SERVICE DESK ANALYTICS

Do you know your Service Desk KPIs?

What is your direct solution quota? Is your backlog growing? What are

your cost drivers in the Service Desk? Which roles are overloaded? How

do you come to the causes of too high solution times on the track?

Where in the organization do 20% of tickets cause 80% effort?

„If you can‘t measure it, you can not manage it“

[Peter Drucker, Pioneer of modern Management]

Easy, transparent and game-changing up from the very first day. The BI

Data Cube processes all your existing transaction data – including all

relations to your organizational parameters and Service Desk processes

Service Desk Analytics knows all your strength & optimizing capabilities as

well as all your dwell times in all phases of your Service Desk processes

Success Scoring!

How do you define Success? By means of which data do you track if initiated activities

take the wanted effect?

Whereby do you recognize where you should optimize? Which activities were proven

and which did not bring the expected effect?

Which parameters show you that everything works well ensuring that the working load

is good balanced and the solution times are on track?

Service Desk Analytics shows you bottlenecks in your processes and helps you to find

out if detected divergences are one-time or recurring.

Let Service Desk Analytics help you to divine where you should go for optimization!

3-Layer Cube Architecture

Service Desk Analytics transforms your data per ETL Processes into three data layers

Every Aggregation Layer features a suitable base for answers for varying analytical

questions.

Combined they form a multi-dimensional Data Cube (OLAP) which allows you to slice &

dice your data in any direction to proove how healthy your Service Desk Business is.

DrillIn to analyse any detail you are interested in.

▪ KPIs

▪ Overview over all tickets in your organisation with monthly averages for all kind of tickets (Created,

Closed, In Progress, Tickets Alive) as well as Ø Solution Time

▪ First Call Resolution Rate, Escalationquote for Reaction- & Solution Time

▪ Monthly trend of your KPIs over time and distribution over your Organisation

▪ Configurable Target Values are highlighting where you left your self-defined corridor. They show

you exactly where you have divergences and give you an assumption why this happened

UUX Pivot-Engine

The Service Desk Analytics – Data Cube holds all KPI relevant data in a classic BI

Schema (Star Schema/OLAP) which is optimized for multi-dimensional Reporting &

Analytics.

Hence you are able to analyze multidimensional data interactively from multiple

perspectives

Dimensions are e.g.

▪ Time (Years, Quarters, Months, Weeks)

▪ Status, Categories, Roles, Priorities, Impact, Urgencies

▪ Organisational Parameters (Locations, Organizational Units, Cost Centers)

and many more

Facts & Dimensions

Facts contain all permutations of all Service Desk Dimensions (events) including the

time until the occurrence of the event (TIME-TO) and the residence time until the

occurrence of the next event (TIME-IN)

Facts & dimensions together form the pivot base for answering analytical questions

such as effort and utilization:

▪ How long does a ticket take on

average from status A to B?

How & where in the

organization has this time

changed in recent months?

▪ Which are my top drivers in

the organization and which

roles are burdened by how

much in volume & time?

Modern visualizations

Interactive visualizations such as SunBurst, TreeMap & BulletCharts give you a good

overview of your expense distribution and top effort drivers. Of course per location,

category or any other evaluation dimensions

Where in your organization do infrastructure issues dominate the Service Desk, where

are the specialized applications that generate the effort? Which one exactly? Are these

exceptional cases that you can specifically address?

Derive measures from this

▪ Which topics do you have to train better in the 1st / 2nd Level Service Desk?

▪ Where is it worth investing in a stabilization of the infrastructure or in the deeper

understanding of a specialized application for your users?

▪ In which roles are there regular bottlenecks?

Ticket Transition

The Flow-O-Mat (Sankey Flowchart) shows you how your ticket volume moves through your

Service Desk organization

Get insights

▪ What are the most common pass paths when routing / forwarding your tickets?

▪ Which topics could be bundled in competence teams?

▪ Where does the specialist role really need it and which utilization would you have to

expect?

▪ What is currently the direct solution quota or the solution time per ticket category? Are

there outliers? (Ø average vs. median)

▪ How has this changed in recent quarters?

▪ Is a trend recognizable?

Open & flexible architecture

The architecture of Service Desk Analytics was developed completely with Matrix42 on-

board resources.

All data transformations (ETL steps = Extract-Transform-Load) are visible & expandable

All data views are based 100% on the basis of the Solution Builder

Orchestration - full transparency

▪ Central workflows orchestrate the data flows and transform your historical and

transaction data based on GDI technology into the central data warehouse

▪ All transformation steps can be viewed in detail with intermediate results

▪ The processing of the data is fully transparent visualized processed because it is

important, especially for longer processing chains to see which steps are currently

being processed and which have already been successfully processed

Power BI Connectivity - on board!

Service Desk Analytics brings a data interface specially prepared for Microsoft® Power

BI. It knows all your dimensions & facts and offers almost unlimited visualization

possibilities

▪ Selection from hundreds of freely accessible visualizations

▪ Interactive dashboards enable target group-appropriate storytelling

▪ Let your data speak and communicate business contexts

ServiceDeskAnalytics@matrix42.com

https://marketplace.matrix42.com/product/service-desk-analytics/