Another way businesses can get more out of their data scientists is to focus on building the department in a way that doesn't just reflect lofty expectations of data, but is based off the actual needs of the business. For example, a business should know before the hiring process begins how many data scientists their business will need, but that can't be determined without first having a clear strategy that outlines what data is needed and how it needs to be translated.
"Data science is an immature, diverse and vaguely defined 'job'. As such, it's impossible to say how many or what kind of data scientists are needed by a company until they clearly define the job as it relates to their business. At SendGrid, we define the data science job and its career path as it relates to our product and engineering process -- this helps answer the questions of how many data scientists we need and directly defines the skill set those employees will have," says Beach.
A coordinated approach
Data science is a new field and chances are most data scientists have a background that includes statistical analysis, domain and business expertise or coding, according to Rattenbury. But he also points out that just because they can do all of these things, doesn't necessarily mean they should. Rather, you should focus on creating a more coordinated approach with multiple skilled people, "In most businesses, the variety of data and the variety of potential applications of data necessitate a multi-person effort that is best accomplished when people take on specialized roles," says Rattenbury.
He says there are two places where data scientists can shine in a business, and where they should focus most of their time and energy. The first one, according to Rattenbury, is around the raw data ingestion or data creation. That means that your data scientists should focus their skills on finding the most useful way to utilize data and the best ways to store and manage that data. The second is looking at how data can benefit the company, what budgets need to be in place to achieve the business' goals and using data to "drive automated process within the company," Rattenbury says.
Don't get greedy
Businesses should also avoid being data-greedy -- because the idea of too much of a good thing, certainly can apply to data. "They may be collecting more data than they have the capacity to explore and assess the value of. One way to solve this problem -- is to be more selective about what data you analyze," says Rattenbury.
And because data is such a new concept in business, Rattenbury recommends a flexible approach to a data strategy -- one that considers what should change as you move along with a new data initiative. This way, businesses can consider what's working, what's not working, who the key players are and the value tied to specific data points. However, prioritizing data this way isn't just a task for data scientists, he says, it's a task that needs to include everyone in the company. Data scientists can't predict or know what data every department will need, so implementing effective data strategies need to be a company-wide task, not an individual effort.
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