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Future trend and challenges (mobile BI (challenges and potential solutions…
Future trend and challenges
ethical issues
BDI supply chain
an information SC
multiple firms exchanging information
adding value to the data
downstream customers
the consequences to the individual
individual's rights are being realized
disrespectful categories
upstream sources(especially bad suppliers)
quality of information
bias in the data
type of users
privacy issue
who should receive the information
how it can be used
hoe long it eill stored
roles of firms
the data stwardship practices
systemic issues in BDI
creating negative externalities
disclosing individual data
aggregated sctions
destrictive demand
secondary use of data
trusted relationship can be a bait
for a sustainable BDI
three important types of firms
possible leaders
organizations with unique influence and knowledge
providers of key products
guidelines
identify can communicate data stewardship practices
differentiate data
implement policies
data due process requirements
identify audit trails
offer interactive modeling
support user objections
quantify activity in the secondary market
institute data integrity professional or board for big data analytics
data scientists
consumer review boards
academic institutions
mobile BI
how mobile BI is used
the user
outside office employees
senior executives(top list users)
mid-level and operational managers
Sales representatives
technicians
types of information
scheduled reporting
pull report
exceptions and alerts
sharing
highest adoption rates for mobile BI of small companies
challenges and potential solutions
creating a roadmap
three stages
successful first project
be business rather than IT driven
specific
be developed quickly
target user
meet expectation
architectures
“greenfield” environment
adds mobility to an existing BI environment
a three or four-tier architecture
native of web-based
screen size
security
control and UX
real-time BI
track business
up-to-date information
definition of mobile BI
Inclusive Research Agenda
information value chain
stages
transform information into knowledge
make decisions
decisions result outcome
convert data into knowledge
people, processes, and technologies
two groups
knowledge derivation
decision making
focus areas
deriving knowledge
decision making
possible directions
deriving knowledge from big data
decisions and actions
IT artifact-related perceptions and behaviors
behavioral science, design science, and the economics of IS
big data information value chain
different people, processes, and technologies
4v
distributed storage architectures
social media and sensor-based data
data scientists
self-service” BI/analytics
implications
theory
problem-solving accuracy
time constitutes
CFT
chaos theory and black swan theory
methodology
reexamine how analyze and validate data
interpret and discuss findings