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Business Intelligence Strategy

Article by Rampant Author Brian Carr

Start with a business intelligence (BI) strategy to get your data analytics projects off on the right track.

Why a Business Intelligence strategy
Developing your Business Intelligence Strategy involves first considering your organizations vision and mission. Melding the corporate strategy with your business intelligence strategy will make a winning combination.

Business Intelligence Elevator Speech
To get key players on-board with your BI initiative you need an 'elevator speech'. This allows you to quickly give stakeholders your idea. Your elevator speech should be: "Business Intelligence allows data to be transformed into knowledge that fuels business growth. Business Intelligence allows better, more informed and faster decisions."

Key players
There are a number of key players to include in your Business Intelligence strategy and initiatives.
- Leadership Key
- Decision Makers
- Business Analysts
- Information Technology
- Other stakeholders

Essential Elements of Business Intelligence
- Good information
- Secure information
- Accessible information
- Analytically structured information

Goal of Business Intelligence: enable better, more informed and faster decisions

How do we enable better, more informed and faster decisions
Different business intelligence tools address these factors in different ways. Reporting delivers regular, timely information, with the ability to author reports or queries to get specific details. OLAP analysis, with its multiple dimensions, allows you to compare and contrast information against time and other factors to uncover trends. Scorecarding presents your key performance metrics and whether you've cross pre-determined thresholds. Executive (Data) dashboards put information in context, and in an easy-to-understand format.

Precursors to implementing BI
The underlying pool(s) of data need to be trusted. For companies to realize successful business intelligence user's must trust the data. Many companies have pockets of data that are considered trustworthy. This trust can be spread to other data by instilling data quality methodologies and also by implementing granular data security. This can be accomplished by mapping key information/data. Then creating data integrity rules/processes on this key data. Through this mapping of key data you will identify security rules which can be programmed at the source of the data, rather than the individual applications and reporting systems that ride on top of the data. Define it one time, one place.







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