Understanding data quality across organisations

Collection of shiny metal nuts and bolts

An organisation that works in isolation and does not share data with other organisations is probably rare. Most organisations will collaborate with other organisations to some degree and will be providing data as part of this collaboration. So, what factors need are relevant when considering data quality across organisations?

When building a machine or structure, you will usually be assembling components supplied by many manufacturers. For this to work, the interfaces between components and items should meet agreed requirements. For example, if one organisation decided to make bolts with a different type of thread, it would be difficult (or perhaps impossible) to join different components together.

In a recent post I considered what is needed to understand data quality from an organisational perspective, however, what are the data quality implications when we have to use data across organisations?


Julian Schwarzenbach guests on the 30th episode of The Data Strategy Show

Julian Schwarzenbach recently appeared on the 30th episode of The Data Strategy Show. The conversation with Samir Sharma covered a wide range of topics including:

  • Julian’s background in engineering and data
  • The strapline “Data Doesn’t have to be difficult”
  • Why we are still talking about Data Quality
  • How business functions need to take responsibility for their data and data governance activities
  • Julian’s approach to valuing data
  • His book: Managing Data Quality: A Practical Guide and the idea of the “Data Zoo”
  • The new British Standards he has launched around big data.
  • The work he does at the British Computer Society

Why asset data is more challenging than ‘normal data’​?

Picture of refinery

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?