Technical writing for big data applications – tcworld India 2016
I’m attending tcworld India 2016 in Bangalore. Surag Ramachandran presented a session called “Technical Writing for Big Data Applications”. These are my notes from the session. All credit goes to Surag, and any mistakes are my own.
Surag Ramachandran works in a financial services business unit, working with data analytics in enterprise big data applications. He presented a couple of use cases from the financial industry. Then he described how the applications evolved, and how that led to a shift in the technical writing processes.
Surag said that financial industry has many use cases, including risk reduction, financial crime detection, etc. When it comes to big data, most of the use cases are in the marketing area, analysing customer behaviour.
The new enterprise IT model is a convergence of SMAC – social media, mobile, analytics and cloud. These all contribute to big data. Big data consists of the structured and unstructured data coming from these sources.
The financial services industry is highly data driven. Analytics is an evolving field. Predictive modelling tools analyse incoming data and give the bank actionable intelligence. Realtime crime analysis can pick up things like insider trading.
Looking at the core banking systems, you’ll find common platforms: frameworks that contain common modules to support big data. For example, the IT team develops an application for the banking sector, then adapts it for the insurance sector. In other words, the strategy is software reuse.
Thinking about technical documentation, there is a clear case for content reuse. Write installation guides and user guides that describe the common modules. Create the content as DITA files. Store the content in a content management system, and use them in the documentation for each individual platform. Within the applications themselves, there’s opportunity for content reuse too.
A lively question-and-answer session followed Surag’s talk, with many questions from the audience about cross-industry platform reuse, training for people interested in getting into the industry, challenges in documenting features in the big data industry, and other aspects of the talk.