Strategic and effective management of data is fundamental to the success of key infrastructure organisations, an industry leader says.

During a keynote presentation delivered at the recent Transforming Infrastructure Performance Sumit that was hosted by Bentley Systems in Melbourne last week, Professor Andy Neely, Professor of Manufacturing at the University of Cambridge, spoke about the need to effectively manage data across entire organisations.

According to Neely, the importance of data should not be underestimated.

However, he says that many organisations are failing to make effective use of their data.

He called for data to be managed and valued as a strategic asset.

“If I land one point today, it’s that data is a strategic asset and needs to be managed and valued as such,” Neely said.

Five key points

During his presentations, Neely made five points.

1. Data driven organisations do best

First, Neely says that around the world, organisations who manage data effectively generally outperform those who do not.

As an example, he cites the case of machinery and heavy equipment provider Caterpillar.

To many, the value of Caterpillar revolves around the physical tractors and diggers which it provides to mining and construction sites.

What is often underappreciated is the volume of data that is generated by the array of instruments which are featured on these trucks. In fact, a single truck may have up to 500 sensors attached to it.

These include:

• the GPS system, which indicates the truck’s precise location and whether it is moving
• scales, which measure the weight of the material in the truck’s bed and indicate whether the truck is being filled with rocks or is at capacity; and
• engine temperature, oil pressure and tyre pressure, which can indicate whether the truck is working as expected or needs repair.

To see how this can be useful, consider the case of a quarry. The Caterpillar truck arrives at the quarry face, is reversed into position and is filled by an excavator.

Using the weight monitoring information from the scales in the bed of the truck, the precise moment at which the truck reaches its capacity and should start moving away from the quarry face can be determined.

This can indicate whether the truck is moving away at exactly the right time or if five or ten seconds’ worth of productivity is being lost. It is significant as many quarries operate on a cycle time of 90 seconds or thereabouts.

Using this data, Caterpillar is able to identify moments of lost productivity and feed information back to the quarry operator, who can then intervene if necessary to improve process efficiency.

2. Data is not delivering

Despite its importance, Neely says that many organisations are failing to capitalise upon the opportunities which are available from their data.

Across many organisations, he says that data is fragmented, difficult to access and challenging to interrogate or manage.

He points to a recent Fortune 500 report, which indicated that around $395 billion was spent on big data analytics and associated activity last year.

Despite this large investment, the report indicated that one third of organisational leaders struggled to justify the impact of their investment in big data.

Meanwhile, organisational leaders report that they are missing 80 percent of the data which they need in order to make informed decisions.

According to Neely, problems go beyond technical matters and extend to a lack of strategic alignment across organisations in terms of how data is managed and used.

To address this, organisations need to carefully consider the outcomes which they are trying to deliver and the role which data can play in helping to deliver these.

In the Caterpillar example referred to above, the company can use its data to assist the quarry operator to operate more efficiently overall.

Take, for example, the identification of potholes which may open up along the haul road between the quarry and the crusher.

These can be identified as vibration sensors in the truck indicate when a pothole has been hit whilst the GPS pinpoints its precise location.

For the quarry operator, this information is invaluable.

Potholes cause trucks to slow down and then reaccelerate. This burns fuel and drives up fuel expenses.

Early identification allows for prompt repair and saves fuel costs every time that a truck drives along the haul road.

Provision of this information is invaluable not only to the quarry operator but also from Caterpillar’s own viewpoint as a supplier.

Using its digital data, the company is able to move beyond traditional sale and maintenance contracts and to work with operators to maximise operational efficiency.

From this, it can better position itself as a supplier of choice by guaranteeing a cost per tonne of mineral extracted.

It has also been able to negotiate clauses within contracts that enable Caterpillar to receive a share of the financial gain which arises out of cost savings which are achieved.

This helps to align the interests of the quarry operator with those of Caterpillar as a supplier.

In another example, this is scaled up to a city level.

In the United Kingdom, one company who was contracted by a local council to repair potholes also bid for the contract to collect refuse waste for that same council.

The idea was to attach sensors to the garbage trucks, which travelled over the entire municipality road network each week.

Data collected from the trucks would be used to identify any cracks that were opening up.

These could then be repaired more cheaply before they became potholes, thus saving the firm significant costs in terms of its pothole repair service.

In such a case, the pothole repair company started with a clear picture of what it was trying to achieve (saving costs by repairing potholes more quickly and cheaply).

It then explored how data could enable it to achieve this and how this data could be accessed.

(AI generated image of a Caterpillar truck on a quarry site. A typicall CAT truck can have around 500 sensors which can feed critical information back to quarry operators).

3. Missed AI Opportunities

Third, Neely says that organisations who fail to effectively manage data will have difficulty in maximising opportunities which are available through AI.

This is because effective AI requires input data which is timely, accurate, reliable and relevant to the purpose for which it will be used.

He says that a common challenge is that many organisations have large volumes of data which may be of varying quality.

This makes it difficult to know where to start when implementing AI initiatives.

4. Manage data as a strategic asset

To maximise the value of data, Neely says that data should be managed as a strategic asset.

A critical step for firms in doing this is to quantify and report on the value of their data.

This provides greater visibility in terms of the data’s importance.

Consider the case of National Highways in the United Kingdom, which manages around 7,000 kilometres of highways and A-roads across the UK.

Several years ago, the organisation undertook a formal evaluation of the value of its data assets. This was done as part of a broader move toward digital transformation.

The evaluation revealed that whilst the organisation’s physical road network was worth £115 billion, its data assets were also worth $60 billion.

In other words, the organisation had one pound worth of data assets for every two pounds of physical assets.

Once this was revealed, data was transformed from being an IT issue to a board level strategic asset.

Leaders began to question how data was being managed and whether the organisation’s data assets were receiving adequate levels of investment and protection.

As a result, the organisation identified 50 priority data initiatives that would deliver between £800 million and £1.2 billion in value over ten years.

Around £1.2 million in efficiencies were identified in early work surfacing. This paid for the valuation exercise in itself.

One insight delivered £20 million in savings from a £300,000 investment.

Across its portfolio, National Highways demonstrated that for every £1 invested in data, it generated about £2.70 of economic value for logistics firms, commuters and the wider economy.

(AI generated image of a highway in England. Image: freepix).

5. Infrastructure sector can take the lead.

Finally, Neely says that with targeted and intentional experimentation, the infrastructure sector has an opportunity to champion and lead the world in these developments.

In particular, the sector has an opportunity to demonstrate the potential to use data as a strategic asset to drive greater productivity and performance.

Consider data as your asset

Overall, Neely says that data should be considered as an asset.

“if you think about the outcomes you’re trying to deliver and what data enables you to deliver those outcomes, and then you start to think about the value of that data, it changes the way you think about this material (data assets),” he said.

“Don’t think just of asset data, think also about how you use data as an asset.”

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