Big Data and the role of data governance in Asset Management

According to the ISO55000, Asset Management is the coordinated activity of an organisation to realise value from assets. It is about a holistic approach within an organisation to take care of valued assets.

Within many organisations, data usually exist in silos and hence, the critical need for a Data Governance process or policy, that enables data to be governed across each department (silos) and presented in a meaningful way to realise value (going back to ISO55000’s definition of Asset Management).
Big Data is a term that describes large volume of data – both structured and unstructured. Doug Laney in 2009 described Big Data as the three Vs – volume (amount of data), variety (different formats) and velocity (speed of receiving data especially in real time). The ‘Big Data’ concept is about trying to predict the future. Data has always existed and the issue is about ‘meaningful’ data. Yes there is an increase in the data generated today and the possibilities are huge. For example, with analysed data you can determine the root causes of failures, issues and defects in near-real time.
Other opportunities include the ability to make insightful planning, fore-casting and predictions as regards asset behaviour and production patterns, which should increase return on investment (ROI) e.g. predicting and avoiding asset down time. Equally, matching data samples around different asset operating parameters and the resulting production output can give us insight into how different operating levels affect the asset and its components. Such information allows for a truly optimised operation as an asset is run to the most efficient production rate, whilst ensuring that equipment and components lifecycles are at maximum capacity.
Failure to produce relevant data for decision making is a major drawback due to the lack of very intelligent analytic tools. This is where a well-articulated Data Governance strategy can have a huge positive impact because without knowing what data you have, how current it is (real time or not), how quick it changes, who uses it and what it is for, an organisation will struggle to get the insights mentioned earlier. So again, Data Governance is about getting value from your data, which ties in nicely with the ISO’s definition of Asset Management. In addition Data Governance is about encouraging good data behaviour.
Whatever it is, the opportunities depend solely on the proper application of Big Data and this can be fostered by proper Data Governance. This means the ability to understand what data sample is needed, how to analyse and interpret these data and of course the flawless application of the findings. Without factoring each of these functions, Big Data may be just too much to handle.
On the flip side, one needs to balance the effort (cost or otherwise) required in sifting and analysing Big Data in order to predict an asset failure with the cost or impact of the asset failure. This I feel is where the focus should be as the practice of Asset Management is about realising value from assets.
So organisations need to avoid operating in silos with a reactive, short-term term mind-set as this impedes Asset Management, while a multi-disciplinary, holistic and long-term approach enhances it. So departments, business units need to work together in achieving the organisation’s goals and this demonstrates the holistic approach of Asset Management and equally confirmed by Mother Teresa of Calcutta when she said “I alone cannot change the world, but I can cast a stone across the waters to create many ripples.”
In conclusion, without proper insights from data (raw or refined forms), then the whole resources spent on gathering and analysing Big Data becomes a waste. And with more focus on Predictive Maintenance strategies, which is a key aspect of Asset Management, Big Data has a major role to play with a robust Data Governance process.

Written on 30 January 2017 by Chidi Umeano (Principal Consultant), Codub Group of Companies.

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