Whether you are developing a new dimensional data warehouse or replacing an existing environment, the ETL (extract, transform, load) implementation effort is inevitably on the critical path. Difficult data sources, unclear requirements, data quality problems, changing scope, and other unforeseen problems often conspire to put the squeeze on the ETL development team. It simply may not be possible to fully deliver on the project team’s original commitments; compromises will need to be made. In the end, these compromises, if not carefully considered, may create long-term headaches.
In the world of Business Intelligence, Excel is the devil and BI tools are the savoir. Spreadsheets are a satanic element were trying to drive from unrepentant departments. This is because centralized data is good and distributed data is bad.
I *love* the term “spreadmarts”.
Data virtualization, which leverages virtual data federation or enterprise information integration (EII) middleware, provides three critical capabilities:
- Data virtualization serves up data as if it is available from one virtual data store, regardless of how it is physically distributed across source data silos. Query optimization and caching enable the high performance required to meet latency objectives without physical replication.
- Data abstraction simplifies complex data by transforming it from its native structure and syntax into reusable views and Web services that are easy for applications developers to understand and the applications themselves to consume. Common higher-level abstractions might include customers, invoices, shipments, payments and more that can be shared across numerous applications.
- Data federation securely accesses diverse operational and historical data, combining it into more complete and meaningful information for a range of application uses.
Web Design vs. Intranet Design
Existing guidelines already tell us how to present regular links; these guidelines also say that link lists can work well without underlining the links. But what about the color of links in lists?
Timely when looked at along with BI for the masses.
The underlying concept for the next wave of BI reporting tool is to deliver information to users through a simple interface where they can also share information. Search tools help users get the BI reports and combine those with structured and unstructured data within and outside the enterprise. These reports can be shared through blogs within the BI community and converted into really simple syndication (RSS) feeds for subscription. RSS feeds will enable easy integration with the office productivity applications like spreadsheets, presentations and email. Key metrics could be easily converted and plugged into desktops as widgets for constant monitoring.
It only took the mainstream 6 years to catch on. 😉
Creating a BI strategy that is easily acceptable to different units within your organization takes much more than just sound BI knowledge. You need the right approach, techniques and few smart moves to make your strategy actionable and attractive enough to remain outside the organizational closet. Before we understand how to make the BI strategy acceptable in the organization, let us define BI strategy. A BI strategy is a long-term plan for an enterprise-wide BI architecture that is designed to make the organization agile, adaptable and efficient by enabling better decision-making. A sound BI strategy would help the organization to leverage information assets effectively for achieving competitive edge in the market.
The name of this column is “Beyond the Data Warehouse,” which implies that there is more to managing information than building a data warehouse. I have emphasized how to accomplish information management and data warehouse efforts as the field matures.
The pyramid model of BI is completely inadequate for today’s world of externalized business, computer-savvy workforces and constant communication. The concepts of hierarchical decision-making and solitary decision-making are simply not tenable in most cases. Problem solving and decision-making happen at every level of today’s flattened and distributed organizations. The second word in the phrase business intelligence is, after all, intelligence. What does it mean to provide intelligence to people and operations? How do systems become intelligent? The enemy of intelligent systems and organization is stasis. Becoming intelligent involves collaboration, sharing and the ability to publish and modify analytical applications, not just data.
Choosing a Course of Action
In my “people” column, I mentioned creating an advisory council for prioritization of activities of the BI team. This council should consist of high-level leaders from each of the major functional groups across the organization that have the authority to make decisions about the BI budget. Choose these people based on their influence within the organization and their passionate interest in using information for better decision-making. They should have opinions about the most important priorities for their function and the entire business because they will be expected to work together to come up with one list of priorities. The advisory council should meet quarterly as part of a regular process to confirm priorities and adjust as necessary.
Like instruments in an airplane cockpit, dashboards help executives see the direction they are heading, gain critical insights before serious problems occur and receive proactive warning signals for real-time decision-making. Effective dashboards display the current status of critical business performance indicators, viewed in context alongside historical results and strategic goals. As a result, decision-makers see flare-ups that demand immediate attention as well as organizational trends that deserve long-term course correction.