3 Reasons to Train in Data Science
This guest post comes from Merav Yuravlivker, CEO of Data Society, the data science training platform for professionals. Among other government and corporate clients, Data Society has trained staff at the Department of Commerce and the U.S. Armyand Data Society Solutions, which provides customized corporate data science training and consulting services. Merav Yuravlivker is scheduled to present at Socrata Connect in March 2017.
DJ Patil, the first Chief Data Scientist of the United States, says that integrating data into government “enable[s] transparency — you create efficiency, you provide security, you use it to foster innovation.” Integrating data analysis into your operations can significantly reduce costs and improve efficiency without increasing your operating budget.
After California released its budget data on vehicle spending, it reduced its fleet by 15 percent. The Center for Medicare and Medicaid Services’ “Big Data” tools saved the government over $1.5 billion through fraud prevention and identifying waste and abuse. But these reductions in cost and improvements in efficiency can only occur when data is leveraged regularly to provide insights.
With budget cuts and hiring freezes, it’s become more difficult to keep regular services up to date and to hire data analysts to work with this data. It’s overwhelming to think of the additional implementations that need to happen to make data into a useful resource. And herein lies the crux of the problem — how can government employees actually leverage the data they have with their current staff and budgets to streamline their processes and target their efforts?
The secret is in the training. Data science training.
When I speak to managers and executives about their strategic priorities, I hear the same responses: “We don’t have time to train up staff,” “We already have a data analyst who’s responsible for this,” or “Our team doesn’t have a background in math or programming.” However, in the long run, data literacy training is a short-term commitment for amazing returns on investment. Here’s how:
- While a comprehensive training may take a few days, once staff is trained in data cleaning and visualization, work that would’ve taken days can be condensed into hours by building algorithms and coding scripts to automate procedures. With this small time investment, employees can recoup weeks of time over the course of a year.
- If a data analyst makes a discovery, but no one around her can understand the results or methodology, was her time wasted? A lone data scientist has a difficult time conveying new findings. When a team understands the basics of data analytics, they’re more likely to speak the same language, process data correctly, and understand the applications that can come out of data analysis projects.
- In my work, both training and consulting in the data science space, I’ve worked with incredible data scientists and analysts who have backgrounds as journalists, economists, psychologists, and English majors. What I’ve found to be most indicative of a successful data scientist is deep industry knowledge, an ability to think creatively, and motivation to solve problems.
Industry experts are prime candidates to learn data science because they can take the new skills that they’ve learned and apply them to problems that they’ve faced in their job. Math and programming, when taught well, can be picked up by almost anyone. Industry knowledge requires years.
In short, data analytics is the antidote to tight budgets and limited staff. It empowers employees to get from point A to point B with a bullet train instead of a horse. More savvy government employees can inform better policies, increase engagement, reduce employee turnover, and the additional time saved by implementing data analysis can be put towards other projects. This is how you can make your budget work for you and lead your team to make a big impact with a small footprint.
Catch Merav Yuravlivker at Socrata Connect, where she’ll lead “Introduction to Data Science, R, and Visualization.”