All right. Hello, everybody, and welcome to today's training. This is environmental data management round table.
All right, so, on behalf of the Interstate technology and regulatory Council welcome, today's training, environment, environmental data management and best practices, this training is being recorded and will be posted online for those not able to join us live just some housekeeping rules here today
as I mentioned, it is being recorded. And at any point, if you have any technical difficulties, or like to ask our trainers any questions, please use the pod to enter those during the training here today.
So the interstate Technology and Regulatory Council is a program of the Environmental Council of States.
We are a State-LED organization, composed of members from State agencies, Federal government, academia, and private stakeholders.
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Norse endorse any specific products from any trainings here.
So with that on Slide 4, I have the pleasure of introducing our expert trainers here today.
Not only were they volunteers of our environmental data management team over the years, but they have also worked very hard to make these roundtables not only possible, but as trainers as well.
So with that, I'm gonna give it give control over to Brian Pointer from North Carolina Department.
Environmental quality together to get us started.
Thank you. Devin. Yeah. Hello, and welcome everyone, and thank you for joining us for the first of our environmental data management roundtables my name is Brian.
As I said, and I have the privilege of being moderator today to sort of help guide some of this discussion with this really awesome group of panelists who are environmental data managers.
Really from around the country, you know. I'll let everybody just go ahead, and you know, if you want to dive into the details, I won't read it.
Anything like the Bios for the interest of time. But one interesting thing is that you know, we sort of look at among us back of the envelope. Calculation.
I think we have about 75 years of expertise combined behind us, and I think one interesting little piece of trivia is that believe we're bypolar.
Not in the in the, in the sense that you know many of us have done a lot of field work, and that includes stuff.
I know a couple of folks are, done a lot of field work in Aaska, and I've had the privilege of being able to go down to do fieldwork, oceanographic fieldwork in Antarctica.
So I believe where we are. We have a wide distribution, a great array of diversity of opinions, expertise, and of course you know the geographic thing too.
I will point out to, you know, with this slide up. This is part you know, of an overall training catalog that we have there is an overview video.
If you go to the website, which I think will, the link will be on the next slide, and I'm I presume that we'll have something put in the chat here pretty soon.
There's a great overview video that we produced which is really sort of an introduction to our documents.
This is the first of the 3 roundtables that we're planning this one about data management planning and field data collection.
We also please mark your calendars for April and May for another roundtable on date exchange.
In another on data quality. In addition, there's also a sort of an audio only sort of a podcast last Youtube discussion about traditional ecological knowledge, which is a really a fun and sort of new piece of environmental data management that we explored throughout this process of of this team and it's
it's it's really engaging, and I encourage you to go check this out as well as all the others. Next slide.
Please.
So, as I mentioned. You know this, and all of these training materials are really going to be based on our documents.
So the environmental data management team spent the last 2 years crafting, coming up with data management.
Best practices for example work. And this is sort of an overview of a broad overview of some of our table of contents.
Kind of how we decided to split things up. One thing to note, you know, I think a lot of folks who are in this data space when it comes to environmental work.
You know, there's a lot of focus. Rightly.
So about a lot of things with data, science and data visualization, there's a lot of cool things that you can do with your data to better understand it however, one of the things that we typically hear from a lot of folks who end up kind of drifting into this space is the difficulty of dealing with the
data at its basic level, right? Managing the data in such a way that you could more readily use some of these, you know, be, you know, nice processes to be able to do statistical analysis and things like that oftentimes much of the analysts time, is, spent managing said data to get things done.
So that's one of the purposes of why we we thought it was a good idea to come together, as a team and put together some best practices.
For actually managing data for agencies. State agencies, private industries, etcetera.
Some of the folks, you know, who, you know. We think that this that's the audience to which we sort of wrote these documents.
Next slide, please, and so one way to think about environmental data management and a way to conceptualize this is by means of a data management lifecycle.
And you can definitely, you know again. Always go back to the D.
We, we have a whole section on the data management lifecycle in our in our planning portion of of the documents.
But I can sort of summarize it here, you know.
One can think about it anytime. You're gonna start something with, you know, doing some sort of a data management.
Say you have a new project, or something like that. You pretty much always have to start with a plan.
You may not have if you're sort of getting started, and you're somewhat new to this.
You may not have, like a written plan, something well, you know.
Well written, or anything like that, but we'll definitely talk about some of the steps you might want to take in order to do so.
We'll talk about that here in a moment. But you may not have something that's necessarily formal.
But you've got to have some sort of a plan.
You have to have an idea of sort of what you want to do.
So everything pretty much does start with that plan from there.
You need to think about how you know acquiring data data acquisition that could be from the field.
It could be from the lab, you know, you've got to get data in from somewhere, you know.
Maybe you're bringing it in from other agencies to do post-doc analysis right?
Those are the kinds of things that you might do from there.
You have to sort of process, maintain. That's sort of the the rough, you know.
What are you going to do with it? Are you going to take it and do some statistical analysis?
You need to put in a database things like that from there.
You probably have some interest in publishing or sharing it.
Maybe you have. You're a state agency, and you need to publish and share this stuff with the general public for knowledge of.
You know you, so that the public can can stay up to date on environmental information.
Or maybe you need to share it with some internal stakeholders or further data analysis, or whatever maybe, if you're a private industry, you may not have that same public component to it.
And then from there sort of our last piece is retained.
And that's basically, what are you going to do with?
Then every project must sort of somewhat come to a close. You have archival requirements, things like that.
This is this is just sort of the broad sense of the data management lifecycle.
Again, for further details. Please go check out our documents for that.
But you know, so this is a broad data management environmental innovation was a very broad and we covered a lot of it in our our documents.
And next slide, please, for the purposes of this particular discussion we are wanting to sort of, you know.
We could talk forever, but in order to kind of keep it manageable, we're really gonna focus on these first 3 components of the data management lifecycle really focused on data management planning.
And then really upon the actualization and processing maintaining thing, we're really going to kind of focus on field data management, right?
Because it's one of the more challenging components of Data Management Act.
You know, data acquisition so we're really going to focus on those 2 things and the reason why we put that better, faster, smarter on this slide is just to key the point that for everyone whether you're in state agencies anywhere like that the demands to be better faster and
smarter when it comes to getting data from the field from wherever you know, whatever is happening with any kind of a disaster or some sort of emergency, the demand to be better, faster and smarter is not going away.
In fact, it's getting increasing more and more so the more that we can do to do a better job of, you know, implementing best practices or planning bet acquisition.
All these things right, the better off will be, and the better we can, you know, answer and serve the public or server client to answer the questions in a data-driven and intelligent way.
Next slide, please.
So, speaking of questions, we want to make sure that you know as Devin pointed out, please be sure and put the put you any questions that you may have either right now or throughout the discussion.
As things come up in Q&A pod. We want this to be a discussion.
We wrote these documents, and we think that they're very good, and they will stand the test of time.
But in doing some in order for them to sort of stand the test of time, we did kind of have to be a little bit general in how we wrote these things.
It's the written word, and sometimes it can be a little less personal but what we're hoping to do in the format of this particular kind of a roundtable venue is to add a little bit of personality to some of the work that we do right we have a great panel of experts.
Here, who can hopefully share a little bit of some anecdotes and things like that.
That maybe didn't quite make it into the into the documents.
But you know, again illustrating the principles of environmental data management, best practices to really sort of help give you the audience a little bit of a sense of how we, how we get some of these things done on a regular basis.
And I think this is going to be hopefully a really engaging discussion.
So please make sure and put your questions in the Q. And a pod to get those rolling.
But to start. I do have a few questions that I sort of have prepared to get the discussion going, and sort of, you know, get things started, and it in order to do so.
I think I to sort of set the stage a little bit of a scenario I'm gonna take myself back, you know, little time machine.
When maybe I had a little bit more here, and the hair that I had wasn't quite as gray, and I was a little bit a little bit more ignorant of some of the things that are done in environmental data management.
Excuse me environmental data management. And I also did not have a great set of documents which to read, you know, sort of like an instruction manual on how to do some of this stuff.
100. And so with that being in in this situation, where maybe starting a new job and of inherited a data management system that doesn't make a whole lot of sense to me.
And I need to go out. And, you know, collect a lot of field data in order to get some sort of a project going.
Still very green. At this process. I guess I just wanna ask sort of the most basic question to the panel environmental data management.
Where do you start?
Brian, I can start us out on that. Typically you wanna start with the end in mind.
Sure!
You need to think about what the end result or product looks like.
What problem are you trying to solve? You're gonna consider the data life cycle that we just showed the data management planning tool in Section 5 of our data management planning fact sheet provides a good framework to work through data management planning you know, start by defining the objectives of what you're
doing who the stakeholders are, what the quality means are, then you're gonna move into how we're gonna acquire your data.
Where are you getting it? How frequently are you getting it?
Where are you gonna store your data? And how are you?
Gonna maintain it over time. You need to consider how it needs to be shared with others, or if you shouldn't be shared, how are you going to control that access?
All this information should be worked through and documented in a data management plan.
You know the other thing. I think we can mention here is when you're talking about planning.
This is an ideal opportunity not just to think about what you need, but also what your wants.
Maybe in the future, you know. Consider how your practice is going to evolve, and what some of those data needs.
May be down the line so that you can, you know, kind of build on that thought process and incorporate the ability to address those needs in the future.
Nothing worse than going back and trying to to fit something in that you hadn't considered previously.
I had a project a while back where the project team brought me a whole stack of Pdfs.
Of analytical data and said, I need to be able to work with this data I needed on a map, and I need tables.
Just just make it happen. So my team pulled it all together and uploaded in the database and gave them a table and put it out onto the figure, and they came back and said, Well, where's the rest of my data I said, what do you mean and we put everything in that?
You have, and they're like, Well, there's an MW.
One up over here on the site, and there's another MW.
One on this side of the site from a different consultant, and oh, by the way, we put in an Mdw.
One down here. I'm like, so we I had to take all the data back out of the database and rename the locations.
So that we could keep that data separated, so that when we put it out onto the figures and stuff the data for that one went up to where it was supposed to.
And this one went here. And this one went here. So naming of your locations and how you're gonna handle legacy data and data from other parties is something that really should be considered in your data management plan.
Somebody put a question into the an apod about how do you keep up with all of the new data management software?
That's coming out, and that is, I think, a challenge for a lot of us.
And the planning piece can help with that. So if you are planning for what like Kristen said, what your wants might be, then you're sort of future proofing yourself for new technologies that may come out in the future.
If you kind of plan ahead, you can account for those changes.
Oh, go ahead!
Gonna put in a plug to. Sorry, Brian, and say that a lot of the stuff like that, like Kelly's talking about associated with data quality will be talked about in the data quality training.
And similarly in organizing and planning for some of this historical data and legacy data and migration will be talked about in our data exchange training, coming up.
So we have kind of piece this out, and it is one huge big topic.
But if you're in those types of things, the other trainings will probably be really helpful for you as well.
Yeah. Excellent point, Olga, please be sure and pick those out once they come out, or you know, if you're listening to this after the fact as a recording, you know, be sure and sure and find those.
And give him a list. So we talked about the data management plan, you know, that was brought up a couple of times.
So, so you don't have a data management plan, or the one you have is really sort of not cutting it.
What sort of things did you be thinking about when crafting a data management plan or updating a data management plan?
Or any of those things.
Yeah, I can take this one. So I'll start out saying, You know, data management plan is specific to a project or a task.
So they're all going to be a little bit unique, depending on what your task is.
But your data management plan should always align with the style or the data governance of your organization.
There are some components, however, to a data management plan that are going to be important, no matter the project or the task.
If you see our guidance on the the Demand management planning overview fact sheet, we go through some of those components, but in general, what you're doing in a data management plan is following the details of the data, lifecycle, so you just want to think through what's gonna happen with
your data from the first moment. It's aired all the way to that final retention, Archival deletion, step and answer all the questions in between.
So one of the things that we did for this guidance document is we developed the tool, I think, that Kelly referenced earlier.
The data management planning tool. Just just a simple figure. That kind of takes you through the steps of the data lifecycle with some questions you want to ask yourself, and by answering those questions, those are the topics that you'd want to put in your data, management.
Plan so you're going to cover things like expected data types, you're going to collect where's the data coming from?
What are the formats that they're coming in? And what's the stata of your data storage system?
And then who's responsible for each of those things along the way?
Because that's a huge part of any successful data management system as having those roles and responsibilities defined.
One thing you want to think about, too, was thinking about some of the different types of data that we can often overlook.
So gis is a is a data type, traditional ecological knowledge.
Brian Referenced earlier. The the conversation on Youtube that we have about that some of these data types that aren't always at the front of our minds, but also need to be managed as part of your data management system and will come with special considerations that you want to incorporate into your data
management, plan. You also, wanna think about when you're looking at the acquire data step, are you gonna be looking at digital field data collection or traditional field data collection?
I think that's a huge question that comes into play.
Nowadays that we're gonna talk about more during this panel and share with the experts.
We have 2. But things that you wanna kind of lay out in your data management plan to get some of those logistics and roles and responsibilities to find.
We also have included some templates that you can actually pull up and look at so if you go into the guidance and go into the data management section, we have some links to some templates that you can use to get you started so the international conference on environmental data management is
STEM has a great white paper, that kind of addresses, data management planning.
And then there's one from Noah. There's one from the Usgs which is kind of like a checklist to go through.
So we do have a whole bunch of different tools that you can look at and come up with.
I see in the chat someone has asked specifically about, like the visual sample plan.
That's from piano. So kind of as a whole group like Brian had mentioned.
We don't really look at specific individual types of proprietary stuff and we don't really ignore like endorse or not endorse any very specific types of things.
Types of programming or proprietary things like that we kind of just give a big, you know, templates and overviews and things that you can apply to your specific situation for for that.
Whatever you have going on like, Katie had said, you know every one of these is a little different.
I'll just mention to while we're talking about data management plans.
You know, we've kind of mentioned every every project's a little different.
Every organization is a little different and one thing you want to make sure with your plan is that it's in line with your organization's data, governance policy.
So if your organization doesn't have a data governance policy, you kind of need to think about those elements of data governance as part of your data management plan.
And that will include things like data access sharing security data, archival deletion, retention and we have several fact sheets on data governance that we've prepared as well, including a data governance checklist that takes you through the major components of what data governance really encompasses but
you won't really think about how your organization's data governance policies need to serve as an umbrella to anything.
In your data management plan and I'll mention to while we're kind of on this this topic, we got a question in the chat about data archiving and deletion and trying to make that actually happen in practice.
So I mean, I'm sure sure we all have some stories, I think, from my personal experience I been more successful with it when I form a collaborative partnership with our it, and I us.
Department. I think I've seen it less successful when that department sort of is siloed from the day to day project work.
But when we collaborate with them they actually already are doing a lot of stuff around organizational security and related to that is data retention.
So I've found they often have a lot of ideas on how to make this stuff easier for us, and are already doing it for us at maybe a corporate or organizational level.
And if we can take their experience and apply it to our projects as well, that I've seen some success with that in the past.
Yeah, that kind of brings me to the question of you know, if you're gonna talk about data governance, that's, you know, maybe a different team of people or well, part of data governance is really just about who do you bring to bring to the table, who are, you stay stakeholders right there's some
of that part of it there, when it comes to crafting a data management plan or updating it, or reviewing it, or whatever who are the kinds of folks that you might want to bring in in an organization to make sure that you know we're covering all your bases
to help craft this data management player.
Yeah, I kinda dovetails off of what I was just talking about.
But really I it is your friend, we need to get paths.
The world where the engineers and geologists sit over there.
And I sits over there, and we don't talk.
We need to really collaborate more. But I think a big part of making sure Linux is the right team involved.
Early on, is remembering your data team, too. I think a lot of times our projects move so quick that we get very focused on the physical energy of things and getting all that square away and contracting and go go go.
And we sometimes forget to ask the questions of what our data team is gonna need. So that's squared away and contracting and go go. And we sometimes forget to ask the questions of what our data team is gonna need. So that they're ready to hit the ground running with our projects to so it's really it's just about interfacing with
the best technical people to align with your scope of work the way you do for any task on your project, but just applying that to your data needs as well.
If you see Section 3 of our data accessibility sharing and storage fact, sheet, we actually outline a lot of the types of data youers to consider getting involved and depending on the size of your organisation and the scope of your project some of these roles can be served by the same person
or some of the roles might be served by teams of people, but that fact sheet doesn't nice job of kind of laying out what types of roles you need to think about.
That will be interacting with your data and managing your data.
And each of those rooms really has a different perspective. And our key components of having a successful data management system.
Another thing to keep in mind and try to make happen is that your data management planning should preferably happen before a state scope of work is submitted to the client.
You need to be able to capture it, particularly in industry.
You wanna be able to capture your data, all of the pieces of data management in your scope of work.
Yeah, something else that I always think of in this instance when you're talking about data management plans versus data, governance is if you're in an emergency response scenario, you may not have time to put together your data governance or your data, management plan before you have to go out and do your
emergency response activities. So having that data governance in place can help you address those sort of emergency scenarios where you don't necessarily have a time to put together a really robust data management plan.
Yeah. And I'll just mention briefly to you.
Know, Megan brings up a really great example of sort of an emergency response.
Scenario, one of the things we kept in mind when we were designing that data management planning tool.
That's part of our data management planning. Fact sheet was the idea that you know, in a perfect world.
You sit down with that tool, you answer all the questions, and it all gets documented and memorialized in a data management plan.
But if you're in a quick turnaround situation, we also that tool is designed to be just a conversation if it needs to be, maybe it's just everyone hop on the phone.
Let's make sure that we answer all these questions, and know what the plan is.
But our goal with that tool is really to make it so. We could scale to whatever the the downs and timelines of your projects were.
I'll add that we don't necessarily, as a whole industry here in the environmental world.
Really, we don't necessarily have it all gotten on board with these idea that data management is its own aspect of a project right?
And so we tend to kind of like shoehorn it into the side, or whatever like.
I think it's taken us a while to get to the point where we understand that data quality, especially with analytical lab data that all needs to be in your work plan.
But we haven't really gotten to the point where you have sections in your workplan, or even a separate plan.
Where you have sections in your workplan, or even a separate plan where you're talking about data management.
I think that's one of the like small incremental steps that you can start making to try.
And like, yeah, this rolled out and better accepted across all the work that we do is suggest. Like Katie is saying, take this little checklist and just use it as a guide to even just write a paragraph, or you know, it doesn't even need to be a whole
separate plan, and at this conference it can really just be something small.
That's part of your plan or your scope, like Kelly had mentioned, or something like that.
And one other thing I just wanna mention, you know, in a lot of these instances, data management planning is not a singular conversation where you develop a plan.
And it's written in stone, and you're done.
Data management plans tend to be living documents that evolve with time, and you know, to Brian's question of who's involved in data management planning.
That list will also change over time of who needs to be involved.
So you know, with the changes in technology and changes in workflow and data needs, these documents should just be, you know, constantly evolving and referred to documents and updated.
Yeah, so I, I think this has been a really good intro to some of the aspects of data planning that we have in our our documents and I think we'll continue to sort of come back to it even in this discussion.
But I do want to kind of turn just a little bit to discussions of field data and feel data collection right?
So you know you've got a plan in place. Now is the time to send you your field crews out and and get them going.
One topic that I know is that we sometimes get asked as as a group, as an industry and it's just one of those things that's sort of related to a question that we got earlier about keeping it up with technology is sort of the question of I like to say, you know, paper.
Versus plastic, right? What's kind of the best way to go, you know.
A lot of folks still use sort of a pen and paper for a lot of field data collection.
But now you know, with the admin of some new technology, we've got the ability to create field forms that you can put on a tablet or a mobile device, can we just have a open it just for a quick discussion to start just about maybe some of the advantages and
disadvantages of each.
Yeah, so we have a field data collection process development considerations, fact sheet for this as well as a decision tree for helping you.
Decide if you should use digital or traditional field data collection methods, they're obviously instances where digital collection is faster, such as collecting photographs or adding captions to those photographs in the field digital forms can also automate data collection by auto
populating similar information like user ids and dates and times, or the weather across several forms.
Or if the form is really short, it can be much faster to collect that data digitally in the field.
We're a digital form, will end up being really long and requires a lot of custom.
Information Handwritten notes, kind of things that can't be turned into valid values.
A paper or a handwritten one can be faster in the field, forms with valid values are often kind of similar.
In time it takes the same amount of time to circle a word on a form or click a dropdown list on a digital form, digital field data collection can also eliminate the need to transcribe data after the fieldwork is completed.
So that can streamline your workflow for faster data.
Turnaround, but if you have error, checking, or other Qc.
Built into the form it can require the users to do a lot of troubleshooting or editing of the fields editing of the forms in the field before they upload them, and that can take more time in the field.
So there's lots of considerations that you can kind of weigh out for your individual projects. And those are described in the the white paper that we have available.
Yeah, speaking about the transcription thing, I think one way to think about the digital versus paper is if you can consider that there's kind of 2 parts of transcription right?
Like, there's the data collection. And then there's the data entry.
And so historically and traditionally, what we do is data collection in the field.
And then the data entry part in getting that digitized has been after the fact.
If at all. And so this is moving data entry into the field and it may or may not like, work well with data collection.
Sometimes it does, and they're kind of the same thing.
But other times they're not so like for an example, if a driller is working on a footage contract right?
And you're just like burying the geologist and chorus to walk.
And we talk about the time. Then your data collection and kind of what you're taking and short handing and all that sort of stuff to work on that contractual situation.
It doesn't necessarily make sense to do data entry at the same time as needed collection in that particular situation just given the contract style.
But you know, like, if you're working in a helicopter and you're login bird sightings and you can get the GPS point at the same exact time.
It is kind of the same thing. Data collection data enter basically the same.
And so so then it would kind of make sense in that situation.
So that's maybe one way to think about it a little I mean, we have lots of ways to think about it.
Like Megan had mentioned. We do have this tool that you can kind of look through a lot of the different concepts and it'll actually just give you like a spitometer meter like, yeah, probably paper.
I don't know. Maybe digital like that and and work through it.
It's meant to just get you thinking about all the different aspects that you might want to consider in that decision.
Yeah. Another example back to the sort of emergency response scenario.
You may not have time to build a custom digital form. So paper makes more sense.
If you can just write things down on paper, and then handle it after they merge your responses, concluded.
But if you plan in advance you may have time to develop standardized digital forms.
And so that can help you make those decisions. The tool that we have can help you think about those things as well.
I could see a lot of stuff coming into the chat, which is really great in the pod.
A bunch of these things are about data exchange, which is awesome.
And you should definitely come to the data exchange training which is going to be happening.
And we'll address a lot of that stuff. I don't want to be dismissive of that, but like we are kind of trying to target the data management planning and the field data collection in this particular training and the data exchange is going to have its whole own you know, hour, and a half 2
hours to talk about all these processes like you're talking about exchanging data between labs or exchanging data between consumultants or across from different things like that.
So just another shameless point.
Yeah, and we will definitely. You know, we will keep track of some of these questions and we'll pass these on to the panelists.
Some of these you'll see some of the same faces but a few different ones in the data exchange round Table.
That's coming up in April. We'll pass these along to them, and so that they're kind of ready for them, so be sure and check it out when it comes.
Be sure and register so alright. So we kind of talked a little bit about the differences between paper versus digital right?
And you know it really is dependent upon. You know your situation right there. Then there's a lot of considerations.
Please check out a lot of the details again in the fact sheet, which we will.
I see a couple of questions about the white paperers or and or fact sheets right?
We will put a link to the, to the documents again in the chat here pretty soon to let you guys check those out.
But so let's take the scenario for the moment.
Alright. You've decided that digital data collection. Right? That's the right.
Answer for you and your organization. What are some of the challenges that come with digital data?
Are? You talked about several, but one in particular that I'm thinking about is tech resistance right?
Certain folks, just really love. And you know we'll take to a new technology really easily.
And some folks just do not, and it's this is not an ageism thing, or anything like that.
Older than older than most. And you know I definitely will.
We'll take my lumps. But you know there's definitely folks who who can, who will be be resistant to change any thoughts or suggestions about how to deal with that.
Yeah, Brian, I can take this one tech resistance or really resistance to any.
Any change can STEM from, you know fears or frustrations.
It could be some prior bad experience, you know, especially when we're talking about.
Yeah, digital data collection. Maybe you had a bad hardware experience.
Things were crashing in the field, or you know, battery life.
Maybe that's forms you were using at the time where we didn't represent the workflow that you use while you're in the field.
You know. So there's a lot of different reasons, and it could be as simple as just unfamiliarity with digital devices.
You know, I think the important thing is when you're developing a digital data collection program, you have to think about your users, and you have to engage your users to understand what their pinch points are.
You know, you need to understand what their concerns are, and really build in.
You know? Are you solution to solve those concerns, or to address those concerns?
And you need to make sure that they understand that their concerns are valid and valuable.
Input, so while you might try to minimize those as much as possible, you know, we certainly can't make everything work the exact way that everyone wants to so really need to take the time to reiterate some of the benefits that you're receiving by using a digital data, collection solution it could be as simple as not
having to come back into the office after a really long day in the field to make sure that you're data has been transferred over appropriately.
And you know, just using them. Other scenario. We all love to be able to look at our vacation balance, and we do that because we do our time sheets every week, and all of that.
You know the accrual, or however that works, that's all.
Going into a database that ultimately you can go online and check and make sure that you still have some time to take off.
It's worth noting that with any data collection.
Program. It, you know, paper or digital having a good training program.
It's critical. And we included a training checklist in the field data collection work products that you can use to help with the development of that training program.
And just kind of as an aside, thinking back to, you know, some experiences I've had.
I had a gentleman who, you know, was trying to bring on board into collecting some digital data, and he never touched a touchscreen product before anything.
His phone was still a flip phone. He didn't really have interest in.
We found ourselves in a scenario where we were dealing with multiple learning curves.
We weren't just asking them to adjust their workflow slightly.
We were at asking to learn a new hardware. We're asking them, learning and one of the ways that we got around that was actually giving him the device.
Many, many weeks in advance, just to play with it. Had, you know, play angry birds, download a game, whatever it was that you needed to do just so that you could be comfortable with that tablet, and then we worried about the form after the fact.
And even with, you know, working with the form, taking a minute to sit in an office or sit somewhere where you have data.
You've already collected and just work through entering that data to gain some sort of familiarity with that form.
It went a long way, and it ended up being a really successful implementation.
I'm sure we all have some other stories where maybe it was less successful, and we learned some more lessons.
But that one certainly comes to mind.
Yeah, I think a big recommendation is always to like, listen to listen to people's feedback.
So do your training and work with those people that are maybe tech resistant and listen to what they have to tell you.
When we started using tablets in the field. We have ipads and if you're in really hot temperatures or really cold temperatures, those ipads don't perform very well, and we kept getting feedback from users that they couldn't see the screens, and
we realized pretty quickly that the screens go dark. If the device is too hot, and so a simple solution is to just take an ice pack from your puller or and put it on the back of the ipad to cool it off, or put it in your aircraft, car let it cool off, and then the screen will go back
to normal, same thing. If it's really really cool and put it in your car, so warm up, slide it inside the packet of your jacket, so it can stay warm so that the screen is easier to see and we didn't know that until we got that feedback from people using the devices in the
field, so it's really important to get that feedback from people.
There was also a question in the chat.
That is, how do you convince the high-level managers and program managers that data governance?
And dia management are important, and should be included on projects, and I think it goes along this line of trying to win hearts and minds, and one good point to make with people is that your data safety should be treated just like health and safety.
That you plan for your health and safety for your project and your data.
Safety concerns, as well the way the landscape is these days, you know, we hear about data breaches all the all the time.
And so data. Safety is very important. Data. Security is very important, and those things can be planned for so that you have a way to secure your data, and that you have a plan in place in case you do have a data breach because it does happen so that's one way that you can kind of win
hearts and minds, by showing that there are benefits to planning in advance that are data, safety related.
And it's risk prevention. Really.
Oh, it too. We've batted around a lot in this committee, and on the with the guidance of this concept that data is your asset right?
That, like at the end of the day, the asset is worth protecting.
And so that kinda it's like the same concept of safety.
But if you think about in terms of asset, you kind of automatically start going down the road of monetary monetary like value, additional value like, where is the value in your data and that can be a lever to win the hearts and minds, and to say if we haven't planned for this and
there is a data breach, and there is a data loss like, how much is that loss?
And you don't wanna be like that. Doomsday, you know, like worst case scenario kind of a person.
It's more. Just let's try to protect this and avoid these potential scenarios which we've seen.
So, yeah, kind of back to the question. I think that we were talking about with Brian here about this tech resistance and fears and frustrations.
I think we all probably have these stories, anecdotes about things that can go terribly wrong, and I certainly had an experiences where people get so frustrated in the field.
They literally just will fling the tablet. They'll just be like, yeah, no, not.
This is like, not for me. And it's like a real thing.
I mean being in the field and working directly with people on that.
It's a real challenge. And like Kristen saying, I mean, there you have to be able to kind of weigh what is their real hesitation.
And what is issue? Because at the end of the day it's real like their frustrations are totally legit right? You can't see a screen.
You can't enter the data that's super frustrating like you have to.
You don't have enough battery life, and you have to be plugging in and logging around like a battery pack.
And now you're tripping on wires that's like Oh, that's a legit hazard right?
And so really listening and thinking like and then not dismissing.
These are, these are real issues. And just because you have this grand vision of wanting your data all nice and tighty and automatically syncing, and all this stuff and running through your data workflow so perfectly, it's like, yeah, it's gotta start at the beginning of really listening to what these
firstustrations are, and trying to solve those individually.
Yeah, I think you know, just listening to Olga sort of talk about those frustrations and thinking about how that drives this tech resistance.
I think an important part of this, too, is just setting expectations both for your field personnel and for your project management, because I think a lot of times.
What happens to is they'll go out with maybe a new tool and a tablet, a new app, and then field the pressure to maybe deliver the same amount of core that they always do in one field day, and then maybe the manager is also pressuring them for that same amount.
That first day, and you know the driller is always pressuring you to crank it out.
So I think just making sure everyone's on the same page up front of it's okay.
If you're first couple of days you don't get as many locations done as you normally do.
What I've seen happen on a few of my projects, especially with soil.
Logging is the first day or 2, maybe even 3.
They're rocky, they go slower because they need some time to get used to it, but then, by the end of the event, a lot of my field texts have actually really liked the apps because it lets you do nice things.
Like oh, I'm in the same unit still. Copy and paste that description.
It can actually start saving you time. And they come around to it.
But just setting that expectation, that for the first few days, like, it's okay.
We know this is new for you. The project manager knows this is new, like, it's okay.
If you're not delivering the same amount of you know, core as usual, or maybe not getting to the same number of monitoring wells as usual, and just understanding that that that we're okay with that because that time savings is coming elsewhere.
It's coming and not having to come back to the office and inter all those boring logs.
It's coming and not having to do that transcription.
So I think just communicating that to everyone on the team can be really helpful. With this tech resistance as well.
And I think this gets to another point, too. That was brought up earlier.
You know, we're talking about the data management plan and who to bring to the table pretty sure.
We mentioned it briefly, but it's worth repetition now, right?
We're talking a lot about communicating with field staff and making sure the field staff is, you know, comfortable with the technology, etc., etc., you know, bringing them in, or you know, at least you know, a representative of 2 of the of that field staff, when you're crafting your data, management.
Plans and you know, you're planning for all this stuff is a really important component as well.
Getting their feedback, their buy-in on. You know what the what's going on and how they're you know, what's gonna what they're eventually going to have to do.
You know, in potentially, you know, adverse circumstances is really really important.
And you know if you don't get their buy-in, things can go badly.
Once things start to roll out. If you know, if you're making their lives harder, she never really want to do so.
Very important. Good. Chris.
I just saw a a question here in the chat asking about moving into the digital world in some Qc.
Concerns with regards, data being loaded directly into a database, whereas with paper forms you can actually have some sort of built-in Qc.
Before you start migrating data. It's a it's a valid concern as part of your data management planning.
You can actually plan for a Qc step in your field data collections, space.
So it may look a little bit different, and without getting, you know, software specific for the system that I use when we enter field data, there's still an approval process.
So while that do me in the database, there's still some approvals.
There's a review and approval setting that needs to be completed before that data becomes available.
So you know, you can work in Qc. It's it's just a slightly different process to do it.
But that's part of the that's part of that planning step.
Yeah, I was gonna chime in the same thing for recommendations for addressing equipment, failure, digital acquisition, failure and the potential for data loss in the field.
You can plan for that and build in your workflow process, making sure that your field staff maybe they do things on paper and in their digital field form the first time they're using their digital field form because sometimes the upload process can be complicated.
And you wanna make sure that you have a paper backup.
A lot of things can be exported before they're uploaded to your database.
You can export it as a Pdf. Make sure you plan for that and communicate it with your field staff, so that they know also simple things like your ipad is not waterproof, and even though it has a case on it you shouldn't put it in a bucket to decontaminate
it, sometimes you have to tell people that cause they may not know.
So planning for that and training your staff on what the workflow is.
Notifications from your field staff to your data management staff when they are done to ensure that the data management staff has received their data, that that even though the field staff thinks they uploaded it, maybe they have it.
Maybe they reset their device. And then you have a data loss.
So that communication needs to be really clear between a field staff and and your data management staff. And that can all get built into your data management plan.
Ryan had mentioned how a lot of us have worked all over the place, and there's often this thing that you're not necessarily working in in a 4G or 5G environment where you can automatically be syncing your data right?
So I think a lot of the stuff about backing up your data right away has been solved by making sure that it's like synced really quickly, and that your access and your online I do a lot of my work offline and remote.
Areas and this is a real concern for people and trying to get tech-resistant people specifically around this tech that around the data loss issue, I have worked in stages and steps where like the first thing is to just be connected to a printer and you're printing it off so that you now have a paper
copy. And that is really an important thing for security, for people that are making this transition slowly over time.
The other thing is every day, and sometimes twice a day, depending on where you are.
I do a full data download backup of the device.
Typically, you know noon, and at the end of the day, and then that's all recorded.
I usually bring several hard drives with me onto the field, and I'm just constantly being backup, and I'll backup many systems like sometimes, you know, 1012 tablets a day, and it ends up being a whole process at the end of the day, which of course, but it's really important for data
security. I do. Wanna address that comment that somebody had about. You know where your like.
If you actually makes you faster in the field like, Katie was saying at the end of the day.
Sometimes you want to be way faster in the field, and if you're working on time materials that doesn't necessarily translate to a great business model.
If you're making less money because you're spending less time in the field.
Well, I guess you know every has different approaches to what that's all about.
But I think in the grand scheme of things, saving your client, money is probably a really good thing and maybe you can redirect them to spend their money on something else that's really important.
Right like, hey! We saved you X amount of dollars in doing this activity.
What would you think about us now doing this new modeling with all this great data that we've collected where, you know, like redirecting a little bit in that way?
And then you're spending less time on the more boring things, maybe, and more time on some cool stuff.
And that is always a good argument for any decision, making doing more interesting things is always is always something to go for well, this this would be great. So let's kind of turn the discussion just a slight bit right you've got right from the data life cycle.
We covered planning. We started. We talked a lot about acquisition, particularly from the field.
Now process. Maintain right that particular step in in our data life cycle you've got these field forms, either digital or paper.
What then? Right? Where does it go? What do you do with that data?
Once you've collected it, where does it go? We've hinted at it a little bit before, but maybe quick sort of around. What happens from there.
Sure, as Olga mentioned earlier. That's called data exchange movement of data from one place to another.
You should really start thinking about. Data exchange at the very beginning, planning phase like we mentioned at the beginning, need to think about where your data is going to be stored and how it's gonna be stored.
That'll also help with where and how it's stored will help determine how to move the data.
The best practices, documents. We have a whole section on data exchange.
And there's going to be a round table on data exchange coming up as well.
The data exchange documents, talk about ballot values which can be built into your digital field forms to help make sure that valid values are values that are can made consistent way to make data consistent across your database.
All of your data. So they talk about valid values, and those can be built into your digital form so that it's the same every time.
If somebody needs that value. Also, talk about electronic data deliverables or Edds which are typically the mechanism for moving data from one place to another.
If you're collecting your data digitally, the data collection app can be programmed to transform the data into an Edd and upload it right to the database automatically and like Kristen was saying then you can put it in couldn't potentially. Put it.
Into draft phase where your room it needs to be reviewed and then approved before use, or if you're gonna fill out paper forms, you have to do some hand entry there, so you'll need to do some manual entry to get that into electronic format that can then
be loaded up into a database.
Yeah, and I think the key thing there, just, you know, harp on it more.
Is there should be a plan. There's always be a plan in place.
So all those those elements that Kelly is speaking about those sort of decision points you have to make of. Are we doing this?
Is this manual? Is this automated thing? I'll ideally you answer all those questions when you go through that data management planning process.
Yeah. And I'll point out to this is kind of right related to this, and also a question that's in the chat about, you know, sort of the database programs that you might want to use.
So to be clear with Irc does is not in the business of endorsing any product or anything like that, and and we don't attempt to do so either.
But we do have, you know, in the documents we did create a section, you know, a white paper discussing data management systems, some of the things that you might want to take into account.
As you know. If you have, if you have there have no sort of electronic data management system, or maybe you have one.
That is showing its age, or, you know, isn't really quite cutting it for for your needs.
Some sort of best practices and things to consider right in terms of just styles of things.
You know there are some, you know, things that run the gamut from, you know up pen and paper type data management systems.
You know, there are ways to sort of manage your data, using sort of pen and paper and and finally, cabinets.
We don't recommend that, but you know there, but you know but it does sort of, you know.
Go from that scale of you know, from down there all the way up to, you know, some commercial products that have lots of bells and missiles and sort of you know, work right out of the box for the most part, in terms, of you know, managing environmental data, so encourage folks you know, if you're in that
scenario. Please go check out that that particular white paper.
It's called environmental data management systems. And we do discuss that sort of, you know, some, some considerations to take into account and all the stuff when we're talking about data exchange does sort of assume that you have something in place.
You know, a place essentially to put that data. And if you are doing, you know, things work for field data collection oftentimes, if you are pursuing a commercial product, something off the shelf.
Many of them do have, you know, field data collection apps forms, things like that.
You can spin up if you're not. If you have something else, there.
Are. There are other ways to do that again. We don't really endorse some of those, any products or anything like that.
But you know there are things out there to look for in some models.
Yeah, there's another question in the pot as well.
That talks about. If you have, you know, hundreds of Pdf.
Reports that you need to digitize and put into a database.
And how do you do that? And the data exchange Round Table will probably touch on that a lot more.
There are same thing, several tools and methods that you can use to digitize Pdf and others sort of handwritten, even documents that you need to put into your database.
So highly recommend. You check out the data, exchange round table that's coming up next.
I see there's another question about, since everybody really loves live data dashboards. Right?
Like we all do. That's like part of this awesome thing about getting data so quickly and so what do you do when you're trying to feed, live?
And so you're just getting it straight from a source and putting it right out to some sort of public.
Or you know, semi public type of viewer that people can see.
Where do you fit in the Qc. Then? Right. And so, yeah, we hit kind of touched on this before.
But one way to think about it is kind of like a development versus a production database right?
That you have this interim step like Kristen has talked about.
So your data flow into one bucket first, and then after it's released, it goes out into your production bucket, or you can call it into a Qc.
Bucket, and then it into a live bucket. So that's one way to do it.
Kind of give yourself like a speed bump, I've like, I said. I do a lot of remote field work, and one of the ways that we handle not having to have some of the stakeholders take these long trips, and you know end up in the middle of nowhere and get stuck
there and, you know, have no resources, and don't want to camp with us.
You know all that kind of stuff is to offer this concept of using some sort of satellite Internet connection to transfer this data to a semi live stream for them so that they can be looking and watching what we're doing in the field.
And we've run into this constantly where they wanna know, like today, what are you doing and what has happened so that they know what to tell us for tomorrow type of thing.
And we've really had to insert a couple of speed bumps there to say we need to look at this a little bit before we can hand it out to you, and I'm sorry, but the delay is going to be 2 h or the delay is going to be 4 h you know something like that just enough to give us
time to get in there, run a few Qc. Checks, and then release it to display.
I'll also add, with the pace of technology changing, there's always more and more options out there to expedite that Qc.
Check. We probably all heard a lot about like machine learning.
And AI it's all over the news these days.
So there are even models out there that you can run on your live data and have the model.
Look at the data and sort of do a preliminary Qc.
For you flag things that it that your machine learning model, or your AI use a suspect flag, those even faster, and then your Qc.
Person only has a limited amount of data that they have to visually look at and inspect.
So there's always new technology is coming along, too. That's part of the fun of this industry is we get to use those new tools.
Yeah, and I think one thing we're benefiting from as an industry is the fact that as much as we all love our environmental data, the reality is, we are not the pioneers in the data world, right?
There are. There are big data companies that are pushing this stuff, and have been pushing it for a while.
And now, because of that, it's reaching a point where the tools are becoming more accessible, they're becoming a lot more easier.
They're becoming more easy to implement. And it's just we're now able to apply these thing like the fact that we can talk about.
Maybe using AI or machine learning while still at the end of the day, mostly being engineers and geologists, is huge.
These tools have just become so much more accessible and so much easier to implement that I think it's really opened up the opportunity.
For you know, like Megan said, to do some more, maybe automated Qa.
Qc. And even beyond that, one day I also wanna mention to you back on August point of sort of the production database versus through your speed bump the that kind of ties in to this full data access concept to which is part of your data governance so thinking
about who's going to have access to that interim database versus who gets access to the production?
One, and being able to build in sort of automated tools of like, okay, maybe everything can go into this interim. One.
But my automated tool says this data doesn't look good.
So it doesn't get pushed to production yet. We have a lot more ability to implement things like that.
Now than we used to.
Yeah, I think a really good example of that that I think I would assume that a lot of people are familiar with is like the Usds Stream gauge network, right?
You can find. You can go there and you can find out what the you know.
You know this stream, gauge elevation is for your local stream and your, you know, near your backyard, as of the last. You know.
30 min ago. But that data, when it's published, has this, you know, little asterisk or something I can't remember exactly what what it has in front of it.
But it's noted as provisional right?
It's got an asterisk on it until such time as the folks have been able to review it, and sometimes it could take a few weeks before or longer, for that happens, and at that point then you know that that little asterisk goes away, and you you can sort of count on that
data that's been looked at, reviewed, and you know you could fill.
But you know, it's it's a trade-off right?
Is that? Do you want it now, or do you wanna perfect, you know, or you know, or checked and reviewed?
And so you have to. You make that you, you know you get the option right.
You you can get it now, but it might be a little bit off.
But yeah, so and of course, it depends on the type of data whether or not you, you can use that as well.
So well for next I wanna just say how awesome the questions have been in the panel. I think we've been able to get to a lot of them as we've been talking.
They are wonderful. Keep them coming. If you guys have, if any of you in the audience have been holding back on a question, go ahead and get them in there because I'm gonna ask sort of my last little plan question there may be a bonus question, if we have time.
But my last little plan question is, we've been talking about planning on the importance of a plan. And you know, we have a lot of really good best practices in our our documents which we encourage everybody to look at for how to create a really good environmental data management plan keep improving etc., etc.
But what happens if you don't have, you know? Maybe this is, you know, you don't have a good plan.
Maybe somebody, who doesn't really have access to these documents, which you know anybody listening doesn't have that excuse anymore, or haven't read some of these sort of does anybody have any examples?
Or maybe some wall shame type stories for what happens as a result of port planning.
And the names can be changed to protect the guilty.
I mean what can't go wrong with poor planning right?
I mean, that's just anything and everything full on mute me.
I think the I think the thing to that. I've always found you in a lot of instances, follow any data management workflow and hearing to a data management plan.
It's not compulsory. It's voluntary.
And so when we're you know, we're really at the forefront of trying to inspire change right?
We're we're trying to work with individuals to get them to adopt and follow.
You know a well thought out plan. And so in a lot of cases, when those plans don't work, you know, we wind up with people who feel like they've been burnt, and they end up not wanting, you lose a little credibility, you lose you know, you kind of lose some of those
followers. So you know the importance of the planning side is, you know, it really can't be understated when when it goes wrong it can go really wrong, and in some cases that might even lead to litigation.
And you know, when you're looking at a consulting firm where you have clients and highlighted sites that could become very expensive.
I always think the results of for planning is poor decision making right.
So a lot of times we're the data we're talking about is site investigation data.
And you're gonna be making decisions about where to implement remediation.
And if you don't have good planning, you may not fully characterize your impacts, and you may end up installing your remediation system or implementing your remediation method in the wrong area, and then you have to go back and do more investigation and put in more remediation so that
planning piece again. It can kind of go back to that risk.
A version. Right? You wanna avoid the risk of making a poor decision.
So that's what I often think of when we're talking about planning.
If you don't plan, you're gonna make or decisions.
Coming at it a little bit sideways, right from like a field perspective.
One of the things that happens say, like, Say, you've decided to go with.
I mean either paper or digital form, right? And you've planned like you're making your form.
And you think you have an understanding about what's going on in the field, and what you need.
But your site conditions are really different than you had anticipated.
Like you haven't been there even thought about it on a paper form.
It's really not that big of a deal right? You just like, add random note, sketch it on the side, you know.
Put something else, write some more stuff in your logbook.
But on the digital side it can be a little bit harder, because you like you end up needing a big catch.
All free form, notes, field, or now, all of a sudden, like your plan, is a little bit ofkew, because now you're gonna have to extract that data.
I mean, it's already kind of a notes, but you're gonna have to sort of extract it out of some bulk thing to try and figure out what you're getting to cause you no longer have clear fields.
You no longer have clear, valid values, and so I think one of the things about the port.
The port planning is to think about how you can build in flexibility to your system.
So I think about that a lot when I'm sending somebody out to Chris's Point like you don't want the person to feel like this doesn't work.
I'm throwing this in the trash because it wasn't well developed, is to try and accommodate the ability to be flexible, that you can be responsive to a change situation that you know we talk about these being listed documents, your plan needs to be like a flexible enough to kind
of accomplish these changes as they occur, so I guess I would advocate for that kind of a perspective that, like we try to plan, we try to come up with all the things that we can think about.
But inevitably there's gonna be 10 of W ones right like you gotta you gotta deal with some point later, and then you can accommodate that.
After the fact. As long as you're aware that, like these things will come up, and you need to then accommodate them.
We we got a good question in the chat about planning asking if the panel could offer any advice on developing effective plans that align with variable or competing needs and approaches between different teams or disciplines that's a great question.
I think that when you, when we think about data management planning in general, the first question, you will see in our data management planning tool in the user section is who are your stakeholders, the other first question, you will see and the data row of that tool is what are your
objectives, I cannot over emphasize how important it is to answer these.
When you're developing your data management plan, you really need to figure that out before you get started anywhere.
So with your stakeholders, you know. I think sometimes that term takes us to a place of oh, there's the regulator, and there's maybe the private client industry and there's our consultant or but it's you know your stakeholders, are internal.
To right. So it's within your organization. Who are your teams that you're dealing with.
Who are the people who have interest in this, and you know to the the question the competing needs of these people.
And if you can get all those people, and in a conversation to kind of figure out what everyone's needs are before you start putting your plan together, that's just going to result in a much more effective plan, going forward.
Yeah, if you think through the items that are outlined in that data management planning fact sheet, it's gonna set you up for success.
And even if you don't have answers to everything that's on the checklist, if you've at least thought through those things, then you can have an idea of what the potential pitfalls for your project may be.
Of course, one thing to note. Sometimes we get in this a mindset where we think, oh, I have this data management plan.
I've created it. Okay. Now I'm done, and I followed it away.
And you know, and that's what happens to it.
I think it's important to remember that it is a.
It should be a living document. You are constantly, you know, every time you go out and you do a a a field event or something that's not routine.
Maybe there is an emergency, or you have to deal with right at A, you know, some different stakeholders who come come to the table and bring a different perspective.
You probably need to go back and take another look and review and update your data management plan to reflect.
You know the situations that come up right. You're gonna learn some things.
You're not always gonna get it right? Nobody does. And so just but making sure that you take the time to towards the end to take a reflective look and and update that plan so that it is more up to date, so that you can sort of have that again as a a lot of people talk about build the
flexibility into your system, so that you know when things don't go quiet.
Is it as planned you? You have some ways to still be able to get your job done and build that value.
And I wanna address. There's been a comment in the chat for a while that we haven't addressed yet about covering covering data ethics in our guidance, and I think there you won't find a very specific like bullet that says this is data ethics section of the document we don't have
its own fact, sheet or anything specific to it. I think one way that we did kind of address this through traditional ecological knowledge, which has a lot of ethics built around it in terms of who's accessible to the data how that data is is kind of gathered and used
and being very sensitive to a lot of the yeah kind of types of agreements and contracts that may need to be done in order to have access and then share or not share that type of information.
But in terms of your specific question about whether license professionals have rules dealing with both define data.
I mean, I think in general, what we think about that is, with the errors and omissions, and you know, sort of things like that in terms of contracts and engineering ethics and hiring ethics, and of that not specific to data.
And so one thing that we did kind of try and pay attention to a lot in developing this guidance was, is this a topic that's specific to environmental data.
And if it wasn't, we didn't necessarily give it as much attention.
So I was probably not a great answer to your question, but it's the truth.
Yeah, I think we touched on it a little bit, Katie.
Remind me in the data Governance section. There was a little bit of it.
We. As we mentioned, we didn't. Yeah, there's not a direct feature I don't think type data ethics into the glossary.
Or you know, find, replace, or whatever, and find find that necessarily.
But yeah, data falsification is just one of those.
I think it's just a given. Don't I? I guess my advice is, don't know false.
My data. And yeah, and I think that's probably pretty universal.
So!
Yeah. Brian, yeah, you're right. We didn't we didn't explicitly touch on it.
And the data governance documents. I think the closest we got was maybe we did touch on data ownership.
Which the reality of data ownership right now, is that it's very unclear.
There are a lot of lawyers at this moment trying to figure out who owns data.
Think good example. We all can relate to is, you know, obviously, I type in my Google search history.
So I generate that data. But just Google own it. Can they sell it?
I don't know. I think a lot of people don't know that right now, and that's kind of what we talk about with the data ownership section.
In our fact sheet, but you know it gets into that ethics arena a little bit, and I think they're just a lot of unknowns that you know, as the world becomes so so much more data driven, at really an exponential rate these are some of the questions that I think people are starting
to tackle.
There's another question like we've had, where does data management planning fit into creating or modifying your Csm conceptual site models often those conceptual site models are generated from data.
So you need to have a data management plan in place before that data is generated.
Hopefully, if you're basing your conceptual site model on legacy data that maybe you've obtained from other sources, then you should put a data management plan in place for the migration of that data, the data.
Exchange group is gonna touch on that historical data migration information.
So the planning piece should be hopefully in place or at least some governance should be in place in advance of you.
Using that data to generate your conceptual site model. Okay, that's helpful.
Yeah, well, we we sort of wrote the when we structured the documents, you know.
If you look at that, there's a you know. If you go to the website and look at our front page, there's a kind of a graphic.
That's our graphical table of contents, and we intentionally put data management planning sort of at the top and almost like with a box.
That sort of covers everything, and that was very much intentional.
That planning, you know, we talked about mostly planning as it relates to field data collection today.
But planning, as it relates to all of the environment, you everything must, we believe, anyway, must really start with a plan, and hopefully a good one.
We give a lot of advice and best practices on how to do that.
So pretty much. Everything needs to start from there and build from there you don't have a good if you don't.
What's the quip when failed?
A plan. Then you plan to fail. Right? It's a little cheesy, but it ringcentral.
When it happens there are some more great questions.
Should I? Well, let me quickly. Okay, okay, go ahead. Go ahead.
I can. I can take that one on chain of custody so yeah, there's a question that says about it's a time consuming practice in the field, particularly to develop papers chain of custody.
And so what should be included in digital chain of custody?
I mean the short answer to that right is that the chain of custody is typically something that's generated by the laboratory in terms of what needs to be in it.
Right, the laboratory is statistic. Dating like these are the things that need to be in a chain of custody so that they can do it.
They can process those samples, and you know, give you the data that you need for your particular project.
So no, yeah, agreed that it makes the lab save time on sample check-in.
Now I have definitely had the situation where some laboratory information management systems, their limb systems actually can't use digital inputs.
Right, that they are a manual entry system for laboratory login.
So I've built systems where you, we use electronic chain of customers.
We're generating them automatically from a digital field tool system.
And we're emailing them to a specific laboratory input process.
And yet they can't then, on their side, use an electronic input, so I do have say that you really have to be in close communication with your laboratories on what is actually going to work for them, not just in terms of what data, they need but in terms of what might actually come and bolt up to their
system in an effective way. And many, many times I think that it's it's harder than it seems, because the information management systems aren't gonna work and play nice together.
However, that being said, getting that chain of custody in electronic format, you know, even if it's just a Pdf with typed sample ids and typed sample date and time couldn't make a huge difference on the laboratory and because they
don't have to interpret anybody's handwriting so that can make a difference, too, even if they do have to.
Then manually type it back into their system.
Okay? Are the questions dwindling? Have we answered them all? I can't imagine.
We answered all of them. If anybody has any more, please go ahead and quickly get them into the, and we'll do our best to get them.
Looks like we do have few minutes left so I'm gonna ask this extra little bonus question right?
Which we sort of saved till the end, and it can be.
It's like winning the lottery ticket right we're winning your lottery.
Who knows what, who knows what can happen? But sometimes the answers can be fairly interesting.
So question is, you've got some extra budget the end of the fiscal year, or you know, maybe somebody did win the lottery. So one of your coworkers won the lottery and really wants to invest in your data management system.
What do you want to do? What would you do? You win the lottery?
You wanna fix your data management system, particularly field data collection.
What do you? What you gonna do, payilis?
I have 2 answers, the first one's super boring, so I am always looking at licensing and hardware no, I'm planning for the future.
I never feel like I have quite enough licenses.
Always looking to see if I can add a few more and really just things I need to do to keep everything running smoothly.
But so let's just assume that I still have money after that, and I can do not boring things, really looking at the future.
And I know very early on at the first question where we were talking about planning for those not just those needs, but those wants.
This is kind of where I put some of that monies.
What can I invest in, or what can we invest in? That's going to either add to our capabilities or improve an existing process?
You know? What can I do to add to our system to add value essentially, and you know that might be some new hardware, some new software.
But maybe it's a new hire who has a skill set that I did.
You know that we don't have it could be any number of things.
But yeah, I'm always looking for an opportunity to address those ones.
So if anyone wins the lottery and wants to invest in my data management system.
Yeah. I'll go next. I would say any opportunities for standardization.
So like. I mentioned a few times over the course of this.
If you're in an emergency response situation, you don't necessarily have time to build yourself a nice, beautiful digital field form.
But if you've built those like standardized forms in advance, then you can pull them out of your toolbox.
And deploy them on your project. So, having the time to think through all of the scenarios, to build some like standardized tools or standardized dashboards, standardized, reporting things that can be deployed at enterprise scale across a wide variety of projects is
always really helpful. I think.
Yeah, I think you know, when Facebook, you know, it's kind of a boring answer.
But I always start with what do I see coming up next year?
That's kind of predictable, because typically those predictable things are the lowest-hanging fruit for automation.
And the more I can automate them, the more I'm hopefully setting myself up for next year to have even more money in my winning lottery ticket.
So I always I like to start there like what is a task?
That is just, you know, driving us all a little bit crazy right now.
That's just like we have to do it routinely.
It comes up with a predictable frequency. There's 2, many manual steps that are time-consuming, and there's definitely opportunity to improve the data processes around it.
The to streamline the whole thing. And I typically try to start there.
If I have extra money to play with.
I'm all about the gadgets, and like the next thing, you know, like what cool thing would be a great way to collect data like in a semi-automated form, right like, is there some new thing out there that makes it a lot easier for people in the field to spend
like, time. Doing so like a lot of you know, the new GPS units other ways to get even higher quality GPS units fully connected.
Are there ways to use like, you know? Get your ysi automatically connected into your tablet. You know all those things that you're just like where you have are still having to like.
Bolt systems together? Are there ways in which we can like reduce that friction between them, and try and get the pipeline to be a little bit smoother.
I'm right there with Kristen as far as and new hire to fill in gaps in our capabilities.
You know, love those great dashboards. But looking a little lean at the moment on people, they actually produce them.
So, you know, filling the the knowledge gap, you know.
Where can we improve what we're already doing?
Well, we could still have if anybody has some other lottery ticket, answers, you can definitely pepper them in here in a second.
But we do have one question in the chat, which I think is interesting, or each of your firms using digital project planning and digital field data collection.
I think it's gonna be a mixed bag.
I think that's kind of the answer to that one.
But I think for those that are one of the biggest advantage you've seen so far.
So if anybody wants to, maybe share a little anecdote or something about what you know.
If you've been using field data stuff, where are some of the big gains in that process for you?
You know I think the the thing that sticks out to me the most is consistency.
It's you know what every time you send a different person.
Send 5 geologists out into the field and ask them to log the same soil, boring.
You will come back with 5 slightly different answers. So you know, that can generally be, you know, acquaintances to other tasks in the field.
So, you know, having some control terminology, making sure that all of the questions are being asked.
It's we are really prompting everyone to answer the same questions.
And where you can control those language. You know items that they're answering them in the in the same way, so that we can actually keep that information.
And you know, I think, augmented earlier. Use that data is the asset that it is rather than spending our time trying to reinterpret data that has already been collected.
So you know, for me, it's definitely the consistency front.
I would 100% second, that. And I'll also add, closing the gap from data collection to data analysis and visualization is that is the goal that's we wanna make those decisions.
And the desire to make those decisions faster and faster is more and more in demand.
And so we're really able to close that gap by using these digital tools and really leveraging to them them to their fullest potential.
I would say to you know, I think, where I've seen the biggest advantage has been the real time communication.
I was on a project this summer where we we're using a lot of real time data collection tools.
But in particular, just a lot of our mapping was being done real-time, and we had instances where it was the type of tasks where it's like, well, we'll see what happens at this location, and then we'll maybe go to the next one and then we'll maybe not and
we'll have to decide. And the fact that you'll maybe not, and we'll have to decide. And the fact that you know the project manager could open up a map and see real time.
Okay, they're doing this one right now. This is what the Pid said.
What should we do next? There were instances, too, that I think you know, really sort of helped product team get on board with this of, you know, going out to a site and having an issue with access of, like, we thought we were gonna put a boring location there.
And it turns out we can't get there, and our field personnel could actually, like walk around.
To like. What about here? Put the point they were standing on into the app.
Have us look at it at the same time in the office, and be like that, might still work.
Or maybe you could go a little this way, cause we wanna get toineation on that but we could have that conversation in 10 min instead of you know, in the past, I think maybe the field tech would just take their best guess at where they should put it, or it wouldn't get done and we'd be talking about another day of mobilization
so that real-time communication between even just internally, the teams has been huge for us.
Yeah, I'll add again the perspective of like remote and offline, and trying to do that, like to Katie's point, not having all the stakeholders in the room when you're trying to make those decisions.
What I've found is giving you all the information at your fingertips, so that you can make a much more informed decision in the field.
So like, for example, going out with a lot of the sites that I'll work on are historical sites that don't necessarily have the same infrastructure that we are investigating right.
So there may have been a building that was there in 1940, and it's not there anymore.
But we need to go out and investigate it. And it's just a field of grass now.
And so like. Where do you even know where to go? And being able to load up and navigate to a historical object that, like you wouldn't have any other indication to be able to get there and then be able to take those samples around a historical building on like a historical aerial
it's like being in a in like a virtual reality.
Environment, almost, like I can see graphs. But my app can see a building and being able to utilize and do that.
And so not just having that data like available to all the stakeholders.
But the data available to you in the field. And so you have all the historical data collected there.
So you know, if you need to do a step out, don't step out to the west because there was already a sample collected there 2 years ago.
Step out to the east. That's where you need to go, you know.
So for me, it's like all about having that accessibility as well, and I will third, the consistency issue but that's where we have 25 different views all coming through for a summer.
7 different companies, you know, and at the end of the day the boring logs yes.
Hey? I'm speaking.
We've been using it to track soil, disposal and some sites where we're having hundreds of trucks a day that we have to keep track of.
What was the truck number? What's the manifest?
I want a picture of what it looked like before it went off the site, and keeping it all in one place in keeping it, organized.
It's been immensely helpful for that.
From the perspective of a State agency, who is still somewhat, who's still trying to develop field forms and getting some of those put in place.
We're very close to it, but we're not there yet.
I think that a lot of field staff and and other staff who are having to deal with the How are you doing it can be to have to do all these data transcriptions, and how you kind of have to data has to jump from one thing to another a piece of paper it needs to go into an excel spreadsheet
which then goes to another data. You know, there's all these steps and if you can get a really good field data management workflow, it's digital you can begin to eliminate a lot of those steps.
Eliminate all the transcription errors that come along with those steps.
You know it. Just and just streamline it and let. Anyway, there are just so many benefits and in our folks are definitely, just really cannot wait to get some of those on board.
So I'm working working hard, guys, if any of you are in the audience working, working on our regist we're gonna get there soon.
Okay. It is. Now, one and a half an hour and a half, and I believe Devin jump in.
If I'm wrong here, that is probably the end of our time.
I wanna. Thank everybody who put questions in the question. Pane.
These were wonderful. They really drove the discussion. I hope that this, our intent here was to again add a a discussion piece, a real-life piece right?
We wrote these the documents with, you know, very general, because they were meant to be best practices that you could sort of review.
But we want you to remember that this yeah, data management is done by real people.
And we have real problems. And we deal with real things in the field.
And it doesn't always go as planned. But that's okay. Right?
Cause. You know what what we've got in those documents are really best practices, and I think you know the principles laid out.
There are really, really good. I wanna thank all of the panelists each and every one of you.
Devin, I want to thank you for helping out in the background, in the chat and elsewhere.
But most of all want to thank all the participants and everybody who joined.
And everybody who's who's interested in this in a data management.
Really happy!
It's like 10 min just real quick here. Thanks so much, Brian, for wrapping us up.
I just wanna have a few closing remarks just for logistics here for everybody I just wanna make sure that you all know that we do have a feedback form available for completion.
And that's also how you would obtain a certificate of completion for today's course. If you do require a certificate of completion for today's course, if you do require any continuing education units, so the feedback form is a on the training page that brought you to today's
webinar. So if you scroll down on that clue and training page you'll see a section called Feedback form.
You expand it, and then you'll be able to fill it out and check a box to certify you participated certificate will be emailed to you.
If you do request that I would also just like to offer that if anybody would like any further clarification on any questions or answers that were provided here today, feel free to follow up with us at Irc.
At Irc dot, or, excuse me, irc@ircweb.org.
I'll play here in the chat in a moment.
But you're also welcome to follow up with our trainers directly.
Their email is also listed on the Cluen training page that brought you to the Webinar as well.
So I'm not training Page. You'll see a whole list of all of our presenters, with their bios and their emails.
So if you want to feel free to reach out to any of them, and they will be happy to help you out.
But again, like we've mentioned, we do have 2 more email tables within this series.
The next is in April. We would love for you to attend that as well, and enjoy, so everybody knows that they all are recorded, and they will all be offered online Archive.
This one will be uploaded shortly. But you know, obviously we gotta wait for the for the next ones to be offered first, and then they will be available for those not able to join us live so with that I'm gonna just thank our expert panelists here, again, and then thank our attendees for
being here with us, and I will put the the link to our email.
And then the feedback form in the chat. Here, in a moment.
But I hope everybody has a wonderful rest of their day.
Devin, do you want us to stick around?
You guys are welcome to leave. I was just close out the room here.
Thanks guys.
Thank you.
Okay, thanks. Everybody. This is fine.
Thanks. Guys, good time.
Thanks.