Wednesday, September 19, 2012

Rapid Risk Identification

If you have read my earlier post on Risk Management in Software Testing , you would know that the risk management process includes Risk Identification, Risk Prioritization (or Risk Assessment) and Risk Treatment. You will now see how you can identify the relevant risks in your software testing project quickly. See my short video, How to Identify Risks? Risk Management Video or read on...

You may have seen some rapid risk identification in action already. During management reviews of projects. During my career, I have been positively surprised  many times when the management was able to identify potential problems (risks) after just listening to the project progress. In fact, risk management is a critical management skill. Other critical management skills being strategic planning, organizing and communication. If you are not management but want to identify risks in your project fast, here is what you can do.

Friday, September 7, 2012

Data Quality and Data Quality Assurance

This post is on data quality and how to go about assuring high data quality. See my video, Data Quality Concepts (I have explained multiple examples in detail in it) or read on...

First, let us understand data quality. Put simply, data are of high quality if they do not suffer from data issues. There are many potential issues with data (see examples below). Now, data are used for a number of organizational functions such as on-going operations, dealing with customers, marketing and analysis and decision making. If the data are not of high quality, there are a number of problems. Users get incorrect reports. Time and money is wasted in miscommunication. Bad data can lead to poor decisions. It can frustrate employees and most importantly, it can frustrate customers.

Although data quality assurance is particularly useful for production databases, it can very well be used in software testing as software testers need to ensure high data quality in gold test databases. Now, let us see examples of data issues that bring down data quality.