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?


Data quality assessment from an organisational perspective

Key factors in a data quality assessment are the data values, data requirements and data subject

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.


Symptoms of data quality problems

Do you have a data quality problem?

A recent conversation about a large organisation highlighted an interesting question – How do you quickly and easily know that you have got a data quality problem? 

Clearly, you may have a data manager/ data team who are stating that data quality is poor/declining etc. But are they just obsessive-compulsive types who want everything perfect? 


Two new data standards for all organisations

Relationship between BS 10102-1 and BS 10102-2

In February 2020, BSI released two new data related standards that should be considered by all organisations: 

  • BS 10102-1:2020 – Big data. Guidance on data-driven organizations 
  • BS 10102-2:2020 – Big data. Guidance on data-intensive projects 

Although the titles state ‘Big data’, this only reflects the committee that created them. They are in fact applicable to virtually all organisations. 

Read on to get an overview of what they contain 


Has lockdown exposed your data weaknesses?

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.


Online training now available

To enable us to continue to support our clients during the changes arising from the COVID-19 pandemic, we are now offering online training at https://courses.dpadvantage.co.uk/.

Our popular ‘Managing Data Quality‘ course has been set up as four individual courses within an overall ‘bundle’:

  • The enterprise data asset – explanations of the basics of data quality, characteristics of enterprise data and the ways organisations tend to exploit this data
  • People and data – explanations about the generic behaviours people exhibit towards data using the Data Zoo concept, what drives these behaviours and how to improve data behaviours
  • ISO 8000-61 – A framework for data quality management – An explanation of the ISO 8000-61 process model and the individual processes within the model
  • Implementing data quality management – Steps to follow in order to develop a strategy to improve the approaches to data quality management in your organisation

All four courses are pragmatic and accessible with a clear focus on the people, behavioural and organisational aspects of data quality management. A free taster course is available here.

Further courses are being developed covering a range of business subjects.