End Point in the Business Intelligence Value Chain
An interesting aspect of Business Intelligence is the fact that there
are many end-points possible in a BI Value Chain. Let me explain
a bit here and build a case for creating “Reference Architectures”
in the BI domain.
In my view, there are typically 5 different configurations for the
BI Value Chain that leads to 5 possible end-points. They are:
End Point 1: Reporting and Ad-hoc Analysis
This is the most common type of enterprise BI Landscape.
The objective here is to provide business users with standardized
reports and ad-hoc analysis capabilities to analyze the business.
With that objective in mind, data warehouses and/or data
marts are created as data repositories and semantic layers
for analysis flexibility.
End Point 2: Data Hub or Master Data Repository
This is a scenario where the objective is to consolidate data
and create master data repositories. The consumption of this
master data is typically left to individual consumers to figure
it out for themselves. The complexity in this type of configuration
is more in terms of data quality and governance mechanisms
around the data hub, as the business value increases only if more
systems utilize the data hub.
End Point 3: Source Systems
This configuration indicates a fairly mature landscape where the
feedback loop from the analytical systems to the operational
ones is in place. The concept of Operational BI is built on this
foundation where the data from transaction systems go
through the analytical layers, gets enriched and reaches
its place of origination with the intent of helping business
make better informed transactional decisions.
End Point 4: Data Mining models
This is a configuration that helps organizations
compete on analytics. Integrated, subject oriented, cleansed data
that is taken out of data warehouses / marts are fed into
data mining models in a seamless fashion. The results obtained
from the data mining exercise are used to optimize business
End Point 5: Simulations
Here is a configuration that I haven’t seen in practice but have
a strong feeling would be the future of BI. I have some
experience in working with Simulation tools (Powersim,
Promodel to name a few) where the idea is to create a model
of the business with appropriate leads, lags, dependencies
etc. The starting criteria (set of initial parameters) would
typically be fed by a business analyst and the output of
the model would indicate the state of business (or specific
business area being modeled) after a period of time. Given
this context, I think it would be more powerful to have
the simulation models being fed with data from analytical
systems in an automated fashion. Presuming that the simulation
models are built correctly by experts in that particular area,
the output tends to be a better illustration of the future
state of the business than compared to “gut feel” extrapolation.
Outlined above are the 5 different configurations of BI systems.
The logical next step from the technology standpoint is
to publish reference architectures for each of these configurations.
This would help organizations get an idea of the components
involved once they decide on a particular configuration
for their business.
Reference Architectures and Simulations in BI environments
are areas that will be explored more in the subsequent posts.
Thanks for reading. Have a great day!