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October 25, 2008
Business Intelligence Value Curve
Every business software system has an economic life. This essentially means that a software application exists for a period of time to accomplish its intended business functionality after which it has to be replaced or re-engineered. This is a fundamental truth that has to be taken into account when a product is bought or for a system that is developed from scratch.
During its useful life, the software system goes through a maturity life cycle - I would like to call it the "Value Curve" to establish the fact that the real intention of creating the system is to provide business value. As a BI practitioner, my focus is on the "Business Intelligence Value Curve" and in my humble opinion it typically goes thro' the following phases:
Stage 1 - Deployment and Proliferation
The BI infrastructure is created at this stage catering to one or two subject areas. Both the process and technology infrastructure are established and there will be tangible benefits to the business users (usually the finance team!). Seeing the initial success, more subject areas are brought into the BI landscape that leads to the first list of problems - lack of data quality, completeness and duplication of data across data marts / repositories.
Stage 2 - Leveraging for Enterprise Decision Making
This stage takes off by addressing the problems seen in Stage-1 and overall enterprise data warehouse architecture starts taking shape. There is increased business value as compared to Stage-1 as the Enterprise Data Warehouse becomes a single source of truth for the enterprise. But as the data volume grows, the value is diminished due to scalability issues. For example, the data loads that used to take 'x' hours to complete now needs at-least '2x' hours.
Stage 3 - Integrating and Sustaining
The scalability issues seen at the end of Stage-2 are alleviated and the BI landscape sees much higher levels of integration. Knowledge is built into the set up by leveraging the metadata and the user adoption of the BI system is almost complete. But the emergence of a disruptive technology (for example - BI Appliances) or a completely different service model for BI (Ex: Cloud Analytics) or a regulatory mandate (Ex: IFRS) may force the organization to start evaluating completely different ways of analyzing information.
Stage 4 - Reinvent
The organization, after appropriate feasibility tests and ROI calculations, reinvents its business intelligence landscape and starts constructing one that is relevant for its future.
I do acknowledge the fact that not all organizations will go through this particular lifecycle but based on my experience in architecting BI solutions, most of them do have stages of evolution similar to the one described in this blog. A good understanding of the value curve would help BI practitioners provide the right solutions to the problems encountered at different stages.
Thanks for reading. Please do share your thoughts.
Posted by Karthikeyan Sankaran at 1:15 PM | Comments (0)
October 5, 2008
Business Intelligence - The Reusability Gene
One issue that confronts me time and again while executing BI projects is "Reusability", actually the lack of it. Let me give an example.
In the many migrations and upgrade projects that I have seen executed, I always find that the number of reports finally migrated /upgraded to a new environment is only 40-50% of the number that is provided to us by the customer initially. Report Rationalization has become such a critical step that we have developed many specific metadata tools that helps rationalize the reporting environment. Coming back to the topic - The reason for such a divergence between the final number of reports and the initial number is lack of 'reusability'. Business users have their own versions of standardized (?) reports stored in their desktops which are nothing but small variations (usually with a new filter added) of an already existing report.
Another similar example on the data integration side is the creation of ad-hoc ETL routines as and when required. This results in duplication of ETL jobs and also results in a non-standard BI environment.
Lack of re-use causes two major problems:
1) BI environment becomes bloated with the increase in the number of unwanted components that use valuable computing resources, resulting in delays for availability of more important information.
2) Any attempt at upgrading/re-engineering the existing system results in high costs and undesirable heart-burn among business users.
The Prescription:
1) Establish a corporate level BI team whose primary responsibility is to ensure that any component addition (ETL, Reports, and Models etc.) is justified based on its purpose. This team has to ensure that existing standards and components are reused to the maximum extent.
2) Strengthen the "Business Metadata" architecture within the organization. In one of my earlier posts, I had explained my view of BI metadata and that is very relevant to the task of improving reusability.
Basically, the "Reusability gene" seems to be a little muted in its functioning among BI practitioners. It is time that BI teams within organizations and system integrators look at reusability as a critical parameter while developing and deploying BI solutions.
Thanks for reading. Please do share your thoughts.
Posted by Karthikeyan Sankaran at 7:00 AM | Comments (0)
