Data integrity is central in solving poll challenges

Electoral team compiles votes. FILE PHOTO | NMG
Electoral team compiles votes. FILE PHOTO | NMG 

Last week the tension was palpable in Kenya in the run-up to the general election. Gladly the voting process took place without too much hassle and the mood across the country was very positive with the interwebs populated with the purple shade “pinkie” that confirmed that the holder had indeed exercised a key democratic right. There was also the ‘Githeri Man’ who helped punctuate the mood with humour, having spawned hundreds of memes.

Things then seemingly took a turn south as the process of counting and transmitting results started, making some people uneasy, leading to all manner of conspiracy theories and the coming to the fore of dodgy dossiers that were quickly disseminated via social media.

We have seen the impact of fake news on even the most advanced economies and the effects are probably compounded in our setup due to a huge rural populace that are several notches more impressionable.

Steering clear of granular analysis of the just concluded election tech, which I personally think operated well on the frontend, a giant step from the previous season performance, I would like to guide our future thinking based on first principles. This will allow us to look at the solution based on what we desire to achieve as opposed to what we have at hand to work with. This will also require us to run experiments and test them at scale to allow for maximum possible buy in from all invested parties, especially the 45 million citizens.