Many professionals (SMEs) and business operation leaders look at data on a daily basis to understand priorities, bottlenecks, highlights, etc. Then they determine a set of activities for the organization using specific criteria that are important for the business.
These people are not programmers; they are domain experts or part of the business operation teams. That means they have strong domain knowledge and very little to no technical (e.g., coding) skills.
For instance, many companies have a call center as part of their core business. In a debt collection business, phone calls are the primary means of contacting and communicating with the debtors. But how is the call list created?
This is a need for many companies that provide virtual or human-to-human interactions requiring clients' conversations. Many industries, such as finance, healthcare, telecommunication, travel, hospitality, etc., have similar needs in different areas and daily workflows.
The Nature of the Work
At the high-level looks like a rule-driven decision, and in fact, it is. But different types of operational decisions. The challenge is that the leadership and executives of the company provide guidelines and exceptions (i.e., business rules). Still, at the operational level and the day-to-day job, those guidelines and exceptions are too generic. Therefore operationalizing them is more than writing some business rules and executing them against the data because there are a few or no specific and concrete business rules, and not easy to predefine them as they may vary case-by-case and day by day.
Operationalizing the guideline and exceptions require looking into data and translating them into specific criteria to determine targets and classify them.
From a different perspective, the process could be seen as working with Excel or other data tools. While keeping the guidelines and exceptions in mind and playing with data accordingly by:
- applying data operations and custom calculation
- including and excluding columns
- building filters
- highlighting values and rules
- flagging records
- cross-checking with other systems
Finally, once the operation team is satisfied with data filters and annotations based on what they see for a population, this knowledge is passed to “a team” e.g., back-end team, data team, development team etc., to bake those criteria and annotations into a pipeline or systems to create the list.
The Business Problem
The problem is this workflow is very time-consuming and expensive. It requires multiple rounds of investigation, verification, and refinement. Also, it requires expertise for the “team” to translate the operation team knowledge of a sample population into a working system.
In short, this workflow is not scalable because:
- It delays the daily process
- It requires constant and frequent refinement from operation teams
- It is error-prone as knowledge transfer from operation to the team is not a straightforward task.
- It highly relies on skilled programmers or data experts.
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Alternatives to Address the Challenge
As we discussed earlier, these operational decisions are rule-driven, but they cannot be easily implemented in a business rules management system or traditional decision management. The reason is the operation team requires to interact with the data and apply the principles of the business to the data to accomplish their tasks. In fact, data solutions such as Excel, Google Sheets, or PowerBI are more appropriate.
The issues with the data solutions are that the operation team should still transfer their knowledge and understanding of the data to the implementation team so they can bake them into the systems. This still is an error-prone workflow, time-consuming and expensive.
And finally, the data filters and views on the data tools are disconnected from business rules as they are purely data exploration tools. The rules of priorities, classifications, highlights, flags, computed columns, etc., can only be translated to proper “business rules” and reused across the enterprise if they are coded into ETL, business processes, and internal/external/IT systems. Therefore this solution is suboptimal at best and troublesome in a medium or large scale business operation!
In the future post, I will discuss how the Booklet can address these challenges for business and operation teams.
Last updated June 2nd, 2023 at 10:04 am, Published May 9th, 2023 at 10:04 am
CEO and the founder of FlexRule – He is an expert in architecture, design, and implementation of operational decisions, business rules, and process automation. Created Decision-Centric Approach, a methodology that brings People, Data, Rules, and Processes together to automate operational business decisions.
This approach is recognized by Gartner as the missing link to provide business value to organizations.
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