Why You Don’t Need More Data Scientists
It seems that we are perpetually facing a shortage of professionals. Attend a cybersecurity conference, and one of the key themes is the lack of cybersecurity practitioners. Drop by an IT event, and at least one panelist would lament the dearth of cloud experts.
And yes, go to a data-related event, and one of the key topics would invariably be the need for more data scientists. But do we really need more data scientists?
What businesses really need
Writing in a contributed opinion piece on InformationWeek, Libby Duane Adams, the co-founder and chief advocacy officer at analytics firm Alteryx noted that while the data scientist might excel at evaluating macro trends, it is often the worker on the ground who is better able to assess the on-the-ground changes or identify hidden insights to move the needle on the bottom line.
“There are hidden insights locked away in… departmental legacy spreadsheets which are entirely invisible to formal data teams without a democratized responsibility for data work,” she wrote.
Moreover, Adams noted that the average data worker leverages more than six data sources, 40 million rows of data, and seven different outputs as part of their analytic journey. This means that data scientists simply “don’t have the time” to solve every problem they need to.
Seen from this perspective, it quickly becomes apparent that what most organizations need is not more data scientists, but a way to amplify their impact. On this front, Adams suggested that existing data scientists should divide their focus between macro insights and harnessing the collective expertise of existing analysts and business managers.
“These [data] experts should be leading on data strategies while supported and buoyed by a raft of in-department experts – workers who can provide valuable context and unique perspectives to color strategic decisions,” she wrote.
The data elephant in the room
But how can organizations adopt a genuine “whole-business” approach to data-driven insights? While the solution will vary across industries, there are two common denominators worth trying: Actively incorporate data-driven decisions from the top and find ways to better leverage in-house data expertise.
In a recent conversation with CDOTrends, Chong Yang Chan, managing director of Qlik for ASEAN suggested that organizations need to drive analytics using a top-down approach. After all, if even the CEO and top managers don’t rely on data for making business decisions, why would middle managers or the rest of the employees gravitate to data?
“To encourage the organization to make use of a dashboard, for example, the CEO should ask for supporting evidence or data points from the dashboard before going ahead with a business recommendation,” he said.
But how can businesses leverage existing in-house data expertise? This is where organizations must tear down ingrained data silos by actively pushing for data democratization. Build awareness and excitement around data through organizational-level transformation, engaging the rank-and-file and encouraging their participation.
Be sure to deliver training where necessary so that employees are not just enabled but also empowered with the necessary skills. And as written before, be sure to identify data champions from within the ranks of the organization to advance the data agenda.
Building a bench of data talent
Finally, perhaps the time has come to hire for data competency. Only then can organizations build a solid bench of talent who are versed in working with data and producing crucial data insights.
These days, we no longer ask if you know how to use “PC” or “Microsoft Office” for non-entry level jobs. But I would argue that we do ask about it, just in different ways. Have you been involved in crafting new RFQs or led the department on digital transformation initiatives, we ask.
Similarly, what we need is not Python knowledge or the ability to use Tableau – even though those would certainly be useful. Instead, we can ask questions about data-driven decisions they made in their previous role or how can data help to improve a hypothetical process?
Or as Adams put it: “Work to utilize, upskill, and enable the experts already on payroll to support your existing data science teams… then refocus and reenergize hiring strategies with an overarching focus on the priorities most important to your business.”
Paul Mah is the editor of DSAITrends. A former system administrator, programmer, and IT lecturer, he enjoys writing both code and prose. You can reach him at [email protected].
Image credit: iStockphoto/36clicks
