Building the Data Academy (Pluralsight LIVE presentation)
•Download as PPTX, PDF•
1 like•300 views
Slides for breakout session given at 2020 Pluralsight LIVE online conference. Description: In September 2019, McKinsey called for the rise of the in-house “analytics academy” to up-skill employees’ data literacy and prepare organizations for the changes from artificial intelligence and automation. In this session, I will share tips for designing effective data analytics training programs acquired from working with leading technology and development organizations. Attendees will gain a step-by-step blueprint for building a data analytics academy, making the case to management, to identifying candidates and topics, to measuring its effectiveness.
Recommended
Recommended
More Related Content
Similar to Building the Data Academy (Pluralsight LIVE presentation)
Similar to Building the Data Academy (Pluralsight LIVE presentation) (20)
More from George Mount
More from George Mount (20)
Recently uploaded
Recently uploaded (20)
Building the Data Academy (Pluralsight LIVE presentation)
- 1. #PSLIVE20 BUILDING THE DATA ACADEMY george mount founder, stringfest analytics
- 4. ROLL CREDITS? NOT SO FAST
- 5. STEP 2: AI- POWERED ENTERPRISE STEP 1: CONNECT COMPUTER TO PRINTER
- 6. AI WE THERE YET? • • • •
- 12. QUESTIONS?
- 15. CREATING THE DATA ACADEMY • • •
- 16. DATA EDUCATION AS ROBOT-PROOFER • • • • •
- 17. DATA EDUCATION AS MODE OF INQUIRY • • • • •
- 18. DELIVERING A UNIFIED EXPERIENCE
- 20. ONLINE STUDY • •
- 22. HACKATHONS •
- 23. QUESTIONS?
- 25. IT’S YOUR WORLD… THE DATA IS LIVING IN IT • • •
- 26. CONCLUSION
- 27. • “Building a Data Literate Culture: Executive Briefing” by Jordan Morrow, on Pluralsight
- 28. • Building the data academy: Getting started worksheet
- 30. LET'S TALK
- 31. QUESTIONS?
Editor's Notes
- I’m going to use “data academy” as the concept here, of course it’s catchier than data training program so that’s one plus, but I also think it as a more systematic “analytics training programs.” but something more involved, more institutionalized. Just catchier and also more holistic. So I’ll focus on four elements here – First, how to sell it, understanding the market forces that are at work and what’s at stake. Then, designing it, focusing on what the learner needs and expects and delivering that. Next, implementing it, so we will look at some more nuts and bolts concepts in developing analytics training programs, things that are specific to data analytics itself. Finally, evaluating it, how can we measure the kind of ROI we are going to get from this program?
- Here are a couple of screenshots I have found recently about this catchy idea of a “data academy.” You will see that these are both coming from banking in the UK and yes knowledge intensive industries like banking are definitely the right candidates for this kind of stuff. But you are likely going to see this idea of a “data academy” in the news quite a lot across industries. [Animation] we can even think about Amazon’s upskilling initiative as a data academy, look here how nearly all these roles are data-intensive, some even have data right in the name! So it’s coming.
- So I mentioned this in my talk description as another one of these “market moving” events in this idea of a data academy. So you would think hey, McKinsey knows what they are talking about, let’s just pull that up everybody and I am going to stop talking! Well, not so fast. I would recommend this article, they have a lot to say, but their perspective is a little different than what I am going for. They are a little off the mark from my own experience and from looking at the market what seems to be the demand. One of the problems of over-emphasizing the AI bit of this is we start to think that all this data stuff is magic, and we start to put the cart before the horse.
- So we talk about all this AI taking over the world stuff and yes it is certainly a game changer but let’s be honest for a lot of organizations your tech capacities are stretched to be able to print something. Very easy to get into the hand-waving about how this data analytics academy is supposed to go, but we need to start where we are at. So what are some of these real-life pain points for data analytics?
- And to demonstrate why I believe this is likely, here are some figures. I like this idea of a data breadline, where you have suppliers of data intelligence and consumers of data intelligence . The issue with this model is that data needs to become baked in (pun, again) to the organization. What I am not saying is that everyone has to be the next AI engineer of their company, that’s kind of the opposite point I want to make. What I am saying is that we are still using data hand to mouth. It’s seen as a scarce good of which some people are producers and some people are consumers. You have to wait your turn to get data and you need to be qualified to get data. And by the time you get that data it’s going to be day-old (ha) and backward looking. So we see hints of these symptoms here in these results, in this survey asking about building a data culture and using data as a business asset. We just aren’t there yet.
- Ok so let’s say that we have been watching the market, we are seeing our competitors building data academies, now we need to think about how to get this going in our organization. Knowing most employers, they can’t get too excited about building an academy, that sounds like a lot of work, so they may come up with some “hacks” to upskill your data analysts, and I mean that in the creative sense. So what are some of these objections going to be.
- Let me know in the chat what you think! What are the blockers? Have you experienced them? What do you anticipate them to be?
- Here’s a common what I call “talent friction” in building a data team, where you are nowhere near to being ready for a hardcore data science team, but you think the easiest way to do it is just to hire a bunch of them. This is going to be very expensive and end in failure, and high turnover. Once your data talent sees that you are really not ready for advanced analytics, they will leave. This is why data scientists have a fairly high turnover.
- I am using broad-strokes “tech talent” here to fill in for workers who are comfortable with building automation and conducting analysis but maybe aren’t at the level to do more advanced analytics. Now this seems like an opposite strategy but it’s kind of the same, the idea being to hire away the problem. You as an organization know you need to do more with data so you hire less-experienced tech talent to do it. Because you do know that hiring a truckload of PhDs is not a good idea. The problem with this strategy is that this talent, if they are smart enough to tame your pipelines, will be smart enough to leave your organization, so again it’s high turnover. So you can hire very experienced tech talent and they’ll leave, or less experienced tech talent and they’ll leave.
- I’m calling this talent “motion” because it’s the opposite of friction! You want a way to bootstrap your organization to success and this is the way. Hiring talent is going to lead to turnover. Up-skilling the talent that you have will help you automate processes with the people that understand the business the best, which is important. Using this data academy is like bringing the bootcamp to your own organization, versus having people teach themselves and then leave. It’s a complex thing and there’s a lot to do, but poor data capacities is a big problem that you can’t delegate away, it’s really a cultural problem, and having this institutionalized bootcamp is the best way to build it.
- So now that we have set the stage, defined the problem statement and the pain points, are there any questions before we proceed to actually designing this thing?
- Ok so let’s say that we have made data-backed claims to some of these objections. And our powers-that-be, probably a CTO or someone like that, is on board.
- We are doing data analytics education right, so when it comes to assessing our learners we want something rigorous? Well yes and no, we want a blend of assessment tools. So let’s start with some more quantitative ways to do this. Self-rating: This is self evident and easy but subjective. Everyone’s rating of their Excel skills on a scale of 1-5 is going to be different. Time estimates: Not to be too crass but time is money and we are in the face of automation so if we can really get a sense of how long things are taking. Where is the problem in terms of loss of manpower? Learning assessments – This seems like it would be the most objective of them all, right? Just ask some multiple choice questions about specific topics, get a pretty cut and dry take on where people are with data analytics. However as you probably know from your own work, problems don’t come to data analysts in the form of multiple choice questions. So maybe our assessment might take on a more qualitative kind of understanding, to get inside the minds of where people are when working with data. Let’s see what some of those things might be. Experiences – really take the time to understand, where are the pain points that they are seeing? What would people consider doing with their data if they knew how? What is holding them back and what can change with training? Emotions – Data analytics is scary as is working with lots of data. There is a lot of impostor syndrome in these technical fields. Many are constantly afraid of making mistakes and just feel like they have lost a sense of control over their work. Goals – Related to the last point, what are the motivations? What do learners hope to achieve?
- For these next few slides I want to focus on more of the nuts and bolts of building this academy in terms of what media we are going to use? How are we going to reach our learners? How are we going to engage them? So let’s start with that last question there, it probably all stems from there.
- How can we make sure that this stuff actually sticks? How are we going to get buy in from the students themselves? We’ve talked before about understanding the motivations of these data analysts. A lot of it has to do with productivity, people don’t like spending all their time cleaning data as we’ve talked about, they want to automate things. So really keep this as a primary motivator, that this will boost productivity and make work more enjoyable.
- And this is something that often gets overlooked -- we may not think this matters but it does. With the right training students will really start to see data analysis as a mode of inquiry, a means of creativity. I really think that if we can get people to see the art and the creativity in what they are doing with spreadsheets that is one of the best things we can do to plant data literacy into an organization. I really believe that data literacy is like “literary” literacy in that, when done right, people start to see it as a fun way to engage with their world. I think this is a somewhat unique trait for data analytics training programs. In high school I worked in a grocery store and I remember learning to nail the right order in which to put the pieces of food on the skewer: meat, pepper, onion, meat, pepper, etc… . That knowledge didn’t have much application outside the grocery store but learning how to analyze data is not like that. You really want to approach it less as a straight recipe as a mode of inquiry.
- Now let’s look at how this information will be delivered and yes it’s easy to just use “blended learning” as a solve-all but let’s dig a bit into that could actually mean for teaching data analytics. It really does mean a virtuous cycle of theory and practice where students are able to be lifted out of their everyday work and learn something new which they can apply back to their jobs.
- We should have some in-person sessions I think that this is really great for providing a distraction free zone for people to start to understand and really begin to articulate questions and communicate their knowledge. Learning should be social! And it should to some extent be done in a distinct place.
- Then there is plenty of online study and there is a lot of educational technology on assessing data analytics skills, things like interactive coding exercises and so forth which we will talk a bit about here soon.
- Where things can really get interesting is by blending the academy’s curriculum with real-world applications. This can be great, employees can begin to look on some problems they have seen in their workflows with fresh eyes as a student and begin to propose some alternatives, there is a sense of immunity that people get when they are students that allows them to speak more candidly about their work, and of course this is definitely a cultural thing, we want to get managers on board and so forth but some of these considerations are not unique to data analytics so I am not focusing too much on them.
- Finally given some of the pain points and scenarios that come out of such sessions a hackathon can be an opportunity to really crack open some of these things. This can be a very democratizing force.
- Any questions on our tour of data analytics teaching techniques? I tried to focus on things that I thought were unique to data analytics and might be unfamiliar to you, I know there are TONS of resources out there on training and instructional design so I wanted to really tailor this to things specific to data analytics. 13 mins.
- OK great, so we have buy-in, everybody’s happy with providing the academy, enrolling, etc. Now how can we actually establish some kind of ROI for this?
- There are certainly some ways to quantify the ROI of a data academy from the revenue side in terms of offering more compelling and reliable services to customers, however I am going to focus on the expense side as I think this can be made a little more concrete and immediate. Probably the simplest victory to declare is with time savings. I have personally seen and been involved with creating solutions that used to take hours and have been reduced into fully automated processes that kick off at the click of a button. That is pretty indisputably a return on investment because ideally that is going to free up your analysts to do more productive stuff. These next kinds of benefits may come further down the road but are still pretty concrete. First, if you really start to enable these processes and this culture, you will begin to disband the data breadline and that will mean that your data teams will be happier with what they are doing, they will stick around longer. There will also be more morale, as people get to do what they were trained for. Last but not least you can begin to lower the bar for data analytics. I really believe that with a data academy like this, it’s going to be a more mutually beneficial solution to upskill than to hire the new grad who can satisfy everybody’s craving for the data breadline until the kid burns out. 2 minutes
- Before we part ways, I wanted to share some recommended resources for you, we can exchange contact information, and if you have any final questions, that’s awesome.
- This is a great introductory course on building data literacy in an organization instructed by Jordan Morrow who is a renowned expert on the topic. It will be an overarching vision of what data literacy looks like and why it matters, which complements nicely what we’ve focused on here, which is institutionalizing that knowledge into a data academy.
- Another place that has been catching on to the idea of a data academy is the public sector, in particular GovEx which is kind of a think-tank consultancy at Johns Hopkins to help cities use data and evidence for their administration. This is a nice little worksheet they have on how to get started building a data academy.
- Last but not least, this isn’t a book but I do want to let you know about the resource library I have on my site. This will give you access to a series of syllabuses with things like learning objectives, lesson descriptions and learning exercises. I also have some pedagogical tools like whitepapers, checklists and I hope to include things like slide decks and games here too. So sign up for access. I have the learning objectives and high level agenda on the first page and then go into the descriptions, time estimates and assessments on the next page.
- Thanks for coming! Feel free to contact me anytime, find me on LinkedIn, I also write frequently on this stuff so check out my website too.
- Thanks for coming! Feel free to contact me anytime, find me on LinkedIn, I also write frequently on this stuff so check out my website too. 2 mins