Unlocking the power of data create strategic and operational advantage across Defence.


Data analytics: making sense of data for more valuable insights across asset management and operations

The recent launch of the Defence Data Strategy to 2023 highlights the importance of data in transforming Australia’s rapidly digitising Defence industry, stating that managing data in a strategic manner is “critical to everything Defence does”. The strategy demonstrates that data is now widely viewed as a game changing asset that can no longer be ignored, and that it is especially important in enabling fast and informed decision making to create strategic and operational advantage.

The five pillar strategy – Govern, Trust, Discover, Use, and Share – signals an intention by Defence to manage data in a more deliberate and disciplined way. It affirms the criticality of taking a strategic approach to how data is governed, collected, organised, stored, accessed, analysed, interpreted and visualised to support decision making processes.

While vast volumes of data are being captured and collected across Defence assets and there is clearly a growing awareness of the importance of data, there is a fundamental problem that needs to be addressed. Raw data alone has little organisational value – to be useful, data needs to be transformed into relevant information and insights, and these need to be put in the hands of decision makers.

At a practical level, raw data needs to be continuously extracted, structured, combined, analysed, interpreted and reported. Setting up the processes and infrastructure to achieve this requires an understanding of the Defence context and organisational drivers, combined with digital, data and analytics expertise and deep knowledge of relevant technical disciplines such as engineering, design and asset management.

Defence context and organisational drivers

The unique challenges for large organisations in unlocking the power of data

For many organisations, unlocking the power of data is hindered by barriers to accessing and organising it efficiently. This is especially true in large, complex organisations with diverse data sets. Their data is often difficult to find and access because it is highly distributed and fragmented across the organisation or trapped in proprietary systems and ‘opaque’ containers such as spreadsheets and documents.

Once accessed, the data may be difficult to use because it is poorly structured, inconveniently formatted or has quality problems such as being inaccurate, inconsistent, incomplete or outdated.

The rapid pace at which data is accumulating amplifies this problem due to a lack of adequate data engineering tooling and capability. A further complicating factor and long-term risk is the fact that the ‘keys’ to certain types of data, including the knowledge of how to access, interpret and use it, may be held by just one or two employees, or an external contractor.

With huge volumes of data being collected across vast networks of physical assets including towers, switches and exchanges, there was a pressing need to unite data sources to provide better visibility across assets for more accurate decision making.

Each physical asset had its own complexities and requirements for maintenance and upgrade, and data located across multiple systems in PDFs making this a challenging task. To achieve this, a single system environment with active 3D models for sites was created, with a consistent approach to data capture, management, organisation and storage across the whole network.

This solved the issue of separately storing data and switching back to historic systems meaning that the workforce can access data they know is up to date and trusted.

By having the right, quality, trusted, information at their fingertips, these organisations are realising significant value including time savings from automating processes such as structural analysis on towers, or power viability audits to understand how much power has been used on their sites. They can now look intimately at everything they do to determine what can be automated, and then push that process into their single source of truth.

Case study
A Telecommunications Perspective

Telco uniting data across disparate asset base

The Defence industry also faces a particular challenge in terms of heightened security requirements. A delicate balance must be struck between keeping data secure while embracing new technologies and the latest tooling (which typically exists in the cloud). Striking the right balance will help to manage large volumes of sensitive data safely and efficiently, enhance organisational agility and ultimately support better, faster decision making.

Context is crucial

To tackle these challenges, and unlock the full power of data, requires putting in place a set of building blocks over time. Having smart data capture techniques is one piece of the puzzle, but to make sense of that data, and do it well, requires combining organisational context with technical expertise.

Technical expertise in working with and analysing data is rightly viewed as a powerful enabling capability, but without organisational context it is largely impotent. Before value-adding insights can be extracted from data it is critical to provide a foundational understanding of your organisational goals and your operating context.

This context makes it possible to connect data to organisational value, because it helps define what data is relevant and where analysis should be focused. For example, it provides the necessary context for analysing where you are relative to your goals, what the key drivers are for achieving these goals, and what data is required to undertake this analysis.

Knowing what data is relevant makes it possible to decide what data to collect and to keep. It also makes it easier to identify precisely what data you need from your partners, and what data they need from you.

Combining data analytics and domain expertise

Collecting and keeping the right data is an important part of the puzzle, but there are others. Wrangling data into shape and putting it to work requires specialised skills. For example, data engineers specialise in accessing, organising and integrating terabytes of information using modern data processing methods and tools.

Data analysts and data scientists have the necessary coding, statistical, data visualisation and reporting skills to uncover valuable relationships and insights, derive meaning from the data, and report and visualise it in a compelling way.

Domain experts in disciplines such as asset management, programme management, and geotechnical, electrical, civil and mechanical engineering can provide a deep understanding of the meaning and significance of the data that they work with daily. Their input is often essential in identifying opportunities to analyse data in a way that supports key operational, planning and sourcing decisions. They also provide an independent view on priorities for addressing key data collection, quality and availability deficiencies.

Working closely with Defence teams, these specialists can accelerate value realisation by:

  • Resolving technical challenges in extracting, transforming, and loading data into repositories, and identifying priorities for addressing gaps in data acquisition and management processes.
  • Integrating diverse data types from multiple sources, for example by merging design information with as-built information, contractual information and asset condition data to improve design accuracy, contractor management, asset maintenance and asset performance.
  • Improving the quality and completeness of key data sets, such as the data held in GEMS (Garrison Estate Management System), to ensure that the data is as accurate, consistent, complete and up to date as possible. Embedding effective processes for improving and maintaining data quality will provide a strong foundation upon which to build a strong analytics capability. Well managed data sets also create possibilities for next generation capability development, such as using machine learning for predictive maintenance or optimising asset performance.
  • Undertaking analysis of data to provide insights in key areas, such as comparing supplier costs and performance; benchmarking of cost in essential categories across different Defence bases to identify outliers; analysing cost drivers and trends; and analysing Estate appraisal information to identify recurring issues and causal relationships.
  • Providing modelling and visualisation ingenuity, using interactive digital technology to present information on projects and operational performance in a more intuitive and timely way, helping the organisation make faster and better-informed decisions. As the Defence data strategy states: “dashboards in preference to lengthy briefs”.
  • Building a mature analytics capability, helping shape a roadmap for upskilling and building analytics skills inside Defence teams.
  • Helping to balance data security with internal agility by using frameworks, methods and tools that are appropriate for Defence’s secure environment.

Using data analytics to transform decision making and operations

When analytics is done well it can transform decision making and operational management. For example, in the context of Defence Estate, robust analysis of the vast amount of data that has already been collected could enable many improvement opportunities including:

  • Optimisation of asset operations and maintenance based on analysis of data from data from control systems, asset inspections and maintenance records. This will help ensure that all assets – from air conditioners to aircraft – are being maintained and operated as cost effectively as possible.
  • Greater precision with capital spending and asset lifecycle management based on analysis of asset maintenance and operational data, and Estate appraisal information (asset inspections and condition assessments). More accurate estimates of maintenance costs, operating costs and remaining useful life will inform optimal timing of servicing and replacement, and this information can be mapped to the forward works programme. It will also provide more accurate data on the Estate for contractors and eliminate the need for recapturing the same data at the beginning of each project.
  • Identifying opportunities and potential risk exposures in supply networks based on analysis of supplier performance and regional market differences, thereby informing future Base Services contracts.
  • Better understanding of trends in key cost categories (e.g. energy, water, catering, facilities management) within and across several bases. A better understanding of usage and cost trends will help identify where costs can be reduced, with potential implications for cost forecasting and forward planning.
  • Gaining insights into cost drivers such as the relationship between age of particular types of assets and their maintenance costs, or how energy cost vary with location, building size, building usage and building material. This allows facilities and assets to be better understood and utilised, may inform design decisions, and improve the accuracy of forward planning of capital and operating costs.

Sydney Water needed to adjust their systems to reduce environmental and social impacts from aged infrastructure but faced an estimated construction improvement cost of AUD 5.5 billion. To minimise expenditure, the Wet Weather Flow Improvement Programme was created using statistical analysis and modelling.

Six independent databases with historic asset data were brought together into a single reference system and combined with digital asset data to create a digital platform that geographically maps the condition and location of each asset based on data analysis and site inspections.

This optimised operational and maintenance planning to lower expenditure on new high-cost assets, but by reducing the discharge of stormwater during wet weather, it enables Sydney Water to improve its environmental and social impacts.

Case study
A Water Perspective

Water’s environmental and social impact

Good data and robust analytics also provide a solid foundation for more advanced digital technologies such as artificial intelligence, machine learning, robotics and autonomous systems. These data-hungry technologies are becoming increasingly important for Defence. They can enable new capabilities such as automatic condition inspection and fault detection, predictive maintenance, and autonomous optimisation of asset performance and energy consumption.

This creates significant opportunities to improve productivity, sustainability, safety and operational effectiveness and help Defence deliver on its strategic objectives.

Building an efficient and scalable analytics capability to drive organisational value

A strong data analytics capability is a powerful insights engine that Defence can harness to help reach its strategic objectives. But as we have discussed, it is fundamentally important that these goals are well articulated and that the analytics engine directly supports the decisions that will guide defence towards these objectives.

It is also important that the fuel supply for this analytics engine is efficient and that the fuel is the of high quality. In other words, the right data needs to be efficiently collected, processed and retained, and this data must be made readily available to analytics teams.

To fully realise the potential of data analytics this end-to-end capability needs to be repeatable, efficient and scalable. This will require upfront and ongoing investment in building and maintaining the entire platform. In particular, it will be necessary to enforce rigorous data management standards, invest in modern scalable and interoperable technologies, and build automated data pipelines.

While Defence has vast reserves of data at its disposal, these reserves have been largely untapped. Data has been treated as a by-product of operations rather than as an asset that can help Defence to achieve its strategic and operational goals. Defence now has the intent and opportunity to change this, and to use the power of analytics to unlock the organisational value in its data.

About the authors

Eric Louw is Managing Principal, Data and Analytics at Aurecon. He has thirty years of international experience with leading management consulting firms and as an executive in the telecommunications industry, focusing on business strategy, technology strategy, digital transformation, and data analytics. He is the co-author of three business books, as well as numerous articles and academic papers.

Julieanne Saxty is a dedicated program manager with experience working across large sustaining capital programs, in both Australia and the United Kingdom. With cross market experience, Julieanne has advised Clients within the Defence, telecommunications, retail and Emergency Services industries. Julieanne is a senior leader in Aurecon’s Defence team, leading and delivering Aurecon’s Project Delivery Services (PDS) Program.

Tim Plenderleith has over 20 years of experience in the industrial, commercial, infrastructure and Defence sectors. He has completed projects throughout Australia, New Zealand, Asia and Africa across business advisory and multidisciplinary engineering and management. industries throughout Australia, New Zealand Asia and Africa. I am involved in advising clients on matters of business strategy and development, supply chain improvement and project design and delivery strategy.

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