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Technological debt in our digital future: the cost of unmanaged data

Effective data management is something every business must become capable of doing now, if they are to remain competitive into the digital future. How organisations manage data today will ultimately lead to future advantage or disadvantage. Unmanaged data will create a technological debt and we will increasingly see a gap between companies that were quick to move and those that were slow off the blocks.

The second wave of Aurecon’s Our Digital Futures research – The Digital Horizon – shared a glimpse of what may come – how the energy, transport and property industries might look and operate, what the workforce of the future may be, where organisations should focus and what it means to properly prepare now. In this article, Eric Louw, Director of Data and Analytics, Aurecon, explores how data is fast becoming the most valuable asset in the world and the importance of its management in our ever-transforming digital future.

The knowledge extracted from data brings major benefits to the way businesses operate, providing insights into customers and every stage of traditional asset lifecycles that enable more informed decisions. Data is also the driving force behind key technologies such as automation, artificial intelligence (AI) and machine learning (ML), which are revolutionising the way many industries, organisations and individuals operate. However, getting the most value possible from any asset is dependent on how effectively that asset is managed, and data is no exception to this rule.

The gaps and differences between company’s data management will further widen as the volume and diversity of data continues to increase, and as the technology and applications that use this data continue their rapid and pervasive integration into our daily life. Less than six years ago the science of artificial intelligence was hidden away in research departments of universities, today however – from Google maps and smartphone facial recognition, to matching candidates on LinkedIn and tailored ads on social media – artificial intelligence and machine learning are now an expected part of people’s experience.

Digital’s full impact still to come

Despite the disruption that has already occurred, we are seeing accelerated demand for these technologies across transport, property, infrastructure, construction and energy sectors. Aurecon’s recent research revealed that the full extent of digital’s impact is yet to come in many industries – especially the sector that designs, constructs and manages our built assets. This presents great opportunities to benefit from using data and analytics tools in areas where digitisation hasn’t yet been widely applied or integrated.

As outlined in The Digital Horizon, the energy, transport and property industries are set to evolve rapidly and data will be at the heart, driving this transformation. What do businesses need to be doing more of now to prepare themselves as much as possible to benefit from the many opportunities available?

Making the most of these opportunities requires effective preparation and organisations have varying capability levels when it comes to data, machine learning and analysis. Many struggle to answer the question of where to start amidst the multitude of data streams and sources and are challenged by the basics of knowing how to collect and hold onto data.

Beyond these first steps is then tackling even more complex questions including: where do we keep data? How do we govern and maintain it? How can we find it, explore it, visualise it, report on it and track it? How is data used in operations and strategy? How do we extract strategic meaning? How can we monetise data? How do we work with it and interpret the analytics to identify where things can be improved or identify issues of concern such as damage, bottle necks, adverse behaviours and so on.

Data cannot be left to manage itself

Answering these questions is critical in managing data and preparing a strategy for this valuable asset. Just like any other asset, data has a shelf life – if it’s no longer fresh it’s no longer necessarily as valuable. To realise full value from data assets, data must be kept up to date, it must be rich – especially given its role as the fuel for automation which is evolving so quickly.

From autonomous vehicles to production robots to speech recognition, these are all trained through rich data, more advanced machine learning algorithms and advances in computer processing power. The richer an organisation’s data and analytics capability, the greater the possibilities for training artificial intelligence models, and the better it is positioned to gain a competitive advantage by leveraging its data assets.

Also, increasingly important to getting value from data is an organisation’s ability to navigate the rich ecosystem of data and capability located outside its walls and leveraging data across organisational boundaries. Agile organisations can more effectively look outwards at suppliers, potential partners, pools of resources, freelance workforce, more porous organisations.

With a better understanding of this external data, organisations can more effectively determine what can be used and leveraged and what can ultimately be combined with internal data sources to achieve the most value possible.

Upskilling vital for long-term success

As we become more reliant on data and it becomes the lifeblood of organisations, it is a top priority for organisations of all sizes to become more comfortable working with data and build the skills and capability of their workforce.

Interestingly, analytics can help drive this upskilling process by identifying capability gaps and informing development strategies. It can help us to understand people’s skill sets, track and guide their learning, shape recruitment and knowledge management strategies, and clarify connections and dependencies between capabilities, data sources, platforms and tools. Aurecon’s Data and Analytics team’s movement through the digital horizons is a great example of this, however for many organisations this is still evolving.

It is also critical to have skills to tackle key challenges, risks and issues associated with data, such as understanding regulatory environments, privacy, security, balancing automation with job loss and creation as humans learn to work with AI and robots. There are high risks for any organisation that stumbles unwittingly into areas where the wrong data is being used, where data is being used in ways that are not ethical, or where they fail to comply with data sovereignty or data privacy requirements.

Just like any other asset, data needs to be controlled and protected, and to do so effectively workforces must be armed with the right skills and knowledge.

Create new opportunities for the future

Advanced analytics and artificial intelligence is fuelling technology that is rapidly reshaping both the physical and digital worlds. As this technology enmeshes itself ever deeper into our working environment, it becomes increasingly important to think about the future, understand what it means for your industry, business model, supply chain, operations and workforce.

The organisations that are best at managing their data assets will be in the best position to compete effectively in our increasingly digital future. No matter where you start, or where you are headed, treating data as an asset, organising it, managing it well and having the right skills around it, will enable you to cycle faster in testing and deploying new technology and creating new opportunities.

The second wave of Our Digital Futures – The Digital Horizon – was released in March 2020. To learn more about the research, visit ‘Our Digital Futures’ at Aurecon.


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About the Author

Eric Louw is Director, Data, Risk and Analytics at Aurecon. He has twenty years' experience with leading management consulting firms and as an independent strategy consultant. His is the co-author of three business books, as well as numerous articles and academic papers.

This article was originally published on LinkedIn Pulse.

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