JK's Diary

How many times have we gone through the routine of seeing data issue after data issue when something goes live in production, no matter how much of due-diligence was put in place? Despite having industry leading frameworks, operating models, air-tight processes, best-in-class templates, data issues creep in at multiple touch-points. Based on my assessment of UAT/post Go-Live scenario at various clients, more than 60% of the issues faced is related to data. Some of the organizations assessed had some sort of a piecemeal approach (or a quick fix in their parlance) to reduce data issues but it was more reactive and not sufficient, foolproof, scalable and/or repeatable.

The project types assessed include data migration, data integration, data transformation and creating/enhancing a bunch of analytical reports. In all the scenarios, data issue topped the charts as the number one pain area to be addressed.  This is because the “data” aspect was either overlooked or not given the required level of importance. None of the processes were designed keeping data as the core/foundation. Everyone understands that data is an asset, yet no one has designed the frameworks models and processes keeping data as the key focus. It is just one module or component in the entire spectrum of things. Addressing all aspects around data in a systematic manner in addition to the existing parameters is key to reducing data issues.

Figure: 1 – Key areas around data

Figure 1 shows some of the key areas that need to be addressed around data as a bare minimum to reduce data related issues.

A conceptual “Data Readiness” framework that can be customized and scaled up as required to suit various project types is shown here. Conducting an end-to-end data readiness check using such a well-defined framework, covering all major touch-points for data will help address data related issues early. While this framework is predominantly helpful during the approval/kick-off phase of projects, it extends all the way till the project goes live and declared stable.

Figure: 2 – Base framework for Data readiness

This highly scalable and customizable framework comes with supporting artifact(s) for each area as applicable. Refer Figure 3 for details. These artifacts are spread across people, process and technology areas. This will automatically address issues like schedule overruns, cost overruns, rework, low user confidence on IT, etc. as it touches upon all the aspects related to data.

Figure: 3 – Supporting artifacts & checklist items

The biggest advantage of this framework is that it can be easily blended with any existing governance model and PMO model an organization might be following.