Business Intelligence & Analytics
adopting the analytic tools
challenges
resistance to change
lack of integrated data management
shortage of data science skills
seed project
identify value/scope
workshop
multiple stakeholder
transfer skills
set goals
identify problems/requirements
data access challenges
data repositories
data harmonization
open analytics platform (options)
develop & implement
agile development approach
update info. to stakeholders
follow-up initatives
moduarity
evlauation
domain
business (strategic)
awareness and acceptance of data analytic
operational benefits
organization
inter-department communication
engagement of other staff
technology
select & develop suitable platform
lessons
leverage process focus
foster data awareness
data access
workshop
agile development
use open platform
reduce fragmentation
enable quick-win implementation
maximize business value
speed to insight
how fast org. transform data to useful info.
drivers/practices
automation
configurable, metadata-driven on-boarding processes
business requirements
agile development approach
reuse
data services
design catalogs
parameterized reporting
persuasive use
drivers/practices
graphics
visually appealing software/dashboard
mobility
mobile devices/ease of access BA
user engagement
gamification
self-service
collaboration techniques
GMobile
easy to use
visually appealing/photographs
data warehouse need to meet the capacity
mobile device/iPad
development team
graphic designer
app developer
business values
transactional benefits
improve productivity
efficiency in finding answer
less meeting
strategic benefits
deeper understanding of business
better decision
identify size profiles, trends
informational benefits
big data
velocity
variety
volume
veracity
big data analytic framework
data source
structured
internal systems
operational systems
machine data
semi-structured
web data
audio/video
external data
data preparation
extracting
transforming
ETL data
data cleasing
analytsis
querying data first
descriptive analytics (summary data)
exploratory analytics
sandbox
predictive analytics
hadoop
data access & usage
users
business users
data scientist
BA
external users
data storage (warehouse)
sensor data
sometimes lies
may not tell the whole truth
to get useful info.
additional data
"multi-genera" analytics
don't measure quantity of interest directly
without comparison or link to additional data/context, provide little value