Design [Decision Analytics Architecture]
Decision Analytics Architecture
Many analytics and business intelligence projects struggle to show business impact and ROI. This problem is solved upstream by linking analytics to business results at the design step, when the business requirements are documented. This solution provides the Decision Analytics Architecture required to systematically link analysis to results.
- List the business decisions that are to be supported by analytics
- Design analytics use cases to link analysis to decisions and results
- Determine the business changes needed to get value from analytics investments
- Align analytics stakeholders towards using analysis to drive decisions and get business results
- Set business expectations for Return on Investment (ROI) from analytics
Why you need a Decision Analytics Architecture
Analytics teams focus on turning data into insights, and rely on their business colleagues to convert the insights into business outcomes. Business decision-makers are faced with a stream of decisions. The decisions differ in scope, magnitude, urgency, and complexity. The imperative to handle the urgent leads to a firefighting approach, and decision-makers often don’t think of how best to connect analytics to decisions.
If you don’t already have a "list of decisions”, how can you systematically design analytics to support the decisions? Worse, if you don’t have a list of decisions, then you won’t have "decision rights”: who plays what role in which decision. Imagine a RACI matrix for decisions.
Decision Analytics Effectiveness bridges the data-to-insights side to the insights-to-outcomes side.
- Close the gap between business, analytics, and IT
- Anticipate questions on how analytics will provide ROI
- Focus efforts on investments that have the best expected outcomes
- Drive business changes that need to be made to gain benefits from analytics
- Build test cases and guide acceptance based on how the analytics enables outcomes
What is the Design for Decision Analytics Effectiveness service?
Design for Decision Analytics Effectiveness is a service offering. Experts work with your business, analytics, and IT stakeholders to build business requirements and use cases. The deliverables provide a design blueprint for actionable analytics.
- Decision Architecture shows the decision points for your business
- Decision Analytics Architecture shows how to apply analytics insights to the decision points and so use the insights to drive results
How is the Design for Decision Analytics Effectiveness service delivered?
- Prepare the Team: we start the project with a review of existing business requirements and use cases. This is done in collaboration with the client to arrive at a first draft of the Decision Analytics Use Cases. During this step the team refines its understanding of how the business operates.
- Build the Decision Architecture: we create a list of the decisions taken by the organization. These decisions range from major capital investments to marketing campaigns, product launches, hiring plans, discounting requests, etc. We categorize and define to build a clean list of decision points:
- List what decisions are made, why, when, where and by whom
- Extend the list with a decision definition and decision characteristics such as: level (strategy, capacity, scheduling/allocation, execution), business impact, data-inputs required, decision roles, decision-skills (expertise), timing (on-request/on-schedule), cycle-time to decide, etc.
- Work with stakeholders to build an enterprise Decision Architecture with alignment across levels, functions, and units.
- Design the Decision Analytics Architecture: we analyze the Decision Architecture to understand how to improve decision effectiveness using analytics techniques. The results are documented in a Decision Analytics Architecture. This architecture shows where analytics can be applied. Existing analytics use cases are mapped to the decision points to assess how analytics drives results. Decision Analytics Designs are created for the decision points, and refined by reviews with business, analytics, and IT teams. The designs are then assessed for costs, benefits, as well as the changes required to get the business benefits.