In the Government of Canada, lack of data isn’t a problem. In fact, it’s the opposite: there’s often too much information available from too many different sources to make a clear decision.
In this episode, you'll learn:
- the definition of data and data analysis;
- the 2 types of data;
- the 2 ways data is organized;
- 5 steps to analyze data to answer a business question; and
- the role of a Chief Data Officer.
In 2018, the Government of Canada published A Data Strategy Roadmap for the Federal Public Service as a guide to help departments think about how they could unlock the value of their data and move towards a data-driven business model.
The volume of data that governments, businesses and Canadians produce is growing exponentially, animated by digital technologies. Organizations are changing their business models, building new expertise and devising new ways of managing and unlocking the value of their data. Governments need to evolve rapidly to keep up. - A Data Strategy Roadmap for the Federal Public Service
We know departments are grappling with similar issues in their attempt to drive value from their data; getting to the end state is daunting. Part of that evolution has been the introduction of the Chief Data Officer (CDO) role.
In 2016, Sandy Kyriakatos was the first-ever CDO in the federal public service. She told us all about it. Listen to the full podcast below (<30 minutes) to learn what keeps a CDO up at night, the difference between a Chief Data Officer and Chief Digital Officer, and how Sandy learned to speak “governese.” Spoiler alert—Sandy’s number one piece of advice for every public servant is:
Figure out how data can help you do your job better.
What does a data-driven organization look like?
Netflix is a great example of a data-driven organization. The company harnesses data to influence decisions on the consumer side (what show should I watch next?) and the business side (what shows should we invest in?).
Now imagine that in the government context—data influencing Canadians’ decisions (what benefits should I apply for?) as well as internal strategy decisions (what social programs should we invest in?). To be data-driven, an organization needs high-quality data, broad access, data literacy, and appropriate decision-making processes.
What is data?
Use of the word data has increased tremendously over the last decade. People, businesses, and governments use it without knowing exactly what it is.
Data is facts that can be analyzed to generate insights.
Two types of data
For some, data is simply what comes with your monthly cell phone plan. However, data comes in many forms such as text, numbers, audio, video, image, code, software and instrument. A typical way to define data is to make a distinction between qualitative and quantitative data.
Qualitative data includes descriptive statements that can be made about a subject based on observations, interviews, or evaluations. Movie reviews and real estate listings are two examples. Photographs, videos, and sound recordings can also be considered qualitative data.
Quantitative data is numerical information acquired through measuring or counting. It usually refers to a certain quantity, amount, or range. The number of individuals attending a baseball game, time spent in traffic, and your height are all quantitative data. Most departments have quantitative data about the number of employees they have, or the number of clients they’ve served this month.
While qualitative data can be converted into quantitative data, quantitative data can’t be converted into qualitative data.
Two ways of organizing data
Structured data is highly organized and easily analyzed. When you think of structured data, think of things that would sit nicely in a spreadsheet. Examples include:
- phone numbers
- postal codes
- client names
- benefit types
Unstructured data is just the opposite. It’s raw, unorganized information that may have its own internal structure, but does not conform neatly into a spreadsheet or database. It is usually text heavy and more subjective, such as responses to open questions, which are potentially all different and difficult to categorize.
Examples of unstructured data include:
- audio and video files and images
- Word documents and PowerPoint presentations
- customer reviews
- text messages
- client notes and chat logs
- call centre recordings
These examples are largely human-generated, but machine-generated data can also be unstructured. Satellite images, scientific data, surveillance images and video, and weather sensor data are all good examples.
Data analysis is the process of evaluating data using analytical and statistical tools to discover useful information. There are numerous ways of doing this including data mining, text analytics, business intelligence, and data visualization.
Now that you know what data is, how can you use it to make better decisions?
In the Government of Canada today, lack of data isn’t a problem. With so much data to sort through, you need to be able to:
- know it is the right data to answer your questions
- analyze the data to draw out actionable insights
- draw accurate conclusions from that data
Isn’t data just for data scientists?
Hard no! Data is everywhere. Everyone needs to use it, everyone needs to get at it, everyone needs to learn. So one thing is the enormity of trying to enable everyone in an organization with data. Everyone needs the right kind of data literacy for their job in order for us to really get the value out of data. - Sandy Kyriakatos
Here are 5 steps to help you use data to make better decisions.
Step 1: Define your questions
When analyzing your organizational or business data, begin with the right questions; they should be measurable, clear, and concise.
Start with a clearly defined problem: for example, a government call centre is experiencing high call volumes, and clients are frustrated. A good question to ask would be, can the call centre reorganize the workflow to decrease wait times?
Step 2: Measure
a) Decide what to measure
Using the example above, consider the kind of information you would need: call volumes, types of client inquiries, wait times, number of staff, and percentage of time spent on necessary business functions. In answering the question, you will likely need to answer many sub-questions. Are staff currently underutilized or overutilized? What process improvements would help? Incorporate stakeholders concerns; for example, if staff are reorganized, how would the call centre respond to surges in demand?
b) Decide how to measure
Considering how to measure your results is important because the method will either support your analysis or discredit it later on. For example, if you collect annual data but need daily data to validate your hypothesis, the data won’t be helpful. Key questions to ask for this step include:
- what is your time frame? (daily, monthly, quarterly)
- what is your unit of measurement? (call volumes, wait times)
- what factors should be included? (types of client inquiries)
Step 3: Collect
- Determine what information could be drawn from existing databases or sources on hand; collect this first.
- Document the data sources (for example, Google Analytics, census data) when presenting the information to others.
- Protect the data you collect and store it in the appropriate place (for example, GCdocs).
You’re not alone! More departments are onboarding Chief Data Officers to help with this work on a grander scale:
The Chief Data Office is the part of the organization responsible for managing data as well as getting value out of the organization’s data assets—whether it’s data management, data governance or data analytics, raising data literacy [...] is about taking care of our data so that we can do the best we can for our clients - Sandy Kyriakatos
Step 4: Analyze
After you’ve collected the right data to answer your question from Step 1, it’s time to look at it closely.
You don't know what data's gonna tell you until you get your hands dirty with it. - Sandy Kyriakatos
Begin by manipulating your data in a number of different ways, such as plotting it out and finding correlations, or create a pivot table in Excel. A pivot table lets you sort and filter data by different variables as well as calculate the mean, maximum, minimum, and standard deviation.
As you manipulate the data, you may find you have the exact data you need; but more likely you might need to revise your original question, or collect more data. Either way, as you explore, continue to go back to your original business question to keep your analysis on track.
During this step, data analysis tools and software are extremely helpful. SAS is one paid option that’s already in use in some departments, while R and Python are free for advanced statistical data analysis. Kaggle is free and doesn’t require downloading.
A lot can be done by leveraging Excel. If you need a review or a primer on all the functions of Excel, check out this YouTube video: Microsoft Excel 2019 Tutorial for Beginners – How to Use Excel. If you require advanced tools, and IT rejects your ask, probe to find out why. A helpful statement to use, courtesy of Abe Greenspoon, is
Show me the rule.
Step 5: Interpret the results
Without context, data is just numbers and letters. The same data can mean different things depending on the business context.
As you finalize your analysis, ask yourself these questions:
- Does the data answer your original question? How?
- Are there any limitations on your conclusions, any angles you haven’t considered?
Present your findings to the relevant people in your organization. If your interpretation of the data holds up after additional questions and considerations, you have likely come up with a viable solution.
Back in the day, advertising executives would pitch clients a concept based on personal experience, intuition, gut instincts, and a little market research. We challenge you to find an agency that works that way today. Feelings aren’t enough; data analysis enables thoughtful decision making.
We have to use data. We have to get value out of it. We have to do the best we can do with it for our citizens with the data we have, but we have to protect people’s privacy. We have to make sure we do it in a way that doesn’t risk people’s personal information. - Sandy Kyriakatos
Rebecca: Alright welcome everyone to the Busrides podcast, I’m your host Rebecca Nava. With me today is Sandy Kyriakatos, she is the very first ever Chief Data Officer in the Government of Canada and she has over 20 years of experience in data analytics. Sandy served as Chief Data Officer for Employment and Social Development Canada for three years and is currently Chief Data Officer at Canada Border Services Agency, CBSA. We’re very excited to talk about the Chief Data Officer role and so, Sandy, welcome to the show!
Sandy: Thank you for having me, Rebecca.
Rebecca: It’s our pleasure. So Sandy, we’re gonna start you off with an easy one, what is a Chief Data Officer or CDO, anyway?
Sandy: Thank you for the softball first question. Chief Data Officer, I guess the stock answer for that or even for Chief Data Office is, I guess, is the part of the organization responsible for managing data as well as getting value out of the organization’s data assets. There’s a bunch of mechanics I can talk about underneath, whether it’s data management, data governance or data analytics, raising data literacy, etcetera. Really, at the end of the day, a Chief Data Officer, much like a lead in HR is about making sure that just like we take care of people so they can help us achieve our mandate, Chief Data Officer’s about taking care of our data so that we can do the best we can for our citizens and our clients.
Rebecca: Absolutely, I have a follow up on that which is, the word data can be a little bit confusing...for me.
Sandy: Me too!
Rebecca: I guess for me, I feel data can be almost anything. Is an email data, are we talking Excel, can you give me an example, when you think about data, what comes to mind?
Sandy: I’m gonna give you two answers. So you’re right, data is everything, data is all of those records in databases anytime you fill out a form an organization captures it, that’s data. Anytime you get paid a benefits cheque or your actual pay, that’s data in a system but data can be in different forms, so what I just mentioned is what we refer to as structured data, you’re gonna see rows and columns of things in a database, yeah it could be in Excel, typically we talk about databases when we’re doing that but documents are data, pictures are data, video is data, this whole transformation, the buzz we hear outside about big data is around the fact that we can take anything and turn it into structured data that we can then do analysis on.
Rebecca: That’s fascinating, I’m happy you mentioned structured data because December is our data month at Busrides and we’re writing about structured and unstructured data.
Sandy: I think that’s why I’m here.
Rebecca: That’s perfect, thank you.
Rebecca: Sandy, a number of C suite jobs have entered the GC scene in the last couple years. Can you tell us the difference between Chief Data Officer, Chief Digital Officer and Chief Information Officer?
Sandy: That is an excellent question. Ok, I’ll try to keep it simple. So we have people that an HR lead takes care of, Chief Information Officer is about managing our technology assets, systems, computers, phones, whatever it is, Chief Data Officer is about managing and taking care of our data assets which is quite different, Chief Data Officer is responsible for data, Chief Digital Officer is a little different. I’m being a bit repetitive but you know, you’ve got people with HR, you’ve got technology with Chief Information Officer, you’ve got data with the Chief Data Officer and you’ve got money with the Chief Financial Officer so this is all the raw ingredients we have to put together to do what we have to do. Chief Digital Officer is more about how, it’s more about how we give our clients or our citizens, how we interact with them and how we serve them in this new world of the Internet so it’s not so much a thing we’re managing like data, people, tech and money, it’s more our citizens go about their daily lives, they don’t necessarily distinguish between the physical and the digital. They’re not saying “oh, now I’m going to be digital and buy a book and now, I’m going to be physical and walk into a store”. A Chief Digital Officer is trying to figure out how do we bundle all those interactions in this age of Internet.
Rebecca: Okay, so it’s kind of the packaging.
Sandy: It’s the how we interact, not the what we use to deliver our services.
Rebecca: Okay, thank you. As a visual person, I feel like I’ve created a picture in my mind.
Sandy: There’s a lot of confusion between CDOs and you know, the word “is it a CDO, is it data, is it the same thing?” It’s different. Data is an asset, we need to store it, we need to capture it, we need to analyze it, we need to put it in the hands of people to do things, but digital is about making sure we are current with how people live their lives today and are able to give our services online.
Rebecca: That really resonates with me, thank you. Now that we know the difference, what keeps a Chief Data Officer up at night?
Sandy: Right, other than the massive expectations? The astronomical expectations? So that’s one, I guess I’ll split that off in a couple ways. So, you kind of alluded to it before, data is everywhere, everybody needs to use it, everybody needs to get at it, everybody needs to learn, so one thing is the enormity of trying to enable everyone in an organization with data. I think the other key thing, and yeah, it’s a stressor for sure, is the duality of the role. We tend to, and we generally have people that kind of take a side it’s like protect data at all costs and there are other people who are like use it, get the most value out of it at all costs but Chief Data Officer, Chief Data Office has to do both at the same time like we have to use data, we have to get value out of it, we have to do the best we can do with it for our citizens with the data we have but we have to protect people’s privacy, we have to make sure we do it in a way that doesn’t risk people’s personal information so it’s kind of, trying to do both things at the same time is the challenge.
Rebecca: Fair enough. Can you talk about those first few months as the Government of Canada’s first ever CDO?
Sandy: That’s such a good question, I feel like I’m reliving that right now. I guess the, well, this isn’t necessarily data-related but the first, sort of, culture shock was based around the fact that I came from the private sector so I’d never worked in government before. So I joke about how, you know, I didn’t speak “governese”, right, everything was confusing to me, nothing made sense, you know, it was as simple as someone talking about writing a BN, I didn’t even know what a BN was, what’s a briefing note? Why do we do those, don’t we have email? So my first few months were, you know, acclimatizing to and adjusting to a culture that was different, and not different in a bad way, just different than what I was used to. I guess the other thing I can say, other than the whole getting used to culture change as I mentioned was getting used to the difference between how private sector was using data and how government was or wasn’t using data and how far apart the two were, it really surprised me how much equivalency was placed on what the private sector was doing, let me give you an example. A lot of news around things going on in the private sector that shouldn’t go on around data really recognizing that nothing even remotely like that was happening in government but we really were reacting to it very strongly and having a really strong negative response to some of those activities going on in the news and you know, we weren’t even close to doing that seemed to hamper us unnecessarily from, and keep us unnecessarily from, serving as best we could with the data we have. So, there’s a real disconnect in terms of, we say the same things, but we do really different things, so there’s a lot of catching up to do in terms of that.
Rebecca: Yeah, and I think we're going to get into that a little bit later because I have a question for you about kind of what's holding us back as a government. So thank you. Now, I don't want to just talk business with you, Sandy, because you're a wonderful person. People just so I want to. I'd love to know, what is your favourite cuisine?
Sandy: OK, so easy. I love Italian food. I am vegetarian, so it's a little tricky sometimes. But yes, a good gnocchi with gorgonzola cream sauce will be my last supper if that ever happened. One day I'll be vegan, but not yet. That's right. I saw on your Twitter. Yeah. I’m working on it, I’m working on it, I just can't give up that cream sauce.
Rebecca: Aspiring vegan. Thank you. So back to business. Where do Chief Data Officers sit in the government hierarchy?
Sandy: So very much like the private sector, the CEO is is not. It's not standardized. There's there's a lot of variance in where it sits. Often government or private sector, the CEO may report directly to the C suite or to the Deputy level, I can say here. But often it will, it will sit and often most successfully, will sit in a in a strategy branch or strategy department we would say in private sector, the key point there being that your data has to be really based on your business objectives, really has to be tied into your corporate strategy. In fairness, it has sat in many different places in private sector. Sometimes it sits in the CFO world, sometimes it sits in the marketing world. But there's a lot of consistency around sitting sitting as close to the strategy as possible.
Rebecca: OK, good to know. How many Chief Data Officers do we have? And how do you keep in touch?
Sandy: That's a good question. I keep losing count because every month or two there's a new one. So we have a Chief Data Officer Counsel. We meet, maybe once a month, once every six weeks or so. So that's so. So we see each other a lot. We email each other all the time. We are we are few and need each other. I think last count there were eight that had the title CDO. But there are there are more I'd say there there's probably about 12 who are actually doing the function, but I think eight actual CDOs in the GC. So that's pretty good. And the numbers are increasing exponentially. Yes, I feel much better that I'm not all by myself anymore.
Rebecca: That's fantastic. Throughout. Throughout. What would you say has surprised you most?
Sandy: Oh, that's a good question. So surprised me the most. I guess I'd have to answer that as someone who came into the public service, I can say I've been floored by the people. So, this is gonna sound terrible but, you know, outside of government, there might sometimes be a reputation around a public servant, a negative reputation.
And I find myself now sort of running around and like, defending the public servant, because really, really blown away by the, the depth of experience, the skills that, that we have. I joke that there's all these people that want to do good and change the world, it's really. It's it's you know, when I joined the public service, in fact, when I joined EDC initially, I wasn't I wasn't a public service. I came in on Interchange and and I shifted over because, quite frankly, that the kinds of people that we have here are just amazing. So it sounds a little motherhood and apple pie-ish. But-but it is what shocked me the most.
Rebecca: Now that warms my heart and I find myself doing the same in terms of defending. All right.
Sandy: I'm always like anytime anyone says something like, I'm serious. People never work so hard as they do in the public service. Yeah. So shush, already.
Rebecca: Right. Moving back over to talking about data itself. What would you say is the biggest myth?
Sandy: I guess I'm going to answer again a little bit high level.
But I think the big myth is, you know, there's this there's this hype around data. And of course, I've been working in data since the 90s, and I don’t want to date myself. So I you know, I drank the Kool-Aid a long time ago. But, you know, data on its own isn't going to do anything. I still say people are the greatest asset and data is number two. So, so just having a lot of data, having tools, this this this thing called big data now is-is really transformative, but only when you apply that human intelligence to it until someone is using it and analyzing it and understanding it. It doesn't, it doesn't really bring the value as well on its own. It could, it could tell you lies, it could lead you down the wrong path.
So, so the biggest myth is somehow that this automation and data is going to, is going to sort of replace human wisdom or intelligence. And,and I think that's, that's very false. I do think it's the most underutilized tool that people have to excel on their own. But, yeah, I think it's just going to sit there unless we do something with it.
Rebecca: Absolutely. So we have to harness it.
Sandy: It's a tool. We have to use it.
Rebecca: Yeah. That's fascinating. Now, I wanted to go back to something you said earlier. You talked a little bit about the differences between private sector and public sector and, what is holding us back as a Government of Canada? What's preventing us from using our own data?
Sandy: Oh, boy, we could do a podcast just on this one depending on my mood, we could get pretty provocative on this question.
I guess I'm going to I'm going to say risk and process. So nothing is without risk. Nothing we do in life is without risk. But we have some some barriers, I think, in understanding how to take a reasonable risk. So that's really holding us back. We seem to be binary. There's got to be no risk or it's the end of the world. So we really need to get into that grey zone and be able to understand what is reasonable and be able to, to take that on in order to really leverage data, because there's no scenario where you can guarantee that nothing will ever go wrong. But with the proper training and environments and controls in place, the risk is really quite low. So we need to kind of get over that in order to really use the data. And this is risk not just in data, but risk in doing things in a different way. We're just having this conversation real recently about using open source. I mean, this is a powerhouse in in getting data scientists specifically to sort of unlock the value of data really in a timely fashion. And I don't mean rush and go fast, but I mean in a reasonable way. So we're very risk averse with with using data. We're very risk averse with using new tools like open source. And that will be the success factor if we can get over that. Now, the, the sort of other side of that coin is that of a similar discussion is process.
Part of reducing risk is doing things in small steps. If something is unclear and then you take a small step, you figure out, you see and you learn so you don't have a huge disaster when you do a little thing. But our processes are all designed to do big, big, huge things. So until we, we really take it as a specific task that we're not sort of, at the side of people's desks or not sort of ask people to sort of innovate in their spare time. But until we actually create nimble processes so that we can do small steps, we're not going to really unlock the value of data. You don't know what data is going to tell you until you get your hands dirty with it. If you need to wait years and years and years to try an idea out, you sort of, the ship’s gone, gone, the train’s left the building. So, being able to take risks and being able to design appropriate processes for quick hits, for iteration are the keys to kind of getting us out of our, kind of the quicksand we’re in a bit now with our data.
Rebecca: You know, it's really interesting because the Canada School of Public Service Digital Academy tries to run on an agile methodology, which lends itself a little bit more to exactly prototyping now.
Sandy: But if it takes you two years to get people or hardware or money. Goodbye agile.
Rebecca: Yeah, exactly. Yeah. So there's some constraints, larger constraints to deal with. So that's a great point. You mentioned before that, I want to talk about data literacy. How important is data literacy?
Sandy: I can say it's probably the most important thing. So, you know, I'm not sucking up to the Digital Academy here, I know you’re interviewing me, and I'm not saying it for that reason.
But I mentioned before that the thing that one of the things that keeps me up at night is these massive expectations of how we're going to you know, now we have Chief Data Office and now we're going to solve all the world's problems with data. That only happens if all of the people can use the data. So if we just have specific teams or a small group of data scientists or an innovative group over in the corner using data, we'll have some benefits. But we won't have enterprise benefits. We won't be transformational. We won't have critical mass of, of, of value until pretty much everybody uses data. And it doesn't mean the same thing for all people. But until everybody can use tools and data to do their specific job better, we won't, you know, get over that hump.
So that's, that's why I say from a from a number one perspective, you know, whether it's the executives in an organization, whether it's someone on the phone dealing with a client or a data scientist who's using advanced modelling techniques or machine learning. Everyone needs the right kind of data literacy for their job in order for us to really get the value out of data.
Rebecca: So data literacy, not just being for a few, but being for all.
Sandy: Everybody. And you know, it's funny, we've had these conversations before and I've had people before say, you really mean everybody, right? I mean, no, I really mean everybody. Right. Does everybody know how to use technology? Does anybody do their job today without knowing how to use a laptop, without knowing how to log into a system, without it, just it would be insane to think that you could have people working in an organization that say, oh, sorry, I don't, I don't do tech. Yeah, it just doesn't make sense. So we need to get there with data.
Rebecca: Absolutely. At the open government for just a while ago, they were talking about digital being akin to oxygen. You know, it's all around us. And so, it's ubiquitous. And we all we're all working with this.
Sandy: Exactly. It's like the other example I use is would you expect only people in a comms branch to write? No, of course not. Everybody writes. Comms has a specialized skill set and a very specific need to have really great writers, but everybody writes. The same thing with data. Yes, there are Chief Data Offices and there is going to be specialized teams of data scientists to do some pretty specific things, but everybody needs to use data.
Rebecca: Well said. Going back to you, is there a book or blog that you would recommend to our listeners?
Sandy: I well, so two things. One, I don't read a lot of blogs, it's really bad, but I gotta say, I get so much information off of Twitter. I cannot explain to you how much I find and I am current with because of Twitter and so that's just one thing I do and I follow all kinds of data people, in all different spaces. And I just keep, it's kind of, one of the things I do to keep current. But in terms of book, I have to say I'm obsessed with the book I just read, it's called Invisible Women, I don't know if you've heard about it, but it's all about how our world is designed and the world of big data is sort of amplifying the fact that our world is designed for certain types of people. And it's and it's a really great book, not because, it's not blaming anyone, it's not talking about intentionally that we're including women or other, sort of disadvantaged groups. But the world is designed for men.
And it's said it gives these really great examples of how everything from how your car is tested for safety to how you know, snow is cleared in the winter, is really designed for certain groups of people. And then if we actually looked at the data and actually designed based on what the data would tell us, we would benefit everybody. So it's a really, really great book. It's called Invisible Woman. I can't remember the name of the author, but I think it's a fantastic read, both from a data perspective and just from a design and society perspective. And also being a woman in STEM, I'm a little bit a little bit on that one here.
Rebecca: And we applaud you. We will add the link to the book and your Twitter with our shownotes, so we'll find it.
Sandy: Follow all data brains that I follow.
Rebecca: Yes. Sandy, if I could grant you two wishes, what would you wish for?
Sandy: OK. Well, personally, I just wish I had more time. So really simple in life, there's nothing more valuable than time. So, you know, that would be the one thing, if you could figure that out for me, that would be great. I think, you know, I kind of mentioned it before in terms of my role and our teams and what we're trying to do with data. I think if I could just if we could fix this access problem, if we could fix this problem of access to data or access to tools, of course, in a legitimate and careful way, we always have to put that wrap around everything. But, I think that would be the key to all of the things that, you know, if we look at all of our data strategies and our plans, it's full of, of so much, but if we can't get to the data and the tools, it's kind of pointless. Yeah, that would be it.
Rebecca: You mentioned that one thing you'd wish for is more time. When you do have time, what's on your travel bucket list right now?
Sandy: Ok, well travel. Well, I used to have an awesome travel list, but then I had kids and they ruined everything. And I love them to death but damn, the travel’s gone way down. Having said that, we are going to go to Machu Picchu next summer and the kids are coming. So it's going to be very exciting. But, you know, honestly, travel is one of those things. Any place I haven't been, I will go.
Sandy: I want to go everywhere and anywhere and not everywhere multiple times. But honestly, anywhere; I want to go as many places as possible.
Rebecca: Yeah, I’m the same way, I don't like double dipping in the same place-
Sandy: Unless it's Paris! Been there six times, need to keep going. So there's a few places that, you know, I'd like to go back to. But, but mostly, yes, it's where, it's what's next.
Rebecca: Oh, fantastic. Yeah, fantastic. So second my second last question is, is data just for data scientists?
Sandy: Hard no! I'm just gonna leave that answer there, like my kids. Hard no, it’s not just for data scientists.
Rebecca: And I think you've explained pretty clearly why. Last question for you, Sandy, is: what is one data skill you would recommend every public servant should have?
Sandy: I think the most important thing for every single person to do is to understand and figure out how data could help them do their jobs better. So, how can you apply data to your work? Is the very first question, because then you can get help from people to do that. There’s a lot of people that will say, “oh, no, no, data is not for me. I don't I don't do that”. And that's the number one thing to figure out, is you know data could help every single person, whether you're at the top of the pyramid, at the deputy level or whether you're working the phones or whatever. Figure out how data can help you do your job better.
Rebecca: That's fantastic. Thank you so much, Sandy. Sandy, so to our listeners, thank you. You can find Sandy in her favourite place.
Sandy: I don’t know if it’s my favourite place, but a place.
Rebecca: One of her favourite places to learn new things, on Twitter, we'll post her handle. And you can find us at busrides.ca or trajetsenbus.ca. You can also follow the CSPS Digital Academy on Twitter and LinkedIn. And, so check us out on the Busrides for a bite sized learning about digital technology and government. Thank you again, Sandy. And thank you so much to our listeners.
Sandy: Thank you for having me.
What we’ve covered
- Data is for everyone!
- There’s an opportunity to leverage Government of Canada data for better decision making
- Five data analysis steps you can apply to GC business problems:
- Define the question
- Unstructured data is often written and harder to organize
- Structured data is often in rows, columns and databases, and is easier to analyze
Learn more about data
- Discover Series: Discover Data
- Beginners Guide to Data | Medium
- Event | Government of Canada Data Conference February 13-14, 2020
- Free Online Data Analysis Courses | Coursera
- Fun Facts | Busrides' 12 Days of Data
- Report | Data Strategy Roadmap for the Federal Public Service