Ignoring Complexity is Not Simplicity

“Very often, people confuse simple with simplistic. The nuance is lost on most.” Clement Mok

For instance, if there is a significant data integrity issue in a project striving for automation, the issue of data purification and reconciliation is often ignored. While the automation functionality may be delivered (often at a high cost), automation ratios continue to suffer.

Simplifying, in the actual sense, involves taking a complex process and re-engineering them to manageable entity. It requires delving deep rather than staying superficial. Simplifying necessitates focus on non functional requirements such as: Scalability, performance, reliability, security and inter-operability.

Techniques for Simplifying

“Simplicity is the ultimate sophistication.” Leonardo da Vinci

Some of the problem solving techniques that can be applied in various business analysis competencies are highlighted below:

Systems Thinking

One of the common problem-solving approaches, useful especially in the initial stage of the project, is to understand the eco-system of the problem domain. As part of enterprise analysis a holistic view of the components of the domain and their interactions needs to be mapped. Context diagram is a handy tool in documenting the results of the systems thinking. System thinking helps to simplify by focusing on:

• Interdependencies (cause effect modelling)
• Goal alignment (ensuring all value streams work towards achieving common goals)
• Convergence (removing redundancies and improving system performance as whole).

Process Patterns

At a business analysis level, identification of process patterns helps to standardize and improve consistency. All business domains over a period of time tend to exhibit entropy. Identifying the essential process patterns helps in implementing control mechanisms that will reduce process deviations.

For instance a campaign management process, irrespective of the channels that is used (direct marketing, phone, internet etc) would have a process pattern like:

Identify target market, contact target customer, promote concepteducate customer, provide offers and initiate fulfilment.

Process patterns help to maintain consistency, minimize and reuse design and improve throughput. It engenders focus and removes activities that do not contribute to the goal of the process.

Think Data

Data elements are the fundamental building blocks in any system. They tend to get more complicated and maintenance intensive, causing data attrition. In a study done by IBM, data quality even in best maintained system has an attrition value of 2%..

Business analysts can simplify the way in which data structures are defined, maintained, displayed to the users. Identifying core and meta-data relevant to business is an integral part of requirements analysis. Naming standards help reducing the profusion of multiple terminologies.

Organizing data structures smartly ensures that business processes operating to maintain the data can be simplified.

For instance, in a telco domain, if the fulfilment process does not define data structures for service level agreements consistently, service assurance processes will suffer.

And Finally

Business Analysts can gain significant benefits by simplifying the way requirements are documented and communicated.

Using relevant diagrams, requirements management workflow tools, modelling data analysis and presenting them innovatively to stakeholders is a critical component of business analysis.

User stories, scenarios and narrative techniques can help the reader to engage and understand requirements better.

Taking a leaf out of Ernest Hemmingway’s book might perhaps help: “My aim is to put down on paper what I see and what I feel in the best and simplest way.”

Article source: http://feedproxy.google.com/~r/BusinessAnalystTimes-BusinessAnalysisHome/~3/ny2INFsZfeE/ignoring-complexity-is-not-simplicity.html

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