DIKW model SLA data

“How the DIKW model (and a new app integration!) can help you manage SLA data betterDiscover how on this episode of Appfire Presents: The BEST IT Service Management Show by Appfire. Emily Peet-Lukes chats with Gorka Puente about how using the DIKW model as well as a new integration of Time to SLA and Dashboard Hub can help you make better decisions around managing your data.

About the guest

Gorka Puente is the Principal Product Manager of BI/Reporting at Appfire.

About the show

The BEST ITSM Show by Appfire brings you expert insights for IT service delivery, so your employees and customers have what they need to succeed. Get the right tech and tips for the right job at hand. Look like you’ve come from the future with all your new ITSM smarts. Every episode is a brisk 10 minutes—less time than it takes to provision a laptop or troubleshoot a tech support issue.

For your convenience, here is the transcript of this episode:

How the DIKW model (and a new app Integration!) can help you manage SLA data better

Emily:  Today we are talking to Appfire’s own Gorka Puente about how the DIKW model and a new app integration can help you manage your data and SLAs more effectively. Stick around for 10 minutes of expert insight.

Hello, Gorka. Thanks for joining us today. How are you?

Gorka:  My pleasure. I’m doing great. I really like the intro.

Emily:  We’re so happy to have you. 

Gorka:  Thank you. 

Emily:  Let’s just start off. Today we’re talking about data and SLAs, correct?

Gorka:  Yes.

Emily:  Awesome. What are the challenges that support and service desk teams are facing with this?

Gorka:  Jira generates a lot of data. When we’re using SLAs, like today we are going to talk about an app called Time to SLA, that is even more data. We have the times when we move from different status, so we have to commit to the SLAs that we sign with our customers, and sometimes we breach them. How we handle that data is important because we use that data to make informed decisions about our support teams or our service desk teams. 

Emily:  Right. That makes sense. There’s a lot of data, you have to manage it properly because it’s the backbone of your decisions.

Gorka:  Right.

Emily:  We’re talking about the DIKW model. Can you tell us about that, what is that?

Gorka:  The DIKW model is a method that sources in knowledge management that helps us to explain how we move or how we transform the data, the D in DIKW, to information, to knowledge, and to wisdom. Sometimes the boundaries can be blurry between data and information, knowledge, and wisdom. This is a process that helps us to understand how we move and how we transform this data. Each step of this creates value based on the data and we can use that to answer high level questions in the different steps.

Emily:  It’s a process. How is this related to SLAs and reporting? There’s this new app integration, so what are these two apps?

Gorka:  We got this really nice integration, and I’m super excited about that. Time to SLA is an app to define and start working with service level agreements. For me, the key thing, the thing that I like the most about this app is that it works in any type of project. You don’t need to use Jira Service Management projects, Work Management, or Jira Software projects. 

We have integrated it with Dashboard Hub from Jira. This app is for creating reporting dashboards, as the name indicates. It’s really easy to use. It’s really easy to share, have more template integrations, like almost 100 metrics. This is going to be a tool to extract the value of that data. 

It has a history with ITSM. We have a couple of customer success stories that you can find in the Appfire Hub with two companies that are using Dashboard Hub to share with their customers in Jira Service Management, in a customer portal, life reports. 

Going back to our how we explained the relationship and how we treat the data generated by Jira and by Time to SLA, think that each building block is a step of this model, it is a step towards the higher level. You get the data, the information, the knowledge, the wisdom. In each of these steps we are doing, we are answering different questions about this initial data, and how we add value to that data, how we enrich our data. Why do we need to enrich that data? Because we need knowledge, we want insights, so this is the key. We can make better informed database decisions about this data.

Emily:  Totally. I understand. That’s why that pyramid is in that structure that it is, because the more you get, the more you know, the better decisions that you can make.

Gorka:  Right.

Emily:  Can you be a little more specific or explain with an example how this model is applied to this integration?

Gorka:  Yes. You’re right, probably best to have an example. When we get the data, think of that as a collection of elements. It doesn’t need to be organized in any form. In our case, it’s the tickets for service desk or issues for an upgrade. With the thing that we have, for example, the time to response or the priority, these are properties. In the context of Jira, properties are fields, so we have context. 

In the first level, we have context where they’re starting to have more value. For example, if we say Mary, it’s just a name. But if I tell you Mary is an agent, then you say in that issue is an assignee. So, we’re having more and more context.

In the information level, what we’re going to do is process that data. Maybe we can use aggregations. We can establish, for example, the average of the Time to SLA. Now we can analyze and visualize that, so we have moved from data, we are processing that data, and we have the information, we can analyze and visualize that in the next level.

Emily:  Okay. Then in this next level is where the integration shows more of the benefits of being able to use it versus not having it?

Gorka:  That’s right. That’s where we are starting to see goals in this integration. We take all of the data generated by Time to SLA and we can start doing things like create a chart to display, for example, the met versus the breached SLA instances over a period time, how many times per day we breached or met the SLAs that we committed to our customers. This is the first metric or the first gadget that you can find in the integration of Dashboard Hub with Time to SLA.

Emily:  That’s awesome. 

Gorka:  Here we are starting to ask questions, so who, when, or where are we breaching the SLAs. This is where the next gadget of this integration gives us the ability to quickly spot what’s going on in a particular project, about a customer request time, are we about to breach an SLA in that period with our client or in a specific customer type. 

Emily:  I see. When do we move the data to that knowledge level part of the pyramid? 

Gorka:  We can move if we ask a different question. Now we have to ask how. For example, how this information that we saw in this chart is relevant to our support team, and how is that relevant to our goals, or how are these pieces of information connected to other pieces that we may have in this dashboard providing information. 

Here is where having different metrics in a dashboard comes in handy because you can compare or you can drill into that information and find relationships that probably were not that explicit just in the information, because you got your experience and you can see that. When we have gained knowledge, we get useful insights, we can use that to make decisions, so we reach the wisdom level.

Here we ask another question. Why? Why are we not meeting our SLAs? What would be the best course of action? Should we maybe increase the number of agents on our support team, or we should provide training to our agents to be more efficient and meet our SLAs? Maybe we have to go to the customer and negotiate our SLAs again. 

Emily:  Right. That’s where you reach that apex of being able to make those very informed decisions, by being able to get to that wisdom level. 

Gorka:  That’s probably the key. 

Emily:  Okay. We learned a lot about the integration of Time to SLA and Dashboard Hub and how that can help manage data. After all this discussion, can you give us a couple of key things that we should take away from this?

Gorka:  Key things, I would say that for the first two levels, take that information, take a look at what happened. For the next two levels, knowledge and wisdom, those are related to what we do now and in the future.

The second thing I would say is that in ITSM there is tons of data, so what we have to do is add value to enrich that data to get information and then answer the right questions to make better informed decisions. That’s the key. 

The last thing is remember that this DIKW model is not something rigid, it’s a process that is going to help us navigate the way of our data. That’s the idea of using this model.

Emily:  Awesome. Thank you so much for joining us today, Gorka. It was really informative and great to learn about this valuable new integration. 

For everyone listening, if you want to try it out, visit the links in the show notes to start your free 30-day trial of both apps where you’ll be able to explore the integration. We’ll also link to some of our customer success stories so that you can learn even more.

Thanks for joining us today. We’ll see you next time. 


Last updated: 2023-06-05

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