Increasingly, organisations are recognising the importance of good data management and how it can improve organisational performance. Managing data across large (and small) enterprises can be challenging, there are a range of standards and approaches that provide guidance applicable to many sectors.
Asset intensive organisations include refineries, power stations, substations, railways, water treatment and distribution networks, highways etc. Asset owning organisations typically have large portfolios of assets with huge variety of types, construction and configuration, ages, condition and performance. Such organisations need to manage their complex portfolio of assets over extended periods of time.
Why is data frequently stated as one of the top 3 challenges for asset intensive organisations? What approaches to data work best for asset intensive organisations?
Most organisations have problems with their data – not enough, too much, not good enough, missing, duplicated, inconsistent etc. Many managers and employees just want someone to ‘sort it all out’, but who is responsible?
Often there is a view that data quality is IT’s problem, but is this correct?
Data quality assessments can provide large amounts of useful information. However, to gain a complete perspective on organisational data quality, it is essential to consider three key perspectives:
The data itself – entries in databases and spreadsheets
The requirements for data – arising from processes and organisational objectives
The data subject – the person, product, activity or event represented by the data
Considering data quality from only one or two of these perspectives are insufficient to understand organisational data quality. Having a lot of information about some aspects of data quality may mask the fact that you are missing key data quality dimensions and insights. Activities to improve data quality may therefore not be correctly targeted.
It is time to consider ‘the value of data’ – a number of recent conversations (such as with Stuart Ravens and Martin Paver), an excellent recent article on Highways England’s data valuation and related events such as the emergence of a proposed international standard to develop a ‘data value index’ show that this is a timely subject.
Staff can often be very resourceful in making poor systems and processes work and to overcome data problems within an organisation. If people are based in a single office then they can call out questions like:
“Do you remember who did…”
“How did we solve….”
“Where is the information on….”
And so on
I used to give a similar answer about why many smaller organisations were less affected by poor data quality than they would be if they were larger – namely, being close to colleagues and able to verbally resolve issues acts as a ‘sticking plaster’ to overcome data problems.
A few years ago we did a series of blog posts on ISO 8000-150 which have been perennially popular. Well, since those posts were created ISO 8000-61 has been published which provides a richer and more comprehensive approach to data quality management.