The Harvard Business Review crowned it the sexiest job of the 21st century. Everyone’s talking about it. Many believe data scientists will be to the current decade what software engineers were to the last decade.
“I keep saying the sexy job in the next ten years will be statisticians. People think I’m joking, but who would’ve guessed that computer engineers would’ve been the sexy job of the 1990s?”Hal Varian, Chief Economist, Google The McKinsey Quarterly, 2009
And to ice the cake, last week President Obama named former Silicon Valley data scientist, DJ Patil, the first Chief Data Scientist for the U.S. government.
After making more than 138,000 data sets available to the public, the U.S. government is making a concerted effort to expand its ability to process enormous datasets and use the insights they hold to build innovative data products that help our country thrive.
— Megan Smith (@USCTO) February 18, 2015
The government just admitted that its facing the same problem CEOs and CMOs across the globe are grappling with.
In 2014, Bizo asked CMOs how well they use data. Less than 2% feel like they’ve nailed it.
So if the Feds need a Chief Data Scientist to help the U.S. government figure out what to do with all its data, is this a sign that every company of every size also needs a Chief Data Scientist?
As the data science field advances, some worry that a talent deficit could slow its growth. Because the highest concentration of the deficit centers around management titles, you’re likely to encounter significant trouble in attempt to hire a skilled data science leader.
A 2013 McKinsey trend report estimates that there will be a shortage of 140,000-190,000 data scientists and a deficit of 1.5 million managers who are capable of utilizing data driven insights.
“We are seeing a proliferation of executive-level roles borne out of the global influence of big data. Businesses are looking for leaders who can not only understand the massive amounts of information available to them but also identify the threats and opportunities that come as a result of this evolving landscape.” Brian Sullivan, CEO, CT Partners, 2014
Hiring top talent in any field is difficult enough, but add a crippling talent deficit on top of your regular recruiting woes, and hiring a Chief Data Scientist also means hiring a Chief Recruiting Officer.
However, if you can manage to hire a top Chief Data Scientist, you will also attract an ensemble of talented data scientists. For the White House, hiring DJ Patil is equivalent to fronting a data science recruiting team.
At the core of a modern data science organization, valuable insights sits like The Hope Diamond inside of a massive dataset. To extract usable value from the dataset, data scientists have to do a lot of grunt work. Hiring a single data scientist to figure out what to do with your data is probably not the answer, and here’s why:
This is how Radius organizes our data science team:
Before we deliver any value to our customers – the marketers – our data goes through an extensive and complex aggregation, cleansing, integration, and canonicalization procedure. This is not a simple process. It’s estimated that 50-80% of a data scientists’ job involves arduous work to sculpt the data before actual science is applied to them.
Thanks largely to advances in technology, a lot of the grunt work of data science is becoming automated – or, at least, easier to manage. Tools like Spark also make it easier for data scientists to do their jobs, and also make it more attractive for data scientists to join organizations that run on that latest data technology.
“Data scientists are developing fewer models from scratch. That’s because more and more big data projects run on application-embedded analytic models integrated into commercial solutions.” James Kobielus, Big Data Evangelist, IBM
While these tools make the life of a data scientist easier, they also require both financial and resource investments.
Building internal resources to figure out what to do with your Big Data might actually cost more than building a container of Big Data did. With over 138,000 datasets just hanging out and decaying at data.gov (and that’s only the data that’s publicly available), the government has made such a big investment in Big Data that data science has become imperative. At the Strata conference, In his first public appearance after being named Chief Data Scientist, DJ Patil petitioned the audience to consider joining White House data science team. Extracting valuable insights from the government’s data is the work of more than one data scientist.
The last few years have witnessed a tremendous rise in data products: software solutions built on data to deliver insights. Using these solutions, companies can apply data science without fronting an entire data science team.
Look at the solutions that have emerged to help business leaders predict behavior from customer data – the largest unused data asset held by corporations across America.
Implisit, for example, helps companies use CRM data to understand which sales opportunities to pursue, and how to find more like them.
Preact uses data science to decode customer behavior so you can reduce churn.
RelateIQ uses data science to help customers prioritize CRM activities.
At Radius, we use data science to help customers understand their total addressable markets.
As more companies develop products using a data science approach, business leaders will stop asking, should I hire a chief data scientist? The question instead will become, which data products should I invest in to drive highest ROI from my data?
At the end of the day, if you have to ask whether or not you should hire a Chief Data Scientist, unless you’re hiring DJ Patil, I think we both know the answer is probably no.