[00:00:01] Speaker A: Hello. Thank you for joining us. This is what counts, a podcast created by Trailblazer Consulting. Here we highlight proven solutions developed through our experience working with companies across various industries, and we talk about how you can apply these solutions to your company. We share our experience solving information management challenges, like creating and implementing a records retention schedule, creating an asset data hierarchy, or helping with email management.
This is Lee, and in this episode, we'll take the back file characterization concept and use it in a slightly different way. In this special episode, I'm going to let Maura explain.
[00:00:40] Speaker B: Oh, put me on the spot. Hi, everyone.
Yes. So if you have listened to our couple of back file episodes where we talk about the sometimes overwhelming collections that people create of both paper or electronic records, right toward the end of the last episode, Lee mentioned something about statistical sampling and taking. Taking a deeper dive into some of the collection holdings. Go look at some boxes, open some folders to either find out more information or prove your hypothesis about. Yeah, I think that's what's in these boxes. And I wanted to talk about. That triggered a thought for me that I wanted to talk about a slightly different application of this same concept that we did for a client many years ago, where we were faced with this challenge.
Figure out what happened here based on the records. And these records weren't about anything in particular. They were just.
They were running an operation, and they wanted to know what happened, but they did not want to go and look at every single box, every single folder. And these were boxes primarily of paper files. They were pretty old, and it actually came up more than once. We've done this more than once. So sitting there thinking about, how do we find out if. And it was, if this thing happened, right. It wasn't just in general what happened? Like, write a history of this organization. It was, did this thing happen? And what do we know about that? What can we find out about it? Okay, so we looked at first, how big was this operation? How big was this organization? And physically, we're talking about a fairly large campus like environment. Many buildings. And the buildings were, at the time we did this project, they were more than 50 years old. They had been in operation, continuous operation for more than 50 years. And some of the buildings had changed purpose, but most of them actually were still the same. So it's a big operation. There were garages, there were labs. There were dormitories. There was a hospital, small hospital kind of setup, a lot of office space.
And people just left stuff behind, because you know what? People leave stuff behind even in continuous operating environments. You find a place, and you put records in it. You put boxes in the attic, you put boxes in the basement. You shove them in a file drawer. You keep them in closets, in corners, everywhere. It just happens. If you've ever been through an office that a company has sat has, that has a company who's been in a place for more than ten years, you will find boxes in every spare corner. Interestingly, a little tangent.
One of the things that often triggers, it's apparently my word today, that often prompts you to go clean up space is the rents going up, the rents going up, and you need to get these boxes out of this really expensive office space. And that's when you send them to offsite storage. That doesn't actually solve your problem. It just moves it to the off site storage. It doesn't solve your records problem. It immediately relieves your rental problem. But this operation had plenty of room. They weren't sending things to off site storage. They were just shoving them in different places. So we then looked at. All right, let's look at the map.
What are the types of buildings? And we came up with ten categories of buildings. And we rated those buildings based on the likelihood that the thing we were looking for, the thing that we thought might have happened, that something in those buildings would contribute to that thing. So maybe we were looking for the creation of an amazing cake. And so where was the most likely place for that recipe to have occurred? To kitchen, dining hall, but also maybe dormitory. So we rated those buildings higher in our ranking. They needed more attention. Some of the other buildings, garage, administrative office, probably lower on the list, in likelihood of a recipe occurred. Lab could go either way. If you've seen one of those new tv shows recently about cooking in labs. So that was in the middle. So we ranked all the buildings. There were 500 buildings on this campus. We ranked all the buildings into these categories. One, two, three. I think there were five categories, maybe. And then we set up statistics. All right, if we go and we look at the boxes and we look at samples of boxes, because, again, we couldn't open them all. We were sending two person teams to buildings for the category one buildings where we thought it was most likely that the thing we were looking for had occurred. We were going to look at a 25% sample of the boxes, and if we found an occurrence of this, of something that hinted toward the thing in the 25%, then we would look at 10% more. And if we found one more occurrence, then we would look at all of them in that building. That then became 100% inventory building. In the lower ranked categories, we would only look at 10% of the boxes. If we found any occurrences in the 10%, we would look at 5% more. And if we didn't find any more, then we would leave it alone. If we found more, then we would go to a 25% review.
And. And we reported these findings weekly because they were. The buildings were spread out. It took some time. We sent the two person teams across campus. We had, I think, eight teams going at once to the different sets, and we prioritized them. So of the eight teams that we had, we sent, say, four of those teams to category one buildings first so that we got the highest priority ones, got hit first. And then we sent two teams to category two and two teams to category one, category three, so that we would start. We started to see patterns pretty quickly. We were right in our assumptions about the administrative offices. Didn't have any indicate in any indications, for instance, of this mysterious recipe we're looking for. But we were wrong a little bit about the garages. Somehow in the garages, they were creating beautiful cakes. They weren't the cake we wanted, but it raised our thresholds. So once we found that in the first couple of garage buildings, we were like, all right, these are actually not category three. These should be in category two. And we bumped up the percentages that we looked at, and we did this across the entire campus.
We captured all of this information. We recommended which buildings and which record sets needed a deeper dive to determine what really happened and presented that to the clients, to the owners of this campus, to the people who were looking for the proof of what happened and where did it happen so that they could make decisions on how much more did they want to do to find out, or did they want to take action based on what we'd already found.
And it was a four month project. Six month project was a while ago. I'm trying to remember. It was at least a six month project to get through figuring this out, figuring out the approach, what would be statistically significant. And I've kind of. The examples I've given today about the thresholds are not the real examples, but we did it based on the categories of building that we came up with and the type of activity we were looking for. And then we tested out in a couple of weeks of pilot processing to see did our theories make sense and did the thresholds make sense, did the escalation process work? And we adjusted slightly based on the findings from that pilot. So it was an intense and disciplined process to go across the entire campus and inventory at a high level.
The occurrence of this type of information so that we could take this. Basically, it's the back file characterization approach, but rather than our goal being to destroy things, our goal was to find something.
So what do you think about that?
[00:09:53] Speaker A: Sounds like an incredible project and a great application of back file characterization.
So that's all I had to say on that one. I didn't work on that specific one. I worked on a couple other ones. And it's pretty intense.
[00:10:09] Speaker B: Yeah, it was. What I found about it was, it was like that different purpose of trying to find something versus trying to destroy things.
Both equally important in the world of records. It's good to destroy records that are out of date, that you don't longer, that you no longer need based on your retention schedule. But the finding of a particular thing that had been. That was hard to find.
It was really an interesting kind of mystery approach, and the team was excited about it, which was good. And the clients were, were felt confident about the results that came, that came out of this process, and they were able to move forward to their next step in the bigger action that they were taking off of the results of this.
[00:11:03] Speaker A: So, great job.
Thanks. If you have any questions, please send us an email at
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