Repost from Forbes article by John Furrier
Evolving quickly alongside today’s business demands, the data scientist profession is faced with the challenges of interdisciplinary rarity. And the job pool is struggling to keep up. Just as we’re beginning to put Big Data in its place, its human counterpart seeks definition within the modern enterprise.
Can the data scientist keep up with today’s growing data demands? The specialized and expensive skills required for this coveted profession are hard to find, making the data scientist a difficult role to scale. So even as the data scientist is hailed as the hero to optimize business as we know it, automation must assist this practice in efficiency.
How do you multiply the role of the data scientist without the workforce to support it? As organizations are asked to do more with the same, they’ll rely increasingly on data to root out efficiency gaps and provide opportunities for workflow automation. And that goes for the data scientist and his workload as well. Automation will empower the data scientist to empower everyone else at the company, and they’ll need the help of software. Merely throwing more data scientists at the problem of data management won’t solve it.
“Adding more humans like expensive data scientists is not the solution – software is the answer. More data, more people, more complicated questions. You can’t just make up data scientists,” insists Bruno Aziza, CMO of Alpine Data Labs.
Learning to automate through collaboration
Software automation can’t improve without reorganizing a company around its data. Consider it organizational self-reflection, learning from every interaction humans have with work-related machines. Collaborative, social software is at the heart of this interaction. Software must find innovative ways to interface data with employees, visualization being the most promising form of data democratization.
Every department within a company today is itching to apply data-driven systems to their workloads. So it’s in the interest of service providers to make data more broadly accessible and productive. That means data must be on-demand, and visually interactive. Most importantly, data analytics must provide insight that decision-makers can take to the bank.
Fortunately for the data scientist, data isn’t bankable without the human touch. To fully address the growing matter of automation in analytics software, the Big Data market must move beyond basic Business Intelligence to more comprehensive solutions. So while we have a growing number of tools to store, manage and run data, its interpretation still requires a skill that can’t be automated – human finesse.
BI is just the tool – It’s time for advanced analytics
“I think the trends are expanding beyond BI apps to more advanced apps, to drill down and answer the questions asked by the BI engines,” explains Alpine Data Labs Chief Product Officer, Steven Hillion. “I see BI tools as question generators. BI tools allow you to understand the business and ask the right questions. ‘Why are we losing customers? What products should we recommend?’
“Advanced analytics are how you answer, and right now advanced analytics has to go down to the basement,” Hillion goes on. “One trend is getting it out of the basement and have the whole company, collaboratively, answer the questions. Amazon’s done well with this. Jeff Bezos has created a culture around this data-driven concept. It’s a cliche but it shouldn’t be.”
The role of the data scientist plays an important part in setting the tone for collaboration within an organization, as these multidisciplinary problem solvers will need to communicate clearly with each other, as well as every other department. For one MBA student of University of Tennessee’s burgeoning Business Analytics program, the workplace is the perfect environment for data scientists to learn from each other.
Haley Hubbard already sports impressive credentials, including a Masters in accounting. But it wasn’t enough to keep up with the increasing types of data crossing her desk at PricewaterhouseCoopers , so she went back to school. Hubbard now interns at Pershing Yoakley & Associates, where her team is comprised of specialists in computer science, statistics and more.
“Now I’m working in a startup environment for bosses not historically part of the consulting industry – [they’re] coming from the lab. They’re very open to innovation,” Hubbard explains. “They’re interested in developing our skills, and in hearing our ideas. I like it because it gives you an opportunity to wear a lot of hats and stretch your skill set. The people I work with are really bright, and I feel valuable because of the business skill I bring.
“It starts in class,” Hubbard recalls. “We have a lot of group projects. When you assemble your group, you don’t look for someone like you. You want multidisciplinary teams.”
The rise of the MBA
As mentioned above, everyone within an organization benefits from the work of the data scientist, but not until this data is better democratized. For the MBA in particular, this is an especially true statement.
“A mentor of mine described it as the rise of the MBA,” says Hillion. “If Alpine succeeds and makes data accessible to a broader set of people, there’s more possibilities for MBAs to better understand business from a deeper level.”
So it’s no surprise that Hubbard is layering business know-how onto her data-driven background. The University of Tennessee understands the growing importance of businesses’ intersection with data, taking relatively early steps to prepare its students for the real world of analytics.
“I think the primary driver is that companies have realized that analytics has to be an integral part of their competitive strategy,” says Ken Gilbert, the department head for University of Tennessee’s Business Analytics program, explaining the explosion of applications for the two year-old initiative.
“The obvious examples are Google GOOG +0.65% and Facebook. They built their business around the use of data,” Gilbert goes on. “But there’s a lot of traditional organizations that use analytics. Kroger for example, has increased store sales thanks to analytics from customer loyalty programs. Ford attributes analytics to playing an important role in their turnaround, aiding in decisions for vehicle development and marketing. Companies such as Amazon and Dell have supply chains driven by analytics, and they’re able to do things for customers that their competitors can’t do.”