Metsä Group uses Data Content Manager to improve data quality in their CMDB. We had a chance to interview Mika Lindström, the ICT Configuration Development Manager at Metsä Group. Thanks, Mika, for joining us.
Metsä Group are an internationally operating frontrunner in sustainable bioeconomy. They are a successful forest industry group with strong roots in the Finnish forests. They’re unique in that their parent company Metsäliitto is a co-operative owned by nearly 100.000 Finnish forest owners.
Metsä Group operates in 5 business areas in 28 countries and has 9500 employees. The group sales are 7 Billion Euros.
Q: Can you tell us how you became acquainted with DCM?
I think I saw DCM already in 2017 at my previous employer. We piloted it back then but did not proceed. However, I had a good impression of the product already back then. When I joined Metsä Group, it quickly became apparent that DCM is the tool we need to meet our targets of improving our overall data quality quickly and transparently.
Q: What was the primary challenge DCM brought in to solve?
We had a lot of unvalidated data in our CMDB based on a large import operation. The data came from various sources, such as disparate excel sheets. The volumes were huge, and making sense of it afterward was challenging.
At the same time, we had established concrete goals for data quality to enable the data users, such as the Service Desk, to do their jobs better.
DCM provides us with the tools we need to get this done. First, we use it to look at the data: what relationships are missing, what attributes are missing, and what is correct. Then, on the other, we use it to follow our progress against our KPIs and communicate confidently with our stakeholders.
Q: So, what is the usage today?
We mainly have Operational Technology, such as factory systems, which 3rd parties usually support. Then we have Metsä ICT applications. Our focus right now is the latter. We want this area to be solid so that we know exactly what is affected and what contributes to the problem if something happens.
Q: You’ve made a lot of progress in a short time. Can you elaborate?
We’ve set a minimum target we want to reach with a single Blueprint, which looks at Applications. We got our baseline already in the first audits, and we’ve then been able to focus on the lowest-hanging fruits. Those are the areas where we see the most deviations.
So, for example, I simply look at the top 10 records with the most deviations and talk to the responsible people. This allows me to get the most gain most efficiently.
In addition to the top-down view, I can now talk to the data providers about the data they are personally responsible for. They are motivated to contribute by seeing how their efforts improve their personal data quality metrics.
Of course, getting results quickly requires cooperation and training, but having concrete metrics and personalized dashboards really helps.
Q: So, is it fair to say that DCM, in a way, directs your work?
Yes, exactly. It helps me understand where we are, what I need to focus on, and with whom. And where I can get the best results.
Q: Since DCM plays a significant role here, how would you do this without it?
That’s a good question. I suppose we would have to resort to a lot of manual effort with various reports and excel sheets. Especially figuring out our missing relationships would be really hard without DCM.
It’s vital that with DCM, we can see the big picture as well as drill down into the details at any time. We don’t have to think about how to get this data together and how to update it. Once the Blueprint is set up and the audits run, it’s all there in the dashboards.
The Blueprint brings the entire thing together so that the rest follows automatically once it is up and running. And if you need to adjust it, the adjustments also take effect immediately without changing your workflow or any of the reports.
Q: What about the future?
We will expand the usage once phase 1 with our Metsä ICT applications is complete. Over time this will include connectivity, server capacity, and databases. So we will add one piece at a time instead of trying to eat the whole elephant at once.
We believe that completing something before moving to the next thing will foster trust in the data and, at some point, enable more automation.
Q: What about Operational Technology (OT)?
The line between OT and ICT is sometimes blurry, but I see these will eventually become a part of the picture, too. However, right now, the main focus is on ICT.
Q: In your opinion, what are the best practices with DCM?
1. Start small when designing the first Blueprints and tie them to targets. For example, create a Blueprint for the end of the year target and another for the following summer’s target. Then track the targets separately but simultaneously.
2. Manage your scope and expectations vs. targets. For example, communicate clearly that against this target, we are at 90%, but the next target is more extensive, and we are only at 60% against that.
3. Make sure you identify the people responsible for your data. If you don’t know who they are, you won’t be able to communicate with them. When you do, you can use personalized dashboards to help them take responsibility for their data.
4. Never underestimate facetime. Explain what DCM is, why this is done, what is expected, and so on. Only then can you expect people to be able to commit to data quality and, for example, accept tasks that DCM may generate. They should understand that they are the ones who stand to benefit from the improved data quality.
Q: How would you summarize the benefits Metsä Group has gained with DCM?
- DCM provides transparency and a holistic view of the state of our CMDB.
- It helps us find and fix deviations as they happen.
- It provides all this automatically, so no manual effort is needed when somebody requests this information.
- It allows us to focus on what’s important to us and supports our way of working.
Data Quality is an investment that requires people and tools, but in the end, it can save considerable amounts of time and money. Unfortunately, no magic button automatically improves things – it requires work. For this, there must be management support.
Mika – thank you for this interview!
To learn more about Metsä Group, please visit their website.
If you would like to learn more about Data Content Manager and see how it can help improve data quality in your ServiceNow, please book a demo with us!
Images courtesy of Metsä Group.