Process analytics is more than a pretty graph

One of the technology areas I've been enjoying getting my head around in my new role is Business Process Optimization as it relates to BPM. As I'm starting to understand it, the optimization of business processes can be represented as a lifecycle, stepping through three main phases:

1. Model & Simulate
2. Execute & Analyze
3. Manage & Improve
   

In the dim and distant past, long before I ever considered joining Global 360, and perhaps when my knowledge of the BPMS market was limited by the constraints of the products I worked with, I had a pretty good discussion about Model & Simulate. I'll probably return to this discussion in the future, since I think there is still some legs in the thinking around simulation where integration (or SOA) is involved.

As for Manage & Improve, in my past I naturally assumed that this was just a function of making sure that the process was well designed and roles flexibly assigned so that fluctuations in workload were well balanced across the available workforce. That is only half the story, but the need to Execute & Analyze effectively are still the foundation for effective processes.

In simple workflow environments a quick report and a simple graph can provide all that is necessary in terms of 'analytics'. It can show a manager at a glimpse where work is building up in a process. But in high volume, complex business process environments, that have constraints applied through contractual Quality of Service agreements or a need to provide exceptional customer service, the analytical capabilities of a system need to be a lot greater. Examples could be credit card dispute resolution, call center customer services, life insurance application processing, or brokerage account opening.

In these complex environments, managers need up-to-date data that can represent work sliced and diced across many dimensions. This enables them to see not only that there is a large mass of work collecting in one activity in the process, but whether that places their highest value clients or service contracts at risk. True process analytics tools can understand the structure and 'flow' of work in business processes, enabling them to produce OLAP cubes for complex analysis. And since it does this by capturing an event stream representing work being processed and routed, by taking the data offline there is not the huge processing impact on the live system that complex database queries would have.

Now that managers can see and respond to predefined analytics, as well as having the tools that enable them to simply visualize the data sliced according to their own local requirements, the job of analytics is done, right? Not really. Process analytics enables slices of data to visualized over time, enabling trends to be spotted or the impact of specific conditions (for example a spike in volume of high value work) to be assessed.

Being able to understand how a business process responds under real conditions seems like the ultimate proof of performance. In a crazy day, where everyone is working flat out, a manager may not be able work out from 'gut-feel' alone how well his process is responding to this big spike in demand. Given data and easy to drive analysis tools after the fact, he or she can quantitatively understand what was different to other days, what went well and where improvements could be made.

I'm really just a beginner in this business process optimization world, but I understand that business process execution can be run in several way: just get work through and out of my sight, or get work done that really benefits the business. With experience, Key Performance Indicators (KPI) can be developed that provide the manager with an 'at a glance' metrics showing if the process is running to plan. The aim of the business is not necessarily to hammer out 10,000 cases an hour, but really to beat the true goals of the business that his teams should be bonused on - be that profitability, customer satisfaction, value of new business, etc.

With a business process that has been optimized based on quantitative experience applied to real metrics, and with analytics that have been built to be meaningful in the heat of the moment, a manager can really work to exceed true business objectives.

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