Applying the lessons of Big Data to Project Controls

In recent years, the construction industry in the UK has begun to harness Big Data to better deliver projects and to monitor and maintain the assets produced by them.

In fact, Big Data is an almost inescapable buzz-word across the construction industry at the moment. The question is; is the hype warranted and what should we be doing about it?

The first area where Big Data is emerging is the

monitoring and tracking of the operational assets produced by the construction industry. Everything from individual rubbish bins through to bridges and entire cities are being designed to be more connected and smarter, using monitoring infrastructure and the Internet of Things (IOT). With this wave of connectedness will come a wave of data that will need to be collected, analysed and used to optimise operations and future new works.

Figure 1- Example of Smart Infrastructure Sensors (Datafloq, n.d.)

The construction industry will be affected by this area through the designs they are asked to produce, and over time, the data-driven decisions they will utilise in order to determine what to build next, but this set of information does not specifically help us answer the challenge “How do I deliver my projects and programmes more effectively?”, the benefits will mostly affect the clients and owners of the operational assets.

The second area where Big Data is indeed already emerging within the construction industry is in the monitoring of hardware/equipment on the construction site and the multitude of engineering sensors deployed to monitor site conditions, safety and environmental impact. Solutions already

on the market and producing large amounts of data include:• Temperature, wind, vibration and other

environmental monitoring applications• GPS monitoring for vehicles, staff and

shipping/transport• Specialised machinery monitoring such as

tunnel boring machine monitoring and predictive modelling for disc cutter wear and maintenance

• Specialised engineering testing and monitoring applications such as concrete strength and density monitoring, visual inspection and machine vision monitoring

Figure 2- Industrial Internet Overview (Ravi Kalokota, 2012)

The application of each of these operational technologies is specific to the type of work being done and the engineering challenges being faced, but all have a significant role to play in ensuring more consistent, safe delivery of construction works which minimises down time and waste. Arguably this is where the construction industry has seen the most benefit so far, and rightly so, but the applications, strategies and data

collection methodologies are inconsistent and fragmented. The applications themselves are not repeatable over a wider range of projects. Moreover, we see a significant loss across the industry where data collected from these applications is lost or ignored as assets move beyond construction, reducing the ability to leverage and apply learnings in the future.

Figure 3- Tunnel Boring Machine Sensor Feeds Dashboard (Maidl Tunnelconsultants, n.d.)

This leads us to the third area that will be

impacted by Big Data: the project delivery,

project controls and programme management

office (PMO) functions. These functions are

consistently present on all construction

programmes and are largely responsible for the

collection and management of information

before handover to operations. These parties

are also responsible for bringing the

programme in on time and on budget.

At present, conducting a quick search across

the internet for ‘Big Data’ solutions for

construction turns up any number of results

that will easily fit into the first two categories,

but you will be hard pressed to find significant

amounts of information or strategies regarding

the third. A look at the definition of ‘Big Data’

lends itself to the explanation:

"High-volume, high-velocity and/or high-

variety information assets that demand

cost-effective, innovative forms of

information processing that enable

enhanced insight, decision making, and

process automation."

(Gartner, n.d.)

Ignoring our role in capturing and utilising data generated from equipment and monitoring tooling (roles that are often overlooked when planning for the project controls function), project controls and performance data is neither high volume nor high velocity, meaning we are not processing gigabytes of data by the hour, day or week, and the reporting we do is more often than not consolidated on a once-per-month cycle. Technically our work does not fit neatly in the Big Data box. Building Information Modelling (BIM) is the closest the project delivery and controls functions come to Big Data in their present roles, but this information is still geared more to assisting the client/operator when construction is completed.

Nevertheless, we are living in a data-driven world where "data is the new oil" (according to Clive Humby, a UK Mathematician). There is also an ongoing revolution in several industries and large organisations in developing practices and processes to harness Big Data to deliver valuable, actionable information. Disappointingly though, the construction industry is lagging far behind. Investment in technology is minimal and data management and utilisation practices are both inadequate and archaic.

A recent study by Deloitte cited the following statistics:

"The construction industry is last in IT spending compared to 19 other industries“

"67% of Construction project management professionals are tracking and reporting performance

via manual processes or spreadsheets"

(Deloitte, 2017)

Across the world, engineering and construction

programmes continue to operate out of

spreadsheets, word documents and

PowerPoint slides, often with an army of staff

at hand, ready to copy and paste from file to

file in order to produce the next round of

reports and analysis.

The cost of doing business this way is

unacceptable, yet hidden, particularly when

most organisations continue to operate with a

headcount only mentality.

Our ability to manage even the smallest sets of

data, including those we are most familiar with,

is well below the performance and capabilities

of any number of other industries. Given this,

the industry is almost wholly underprepared to

deal with and most importantly, mine and

make use of the additional data soon to be

coming our way.

Planning/Schedule

Financial

Estimation

Design

Risk

Change

Commercial

Whilst we aren’t yet mature enough to deal

with the information, what is the tipping point

for our traditional project delivery dataset

becoming ‘Big Data’? Perhaps large current

and historical sets of project delivery data -

estimations, durations, work rates etc?

This requires conscious effort from industry

players to standardise the archiving and

processing of such data, a significant effort for

all but the largest organisations. To date, most

initiatives in this area have been patchy,

proprietary and disjointed. With little drive in

this direction and limited pull from client

organisations, it is unlikely that Big Data will be

arriving to save the day for project controls

any time soon.

Given this bleak state of affairs, it’s no wonder

that, since data usage practices have not kept

pace with other industries and have arguably

not evolved much at all, there has been no

significant improvement in project delivery

performance.

THE LESSONS OF BIG DATA

Despite the challenges, and the somewhat

unfulfilled promise of Big Data in the project

controls space, there is plenty more to do as

we prepare ourselves for its eventual arrival.

Most importantly, the construction industry

must improve its data management practices

from top to bottom and project controls is

uniquely positioned to support this process.

It’s certainly not as exciting or buzzword

worthy as Big Data, but similarly, the

foundations of your house should not be built

of sand.

Fortunately, we have the advantage of looking

outwards to the industries that have paved the

way to Big Data. Rather than make the same

mistakes, we can harness the practices they

have pioneered along with the modern tools

that they have created.

This first step in any Big Data initiative is to

know where you are going, what you think you

need to measure and why it’s important.

Rather than focussing on eventual outputs and

where the information will be positioned in a

report, it is far more valuable to identify all of

the stakeholders, their business objectives and

the key metrics and measures that they believe

will help them in their decision-making

process.

Know what you want, who

wants it, and why

By building a dictionary of all of the

measures, it will then be possible to assess

your existing sources of data to make a

determination of readiness or to identify a

roadmap to reach your desired end point.

This process will also help your organisation

clarify exactly what the roles, responsibilities

and expectations are across the organisation

and will potentially help to identify

misalignments that need to be addressed.

‘The Golden Thread’

Understanding how all your

organisation’s/programme’s information

relate to each other is also an important step

in the process. This process requires you to

identify the classifications or breakdowns

that segment all your information.

These breakdowns are the first step in being able to answer all the questions borne out of the Key Metrics and Measures Dictionaries you have developed.

Once a number of breakdown/classification schemes are agreed upon, then these will form the lowest common denominator

across all of the information sources that will be implemented in the business. It will be possible to review everything currently in existence for compatibility and will form a minimum set of requirements for any new information systems and processes brought online in the future.

This ‘golden thread’ throughout your organisation’s data will also simplify the data collection and transformation process for future and current integration projects.

Efficient processing and analytics relies on high quality information. The more repeatable the end to end process of inputting, tracking and utilising information across your business processes, the more reliable and trustworthy the analysis results will be.

The construction industry needs to make a

much greater commitment to move away from stop-gap data management solutions such as Excel, Word and PDF as its primary stores of business data. These systems, whilst convenient at first blush (everyone knows them, and don’t have to pay any more), can become prohibitively expensive when you factor in maintenance, error correcting,

Structured Systems

Source: https://www.theartoftheneedle.co.uk/product-page/japanese-gold-thread-no-8

data collection and summarisation and incompatibility across the wider organisation. Instead, advances in cloud services and modern rapid application development mean that organisations should have little difficulty either identifying an off the shelf product that will meet their needs, or implementing a solution at an acceptable cost.

Moving to more structured systems will bring a variety of benefits to an organisation often including:

▪ Standardisation of processes▪ Less training/retraining of staff as they

move between projects▪ Centralised store of information▪ Improved data security/backups▪ Easier integration into reporting systems▪ Traceability of changes/updates▪ Workflow automation▪ Correct information always available▪ Mobile access▪ Automated data quality/input checks

The way that information is recorded in a

source system is not always fit for purpose in a

reporting system. Sometimes information

needs to be rolled up higher, sometimes it

needs to be filtered. In other scenarios, it may

need to be joined with other datasets from

across the organisation before it will make

sense. In true Big Data applications with

regular reporting/monitoring, none of these

processes are done manually, it would be too

time consuming. Instead, Data Transformation

or Extract, Transform, Load (ETL) toolsets have

been developed to support the automation of

these processes.

These are not new concepts, but new generations of tools that have advanced the capabilities available, often providing integrated organisational metadata management and the ability to view and create ETL workflows in a visual manner, enabling the possibility to model and confirm the process with business representatives in a

much simpler format.

These tools also offer a wide variety of opportunities for project delivery and controls teams; Whilst we do not have enormous data requirements, ETL tooling can help us to:• Streamline month end data collection

processes• Re-map information between different

systems where code structures are not consistent

• Automate data quality checks• Provide an integration interface between

project systems• Significantly reduce the cost of reporting• Free up resources for analysis and

decision makingETL tooling should be picked up by the Project Controls team as an opportunity to introduce a greater level of standardisation for information gathering and report generation.

Automation Technology

https://www.building-blocks.nl/content/blogs/different-types-of-data-sources

Figure 4- Unifying Datasources (SnappyData.io, n.d.)

The toolsets with graphical design capabilities also provide a level of self-documentation on how data is collected and what is done with it which can provide a level of assurance against the analysis processes. Getting up to speed in this technology will also enable the project

controls team to prepare for future Big Data initiatives, giving the team the capabilities required to act as business analysts and implementers for new analytics requirements.

A lot of Big Data platforms and modern

technologies are beginning to shift towards

open source models for their software tooling

and platform offerings. There are some

obvious advantages to looking for solutions

within this space:

• Lower cost of ownership

• Pooling of knowledge/experience

• Standardisation of solutions

• No vendor lock-in

Project controls and project planning are not

yet well represented in this space and there is

the potential for significant advantages in

identifying and/or supporting the buildout of

applications using this methodology in the

future. Taking this hand in hand with advances

in software development and cloud hosting,

this combination may offer attractive options

in the future.

Open source also extends well beyond the

creation of software and may be an attractive

area of exploration when it comes to

standardisation of processes, project delivery

models, quantity and rates databases and

schedule performance datasets.

These will require a more creative outlook of

what could or should be shared outside of an

organisation’s boundaries but could offer

significant advantages to those willing to

participate.

Open Source Opportunities

The construction industry and the project controls function will truly not be ready for the onslaught of Big Data until such time as Information Management and Data Analysis are embedded as core functions both within programmes and within the organisations running the projects. This extends well beyond just the selection and implementation of software systems, also influencing how an organisation should recruit, train and manage their staff, and how they should assign management or ownership of their data.

The vision for how data is managed within an organisation must also be pushed up into the corporate space. It is simply not sustainable to allow projects and programmes to implement their own standards and processes without overarching coordination. This does not mean an organisation must completely lock down their systems, however a well thought out model needs to be implemented. We see project controls and PMO’s as having a signification influence in this space. Within project driven organisations, these bodies are well placed to sponsor and influence the adoption of better practices. Each of these functions have a wide reach across organisational units and collect and report significant amounts of data. The PC/PMO function should look to align itself as the business owner or business representative for the organisation with respect to project and performance data and may also have a role to play in becoming the initial business analysts for the organisation.

Modern data driven organisations and IT organisations have been dealing with similar

problems for a long time, and it is possible for the construction industry to leverage and tailor some of the existing information and organisational architecture management frameworks in order to kickstart their organisational transformation.

One possible starting point is to look to the open source TOGAF 9.0 Enterprise Architecture Methodology and Framework; a simplified model for project delivery information management could be developed to satisfy the needs of an organisation looking to increase their information management capabilities. Organisations may also look to resources such as www.datagovernance.com or the DAMA Data Management Body of Knowledge to begin to shape their strategies.

Organisational Change

Figure 5- TOGAF Framework (TOGAF, n.d.)

A key risk to implementing strong data

management practices can be found in the

common construction model of employing

Joint Venture organisations during the

delivery phase; When clients select a Joint

Venture, they must be cognisant of the fact

that they are in effect sanctioning the

creation of a new organisation, along with all

the problems that that will bring. Joint

Ventures must redefine all of the tools,

processes and data management standards

that they will employ, often under

competition with the existing processes that

are being brought to the JV from each of the

parent companies. Moreover, those brought

in to deliver the JV programme of work are

focused on the day to day delivery of the

programme; it is extremely unlikely that they

will hold a strong strategic vision of

integrated data management.

Without either strong guidance from client

organisations, or forward thought between

the JV participants it is unlikely that well

thought-through data management and

integrated technologies will be brought to

bear on the programme. We believe that

efforts should be made to develop and

standardise the operations of JVs with

Project Delivery charters that set out ahead

of time the systems, processes and

procedures that all participants will follow, in

doing so a more efficient operation will be

possible within this space.

A Note on Joint Ventures

A key embodiment of all the principles discussed in this paper may be the introduction of a new organisational role: The Project Controls Architect. This may either be a person, or a partner organisation brought in to assist in the design and implementation of a cohesive

Data Integration and Information Strategy along with the development of integrated project controls. We believe that the ability to identify and work with representatives capable of creating and seeing through a strategy will be the greatest factor in successful, efficient delivery and readiness

The Project Controls Architect

We are an award-winning consultancy that helps businesses achieve better results in the delivery of projects, big or small. At LogiKal, we can offer a range of services including consulting and advisory, information management and managed services to help your project succeed. To enhance industry capability, we also provide coaching, training and professional development services with a range of accreditations and non-accredited specialist skills courses.

Our expert advice and performance management solutions improve operational performance, reduce cost and mitigate risk. Our unique mix of specialised and proprietary

systems and services integrate into our clients’ project controls. This means our that our clients can make effective decisions faster and easier.

We were founded in 2002 and now have project planning and controls specialists across the UK, Europe, Australia and Asia. Everyone at LogiKal shares a vision for building and enhancing capabilities within teams so they can achieve consistent and sustainable results.

There are numerous lessons from the realm

of Big Data that can be learnt and applied to

project controls that will enhance data-driven

decision making and ultimately, improve

project performance. The good news is that

these can be applied to a fledgling project at a

fraction of the cost and complexity of Big Data

players and consumers. It will require strong

leadership to engender a culture that values a

modern technology infrastructure and data-

driven decision making. It will also require

one or more consummate professionals to

own the design, implementation and

management of systems, data and related

processes for the duration of the project and

beyond. Organisations who desire to remain

relevant in the future will need to start

investing in the development of staff and

technologies within the data management

space at a corporate level, not as a “nice to

have” project level expense.

Summary

About LogiKal

1. Datafloq. (n.d.). 5 Ways Big Data Will Improve Civil Infrastructure. Retrieved from Datafloq: https://datafloq.com/read/5-ways-big-data-will-improve-civil-infrastructure/1477

2. Deloitte. (2017, June 26). Data Driven Management for Digital Capital Projects. Retrieved from deloitte.com: https://www2.deloitte.com/content/dam/Deloitte/us/Documents/Real%20Estate/us-engineering-construction-data-driven-management-digital-capital-projects.pdf

3. Gartner. (n.d.). Big Data. Retrieved from www.gartner.com: https://www.gartner.com/it-glossary/big-data/

4. Maidl Tunnelconsultants. (n.d.). Procon II. Retrieved from maidl-tc.de: https://www.maidl-tc.de/en/Procon.html

5. Ravi Kalokota. (2012, March 26). IoT, Machine Data and Cloud Analytics: Splunk. Retrieved from practicalanalytics.co: https://practicalanalytics.co/2012/03/26/machine-data-analytics-splunk/

6. SnappyData.io. (n.d.). Unify Data Sources. Retrieved from snappydata.io: https://www.snappydata.io/highlights/unify-data-sources

7. TOGAF. (n.d.). The TOGAF® Standard, Version 9.2 Overview. Retrieved from opengroup.org: https://www.opengroup.org/togaf

References

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