On Stage in Nashville, Tennessee: A Date with Data at ii22

A Date with Data

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### Episode Transcript ###
00:00:01.52 >> You're listening to "A Date with Data" with your host, Amy Bitterman.

00:00:07.34 >> Hey, it's Amy, and I'm so excited to be hosting "A Date with Data." I'll be chatting with state and district special education staff who, just like you, are dealing with IDEA data every day.

00:00:19.50 >> "A Date with Data" is brought to you buy the IDEA Data Center.

00:00:25.10 >> Thank you for tuning into a very special episode of "A Date with Data." I had the opportunity to chat with data experts from states across the country at this year's IDC Interactive Institute live in Nashville, Tennessee. We talked about what it means to be a data quality influencer. Join me as I chat with Amy Patterson, IDEA Part B Data Manager with the Kentucky Department of Education, about what her role as a data quality manager looks like in Kentucky. We also touched on some challenges that she faces and future plans to impact the collection of high quality data in her state. Well, thank you Amy so much for take go-ahead few minutes to stop by the podcast. The theme of this institute is data quality influencer and how all of us in some way or another are data quality influencers through what we do, or what we don't do. So wanted to just hear from you and from your perspective, what is being a data quality influencer really mean to you?

00:01:23.63 >> I think my role as a data quality influencer is just to ensure that we get the best data possible and help people understand why it's important to get the best data, how to use that data, what the data mean, and really just to understand the importance of good data and what it means if we don't have good data.

00:02:00.60 >> Yeah, absolutely. How do you think you influence the data quality in your state? What are some things you do to have an impact?

00:02:09.30 >> I provide a lot of tools, some IDC tools, some tools I've developed, to help people in this state in the districts understand, or in the LEAs understand what I'm looking for, where the data come from, the best way to enter the data, the best way to get the data out, things to look for that may lead to not necessarily great quality data. So I feel that is a big portion of my job. And internally as well within our SEA, I feel it's important to help people understand how we use the data and what is good data, what to be looking for those sorts of things, even within the SEA.

00:03:06.47 >> Yeah, it's an internal focus and external ...

00:03:09.49 >> Yes.

00:03:09.77 >> ... job, I would imagine. What are some challenges that you've encountered in your kind of data influencer role, and how have you addressed those?

00:03:18.42 >> A lot of turnover. Turnover within the LEAs, turnover within the SEA, just helping people to ... I think it's difficult to communicate with the right people. We provide trainings. We send out e-mails. We do ... We provide a lot of communication, but the ones that don't read that communication, don't attend those trainings, they're the ones that need it the most.

00:03:48.24 >> Yeah.

00:03:49.01 >> And that is ... That's probably the most difficult piece, and a lot of times if we have a new Director of Special Education, they're so overwhelmed. They don't even ... I could talk to them, and they just wouldn't even understand ...

00:04:07.04 >> Yeah.

00:04:07.54 >> ... because their minds are on so many different things. And they really don't understand until after the first year of data collection, really what's important and what's not.

00:04:17.88 >> Yeah, a lot of it is kind of on the job training.

00:04:19.78 >> Yes.

00:04:19.90 >> You need to really experience and go through it until you really understand what it's like and how to do it.

00:04:25.27 >> Exactly. Yes.

00:04:25.96 >> Yeah. What do you have coming up next? Do you have anything, future plans for a how to be a data quality influencer? What are some things you ... additional things you could be doing?

00:04:38.30 >> So we have a statewide dashboard that we are partnering with another agency in Kentucky to develop, and I feel that is going to be a key to helping people understand how to use the data and, therefore, maybe how to ensure that the data that they provide are better. So that's one thing. We do trainings twice a year, every year. So, and I'm working on ... I'm constantly working on tools on better reports in our Student Information System ...

00:05:15.80 >> Mm-hmm.

00:05:16.37 >> ... statewide Student Information System that will help people be able to look at data and not only get data correct for their data submission, but also, how can we make this better for kids? What can we do for kids to really help them?

00:05:36.89 >> Yeah.

00:05:37.42 >> And so we're constantly working on tools like that within our statewide Student Information System.

00:05:43.02 >> Mm-hmm.

00:05:43.72 >> And internal communications with other offices are also key.

00:05:48.01 >> Yeah. How have you kind of made some of that happen internally and with what other offices?

00:05:55.14 >> Yeah, it's ... It requires relationship building.

00:05:59.53 >> Yeah.

00:06:00.01 >> And sometimes it's hard to break out ... break down the silos and the walls. I think Generate has actually helped us because we're forced to work together, and I don't think they ever really ... The Office of Educational Technology within the Kentucky Department of Education, I don't think they really realized how complex our data are.

00:06:23.27 >> Until they have to get in there and do the programming for it ...

00:06:25.64 >> Exactly.

00:06:26.02 >> ... and the specs and ...

00:06:26.72 >> And how complex it is and it's also how much we are monitored by OSEP ...

00:06:33.22 >> Yup.

00:06:33.64 >> ... and by the federal government.

00:06:34.84 >> How much is really required.

00:06:36.01 >> Yeah, how much is required and follow-up, data nodes, those sorts of things. I don't know that other programs are required to do that ...

00:06:44.73 >> Yeah.

00:06:45.15 >> ... which I don't think I realized myself, but I don't think they realize how important it is to get things right ...

00:06:52.22 >> Yup.

00:06:52.74 >> ... for us, more so than most ...

00:06:54.78 >> Yeah.

00:06:55.18 >> ... other offices.

00:06:56.16 >> And they're data quality influencers themselves too ...

00:06:58.88 >> Yes.

00:06:59.08 >> ... and probably haven't realized it or realized ...

00:07:00.95 >> Exactly.

00:07:01.25 >> ... what that means or what their responsibility kind of is in that role. Well, thank you for stopping by.

00:07:07.56 >> Thank you for having me.

00:07:08.13 >> Thank you so much for answering the questions. We really appreciate and hope you enjoy the rest of the conference.

00:07:12.94 >> Thank you.

00:07:13.98 >> I want to give a special thank you to Amy for joining us on the pod. Next, I'm joined by Rochelle Davis, an Education Program Specialist with the Office of Special Education Programs at the US Department of Education. We get into the importance of communicating the why behind collecting the data to states and districts and how the data drives policy and decision-making. So I'm joined by Rochelle Davis from the Office of Special Education Programs, and we are live at the Nashville Interactive Institute. Thank you so much for jumping on and chatting briefly with me.

00:07:48.07 >> No problem. I'm happy to be here.

00:07:49.98 >> Yeah.

00:07:50.59 >> It's great to be back in person. I can tell you that for sure.

00:07:53.63 >> Yes, it is. It's wonderful.

00:07:54.88 >> Lots of smiling faces, so.

00:07:56.77 >> Yes, it has been very good to see everyone. So as you know, the theme of this Interactive Institute is data quality influencer and how everyone in their own way, whether they know it or not, can influence and does have an influence on the data. And what is being a data quality influencer mean to you?

00:08:17.29 >> Well, it means a lot of different things because from where I am at the department, I am a data quality influencer as it relates to kids with disabilities in every meeting that I'm in because I always want to make sure that our students are counted, that they're at the table, that they're a part of the conversation. So I'm always talking up the data that we have and how we can make sure that it's utilized so that we are getting the best programming, the best policy designs for our kids with disabilities. From the states, the state staff have no idea how much they influence federal data and what that does. We have states who, they come up to us with an idea. And so it might take us a few years, but eventually, we warm up to it. And so the change, the recent change to go ... change how we count five-year-olds in kindergarten or not in kindergarten, that was something that came from the states. And it's been a really huge data quality, positive data quality influence. It's really boosted the data. I think we have a much stronger idea of what the programming kindergartners are receiving that we really didn't know before.

00:09:35.86 >> Yeah. How do you feel like you and the department influence IDEA data?

00:09:43.98 >> Well, obviously we just sat in a session where we talked about the different hats that people wear. And you can wear the data police hat, and unfortunately, sometimes we have to wear the data police hat. But more than that, I think that we are able to influence that data by the policy decisions. We're talking about how the data is collected. One of the things that was really interesting to me is how just a data collection can really drive policy. And so not only are we data quality influencers, but by being a data quality influencer, we're also a policy influencer, just merely with the data that is collected and how it's used.

00:10:30.45 >> Yeah, absolutely. And I think a lot of times, especially states and districts don't always see that connection, and the more I know we're hearing from states saying, "We have to get districts and schools to understand what this data is ultimately used for and why it's important and what they can do with it too," really can make a huge difference in their quality.

00:10:50.28 >> Yeah, I remember being a first-year teacher. Maybe I was a second year, I don't really remember, but someone waltzed into my room on December 1st, and they're like, "How many kids are in your room?" I was like, "four." And so what I didn't realize was that I didn't make the connection at the time, that what that actually was, was the child count that was being done. I think it was a little higher quality than that, but I didn't make that connection until I got to the department. And I don't think that the teacher that's sitting in the classroom, certainly not the first-year teacher that's sitting in the classroom, realizes that on October 1 or December 1 or November 1, somebody is actually counting how many kids are receiving special education services.

00:11:35.69 >> Yeah, and what that impacts.

00:11:36.88 >> Mm-hmm.

00:11:37.72 >> So I think that's a good lesson that trying to get across to all levels, we're not just asking for this information just to ask for it.

00:11:46.04 >> Yeah.

00:11:46.38 >> Here's more specifically why we need this and what happens to it along the way.

00:11:50.14 >> Exactly, what are we going to do with it? Why do you want it? And I think once you know the way, it becomes ... You have a little more skin in the game, if you will, to improve the quality of what it is you're giving once you understand what's going to happen to it and how it's going to be used.

00:12:05.98 >> Yeah, I feel that comes up again and again. It's the why. Why are we doing this?

00:12:10.60 >> Mm-hmm.

00:12:10.89 >> That's really what is the foundational piece to all of this. Do you have examples, not necessarily names of states, but things you've seen in states where you would really point to and say, "That's a really great data quality influencer example"?

00:12:27.89 >> There is a state that we've been working with around significant disproportionality. They have a system for identifying significant disproportionality for utilizing the data that they have, and it's not simple, but it is so strong. And being able to utilize the data in the manner that they have really lets them pinpoint where they are seeing significant disproportionality and how they can go in and address it. They're able to ensure that you're not pinning one LEA with a bunch of discipline referrals that they didn't actually get. And so having that for them, I think, is really important, and it lets them provide assistance, technical assistance to the district that actually needs it, not to someone who just randomly had a student that had a lot of suspensions and expulsions somewhere else.

00:13:28.92 >> Yeah, no, that's a great example because that is an area that is so complicated and states and districts struggle with so much, but you have that opportunity there where you're using the data to identify these potential issues, but then doing that root cause analysis.

00:13:43.12 >> Exactly.

00:13:43.65 >> Then leading to you have to show in order ... When you're doing the Comprehensive Coordinated Early Intervening Services, it has to be connected to those root causes, so there's ...

00:13:51.65 >> Exactly.

00:13:52.41 >> It's really a good example of how that ... why you're using that data, how you're using it, and all that.

00:13:58.23 >> Mm-hmm, yeah.

00:14:00.37 >> What do you see as some of the bigger challenges around kind of being a data quality influencer, whether that's within OSEP or states or districts, and how were some ways it's been addressed that you've seen?

00:14:11.33 >> I think one of the hardest parts is telling people what they don't want to hear.

00:14:15.11 >> Yup.

00:14:15.82 >> It's when you have data that doesn't show what somebody actually wants to see. I think it's very difficult to get that influenced in there, and so making sure that having a story. We had a session this morning, and I thought that the speaker was ... Dr. Rincon was excellent at really showing us kind of how to break bad data news to other people. And I think that that is something that I hope folks are able to take away from this because that is one of the hardest aspects, certainly in my job. I have folks who have ... They have a new administration. They're coming in. They're ready to make things happen. They want to do good, and you show them something that is completely opposite of what they think. And they're like, "No, you're wrong." I'm like, "Well, I don't know that I'm quite wrong. Let's try this again."

00:15:12.21 >> Yeah.

00:15:12.45 >> Or they hear a story when they're out on the street and that ... Stories are really important, and they really stick in there. And sometimes having to maybe show data that isn't ... that doesn't align with the one story that they heard. It's just ... It's hard to do that, but similarly, translating data from the data geek speak, if you will, into the policy speak is also a little challenging at times.

00:15:42.76 >> Yeah, absolutely. So what do you have coming up next as a data quality influencer?

00:15:50.09 >> We always are looking at how to improve our data collections. The EDFacts modernization is coming up, and I think that that's going to be ... it's going to change how we do ... It's not going to change the underlying aspects of it, but what the data timeline looks like is going to be a little bit different. And I think in the long run it's going to be a really positive thing, but I don't doubt that there are going to be a few hiccups along the way. And change is hard. So that ... That'll be something that's big coming up.

00:16:24.43 >> Yeah.

00:16:25.47 >> Obviously, just looking at the collection packages and trying to figure out how to look at data and best align it to make sure that we're collecting what it is we want to be collecting, that's always on the agenda as well.

00:16:42.01 >> And the potential changes I guess with the EDFacts data collections still ...

00:16:45.82 >> With ... Yeah, exactly.

00:16:46.90 >> ... which maybe is what you were ... Yeah.

00:16:47.81 >> Yeah.

00:16:49.05 >> Yeah, well, a lot of great stuff coming up. I'm sure it's never a dull moment.

00:16:54.05 >> No, never a dull moment.

00:16:55.65 >> No.

00:16:56.22 >> It's good, though. It's fun to be able to be, as I said, making policy, but making it through the use of data and through the data collection, so.

00:17:07.80 >> Thanks so much for joining us from Nashville live at the Interactive Institute. To check out any resources, or if you want additional information about being a data quality influencer, take a look at the podcast notes for some links.

00:17:22.79 >> To access podcast resources, submit questions related to today's episode, or if you have ideas for future topics, we'd love to hear from you. The links are in the episode content, or connect with us via the podcast page on the IDC website at ideadata.org.