Business processes seem to come in two flavors: those that produce transactions or content and those that produce decisions. The quality of decisions from the latter category often drives the trajectory of the business. Well-executed, insightful decisions can lead to superior results.
1. Focus on the Processes that Matter most to Your Business
Organizations improving insightful decision-making carefully pick the key processes and operational variables on which to focus. Clear alignment exists between a successful organization's market strategy and its processes and operating metrics to implement the strategy.
W. Chan Kim and Renee Mauborgne, in their groundbreaking book, "Blue Ocean Strategy," developed an interesting approach in which they recommend picking operational variables in the context of strategy development. They point to well-known examples of companies with clearly differentiated strategies, including Southwest Airlines and Cirque du Soleil.
In the high tech electronics industry, clients select variables that include forecast accuracy, order fulfillment rate and inventory levels. Making planning decisions on a weekly basis at the SKU level, based on insights across those variables, resulted in tremendous improvements in all three.
2. Stay Focused on Your End Goal
The improvements you target should be expressed as changes in the specific selected variables. For example, if the goal is to reduce inventory by 30 percent, the initiative should fit that objective clearly.
This kind of Deming approach of "you get what you measure" is well-documented, but it is surprising how many organizations do the first step without then taking the time to set clear objectives in the second.
Deriving insights from a business process requires a good balance of freedom to efficiently explore information and decision alternatives coupled with a clear idea of the objective.
3. Ensure Your Data Supports Your Insights
Taking into account the processes, variables and objectives selected in the first two steps, the third step in improving decisions is to determine the readiness of your data and infrastructure to support the kind of insights required.
Organizations often get caught in the trap of believing that their data or infrastructure are not up to the task and assuming that progress cannot be made without solving those issues. And yet, decisions must still be made, and it falls on business analysts to cobble together information manually and come to meetings armed with spreadsheets.
These discussions based on suspect data often lead to finger pointing and fact questioning instead of insightful decisions.
If the data is suitable to drive the required, but often ineffective, discussions, would it not make more sense to leverage the data in a smarter way to derive insights more systematically and in a way that improves over time?
One of these techniques is to provide analytic reports showing all variations of a particular data field along with their owners. The process designers indicate which field variant is authoritative for a particular value, and technology can be used to manage the communication with other owners as they align their data.
As alignment is achieved, the quality of the insights steadily improves. This "peer pressure" approach to data cleansing at the source is reminiscent of rating systems used for sites like eBay.
There is incentive to getting the information right at its sources because everyone sees the impacts of good and bad data downstream.
This technique is distinct from the traditional approach of creating large data warehouses that attempt to consolidate schemas and provide highly cleansed enterprise data from a central source for driving analyses and processes.
Many organizations have struggled with the data warehouse approach, in part because their businesses don't remain static long enough to even finish the warehousing project.
It is always beneficial to leverage warehouses that are in place, at whatever level of completion, and then use the peer pressure approach to fill in the gaps and address new gaps as they emerge.
4. Parlay Processes & Insights into Smarter Decisions
The fourth step is to design and engineer the process and business analytic capabilities required to produce the insights and execute the resulting decisions.
This work might seem straightforward, but it is fraught with subtleties and traps typically resulting from experience biases among the team involved in the work. For example, an IT team charged with deploying a company's business intelligence technology of choice would naturally focus on the reports required.
The reports are a critical part of deliverables, but if the business analysts still have to manually transform the information and engage in offline or disconnected interpretation discussions spanning a company's functions, driving insightful decisions remains difficult.
Alternatively, if the team is adept at business software development that supports transaction or content production processes, the tendency is to try to develop analytical processes using the same methodology. This typically results in elongated development cycles and solutions that still miss the mark.
Improving insight requires a careful combination of flexibility and context management in some kind of guided analytics environment, as opposed to an exact, step-by-step approach.
If the team comes from a process development or consulting background, the two traps I see most are biasing the work more toward the process than the result and producing one-time deliverables that may not transition well into an ongoing change vehicle.
While many of the skills of these teams are often highly valuable, agile development processes coupled with the right amount of business-focused domain expertise are more suitable for business analytics.
Getting capabilities in the hands of the process stakeholders quickly and then letting them evolve as the methods of gaining insights emerge usually adds more value quicker than locking down exact requirements and following traditional development methods. And it is equally important to ensure that the resulting process captures the entire insight loop, including planning, reporting, analysis, collaboration, decision-making and execution.
5. Use Your New Processes to Drive Improvements
The fifth step is to operate the new process and drive the targeted improvements. Here, it is important to make sure resources are provided for properly interacting with the process, data and stakeholders to facilitate the emergence of insights and decisions.
Initially, the new process might require more work than the old process, especially until the stakeholders get comfortable with the differences in the new decisions versus what they would have done in the past. This initial increase in work should be planned for, and if your program is successful you should soon see a much sharper net decrease in work versus the old process.
The best insight-driven processes eventually require tremendous effort to stop, as opposed to tremendous efforts to keep them running.
Even if you do not have all the skills needed to implement these changes, you don't have to go it alone or wait to get started until your team is fully in place. Business models and other resources are emerging quickly to help organizations holistically with these kinds of programs and will be of tremendous value as you develop your program.
As you move through the steps, you can define the gap between the resources you have and those that are needed and build your case around the target variable improvements that process insights will incrementally deliver to your business.
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