With all the mergers and acquisitions in the business intelligence (BI) space, it’s easy to forget that BI is about much more than the technology that’s behind it.
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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.
5. Understand what users need. The three broad classes of business intelligence users are strategic, tactical and operational. Strategic users make few decisions, but each one can have a profound effect—for example, should we close operations in Europe and open them in China. Tactical users make many decisions a week, and use both aggregate and detail-level information, and likely need updated information daily. Operational users are the front-line employees, such as call center staff. They need data within their own set of applications to execute the enormous numbers of transactions. Understanding who will use BI and for what purposes can show the type of information needed and its frequency, and help guide BI decision making.
6. Decide whether to buy or build the analytical data model. One size does not fit all. In general you may benefit from an out-of-the-box, industry-specific data model if you have a more homogeneous IT environment—such as one ERP, one CRM system. Do watch for extensibility and hierarchy flexibility. More complex enterprises may benefit from customization, although you may still want to consider beginning with an industry-standard model as a template or a set of guides (such as typical facts, dimensions and so on).
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7. Consider all business intelligence components. Components that affect the success of business intelligence implementations include: metadata, data integration, data quality, data modeling, analytics, centralized metrics management, presentations (reports and dashboards), portals, collaboration, knowledge management and master data management. Be sure to define the architecture for all layers of the business intelligence stack; even though they may not be part of the BI strategy itself, they will effect the success of implementation.
8. Choose a systems integrator. Business intelligence implementations require guidance from a partner who has deep experience. Evelson says be prepared to spend $5 to $7 on services for every $1 on software, and cautions: Do not outsource the fine-tuning of business intelligence. The process requires a high degree of collaboration among end users, analysts and developers.
9. Think “actionable” and “baby steps.” Choose an end user, business analyst and developer to create a first proof of concept within a few days. Choose a few key performance indicators and build a few reports, then add new releases every few weeks.
10. Choose low-hanging fruit to start. Evelson recommends choosing high-value, simple components to begin. For example, a sales analytics data mart may present high-value targets that also have plenty of existing models and best practices.