Team Tariff, our data management workforce


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Genability is all about elasticity.  We’re focused on how to make the grid more elastic and more efficient.  But our elastic mantra goes beyond our product lines and extends to our workflow as well.

We are organizing and crunching data that is complex and enormous at the same time and requires increasing domain knowledge.  To attack this problem, we needed a large workforce and a model which is simple and scalable but at the same time can address data complexity.   Options were  numerous including outsourcing and crowdsourcing.  We ruled those out because we did not want a third party cultivating a core competency at our  expense and at the same time,  the crowd in crowdsourcing doesn’t have enough domain knowledge to contribute in a meaningful manner.   So we blended the two approaches.  A group of freelancers and an internal workforce connected on a common platform who can learn, share and build.  From Manhattan to Manilla, we employ a distributed team of people who are meticulously growing our core pricing data. This distributed force could also help us in other areas. If needed, we can now expand globally into different markets quicker and with less pain. A contributor gets trained, contributes and then contributes some more by training other personnel.  We believe in training the trainer.

A bit more about the system we have built.  The backbone of our system is called mother (as in mother lode).  The data collection tasks are definitive and standardized and we are continuously challenged to make it more intuitive, more self learning and to require less training for new contributors.   From Turkers on Amazon to freelancers on ODesk,  we are working with and blending varying models and ultimately designing a system that engages the crowd in a way that meets our requirements. No model is perfect and there is no universal best practice and it is ultimately the task at hand which dictates the approach.

Do you work with a distributed workforce?  What’s working well for you?  We’d love to hear your thoughts.

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