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Why hiring a Data Scientist isn’t enough

Data Scientists are captivating the corporate world. What Harvard Business Review once called the “sexiest job of the 21st century,” has yielded billions of dollars invested to extract talent and analysis from data. Data is like a black box. Information goes in, and, apparently, out comes a wondrous solution.

Data Scientists can seemingly solve all the problems in the supply chain: As one New York startup explained in their job description, a good Data Scientist is expected to “design, build, and maintain analyses and performance visualizations” for both internal and external projects, “democratizing data.” One-part engineering, one-part design, the math and statistics pave the way for deeper understanding. With a deep-seated knowledge of algorithms processing, the Data Science sorts through mass quantities of data. So they are suddenly the glue that mobilizes an organization.

Thanks to the battle cry sounded for one of these superheroes, we know they’re desirable–without question. But for all their cache and high salaries…will hiring one actually solve your problems?

Spoiler alert: Not necessarily. The skills of one person are not enough–you have to rescale your whole organization to reap real benefits.

Don’t just leave it all to the techies! A data scientist may not be necessary. Bringing in an external vendor can solve the data mining challenges facing an action-driven organization. If and when you do hire a data scientist, make sure you’ve identified the right problem for them to be working on. Only then can they provide provide value to the company. Otherwise, hiring them would be pointless.

Dream data science teams are not about hiring unicorns. They’re made up of different skill-sets: Data science, engineering, and visualization. As David Robinson from Datacamp said at SXSWEdu, “Data Science is a silver bullet if you’re trying to kill a werewolf by moonlight–but otherwise it’s not.”

The Data Scientist role seems so alluring because it strikes the perfect, elusive balance between hard and soft skills: it’s not enough to be a team player; SQL and Python programming can put you ahead of the game. It’s not enough to be a stellar statistician; if you can’t create productive partnerships, then the work is done in a black hole.

In other words, a Data Scientist won’t solve all your problems…it’s more complicated than that.

First of all, great data alone has the capacity to outpace a data scientist. Besides, this data might be incomplete or inaccurate, or unusable due to security and privacy concerns. So, what are some ways to include data science in your directives, beyond the unicorn?

A strong organization should:

  • Communicate
    Effectively identify the issues at hand in your organization. Communicate in common language, speak to stakeholders and determine their pain points.
  • Define the problem

Build the right capabilities together… otherwise, there’s no point in having a data scientist at all.

  • Collect the right data
    Identify the attributes you need to strengthen the chain of efficiency.
  • Create the right algorithm
    Analyze the numbers to present feasible strategies, which can make the difference between mining meaningless data and driving forward business KPIs.
  • Cultivate a decision-driven culture, informed by data.
    Only once your organization is streamlined can you synthesize the data correctly. Then you can collaborate to achieve number-driven goals

Leadership is critical to ensuring your organization has the right data at the right time. Isn’t just about data science or programming, it’s about the right culture and skills to hone in one what’s not working yet. Those skills are immeasurable when tackling the great wave of data, and solving problems at hand.

Adaptability is just as important as high-level programming, and we can all take on these critical skills. There is no one person or position that is going to float and organization to the top. Workflow varies from case to case, and we must do it together.

Whatever your industry holds, investing in data requires time and attention. Focus on the questions you want to solve, and seek out the right people with the right blend of skills. Data Scientists are so desirable because they have the capacity to adapt. Now it’s time adapt as an organization.

 

Niall McKinney

Niall McKinney

Niall has been passionate about high-quality professional development throughout his career as a leading leader in blue-chip companies, and a VC-backed founder who built a multi-national business to successful sale and exit.

Posted May 1, 2019