How do you calculate the Return On Investment (ROI) for Data Quality improvement? That’s a question we get asked almost every day. And every time, answering is painful since we’ve not come up with a magic formula that fits every scenario. Believe me, we’ve tried.
So, I’ll narrow it down to offer a meaningful example and calculations. For this article, I chose ROI for improving CI data, and I’ll look at it through the lens of the Service Desk:
- I want to reduce incident resolution times
- I want to reduce the number of escalations and reassignments
Of course, this is not a random choice. Our product Data Content Manager excels in this use case.
In my example, the ROI for improving CI Data quality by 10 percentage points produces a ROI of a staggering 1436%.
Let me walk you through how I got to this figure.
What is CI Data?
In the context of the Configuration Management Database (CMDB) and Foundation Data, Configuration Item (CI) Data can include things like:
- Identifiers like Name or ID
- Description – what is this CI in human language
- Location, either physical or logical location of the CI
- Criticality or attributes that impact the urgency of any problems
- Persons and Groups responsible for the CI
- Support/Assignment group, which is necessary for auto-assignments
- Relationships to other CIs and services needed for impact and root cause analysis
So, as an example, for a Server, we should know where it is, what it does, who owns it, who manages and supports it, what applications run on it, and so on. Seemingly very basic information that is surprisingly often incomplete, is not up to date, or is missing entirely. A common reason for this is that these are non-discoverable attributes.
The Consequences of Invalid or Missing CI Data
So, what happens when CI data is not up to date or is missing entirely? From the Service Desk point of view, this might happen:
- A server fails. The Service Desk gets a ticket from an automated monitoring system or a user reports a problem.
- The Service Desk agent finds that the ticket does not contain valid information for the server. So, for example, there’s no information on who is responsible for the server or what applications run on it.
- The Service Desk Agent then needs to start calling around to figure out whose server this is, what it does, and what the impact of the failure is.
- Only after the agent has enough information can they begin working on the actual problem.
All of this can take hours or even days, and during that entire time, the server is down, and whatever applications rely on it are potentially down, too. The people who need to use that application cannot do their work, and possibly many people are contacted before the necessary information is complete.
Had the data been up to date and available, the Service Desk agent could have immediately alerted relevant stakeholders, and work to fix the failure could have begun. Disruptions would have been minimal.
The Consequences Add Up
The worst-case scenario can be pretty bad. Read about a real-life example: The 2 million Euro Incident. In that case, a server failure escalated into a major incident. The root cause was incomplete CI Data.
Of course, not all incidents are business-critical and can be resolved quickly, even without the CI data. Nevertheless, the numbers add up amazingly quickly in a large organization with many requests.
We sometimes hear that this is not our problem. Great if it isn’t, but you might want to look at this Harvard Business Review article: Only 3% of Companies’ Data Meets Basic Quality Standards (hbr.org). Although a little dated, it is eye-opening.
For ROI We Need to Make Assumptions
Cost Per Ticket
I’ll use HDI’s information from 2017, which was readily available. It is from 2017, so the numbers are probably considerably higher today. I’ll round them up to the nearest dollar. According to them, in North America, the Cost per Ticket varies from $3 to $50, the average being $16.
So, I’ll use $16 as my Cost per Ticket. Metric of the Month: Service Desk Cost per Ticket (thinkhdi.com)
Number of Tickets
For this, I will again use information from HDI. Their benchmark data is for tickets per user per month, split between industries. This metric ranges from an average of 0.54 monthly tickets per user in the Equipment Manufacturing industry to 1.38 in High Tech.
Without getting scientific about this, I’ll just split the range in the middle and use 0.96 for my example.
So, based on that, in my fictional 10,000-employee company, the Service Desk gets 9,600 tickets per month or 115,200 per year. I’ll use this number in my math.
Cost of Handling Tickets with Missing Data
This is a difficult number to come by, but a reasonable approximation can be made from the well-known “Rule of Ten,” which states that “it costs ten times as much to complete a unit of work when the data are flawed in any way as it does when they are perfect.”
So, in our example, that means that if a ticket has the necessary data when the Service Desk agent gets it, it will cost $16 to handle, but if the data is incomplete or missing, it will cost $160.
For my example, I’ll add a couple of more assumptions:
- 50% of my CI Data is somehow bad. You can easily get the real number from a Free Guided Trial with DCM. For free.
- 50% of Service Desk requests rely on CI Data. This, I’m conservatively guesstimating based on experience.
Getting Toward the ROI
We already established that the Cost per Ticket is $16 and that the example Service Desk handles 115,200 requests per year.
With this information, I can quickly estimate that about 25% of all requests will require additional work because of missing or invalid CI data. That’s 28,800 tickets. If these tickets did not need extra work, handling them at $16 per ticket would cost $460,800.
But here’s the catch: the cost of handling a ticket that requires additional work, such as calling around to figure out missing location data and then handling everything manually, is 10-fold – so the cost of resolving these data-missing tickets is an enormous $4,608,000!
Remember that productivity is not only lost by the Service Desk agent and the immediate stakeholders that participate in data gathering. Productivity also gets lost by the people who cannot do their work because the ticket takes longer to resolve.
Without going into the discussion about whether 7, 10, or 13 is the correct multiplier, it’s clear that the financial implications are as enormous as they are largely unnecessary.
The Return on Investment
Let’s then get to the Return on Investment. If you invest in Data Content Manager to improve your data quality, the first-year cost for a Starter license is about $40k.
We can reasonably assume that after a year of using DCM to guide your efforts, only 40% of the CI Data would be invalid. That’s 40% instead of 50%, a very achievable outcome in the first year.
Now, the math looks very different:
- Instead of 28,800 requests that require additional attention due to missing location data, you’re down to 23,040.
- The cost of handling those 23,040 tickets with the Rule of 10 applied is $3,686,400 instead of the $4,608,000 we came up with before the data quality improvement.
That’s $921,600 saved. Almost a cool million!
Since you can improve your CI Data quality with the DCM Starter License for $40,000 per year, the ROI is a respectable:
Return on Investment
And it’s not a one-time return, either. If you get 115,200 service requests this year, you’ll probably get the same next year and the year after, so the improvements you make to your data quality will accumulate over time, and with DCM, you can keep it up to the quality level you choose.
The example is simplified, of course, but it is not unrealistic. In fact, in my opinion, this is probably on the conservative side. The data points I used are solid.
You don’t need additional people to run DCM. Instead, those already working with data quality can do their jobs MUCH more efficiently. The cost of training to use DCM is negligible.
In addition, if you plan to automate workflows, your data needs to be high quality. Being able to automate simple but manual steps due to better data quality can increase your ROI even more.
Most likely, many people will be involved in improving and fixing data. With DCM guiding the effort, they will know what to address instead of poking around blindly. You can track and communicate progress with data-based KPIs, not guesswork. When you know you’ve hit a milestone, you can celebrate your wins as you achieve them.
Do it with Your Numbers!
As mentioned in the beginning, we sometimes struggle when asked what the ROI is for Data Content Manager. It is a tricky question to answer on the spot because it depends so much on the organization, cost structure, pain points, current state of the CMDB, and whatnot. Generally speaking, information we are not privy to.
This simple example demonstrates that the payback for investing in data quality pays off big.
I encourage you to do this calculation with your numbers. Better yet, we can help you get some of those numbers in a Free Guided Trial run in your non-production instance. Chances are the outcome will be surprising, justifying a solid business case.
As far as Business Cases go, you might find this article helpful: Build a Business Case for Data Quality Improvement.
Thanks for reading, and feel free to reach out if you have questions. If you want to go through this exercise and get a baseline for doing your calculations, please book a Free Expert Consultation with us!