Focus
The issues are complex and ever changing surrounding
Business Intelligence (BI) broadly defined, such as Data Warehousing,
Business Analytics, Enterprise Portals, and Enterprise Business
Integration. Over the past thirty years, I have experienced the amazing
evolution from fairly crude technology (e.g., time-sharing over 1200-baud
modems) to fairly sophistication applications (e.g., neural network models
driving real-time website interactions). However, I am also amazed that
many basic issues remain the same (leveraging a return-on-investment and
enhancing productivity). An open mind and a healthy curiosity is a
definite requirement for sustaining excellence in the BI/DW area.
Here are the focus areas that we are currently pursuing.
 | Active BI in the Intelligent Enterprise: An
area that evolves data warehousing toward operational applications
(real-time and atomic level) impacting the minute-by-minute business
processes. It's not real-time; it's right-time for real-value. (PDF
105KB)  |
| Science Intelligence: There are many
similarities for the information support of Business and of Science.
And, the infrastructure of both may be similar. |
| Associative Link Analysis: A new approach to
analyzing enterprise data by adapting Graph Network Analysis to the
enterprise data warehouse. |
| BI/DW Outlook: Annual assessment of the
critical trends and issues in Business Intelligence, broadly defined
as to its role in enterprise systems. (PDF 95KB) |
| Ethics of BI/DW: A neglected area that deals
with the tough decisions about how we should (rather than can) use
BI/DW within our companies. |
| Information Visualization: A missing tool in
the BI/DW toolbox. High-density images of warehouse content can
provide actionable understanding of complex business systems. |
| Business Integration: At the convergence of
Enterprise Application Integration and Enterprise Data Warehousing.
(PDF 141KB) |
| Business Velocity: Does increasing the speed
by which we respond to changes in market conditions and customer
demands really impact the success of the company? |
| Collaborative BI: BI/DW usage has shifted
dramatically from supporting me to supporting we. The user base for a
typical data warehouse has increased from ten or twenty to hundreds
and even thousands. Yet, we continue to architect DW systems as if
users are isolated. |
| Enterprise Memory: Data warehousing as
institutionalizing a single image of business reality over the past
decade. The next step is enterprise memory in which the single image
exists for ALL data about the corporation. |
| Intelligent Web Farming: Information from
external sources is playing a critical role in many business
processes. Yet, the application of external data is isolated and
haphazard. Need is a systematic method for discovering, structuring,
and disseminating data from web-based information resources and
integrating this data into business processes. |
| BI for Mergers and Acquisitions: If there was
ever a need for a data warehouse as a single image of business
reality, it is in the situation of a merger or acquisition. Executives
are clueless; IT managers fight for their warehouse as the winner in
the resulting company. A project data mart that is specifically built
to drive the M&A process is required. |
