Episode 51: Shadow Analytics Teams as a Means to Corporate Data Literacy

Today’s episode is all about an attempt to upskill the team members at my organization. Jordan talks about an attempt he is currently making to train employees at his organization in the methods of analytics.

We would love to hear the ways you have attempted upskilling employees at your organization! Be sure to leave a comment below!

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Music for the show: Foolish Game / God Don’t Work On Commission by spinmeister (c) copyright 2014 Licensed under a Creative Commons Attribution (3.0) license. http://dig.ccmixter.org/files/spinmeister/46822 Ft: Snowflake

Transcripts:

Welcome to data tour the podcast about data culture at work at home. And on the go. I’m your host, Jordan Bohall.
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Welcome to data couture, I’m your host Jordan, on today’s episode, we’re going to be talking about something that it’s kind of close to the chest for me, frankly, you know, I’ve talked, and I’ve spoken a lot. And I’ve interviewed quite a few people all around the topic of digital transformation and digital literacy and upscaling employees. And so today, I want to talk about what I’m attempting at least, you know, it’s all about seeing what works, what doesn’t, in this industry, and I’ve been trying something kind of interesting, at least in my mind, about her at least around the notion of getting everyone outside of my team up to speed on some of the tool sets my teams using as well as exactly how to do analysis and how to publish that out and how to automate it and how to visualize it. And something that I call a shadow analytics team, which for any of those people listening, who I’ve been, at least some sort of it function for any number of years, of course, shadow IT teams have been a thing in the past. And hopefully I can learn from the failures around it shadow teams or shadow IT teams in my own endeavor. But nevertheless, today’s episode, I’m going to be talking quite a bit about how I’ve gone about creating my own shadow analytics team and how that’s going and what I hope for the future. So I hope you guys stay tuned.
Okay, welcome the first part of the show. So I think it would be very helpful to many of my listeners if I explained what I meant by a shadow analytics team. And so I’m going to do that, by starting with what exactly the causes were for such a team. And then I’ll get into my vision for what a shadow analytics team actually looks like, as well as what I’m hoping to achieve with this first round of so called shadow recruits. So, you know, I, I spoke about this a little bit with my students and the grad class that I’m teaching at the moment. And, you know, the real cause for this is a kind of problem with in my own team. And that is, within my own team, we are, you know, fairly small, we’re all very early, very talented and very capable at what we do. But nevertheless, now that the organization is well aware of our capabilities, at least at some level, are aware of our capabilities, we are just getting inundated with so many requests, so many, so many needs across the organization. In my mind, that’s incredibly amazing. That’s the best one could hope for when you’re coming from my position, namely, somebody who is starting an analytics team almost from scratch at a company that has not yet seen one, right. And now that we’re, you know, year, year and a half end to this endeavor, it’s wonderful. I mean, it’s it’s almost kind of proof in the pudding of what my team is able to do right is that everybody is deeply interested in getting their hands on some of the sweet sweet data and automation and insight generation that my team is able to do. So I take the inundation as almost proof positive, what we’re trying to do, what we’re attempting to do, what we’re putting into practice is starting to work, right. And for any organization, that’s, that’s huge, right, you’re getting people who want to get data that want to use data that want to take this, these bits of data that these data points and draw insights from them, so they can run their business units. That’s, that’s awesome. That’s, that’s, that’s the goal of, I don’t know, the data profession at large, right. So I’m very thankful for that. And I’m very thankful for my organization for allowing me to run my particular department and the way that I see fit, which, of course, hopefully aligns with the strategy and the overall track that my business going. But nevertheless, you know, we do face a problem, it’s a serious problem. It’s one of limited resources. And so how do we overcome that? How do we overcome this situation where we have only so many people who can really produce the sort of data products that are needed for the entire organization, and the entire organization is much, much, much, much larger than my own department anyways, right. And my team, you know, like, we’re people and it takes quite a bit of time to determine business set of requirements to start figuring out where that data is coming from, to taking that data, putting it into our warehouse, to then doing typical data mining exercises, before putting into a visual, as I said, Of visualizations, and an automated dashboard. And even beyond that, doing some predictive analysis around it, right. And then, of course, implementing that, and its own unique space. And so that all takes time. And, frankly, now that everybody kind of starting to see the power of data, I think, Hey, I don’t have enough time in the day, my team doesn’t have enough time in the day to really satisfy the requirements and the needs of everyone across the organization. And so you can see the reason why I’m looking elsewhere to, to supply my company to supply my colleagues and their various teammates with the sort of data that they need. Of course, one route of doing this is by going through third party vendors, which, you know, I’m certainly not above it, I’m not, I certainly don’t mean to say that I will not use the various and many wonderful vendors out there who can supply the sort of analysis and predictive capabilities that I need, given my limited resources. But nevertheless, I also want to make sure that my company is going to survive in the next 510 20 years. And to do that, as I’ve spoken about quite a bit, is to make sure that enough people or at least most team members in the organization are data literate, digital literate, and can carry out their own analysis. And so while third party vendors are great, it’s not going to solve this other deeper need in my organization, namely, that of upscaling everyone in the organization. And so one last final, I suppose you can call it a business case for why I should be creating a shadow analytics team is one of geography, interestingly enough, and so where I happen to reside, where my company operates, and is headquartered is in a part of the country where, you know, it’s, it’s not necessarily a tech hub, and the United States, even though I am very, I should say, motivated and very impressed by the many political leaders around the Quad Cities of I don’t know, it’s called the Quad Cities, but I guess it’s five cities Incorporated. Nevertheless, in the Quad Cities, which is on the border of Illinois and Iowa, they are very committed to creating an environment that is very open to new technology to startups to all sorts of interesting people and ideas that will at least keep the Quad Cities IE five cities afloat in the coming future, right. And so that’s very motivating, and it’s very awesome that they have the future in mind.
Nevertheless, we’re in a position where we don’t have the body count, we don’t have the skill set that, say places like San Francisco or Seattle, or LA or Chicago, or New York, or Austin or Washington DC has when it comes to analytics capabilities. And so I can’t just put a job posting out there and expect to receive 100 applications for people who have the requisite skill set to do what is necessary for my organization to succeed and our analytics initiatives and endeavors. Right. And so, these, these reasons are hanging these, these challenges are why I am focusing on creating a shadow analytics team. And so what I mean, I don’t know if you’re aware of what I mean by that. But what I mean, and I guess my thinking about what a shadow analytics team is, is really a group of people who, I guess previously were working in other roles across my organization, but nevertheless show a strong propensity towards technical sort of topics like programming, say, and sequel, or Python, or our DAX, or Java, any of these kinds of things, right, your typical analytics stack, as well as a sort of creative mindset. So they can properly visualize the data, and visualize the data in such a way that it’s an honest representation of what the data is saying, right. So they’re not leading people astray. And the final kind of, I don’t know, barrier to entry, or the final hoop have to jump through is that they have to be ethical human beings, they have to have a strong sense, they have to have a strong ethical core, so that if they see something happening, that is clearly unethical, either to our members, or customers, or it’s unethical, and some sort of business operational sense, they need to be strong willed enough to raise a red flag, they need to be able to say, hey, this isn’t right, we can’t do this, I’m not going to do this, right. And so these sorts of people are the ones I’m looking for. And my shadow analytics team and the shadow analytics team comprised is comprised of these kinds of people. And what we’re doing is training them. And some of the tool sets some of the language sets that my team is fluent in and uses every day, as well as bringing them into our development lifecycle so low, so that not only are they having access to our data warehouse, but they’re, they’re interacting with my team, they are going through our QA processes, they’re saving their own work in our, our areas that we save our work in. And they can be assured that they’re not alone, that they have people to support them if they don’t know what’s going on. So even if they might not have the requisite skills that they need, they can always ask my team or my team can lead them to the right resources to figure out how to do what they’re attempting to do. And so the shadow analytics team is, you know, it’s an analytics team, but it’s just not. It’s not located. It’s not focused in a single area. It’s not focused on my analytics team, right? It’s spread across the organization. And so this is, I suppose the reason why I’ve created an MI, Haitians have created it yet, but I’m working on creating a kind of robust shadow analytics team, as well as what it means to be on such a team. So for the next section, let’s talk about how I’m doing this.
Welcome to section two. So on the first section, we talked about the YY create a shadow analytics team. And the answer is pretty straightforward. One, there’s a lack of analytics skill sets and my particular location and the US to, there’s a deep need to make sure that everyone across the organization is upscaled, that they’re trained and analytics capabilities. And finally, my own analytics team is overwhelmed with requests, right. And so we have these three areas that generate a need for creating a shadow analytics team. And so we also talked about what one of these looks like, the sort of people that are being selected for that, at least in this trial period. But in the second piece, let’s talk about how I’m actually going about creating the shadow analytics team. So, to begin, I’ve identified a few people, not many, three to four people in the organization that have consistently shown a propensity towards more technical abilities. But that’s not necessarily the only thing that matters to me, right? One of the big factors is finding people who are eager to learn who are eager to try out new things, it doesn’t matter if it fails, right? It matters that they are there, they’re ready to learn. They’re, you know, they, they want to be a part of something in this organization that clearly will drive all of our initiatives forward across the entire credit union. And so I found, handful of people, like I said, I found what is it for people. And what we’re doing is going through a series of one to two hour training sessions, and in these training sessions, I should back up, I’ve given them all quite a bit of online resources that are through me or Coursera, or, you know, the normal online learning platforms to at least get acclimated towards the tool sets that we’re using. So I’ve had them do that. And, frankly, you know, they’re going through at their own pace, because of course, they have other job duties that and like, this isn’t their job, their job isn’t to be on the shadow analytics team, their job is to do other highly important functions across the organization. And so nevertheless, at their own pace, they’re managing to get through these training sessions, which, you know, to be honest, at times can be quite boring and quite quietly group. Nevertheless, they’re doing that. But on top of it, we are meeting with them. every couple of weeks, I think at this pace, and every couple of weeks, we come together. And we started with a kind of introduction to what it means to do analytics. And we did that. And I’m not going to give the whole piece away. But it was an introduction to what analytics is how to ask the right questions to get business requirements, the sort of tool sets were using, as well as a high level outline of the the steps that might take my team takes and the development cycle. Now after that, we’ve gone into a session where we did a Hello World introduction to how to use our automation and visualization tool sets as well as how to manipulate the data in such a way that they too, could create a similar hello world dashboard. What I find funny is some of the people on my own team, I thought that hello world referred to a very specific data set. Which made me laugh because of course, as many of you will know, hello world is just a trope in any sort of programming language to, you know, print hello world, it’s just, you know, the bare bare bare bare bones of what it means to learn a new programming language right? After that was funny. Nevertheless, we did a Hello World dashboard, automated dashboard visualization piece and learned how to do certain visualizations how to avoid how they’re pitfalls and visualizing data, that kind of thing. Next, the plan. And we haven’t gotten to this part yet, but I saw coming up this week, maybe it’s next week, I don’t know. Nevertheless, it’s deep dive into our data warehouse, the architecture of said warehouse as well as how to extract the kind of data that you need, at least the shadow analytics team needs in order to solve the puzzle problems that they’re facing that they’re being requested of, by their own business leaders. Right. And so after that, then comes more advanced topics and automation and visualization techniques, as well as perhaps some statistical methods for
forecasting bits of data and very, you know, so I’m, we’re doing kind of a crash course and what it means to do analytics. Now, this is just my trial group, my my plan, and I’m very excited about this is to expand the so called shadow analytics team to where I have, you know, 20 3040, how maybe 100 people across the organization who all have access to the tool sets who all have access to our data repository or data warehouse, and can you know, access the data and learn things about what they’re attempting to do outside of their normal job functions and so on. Over time, I’m hoping to expand the shadow analytics team into something that it’s just a normal part of everybody’s job, they, you know, they open up the the visualization it and they sorry, integrated developers environment for analytics virtualization, and they, you know, the access or warehouse given the specific schema that they need. And they answer a question, they don’t have to submit a request, they just have access to that data, they have access to their own understanding of how to manipulate the data, how to statistically analyze the data, so that they can then make better decisions on their own. And, you know, I don’t have a timeline for spending from four people to the entire organization. But I’m hoping that as this first group, becomes proficient, that almost grows organically, and instead of having to go through these one to two are training courses with my own team, you know, there people are helping each other out. And so so it just sort of multiplies and exponentially grows across the organization so that this isn’t something that’s a black box technique, it’s just something that is a part of what it means to be in my credit union, and I’m so excited about it. Nevertheless, you know, you’re getting kind of to the end here. And I would love to hear how you have tried to do this, how you have tried to get people on board because you know, like, again, this is just a test for me, I I don’t know where it’s going. I don’t know how well it’s going to work. But if you’ve if you’ve tried something similar if you’ve experienced something similar in your own workplace, let me know. And hopefully your knowledge your experience will allow me to avoid the pitfalls that you experienced. Nevertheless, until next time, I will talk to you soon. That’s it for the show. Thank you for listening. If you liked what you’ve heard,
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comment or like down below. Stay up to date on everything data couture, be sure to follow us on Twitter at data couture pod, consider becoming a patron@patreon.com forward slash data couture music for the podcast. It’s called foolish game. God don’t work on commission by the artist spin Meister used under a Creative Commons Attribution 3.0 license,
writing, editing and production of the podcast is by
your host, Jordan Bohall. We’re
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