How to accelerate the Data Economy for the Next Workforce with Merav Yuraklivner
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Merav Yuravlivker is the Co-founder and CEO of Data Society, which builds and delivers tailored data science academies to Fortune 500 companies, government agencies, and international organizations. From assessing your current staff capacity to implementing data-driven culture, they can unleash the workforce’s potential to solve your organization’s toughest problems and prepare for the future.
Episode Links:
Merav Yuravlivker’s LinkedIn: https://www.linkedin.com/in/meravyuravlivker/
Merav Yuravlivker’s Twitter: @Merav_Yurav
Merav Yuravlivker’s Website: https://datasociety.com/
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Outline:
Here’s the timestamps for the episode:
(00:00) – Introduction
(01:34) – Data Society is a data science training and consulting firm. And we work with government agencies as well as large organizations and corporate clients to help them understand their data, to solve problems. So whether that is through customizing training programs, to their use cases, to train up their workforce, to understand data, or whether that is building customized software and algorithms to help them make predictions about trends that they are seeing, we are there to provide solutions
(03:01) – What has been truly amazing is just the way that our team has handled the transition from more in-person training to more live streaming. Since we switched to live streaming, we have a lot of students from South America who are joining us now, and it has been really wonderful to see that additional impact that has had and the different points of view that they are bringing to the table.
(05:11) – This is really going to shift the way that people think about education. can we really provide support for each other at a time when people are still trying to work out what support they want. now we are chunking it into smaller portions over longer periods of time to make sure that we are maximizing that learning and that retention.
(06:39) – Data is the only way that we are going to get through this successfully and make sure that we prevent it in the future. So it is really important for us to understand that data that we are collecting about this pandemic is truly for the benefit of the entire population. While there is a lot of politics that seems to be involved in this pandemic, it is important to understand that data is apolitical and it is important to use it in order to inform our decisions.
(11:01) – There is a lot of that misconception going around. And in fact, we did a study last year of data scientists and asked them what their biggest pain points were in their workforce, and what we found is that they had a lot of difficulty communicating insights to their managers and to their staff outside of their data science teams, because there is not a common data vocabulary.
(11:44) – Another misconception that a lot of people have is that data science is magic. You push a button and all of a sudden, you know exactly what is going on, and I am sure you could also speak to how much time data collection and data cleaning actually takes. Usually it is 80% of any data project and a lot of the data scientists that we surveyed said that there was a lot of frustration on the part of their bosses because they do not understand exactly how time-consuming it is to collect that amount of data and then to collect it accurately and make sure that it is clean and ready for processing.
(14:11) – There are some very valid concerns that have come up, people do not want to be tracked by a company without getting certain assurances about how their data will be used.
(16:22) – What if we could connect with data inventories from grocery stores and then build an app to be able to share that information with shoppers so that they can check the supplies before they go. And that way they will only have to make one trip because the other concern is that the more trips you make outside, the more exposure you have to COVID. So our aim is to reduce that, so you only have to go out one time to get the essential products that you need. And what we found out very quickly is that groceries had their hands full already. And a lot of them do not have up-to-date inventory APIs, for example, that we could tap into. So we ended up partnering with another local Washington D.C company called OurStreets, and they have built an app called OurStreets Supplies, which helps people find out what is in stock at a grocery store near them.
(20:54) – Furloughed workers are workers that are still technically employed by companies, but are not receiving paychecks. And what is really unique in this situation is previously when employees were furloughed, they were not eligible for unemployment insurance, but because many companies are anticipating this to be a short crunch as opposed to a long lasting effect, they do not want to lose some of their employees by letting them go too soon.
(22:11) – My company is working on helping prepare those individuals to re-enter the workforce with very highly prized data analytics skills. Bring that industry knowledge that they already have and have taken years to learn and then pair it with that data analytics skill set to create something completely new and help them become more agile in this environment.
(28:45) – Even though the levels of productivity might be the same, there are a lot of intangibles that are very hard to measure that encourage innovation and collaboration that really only occurs in an office space. There is going to be a big shift towards data literacy. And what I mean by that is an understanding of how to ask the right questions of data, understanding what the terminology means, what the potential means and feeling comfortable to manipulate data, visualize data to a certain extent. We are going to see some little robots that are running around on sidewalks, delivering our pizzas inside and stuff like that. So I think we are going to see that type of shift and we are going to see a lot more jobs in that type of automated, like automated behavior.
(37:36) – It is becoming more imperative now more than ever for companies to make that shift to become more data informed. If you are not starting to plan for this data economy that we are in, it will be like competing in a race when you are in a rowboat and your competitors are in motorboats. You will get there eventually, maybe, but you are probably going to spring a lot of leaks and you are definitely not going to be ahead of that pack. A lot of that has to do with the ability for an organization to be agile and to empower its workforce, to think independently, to ask the right questions and to be able to solve challenges effectively.
(43:01) – Take an inventory of where you are currently. So assess what data tools do you have? How is your data stored? How is it stored securely? And then thinking through your workforce; Who are your powerhouses? Who are your people that really are leveraging data and how well is it understood in terms of data governance and data policies?