Is my Data Lead communicating with me in a way that I understand and measure status & risk?.Can we lead and implement an end to end Data Delivery process for Migration? Have we done this before?.Next time your Systems Integrator says '.the business/customer is responsible for preparing data, we'll just load it.', THINK: We get involved in far too many Data Workstream recovery projects to unpick the pieces and quickly get the client on track again. It's not difficult to spot if you are carrying risks in your Data Workstream, they are visible at any stage in the delivery lifecycle and they become more obvious the further into the programme you are.īased on many Data recovery & remediation projects we are asked to engage with, there are 4 failure modes that are common across all Data Workstreams we have been asked to turn around: This is a common chain of events across all failed migration projects we seen, where the root causes are never explored and teams react to the symptoms.( Why did the aeroplane wing fall off in the first place?) The risk level has now increased significantly, the next time the Data Workstream activities will get on the radar is Pre-production or cutover loads which ends this story. Now that the Data crisis has been solved, Management attention moves back to supervising the important Test activities, oblivious to the fact that they've done the equivalent of sellotaping an aeroplane wing back on after noticing it had fallen off. Leadership, ever keen to keep the ship moving, agree the usual approach of de-scoping and defaulting data to force the data in - the net result is 100% load success for SIT/UAT - WOW! what an amazing turnaround high fives all round.

Programme Management having previously not spent much time with the Data Workstream struggle to understand the complexity of the process, don't have time to fully understand it and look for quick resolutions.

Data Leads can be unable to articulate status, risk & metrics in a clear and transparent way to leadership.

The Data Workstream status remains low on the radar frothing with ever growing risk, invisible to the programme, and won't reveal it's ugly status until key Data Delivery milestone events like SIT or UAT when it's discovered that insufficient quality data can be loaded.Įven when the first warning sign emerges that there is a fundamental problem with the Data Workstream (normally SIT data prep), the obfuscation of poor quality metrics and misleading & bamboozling position statements further hides the true reality of status from leadership and what the root causes are. 'Data' is wrongly considered a B lack Art, that programme management can leave the Data Workstream to ' just get on with it' with light touch involvement.in these projects, leadership considers their biggest risk areas as Functional Build or Test activities which attract significant attention and oversight.

Warning bells ring at regular & predictable intervals along the Programme lifecycle for all to hear if you're listening, unfortunately these bells trigger the wrong reactions by leadership teams pushing programmes further in the direction of failure.

The seeds for these failures are normally sown at the very start of the programme and failure is generally avoidable. This is an expensive discovery as it invariably results in a programme delay costing £millions. This article shares insights on common themes observed and offers some tips for leadership to spot warning signs which are there for all to see, only if you're looking though.įailed Data Migration workstreams traditionally reveal themselves very late in the delivery cycle, normally days or weeks before cutover. This year alone, unfortunately, Kagool is in double-digit figures for SAP programmes requiring Data Workstream rescue & recovery. In many instances these workstream issues have come as a last minute surprise to the programme and yet were entirely avoidable with risks clear throughout the projects but not understood by the leadership. As SAP Data experts, we are commonly engaged to review and recover failed Data Migration workstreams, which have caused large SAP programmes to delay cutovers.

Data rescue 4