Data scientist job seekers beware
To avoid this position, data scientists should review all the technology and platforms their prospective employer has in place. From the first interview, you should ask questions to ensure that you fully understand the state of data at the company. Make it clear that you aren't there to organize the company's data, but rather to glean insights easily and quickly from an already organized data set. If their data is disorganized, ask about solutions to fix it, or what budgets are in place to purchase the right tools and software.
"Data scientists need to be able to derive insights easily and quickly in order to support business decisions, and the more scattered the data is, the harder that becomes," says Leong. So you don't want to get into a position where you are faced with the monumental task of searching through tons of scattered data points.
Get problems and issues out in the open
If you do find yourself hired as a data scientist, but are instead acting as a more of a data janitor, Leong says to speak up. Emphasize the importance of data on the bottom line of the company and point out how data isn't useful if it's scattered and disorganized. "Disorderly data isn't an IT or data science problem, it's a business problem, and data scientists are in a unique position of power to communicate this to other stakeholders in the company," says Leong.
Hiring a data scientist?
Alternatively, if you're an employer looking to hire a data scientist, you need to take a step back and evaluate the reality of the role you are hiring for. Understand the importance of a data scientist; they not only help discover insights into the business, but they can also identify and rectify problem areas.
You can expect a data scientist to fix problems to some extent, but you can't hand them a complete mess and hope they'll do their magic. "Data scientists are not magicians," says Leong. And ultimately, how successful they are at their role will also depend on how much the business supports the role. Entering into 2016, data is becoming more important than ever, and businesses that wait too long to put a plan in place will likely regret it.
"The more scattered and overlapping systems are, the less valuable the data becomes for large-scale analysis. The miscommunication often stems from the cultural chasm that can exist between business leadership and technical teams, as few businesses have adopted a common 'language' to discuss data and analysis and data management issues," says Leong.
Get big data under control before it's too late
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