Patterns of enterprise intelligence
Published: July 3, 2019
This is the second in our Intelligent Enterprise Series of articles. In the first article we introduced Continuous Intelligence, the virtuous cycle of steps in transforming data to insight to action and of continuously improving this process through iteration. We also introduced the Enterprise Intelligence Model to overlay this cycle onto enterprise operating models in which the business value and experiences of customers, colleagues, and partners may be improved through insights and automated decisions. We also introduced an Intelligence Maturity Model for helping you evaluate how advanced your organization is in creating insights from data and using those insights to drive decisions, actions and learnings. These thinking frameworks serve as a basis for this and other upcoming articles in this series. We’ll be examining the technical and business enablers for speeding up the Continuous Intelligence cycle in your organization.
In this article, we’ll explore common patterns of enterprise intelligence and identify the points of friction and opportunities for improvement in the Continuous Intelligence cycle. This article series is best read as a collection of dimensions for incremental improvements — things that are usually best taken in steps, on a test-and-learn basis, to find what works best for your unique situation. This approach requires executive support and sponsorship as well as grass roots buy-in from the knowledgeable colleagues in your organization.
The Enterprise Intelligence Model introduced in the first article in this series offers a means of enabling Continuous Intelligence by examining those points of friction and opportunity in the intelligence cycle which are most likely to deliver results quickly, and to mature capabilities. In our experience, those considering how to enable Continuous Intelligence in their organization find it most helpful to identify common stages along the path of intelligence maturity as organizational archetypes. We’ve found framing these archetypes in the Enterprise Intelligence Model provides a clear way to express these stages of maturity. Future articles in this series will unpack the what, why, and how of the techniques in this article that we identify with the more mature stages of Enterprise Intelligence.
- From an organizational standpoint, we’ve observed that the following hurdles most commonly cause friction in creating value from data:The data being captured doesn’t reflect the richness of the customer and user experience
- Insights gained losing much of their practical applicability because too much time is spent refining data models and schema for insight enablement and too much focus on reports as standalone artifacts
- There is a disconnect between the decision-making process and the insights needed to support that process, leading to suboptimal decisions.
- Feedback loops are ineffective because decisions aren’t carried out entirely as intended and so aren’t reflected in the user experience