Our labor costs are half what they would have been. And then there was the lack of quality or the lack of productivity. So it's had a huge impact on all those things. That's why the money is more than recouped.

My three-year plan was to recoup the $3,000,000 we spent in the previous three years, and this year we have already recouped $2,400,000 of it. We have two more years to recoup to break even. I don't know if that's directly related to just the Data Governance. It might be that because of governance it has allowed us to do all the other things. The ultimate goal was to get data into the hands of the decision-makers and have it as accurate as it could be, so they could make better decisions.

We have improved our time for report servicing and our capabilities in turning things around quickly.

One thing we missed in our estimates of cost savings was the reduction in the number of requests or the time it would take to do a request. The issue was that we created a standardized report, and it worked so well that we stopped getting requests altogether. We never thought they would never send a new request, so we saved even more. But that's because we knew the fields, we got it right the first time, and we created standards around governance that allowed us to really simplify our business. I have 29 standardized reports around memberships, providers, and claims, and they are used throughout the organization now. Those are reports we didn't have before.

In terms of time-to-value, I wouldn't say standing it up was quick, because a day would be quick. But it was under a month. It could be set up pretty easily, especially once you understand all the components you need and all their modules. The erwin governance solution is on the cloud while the modeler is on-premise and the suites are also on the cloud. And the fact that it's cloud-based made it simple and straightforward. It was just "boom," we got our logins and we were fine, for the Data Governance software.

We are in the early stage of ROI. The ROI is more in terms of the time that we saved from the analysis. If the analysis is much faster, let's say by 30%, we can get some of the insights faster. We can then accordingly make business decisions, which will give the business value. So, right now, the ROI is in terms of being able to be faster to market with some of the businesses.

I can't quantify our return in a dollar amount. However, we can now answer how the system works down to the transformation level as needed. Previously, we would need to start a "project" to obtain such information.

We have absolutely seen return on our investment with Data Intelligence so far. There has been an increase in delivery speed and the decrease of project costs that results. The decrease in time to find the information you need to do your job, versus the larger amount of time needed to research without erwin, has been invaluable.

If I use a traditional ETL tool and build it through an IT port, it would take five days to build very simple data mapping to get it to the deployment phase. Using this solution, the IT cost will be cut down to less than a day. Since the business requirements are now captured directly in the tool, I don't need IT support to execute it. The only part being executed and deployed from the metadata is my ETL code, which is the information that the business will capture. So, we can build data pipelines at a very rapid rate with a lot of accuracy. 

During maintenance times, when things are changing and updating, businesses will not have access to their ETL tool, code, and the rules executed in the code. However, using this tool with its data governance and data mapping, the data captured is what actually it will be. The rules are first defined, then they are fed into the ETL process. This is done weekly because we dynamically generate the ETL from our business users' mapping. That definitely is a big advantage. Our data will never be off the rules that the business has set up.

If people cannot do discovery on their own, then you will be adding a lot of resource power, i.e., manpower, to support the business usage of the data. A lot of money is saved because we can run a very lean shop and don't have to onboard a lot of resources. This saves a lot on manpower costs as well.

We have seen an ROI, and it is more about speed and that opportunity cost.

We used to calculate ROI several years ago but are no longer concerned with it. This product is very effective and it has made our jobs easier, which is a good return.

The client is thrilled with higher quality, lower-cost products, and the services.

ROI is a bit hard to come at. There is peace of mind knowing that we now have visibility into the business. To be able to know that I'm instantly pushing all the data definitions out to the business, even though culturally I haven't changed everything so they are looking at it on a daily basis. This is still hard to put a price tag on. I know I'm doing my piece of the job. Now, I have to help them understand that it's there and build a more robust data set for them.

If you count that it takes 70 percent less time to deliver and multiply this by 40 people who work around the development process, this is a big time savings that we can use for more development. From my perspective, there is a very big return on investment for this tool.

We struggle when it comes to ROI because data governance and data management are parts of an enterprise strategy, as opposed to a specific, pinpointed problem. An organization might be able to use the overall data management strategy for multiple things, whether it's customer satisfaction, customer churn, targeted marketing, or improving the bottom line. When we clean the data and bring some method to the madness, it creates a base and, from there, an organization can really start reaping the benefits.

They can apply analytics to the clean data and have right ownership of the data. The overall process is important as it is the base for an organization to start asking: "Now that I have the right data and it is quality compliant, what can I deduce from the data?" There may not be a dollar value to that straight away, but if you really want to bring in dollar value from your data, you need to have the base set properly. Otherwise it is garbage in, garbage out. Organizations understand that, even though there is no specific increase in sales or bottom-line improvement. Even if that dollar value is not apparent to the customer, they understand that this process is important for them to get to that stage. That is where the return on investment comes in.

We have seen a return, but I can't say how much. 

As an experimentation, we are not fully in production. Therefore, it's absolutely impossible to have a return on investment right now.