I’m a self-described “reluctant data scientist”. Wait? Didn’t Harvard say that data scientist is the hottest job of the 21st century? Why would someone reluctantly claim title to the rockstar status, money, and glory that come along with being a data scientist?
In my personal experience, there’s a big gap between data science hype and the real world. For several years, I’ve helped companies with data science. These companies have big ambitions, and data science can add a lot of value to their business. Sadly, most of them aren’t ready for data science. Usually nowhere close to ready. Some symptoms of not being ready for data science include a lack of data, bad data, insufficient data architecture and technology, and weak data culture and processes. Any one of these factors alone will hamper data science efforts. When these factors are combined, data science isn’t happening.
I don’t have exact figures, but I’m guessing that many companies have a giant gap between full actualization of their data’s full potential, and their current capabilities with data. The timing couldn’t be worse for these companies. As the Economist says, data is the new oil, and the rate of change in business is accelerating. Smart, data-driven companies are fully utilizing their data across the organization. Companies who fail to fully actualize their data will swiftly die. Speed and intelligent, data-driven action win. Sloth and gut-driven actions die. The decision to take action to win with data starts today.
But winning with data requires a solid data foundation. Without a solid data foundation, it is unlikely that a company will effectively do anything with data, let alone data science. Instead, the company will instead waste valuable time and resources, meanwhile being surpassed by competitors who are making their right moves with their data.
So where does this leave a reluctant data scientist? Ironically, in order to truly help companies get value from data science, I’m stepping back from data science for a bit. To make the biggest impact, I will help companies get a solid data foundation in place before embarking on data science – data architecture, data technology, data culture and data processes. As far as I can tell, more companies need a solid data foundation than a haphazardly implemented deep net. Companies need the data upon which to do data science.
To this end, I’m starting a new company this month, focusing exclusively on data architecture and data engineering. The goal is to help companies build a solid data foundation so they can win in today’s brutal competitive and technological business landscape. More updates coming soon.