It has been stated that the most common failure of a software project is caused by not capturing or understanding what the system is required to achieve. This is equally true of your information needs; have you truly captured your data requirements to meet your information needs and organisational objectives?
Is your data over or under specified?
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You may wonder why you should bother improving your data quality or what the benefits of this activity may be. You may wonder how to secure suitable resources and funding to deliver improvements to data quality. Read on to discover why ‘data quality is free’.
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Data quality problems all, at their root, involve some form of human error. Whilst this is easy to say, it is perhaps harder to identify and resolve the causes of these human errors.
In this blog post, I will explore ways to categorise human data errors and propose strategies to reduce the severity of these errors. (more…)
When talking about data quality, it is usual to consider different aspects or ‘dimensions’ of data quality – validity, completeness, uniqueness, consistency, timeliness and accuracy. These six dimensions were agreed as the most relevant and representative of data quality as part of work led by DAMA UK that I contributed to in 2013 and published in this White Paper.
Some recent work and discussions suggest that there may be another dimension to consider – continuity. This blog post explores things in a bit more detail.
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The world is changing, people are changing, organisations are changing, and this is no different for data requirements. An organisation needs to accept this and makes sure that change requirements are captured, impact assessed, acted upon and communicated.
During my career I’ve seen many approaches to configuration changes or data change management. I have seen approaches in organisations range from being totally non-existent right through to exemplary practices. It’s true that the process takes resources i.e. time and effort; to firstly put Change Management in place, and then the effort to maintain the process as a business as usual activity. However, this time and effort will reap dividends by reducing rework, ambiguity, process failure, etc. and create a defined and agreed data specification that will meet the needs of the business in terms of information requirements.
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Most people should be familiar with the old adage “Garbage In, Garbage Out” intended to remind people that if your input data is poor, then any outputs will also be poor.
There is a variant of this that cropped up in a discussion recently “Garbage In, Gospel Out”. So what can this mean and does it apply to your organisation?
A colleague of mine recently posted a very interesting blog titled Do you trust your data?
This led me to think about the issues from the perspective of the data i.e. if I am a data set can I trust the people who handle me to do the right thing with or to me………
Those days when you need to make an important decision can be trying at the best of times. The old saying “Garbage In, Garbage Out” is never more relevant (particularly if an AI tool is making automated decisions). (more…)
Those of you that know me well will be familiar with my comparisons between attitudes to data and attitudes to health and safety.
In this post I will expand on these themes and consider how modern organisations could be considered as having a ‘Victorian’ attitude to data quality.
The social housing sector in the UK continues to operate in challenging times – the combination of a continued shortage of housing, reduction in grants from government and ageing asset stocks means that there needs to be a consistent drive to ‘deliver more for less’. Housing associations need to ensure they are:
It’s not an easy ask, is it? With resources sometimes already at the bare minimum, it seems almost impossible to produce more, but if we take a step back and think about how we make decisions at work, then it can become a much clearer picture. (more…)