Organizations that rely solely on the IT department or analytics team to fulfil queries around analytics are likely to be dissatisfied with the results, says Alan Jacobson, the Chief Data and Analytics Officer (CDAO) at data science and analytics firm Alteryx.
In an interview with CDOTrends, Jacobson cited a Harvard Business Review study which found that not a single organization that adopted such a model expressed satisfaction with the results.
“Whether you are an accountant, an engineer, a logistics professional, or a knowledge worker, you likely have data and are asked questions every day. If you had to go to IT or the analytics team for answers, it would be very difficult to get answers as fast as you need them,” explained Jacobson.
Helping citizen data scientists help themselves
The explicit knowledge of the business, whether by the accountant or the sales director, plays a vital role in helping structure the right questions. Giving these domain experts access to the right tools and capabilities to leverage data science is hence incredibly important. That’s not all, however.
“To make matters worse, until you explore the data yourself, with your knowledge of the business, you likely wouldn’t be able to structure the right questions,” he said. “We believe that the domain experts need to be able to self-serve, they are the people closest to the data and the best positioned to do the analysis.”
Indeed, Alteryx is having great success with this self-service model with 85% of its customers seeing success and an ROI in the first two weeks of using the product, says Jacobson. “We see companies that have this type of analytic maturity, where analytics are being prosecuted in every area of the business are outperforming those that are not.”
The human touch
As you may expect, the challenge is less around the technology and more on the human challenges, according to Jacobson. Of course, having human-centered technology that is easy to use and intuitive will go a long way towards guiding the novice to the “deeper end” of the analytic spectrum. Unsurprisingly, Jacobson recommends Alteryx for its ability to do this “exceedingly well”.
Yet he is also cognizant that genuine success requires a blend of training programs to raise competent citizen data scientists, a culture of analytics, and a willingness of the organization to change – by swapping out outdated processes and decision making processes for a data-centric approach.
“Blockbuster didn’t go out of business because people there were unable to perform analytics, but instead, likely suffered from the difficultly in changing their current processes fast enough. And with that, Netflix ultimately beat them with analytics and data-driven processes. This is the top challenge in most companies and for most knowledge workers, the challenge of change.”
Do not fear
A perennial fear for most is the innate – and irrational – fear of data science for being “too technical” or difficult to implement for the average organization. The result? Most organizations end up intimidated and simply never get started. You are not alone though.
“The reality is that much of the math that these algorithms use is quite easy to understand and learn. In addition, today’s knowledge workers are incredibly bright and understand the basic math these tools leverage,” said Jacobson.
While there are certain concepts to learn and master, data science is a skill that all knowledge workers can obtain, he says. Conversely, not curating these skills can pose a significant risk to both the individual and the company as they become less competitive.
This can result in them being left behind. Jacobson summed up: “We are seeing an Analytic Divide forming that is much like the Digital Divide between companies and individuals that can apply analytic process automation skills and those that are unable.”
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