In my first post on this topic, I explained my basic premise that there are major differences between Strategy Execution software and the broader category of Performance Management software and why I believe that only Strategy Execution software really helps an organization become more performance-focused. I've since talked about accountability (Part 2), driving action (Part 3), performance improvement (Part 4), and communication (part 5). This time it's all about data.
All types of Performance Management, Business Intelligence (BI), and Strategy Execution systems rely on data.
BI systems typically have a medium number of measures (up to many hundreds) that can be broken down in many ways – for example: sales by region, product, channel, customer, color, etc. BI systems are great for slicing and dicing data to find trends, problems, and possible inefficiencies.
BI systems are also great interfaces to large enterprise data warehouses. Enterprise data warehouses typically contain the most important business measures at the enterprise level. Data warehouse projects are notoriously expensive, take a long time to implement, and have a high failure rate. When successful, these projects give reliable access to key measures and a “single source of the truth.” BI systems are also commonly used to access data marts, which are smaller versions of warehouses that focus on one area – finance, for example.
ActiveStrategy Enterprise (ASE), which is a Strategy Execution system, focuses not on collecting metrics per se, but on using metrics to drive accountability, action, and communication to deliver performance improvement. Data marts and warehouse metrics can definitely be useful contributors to ASE. Usually, however, only a small subset of the detailed data for any given metric will be required in ASE (where, again, the focus is on the actionable data). Take sales for example. Users of ASE might wish to drill down from top-level sales data to a low-level geographically, but at a higher level by channel.
The key is where the accountability lies. Strategy Execution systems should not be used to provide all of the data for everyone in an organization to analyze; BI systems are great for that. What a Strategy Execution system should do is link data to those elements of accountability, action, and performance improvement.
As a rule of thumb, if you can’t put a person’s name next to a something as an owner, it probably shouldn’t be in a Strategy Execution system. On the other hand, there are typically thousands (or millions) of data points in BI systems that are not truly “owned” by anyone.
Another critical distinction is that Strategy Execution systems link high-level data to leading indicators. Leading indicators (also called causal indicators) drive performance of lagging indicators (also called outcome metrics). Continuing with a sales example, a simple leading indicator might be product quality – the better the product, the more we should sell.
A typical large organization might have several hundred important measures at the enterprise level – both leading and lagging. These measures can be broken down and analyzed across a myriad of dimensions, but they still represent a relatively small set of things to measure. But below the enterprise level, where employees are accountable for the actions that drive performance, there might be 50,000 or more leading measures. These leading measures are different in each organization.
Here are just a few examples of leading measures for a sales example:
- Penetration of direct marketing in the western region
- Number of pre-manufacturing quality reviews of each new product
- An individual sales rep’s achievement of product training goals
- The number of positive national PR mentions for the organization
- The brand awareness of the organization
- The number of milestones on time for the new sales tracking system implementation
The point is that there are many of these types of measures within an enterprise. They change often, they need to be linked to the strategic enterprise objectives, and they typically have a smaller scope of people interested in them. Because of these characteristics, it is not feasible to build a centrally managed data warehouse to hold all of these measures. But these are the measures that need to be managed to really drive performance.
This is precisely the problem that Strategy Execution software solves.
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