WHAT SCRIBBLE DATA DOES
We're the bridge between data science and data engineering.
Most Data Engineering is focused on the problem of moving, storing, or retrieving data. Most Data Science is close to the business, applying domain knowledge and statistics to the right data sets to create eureka moments or little crystal balls.
It’s this void between data engineering and data science, where the raw data needs to be nimbly, scalably prepped for every data science need, from analytical to ML-predictive, that Scribble Data operates.
Our platform, Enrich, helps prep data at scale (feature engineering) for data science, and our consulting services are aimed at turning every data science team into well-oiled machines.
Indra loves learning about how people interact with technology. How they adopt and when they discard - the little niggles when they hit technology bumps that should've been ironed out, the half-smiles when something is easier or works better than expected. Indra did his bachelor’s in electrical engineering from McGill U. and his MBA from UToronto.
Why we're doing this
So much of our decision making happens in uncertainty.
Historically, we’ve figured that data would help us combat this uncertainty, to help us make more accurate predictions and better decisions. But the reality is that data is being generated indiscriminately. We are now grappling with all the noise that data causes - our continuous ‘data exhaust’ simply causes ‘data pollution’.
In this context, as businesses lean on data for their machine learning to make better decisions, there are opportunities, and dragons on this path.
Few things excite us as much as our journey down this path.