Improving data quality root-cause analysis

A great article was posted here on data quality PRO about 8 Simple techniques to improve data

quality root-cause analysis. Definitely worth a read, it lists the following techniques:

Focus on problems that impact customer satisfaction
Eliminate the short-term practice of data cleansing
Ensure information chains are accurate and detailed
Include external data sources in your root-cause investigation
Use the 5 Why's to discover multiple causes
Develop a root-cause analysis process that is commonly adopted across the business
Learn how to perform Pareto analysis effectively
Learn how to construct a fishbone or ishikawa diagram
Validate your root-cause assumptions with further data analysis and investigation
Monitor any improvement or preventative measures implemented

Comments

Why Madge Networks, the token-ring company, went titsup

There I was shooting the breeze with an old mate. The conversation turned to why Madge Networks which I wrote about here went titsup. My analysis is that Madge Networks had a solution and decided to go out and find a problem. They deferred to more incorrect strategic technology choices. The truth of the matter is that when something goes titsup, its not because of one reason only, but a myriad of them all contributing to the negative consequence. There are the immediate or visual ones, which are underpinned by intermediate ones and finally after digging right down, there are the root causes. There is never a singular root cause for anything but I'll present my opinion and encourage everyone else to chip in. All of them together are more likely the reason the company went titsup. As far as technology brainfarts go there is no better example than Kodak . They invented the digital camera that killed them. However, they were so focused on milking people in their leg

Flawed "ITIL aligned" Incident Management

Many "ITIL aligned" service desk tools have flawed incident management. The reason is that incidents are logged with a time association and some related fields to type in some gobbledygook. The expanded incident life cycle is not enforced and as a result trending and problem management is not possible. Here is a fictitious log of an incident at PFS, a financial services company, which uses CGTSD, an “ITIL-aligned” service desk tool. Here is the log of an incident record from this system: Monday, 12 August: 09:03am (Bob, the service desk guy): Alice (customer in retail banking) phoned in. Logged an issue. Unable to assist over the phone (there goes our FCR), will escalate to second line. 09:04am (Bob, the service desk guy): Escalate the incident to Charles in second line support. 09:05am (Charles, technical support): Open incident. 09:05am (Charles, technical support): Delayed incident by 1 day. Tuesday, 13 August: 10:11am (Charles, technical support): Phoned Alice.

Updated: Articles by Ron Bartels published on iot for all

These are articles that I published during the course of the past year on one of the popular international Internet of Things publishing sites, iot for all . These are articles that I published during the course of the past year on one of the popular international Internet of Things publishing sites, iot for all . Improving Data Center Reliability With IoT Reliability and availability are essential to data centers. IoT can enable better issue tracking and data collection, leading to greater stability. Doing the Work Right in Data Centers With Checklists Data centers are complex. Modern economies rely upon their continuous operation. IoT solutions paired with this data center checklist can help! IoT Optimi

Thinking problem management!

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