I know it’s been a while since I’ve posted anything. This is not by any means due to a lack of content, it’s more a lack of time. Or - to be more accurate - time management! I was however moved to write a short article on why Universities should persevere with their initiatives to improve the quality of the data asset, in spite of the news this week that Data Futures has been put back at least another year. WONKHE were kind enough to publish it on their website Hopefully this will kick start my approach to dealing with the [...]
The answer should be a firm yes, but first let me explain why it is often a definite no. Assessment scores are amongst the dirtiest data you can collect, with most methodologies being entirely qualitative Completing the assessment may give you a grade or a level, but other than printing it out and sticking it on the wall, what do you do with it? The HEDIIP programme originally envisaged publishing a data maturity assessment across the HE sector. My view was without a framework for that assessment to operate in, the cost of collection was not commensurate to the value we [...]
This template is a version of the Stanford EDU data maturity assessment model. It is formatted to allow each question to be assessed via a drop down box. When completed, a number of graphs representing the scores will be available for review. The original material is copyrighted for Stanford EDU, but free to use. Please find more details here.
A recurring problem with resolving cross domain data quality issues is the asymmetry of benefits. Essentially the data producer (responsible for entering or uploading data at the point of collection) has little visibility of how the quality of that data will affect the data consumer (the person or persons who use it). The utility of data is often scuppered at this collection point, as the producer - understandably - will apply only the business and quality rules relating to their own use cases. This is not simple to fix. I used to believe merely showing people the implications of these actions [...]
In my last blog post, I introduced a mapping tool linking the HESA Data Futures schema (2.01) to the UCISA HE capability model. This generated an enormous amount of feedback and interest. This interest made me appreciate - again - how powerful the capability model is if tuned to a real world scenario, and that I'd created a bit of a monster in terms of the tool itself. Having said I wasn't going to enhance it, the number of requested changes, and a bit of spare time over the break has brought forth version 2.1. The new functionality includes: A query function [...]
What is it? A business glossary is a cornerstone of any successful data governance framework. It underpins much of the effort to assess, track and improve the data asset. A properly formed glossary is the foundation for driving up the utility of data. It does this by generating trust in that data because we know what it means, and the quality at which it is held. It is therefore an institution wide, agreed business view of the most important data and where it is used. As such, it's a key tool for data stewards and owners to move data out of [...]
This template gets you started with a place to define, store and understand the most important business terms in your institution. Details of how to get the best out of it can be found in this blog post.
This template is a powerpoint slide. It is the basis of a framework for a Data Governance accountability matrix and operating model. Many of the other templates will reference this model as it has been designed and implemented specifically for UK universities. The main features are: Business ownership of data. This is shown by the key business roles of Data Owner, Steward, Producer and Consumer all being held within the areas of subject matter expertise (roles and responsivities will be covered in more detail in a future template) Clear lines of accountability. The owners set the policy based on the university [...]
This is not an exhaustive list of all data governance activities you may wish to undertake. Rather it is a set of themes that are generally involved in implementing successful data governance. Each university needs to look at their own requirements, priorities, current state of maturity, resources and budget and senior management buy-in to understand which activities are most important, and in which order. The four themes are: Data Strategy and Policy: How to build the 'rules of the road' for data and showing how this approach visibly supports the wider objectives Development of a framework: What will data governance look [...]
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