You need to establish your vision for your business intelligence strategy before you bring technology into the conversation, says Boris Evelson, a Forrester Research analyst and lead author on the upcoming study “It’s Time to Reinvent Your BI Strategy.” Here’s how.

1. Choose a C-level sponsor (who’s not the CIO). Business intelligence implementations should absolutely not be sponsored by anyone in IT, says Evelson. Instead, BI should be sponsored by an executive who has bottom-line responsibility; has a broad picture of the enterprise objectives, strategy and goals; and knows how to translate the company mission into key performance indicators that will support that mission. This executive is often the CFO. This sponsor should govern the implementation with a documented business case and be responsible for changes in scope.

2. Create common definitions. Without common definitions, a BI implementation cannot succeed. And lack of agreement is a widespread problem in companies today. For example, finance and sales may define “gross margin” differently, which means that numbers will not match—in effect, negating the value of automation. To combat this problem, get subject matter expertise throughout lines of business from front-, middle- and back-office staff. At this stage, IT's participation should be limited to running the project management office and taking ownership of compliance and business standards and policies. Secondly, start small and choose only 10 to 20 key performance indicators and create standards and governance with them in mind.

3. Assess the current situation. You should analyze the current business intelligence stack and processes and organizational structures surrounding current BI implementations. Both IT and the business should be involved. Evelson cautions against underestimating this phase, and points out that a full “BI diagnostic” from Accenture contains 1,500 questions against 325 best practices and 75 subject areas.

4. Create a plan for data storage. Many organizations begin with an isolated data mart, since it’s quick and cheap, but consider that this tactic means additional silos will need to be created as additional data storage needs arise, which can grow out of control within a few years. Something else to consider is whether to build and maintain a physical data warehouse or go with the virtual, so-called “semantic” layers to link operational systems. Traditional data warehousing means duplicating data, which means bringing in operations systems in real time will be next to impossible. You can save space with an abstract definition layer, but this is difficult to design, as is any metadata repository. Before even considering which vendors to choose, you must resolve this issue.