Feature The misunderstood asset Data is a bit like politics loved by some, feared by others, ignored by many. But, asks Ben Meek, why is it so misunderstood? While some industries, such as banking, have embraced data as one of their most prized possessions, many organisations particularly those dominated by physical assets, such as utilities have not embraced the management of data with the same vigour. Many experts have explained why data meets all the essential financial accounting requirements to be deemed an asset, but few have done better than Moody and Walsh in explaining why data meets all the essential characteristics of an asset from an accounting perspective: 1. Data has the potential to enable future services or economic benefit 2. The data controller has the capacity to regulate (or deny) access to that benefit 3. Data is the result of past actions for example acquisition, discovery or development. Unlike other intangible assets, such as staff and customers, data cant resign or change suppliers. The key notion of data management is to ensure that organisations take steps across the life-cycle of data acquisition, use maintenance, protection, enrichment and retirement, to ensure they govern and improve the realisation of current and future value. Is it too new? One of the earliest examples of advocacy for managing data as an organisational asset comes from the founding President of this very institute, Norman Eason. His groundbreaking Data as an Asset paper presented to a conference in London in 1985 highlighted the compelling reasons to manage data as an asset. In this paper, Eason made some astonishingly prescient observations about how to preserve and amplify the value of data. He also highlighted the destruction of value that inevitably results from imprisoning data inside applications. During the past 30 years, there has been a slow but steady flow of publications building on Easons seminal thesis, including Douglas Laneys excellent book Infonomics: How to Monetize, Manage and Measure Information for Competitive Advantage. Most recently, the theme of freeing data from imprisonment has been most eloquently put forward by The Data-Centric Manifesto coalition. This theme has been amplified by Dave McComb in his recent book, The Data Centric Revolution. Intangible assets now dominate the balance sheets of many organisations. For example, while the intangible share of S&P 500 companies amounted to less than 20 per cent of asset value in 1975, it has steadily risen since then and now accounts for more than 90 per cent of market value. It is difficult, therefore, to sustain an argument that the idea of managing data as an asset is too new. It is more a question of (a) what proportion of enterprise valuation should be attributable to data; and (b) what level of ongoing investment and management oversight is warranted to preserve, protect and promote the use of enterprise data. For some highly regulated organisations, there may also be the question of convincing the regulator to permit investment in intangible assets such as data. However, this should not be an excuse for deferring the journey to managing data as a highly valuable asset. Author bio Ben Meek is Executive Vice President, Data and Innovation, at Utilligent. He is currently responsible for promoting leading practices in data and analytics with utilities around the globe. He spends much of his time curating enterprise programmes that drive data value and quality, and has a special interest in moving grid data science from science experiments into sustainable operating controls and innovation. Case study: China Light and Power China Light and Power (CLP) is a leading power provider in the Asia-Pacific region. Established more than a century ago, CLP delivers electricity to more than 80 per cent of Hong Kongs population and has a significant presence in China, India, Australia and Southeast Asia. As CLP faces the challenges of the energy transition, it has emphasised even more the importance of data as a strategic asset similar to its physical assets. It sees data as key to its decarbonisation journey. An example is its smart-grid programme to provide better prediction on renewable energy yields. CLP believes it would not be able to achieve these things without its investments in data. CLP recently carried out an exercise to arrive at a valuation of our enterprise data assets, explains Pubudu (Pubs) Abayasiri, Director, Digital Services, at CLP. This involved looking top-down at benchmark comparisons with peer utilities, as well as engaging with stakeholders across all of our regulated and unregulated business areas to build a solid bottom-up view of how to drive more value for customers through our data-centric programmes. This has proven extremely valuable in helping us improve our understanding and appreciation of this critically important intangible asset at all levels in the company. The data valuation has also helped better align the CLP enterprise data roadmap with our strategic business plans. Is it too rubbery? Perhaps a better explanation for the slow adoption of more rigorous and expansive data asset management practices is that there are no standards for measuring data value. Data, like many intangible asset classes, possesses some of its own unique characteristics and can be notoriously hard to pin down. In one sense, data is like physical assets in so far as without maintenance, data tends to perish over time. But, unlike physical assets, data is not depletable it can be used over and over, and new data and insights can be derived in the process. Data is also unique from all other assets in that it is infinitely shareable the owner of the data and any other authorised person can simultaneously use the same data repeatedly at any time (without loss) to their individual and collective benefit. While methods for measuring and valuing data are not standardised, there is a growing body of knowledge on methods for estimating the value of data, including so-called top-down and bottom-up methods. A recent project at China Light and Power (see case study box above) provides a practical example of applying both methods. Consequently, there is increasing understanding of how to develop a data maintenance and investment budget. The methods to develop these budgets are not perfect, but they are sound, and the absence of perfection should not be an excuse to defer the journey to managing data as a highly valuable asset. Is it too difficult? Possibly the best explanation for the slow adoption of advanced data asset management practices is that most organisations, regardless of size or industry, find advancing their data maturity is no easy task. The data maturity journey for the vast majority of organisations is not straightforward. It generally starts with various functional teams or departments implementing applications to automate and/or improve a particular capability, be it marketing, sales, finance, supply chain, and so on. To achieve this, there will be work to design, store, secure and monitor data. All good so far. Immediately, however, the practical challenges of data management begin to materialise. Different departments want to share and combine data from different applications; users find limitations in how their applications represent reality; data quality begins to fall off; standard reports cant answer important questions and new data warehouses are developed to close the gaps. Soon, there is a data labyrinth a mess of sometimes crudely interconnected silos where poor-quality data is imprisoned. Fortunately, during the past three decades, there has been a slow but steady development of a data management body of knowledge where good, better and best practices have been distilled from around the world. The best known of these is the Data Management Body of Knowledge (DMBOK2), which is curated by DAMA International. DMBOK2 contributors include leading data management practitioners such as Dr Peter Aiken. For example, Aikens concept of the Golden Pyramid provides a simple yet highly robust framework for thinking about how organisations should approach improving their data asset management practices. As with traditional asset management, developing and improving data asset management is a journey that will typically take several years. The journey usually starts with a coalition of the willing, who recognise that the benefits of improving data maturity can far outweigh the efforts to get there. This is often followed by a maturity assessment that will review the gamut of data management practices that include data strategy, and data life-cycle processes. This usually results in a scorecard or heat map. Finally, a roadmap is created and the journey begins. Data is also unique from all other assets in that it is infinitely shareable the owner of the data and any other authorised person can simultaneously use the same data repeatedly at any time (without loss) to their individual and collective benefit Case study: WEL Networks WEL Networks (WEL) is a community-owned electric utility in the Waikato region of New Zealand. Like many other electricity utilities around the world, WEL faces many challenges in managing its network safely and efficiently to support the regions electrification and decarbonisation goals, as well as providing affordable and reliable energy solutions to more than 90,000 customers. In progressing its vision as a customer-centric network operator, WEL has successfully deployed the capability across its low-voltage (LV) network of more than 67,000 household smart meters to record and transmit back to its control room, in near real time, both consumption and technical data, including fine-grained voltage records. This is the first network-wide street level deployment of live consumption and technical data from the customer premise in New Zealand, and marks a significant milestone for WEL. This data foundation provides the street-level visibility of its networks that has been eluding many in the industry. With the new data foundation in place, enhancing the network visibility, WEL has commenced a comprehensive data-insights programme, with a focus on network operation support and asset management, says Garth Dibley, WEL CEO. New features that are currently under development include line-down detection and unbalanced LV network utilisation. The next step will be around developing an integration standard and management interface for distributed energy resources. The ultimate goal is to offer flexible services to our customers and the wider electricity market by combining the advanced functions enabled by network visibility and system operability. This strategy of visibility and control based on more granular real-time network supply quality data is central to WELs overall transformation to a distribution system operator network model. Should it be a priority? A brief survey of Assets provides ample examples of physical asset managers across the globe driving significant value from their enterprise data. These include Downer Rail, Telstra, National Grid, INEOS, Oman Gas Company, ISA and Colorado Springs Utilities. The case studies in this article, from CLP in Hong Kong and WEL Networks in New Zealand, provide even more recent evidence that value can come in many forms, both tangible and intangible. Now, more than ever, that we should heed the prescient advice from our previous IAM President Norman Eason and start managing data as an asset on a par with physical assets. Endnotes 1 Measuring The Value Of Information: An Asset Valuation Approach; Daniel Walsh and Peter Moody, 1999 2 Data Management Book of Knowledge, 2nd Edition, DAMA, 2017, pp.39-40
