We're a medical company and we have our own source systems that process claims from multiple organizations or health plans. In our world, there are about 17 different health plans. Within each of those health plans, the membership, or the patients, have multiple lines of businesses, and the way our company is organized, we're in three different markets with up to 17 different IPAs (Independent Physician Associations).
While that is a mouthful, because of data governance, and our having own data governance tool, we understand those are key concepts and that is our use case: so that everybody in our organization knows what we are talking about. Whether it is an institutional claim, a professional claim, Blue Cross or Blue Shield, health plan payer, group titles, names, etc., our case represents 18 different titles. For us, there was a massive number of concepts and we didn't have any centralized data dictionary of our data. Our company had grown over the course of 20 years. We went from one IPA and one health plan to where we are today: in five markets, doing three major lines of businesses, etc.
The medical industry in general is about 20 years behind, technology-wise, in most cases; there are a lot of manual processes. Our test use case was to start from fresh after 20 years of experience and evolution and just start over. I was given the opportunity to build a data strategy, a three-year plan where we build a repository of all sources of truth data used in governance. We have our mapping, our design, our data linkage, principles, business rules, and data stewardship program. Three years later, here we are.
erwin DI needs the Data Modeler, obviously, to be able to harvest the data directly from an existing database, or even a brand new one as you're designing it. That is a huge step in the right direction, although erwin has been known for that for 30 years. But the ability to take that model and interface it directly to the data governance makes it an easy update. It makes it simple for me to move from a development/design stage, for each environment, and into production, and to update the documentation using the data harvester and the Metadata Management tool and data cataloging module. That really brings it all together.
If I were to note any downside, it's that there are multiple modules and you can't have one without the other if you want to be world-class. But when you have them all, it makes life really easy for something like data profiling of an existing database to know if you want to keep it or not, given that there are so many legacy changes all the way through. The way we do it, when we make a change to a database or we add a database, the model is mapped, we import it, and then we have the data stewards populate any of our descriptions in their glossaries. The tool allows us to see all that instantly, unlike before.
I mentioned we have a data steward program, which is not part of the tool. While the solution has ways of using issues and for requesting data access within it, we're still stumbling with that. Sometimes it's just easier to talk to people. But we find that getting requests, getting data, and updating it, is actually a much easier process now.
In addition, the fact that I can always refer back to a centralized location with executive approval has helped me.
For our business analysts and data analysts, especially for some of the wannabes and the data steward program, we have been able to centralize a tremendous amount of data into a common standard. One of our mandates was to have a Tableau-type business-intelligence component. We went live with our entire enterprise data warehouse, all the tools, in January of 2019, even though we started in 2016. We spent most of the year in massive amounts of discovery just around our organization's members. We didn't even get to claims or provider-contracting because they are so complex. The tool itself has expedited our getting to brand-new levels we've never seen with our members, because now things are becoming standardized.
People can refer to an inventory of reports and they can see that we don't have the same report in 20 different places, having 20 people support them. Now, there is one report in Tableau with one dataset. That dataset has become a centralized dictionary/glossary/ terminology inside the tool. Anybody who needs to get access to our data can access it.
It's enables efficiency. Just in our marketing department alone, the number of new ways they have to think about our membership and growth has completely changed. They have access to data to make decisions.
Executives can now look at what we call a scorecard of our PCP because we now have standardized sales. Everybody knows what they mean, how they are calculated.
Very high-end statistics and calculations are now easily designed. Anybody can go look at them, they know where to go. And if they want something because it helps make their business grow, it's almost a 24-hour turnaround, as opposed to a four-week SDLC process. It has expedited our process. The goal was to build a foundation and then, for the next couple of years, to really expand it. We hit that and I don't think we could've done it without these tools.
Recently we had to bring on a brand-new entity, a brand-new medical group. One of the minimum requirements was that we had to take 10 years of historical data from whatever system they had and to convert it, transform it, map it, and log it into our existing source of truth. We did this about four years ago for an entity, and it took us almost nine months just to get a dataset that somebody could use. This last time, it took us three weeks from start to finish because, outside of the governance tool, we have erwin's Mapping Manager and harvester. It also allows us to do source-to-target, so we have all our target mapping to our own repository, and then we have all our targets to EDW already mapped. Our goal was to bring a 100 percent source of truth. We had a complete audit, from when it came in from outside the building, to a location in the building. Then we would transform it into our EDW to whatever attributes, facts, or dimensions we wanted to. The tool allowed us to do that almost in hours, compared to what used to take months.
Another thing with their DI, not necessarily governance, but some of their other tools — which, of course, all feed back there — is that as soon as we do it, it's available to anybody. Not that a lot of people look at it, because a lot of times they just come and ask us, but the difference is that we're giving them the right answers within minutes. We don't have to tell them, "Well, let me go back and search it for six days."
We have downstream departments, like our risk department which manages our Medicare patients, and makes sure that we are taking care of them, which involves a very data-intensive process. Our ability to bring in historical data from an old system, a different type of a computer system, and convert it to make it look just like ours, no matter what it looked like before, is all because we have a data governance program. People can look at the changes from before and after and determine if they need certain data.
A year ago, if somebody in our company's "left hand" brought in new data, no one but that left hand would know about it. Today, if somebody brings in data, all my data stewards know about it and they can choose to subscribe to it or not, today or later. And that is a matter of a flip of a switch for them, once we have brought it in and published it to anybody in the company. That's really important, for example, from the point of view of a human being. If someone has been around for 20 years it would be nice if we had all their records. Because of our data governance and what we built, all those records are maintained and associated with that person, and that's huge from a medical point of view. Data governance is helping us become even a better company because we know our data and how to use it.
The fact that erwin DI for Data Governance has affected our speed of analysis is a given. The DBAs are starting to use it more and even some of our executives are wanting to get to it for the data dictionary. It can happen that somebody from one of our departments sends them something and it doesn't make sense to them. Our goal was that if that happened we would try to find out and try to centralize it. We ended up creating our own dashboard reports on our Tableau server and published them to the same parties, so we could get rid of old habits and focus on new ones that have now been validated and verified, with the rules checked.
The data governance allows us a real-time inventory. Every time there's a new request or a new ask, we put it in there and we track it and we make sure that our attributes are the same. If they're not, we have an explanation with a description for the different contexts in which the data is being used.
In addition, part of our ingest of an ask is that we take a first look at it and we provide as-is documentation so that the functional design can be tracked. That's a huge advantage. That has saved huge amounts of time in our development cycle, either for data exchange or interfacing, or even application development. The ability to just pull up the database, to be able to look at the fields and know what's important and what isn't important, note the definitions — we're able to support that kind of functionality. I'm one of the data architects here, and we work with everybody to make sure that our features and our epics are managed properly. For me to be able to quickly assess something, within a few minutes, to be able to say, "Here's the impact, here's what we have to do," and then hand it off to the full-blown design teams; that saves a month, easily. And that's especially true when there are 10 or 15 requests a week.
As for how the solution’s data cataloging, data literacy, and automation have affected the data used by decision-makers in our organization, on a scale of one to 10, I would give it a seven. It depends on which stakeholder or executive we're talking about. But has it had an impact? Every one of them has brand new reports, reports that didn't exist a year ago. Every one of them now sees data in a standardized format. The data governance tool might not have a direct impact on that, but it has an indirect impact due to the fact that we now govern our data. We treat data as an asset because of the tool. It's not cheap, it's an expensive tool. But my project has a monthly executive steering committee and, for 36 months, they never had a question and never second-guessed anything we did, and they loved any and all tools. So being able to sit with them and say, "Hey, we had an issue," and immediately give them a visual diagram — show what happened with the databases and what somebody may have misinterpreted — is huge; just huge. For everyone from our chief operating officer to our network operations, physicians' contracting, our medical management group, and our quality improvement group, it definitely has impacted the company.
We've only taken it out to about 50 percent of what it can do. There's so much it can do that we still don't do, because we ourselves are maturing into the program. It really has helped when it comes to harvesting or data profiling. For those processes, it's beautiful — hands-down the best so far. I love the data profiling.