5 quick rules for charting – Chandoo.org

Good charting tips from Chandoo.org :

Read more here.

Impact of IT Ops on Business!

A two part article by Damon Edwards on measuring the impact of IT Operations on Business. Please click on the links below to read!

Part 1: Putting a metrics/KPI program into place in 6 steps

Part 2: Identifying candidate KPIs to evaluate

CIO’s IT Dashboards

Excellent internal links around CIO Dashboards on ciodashboard.com

Get links to

Dashboard Tips

Elements of a CIO Dashboard

Problems with IT Benchmarks… amongst other articles!

Click here to read more!

Balanced Scorecards and Metrics for Service Support

In this article by Rob England he provides a point of view on the some not so desirable metrics for Service Desk and an approach towards a Balanced Scorecard. What I really admire about Rob’s approach is his different point of view on the usual norms and the way he would force you to validate your thinking and provide a different point of view.

In this article he has written about too much focus on Abandoned Calls undesirable. While it might be ok for an in-house service desk to not give too much focus on Abandoned Call Rate, more often than not SLAs which are written between third party/external service providers and customers have Abandoned Call Rate as an important statistic. This is something that is an indicator of the service to customers and drives customer satisfaction. Rob mentions that too much focus on ACR might force Service Desk agents to finish the calls in a rush, and a similar situation would occur with focus on AHT (Average Handle Time) for calls. I personally feel that these two metrics are an important indicator of the Service Desk quality and need to be measured. Some of the actions which need to be taken with these numbers going up could be

1. Identify a trend in Call Abandon Rate and staff Service Desk agents to handle those volumes (most call centers do that)

2. Identify agents which high AHT and identify reasons for high AHT. This might trigger a need for some training to be imparted to those agents

In my opinion, its not possible to reach a conclusion on these metrics unless a deeper analysis is done on them and over time a consistent performance should take these down the priority list.

Dashboard Gallery @ www.dashboardinsight.com

Click here to go to a gallery of dashboards with spans more than 10 pages. More than 50 dashboard views to look at. Some of the dashboards have a live demo as well.

You can’t improve IT, if you are not measuring IT

 

The 7-Step process in CSI phase of ITIL has identification of what needs to be measured as one of the steps.

There is no way of improving a service or a process, if we are not measuring. Measurments allow us to do two things

1. Know where we are

2. Help determine where we want to go

Often in my experience, we take the standard set of metrics and put all of them on a dashboard for various stakeholders to review, irrespective of the relevance of these beautiful looking graphs. I would in this post and in future try to put together some  tools which help in identifying what needs to be measured. One of such tools is GQM – Goal Question Metric Approach. In some of the next posts, I would write about illustration of the GQM related to ITSM Processes.

Read more about the GQM here and here.

 

Eight Levels of Analytics

I came across a lovely article on levels of Analytics Maturity on SAS website. The 8 levels mentioned in the article are

Standard Reports

Ad Hoc Reports

Query Drilldown

Alerts

Statistical Analysis

Forecasting

Predictive Modelling

Optimization

I am yet to see maturity at anything more than a level 4 in most of the organizations that I know about, however I have been a part of Statistical Analysis and Forecasting in the BPO World. Click here to read the article, or here to download the pdf version.

Dashboard or a Scorecard or a Report or what?

I have been talking, thinking, searching on these terms for a while, and though there is a lot that is available on the terms, mostly I see that they are open to individual interpretations and organization use and perception. In future, I would attempt to bring together some thoughts around these terms starting with this article on the topic.

Do read!

Analysis and Analytics

In continuation with some definitions of Data Analytics and Analysis here, the following graphic came up after a small discussion of how and when the Analysis and Analytics components play a role.

Will write more about various methodologies and tools as I learn more!

Definition – Data Analytics

A detailed definition of Data Analytics from Search Data Management Definitions. Here is what it says :

Data analytics (DA) is the science of examining raw data with the purpose of drawing conclusions about that information. Data analytics is used in many industries to allow companies and organization to make better business decisions and in the sciences to verify or disprove existing models or theories. Data analytics is distinguished from data mining by the scope, purpose and focus of the analysis. Data miners sort through huge data sets using sophisticated software to identify undiscovered patterns and establish hidden relationships. Data analytics focuses on inference, the process of deriving a conclusion based solely on what is already known by the researcher.

The science is generally divided into exploratory data analysis (EDA), where new features in the data are discovered, and confirmatory data analysis (CDA), where existing hypotheses are proven true or false. Qualitative data analysis (QDA) is used in the social sciences to draw conclusions from non-numerical data like words, photographs or video. In information technology, the term has a special meaning in the context of IT audits, when the controls for an organization’s information systems, operations and processes are examined. Data analysis is used to determine whether the systems in place effectively protect data, operate efficiently and succeed in accomplishing an organization’s overall goals.