Data Crunch

The Good Fight against Shadow IT

09.12.2019 - By Data Crunch CorporationPlay

Download our free app to listen on your phone

Download on the App StoreGet it on Google Play

Simeon Schwarz has been walking the data management tightrope for years. In this episode, he helps us see the hidden organizational and economic impacts that come from leading a data management initiative, and how to understand and overcome the inertia, fears, and status quo that hold good data management back.Simeon Schwarz: Fighting against shadow IT . . . you have to find a way to adopt it, you have to find a way to incorporate it, and you have to find a way to leverage it. You will never be able to completely eliminate it. Ginette Methot: I'm Ginette.Curtis Seare: And I'm Curtis.Ginette: And you are listening to Data Crunch,Curtis: A podcast about how applied data science, machine learning and artificial intelligence are changing the world.Ginette: This might come as a surprise to some, but......tools won’t build a data-driven culture. The right people will. Read more at mode.com/datadrivenculture. m o d e dot com slash data driven culture.Ginette: Today we speak with Simeon Schwarz. He’s been working in data management for over twenty years and owns his own consultancy, Data Management Solutions.Simeon: Being in the data management function, you're de facto seeing the life blood of how the business flows, how the uh, where the information goes, how the decision are made. Curtis: So have you been focused mainly in a, in a specific industry or have you spend a lot in your career? Simeon: I've started in telecom. I've built first cell phone carrier back in my home country. I worked in academia, in a retail, ecommerce, and then 10 years in financial services, most recently, and now I do insurance. So a lot of different fields. Curtis: So you've run the gamut. That's interesting. And now that you've done this in several different fields, do you find that the principles and your approach is basically the same or or is it different depending on the problems that you're trying to solve? Simeon: The approach is the same, and there are two parts to this. We'll talk about what's difficult in this role a little bit further in this conversation. The second part is you really need to understand the domain you're dealing with because, one, if we, if we're talking about data management in general, one of the key functions, one of the key challenges that you're going to be facing is establishing and building your credibility. Without knowledge of the domain. B insurance or financial services or manufacturing or any other field, you simply can't have intelligent conversations with your stakeholders in a way that would lead to good conclusions. So you will absolutely have to know the domain, which is large portion, of your value. Curtis: So as you've gotten into a domain that maybe you weren't as familiar with in a data role, how did you overcome this need to understand the domain better? Simeon: Let's step back and talk about what a data genuinely is right now and specifically talk about data management. You are running a data function or sometimes called data services because what used to be DBA teams or data analysts or various forms is really becoming a practice and looking at it as a practice. You have a certain set of clients, the are paying you for the services, you have certain amount of resources and you trying to optimize those resources to serve your clients better. So what are the challenges that you're going to face in any data management role? So you're in this interesting balance between moving forward very rapidly as well as not destroying what already exists, not destroying the services that are already provided. People have to breath, people have to be able to, to leave. You can't disrupt too much the services that already exist, your reports, your, you know, our auditing work your work with, you know, regulatory agencies. Anything else that the business needs to produce has to continue to happen. The people who are doing their jobs in the current way simil..

More episodes from Data Crunch