There’s no doubt about it – data has become one of the most valuable currencies of modern times. Whether data is applied to quality assurance, customer service or financial outcomes, organizations worldwide are beginning to realize the benefits of investing in data collection, analysis and application. In the current climate, one of data’s most common uses is in performance monitoring – a practice which has become commonplace across a variety of industries, particularly in the evaluation of a connected system or network. Utility companies for instance collect huge volumes of data when assessing the performance of a network, which is then analysed to provide insights into efficiency across the grid.
The true method of extracting value from data however, as this article will explain, requires the development of a ‘data-driven’ operation; by aligning processes, systems and organizations, utilities are able to create an ‘as-operated’ paradigm as opposed to an ‘as–designed’ model.
The 'big data' that is produced by the utility grid possesses enormous transformative potential for utility managers. Those utilities that use data to help enhance operational performance, improve financial outcomes and boost customer service can be viewed as 'data-driven'. These types of organizations are seeking to overcome constraints (organizational, operational and technical) that limit the power and usefulness of the data generated and used every day.
Data-driven operations are not a new concept - big data in utilities has been around for a number of years, and many utilities have consequently been seeking to exploit the insights it can provide to help improve performance. Today however, the increasingly complex utility service marketplace and less forgiving regulatory environment is causing the pace of this benefit to increase rapidly, which in turn is dramatically altering utilities’ historical approach to data. Part of this has come from refining, applying and analysing data, which allows utilities to considerably improve their network and operational effectiveness. For instance, by capturing, tracking and analysing voltage, they can better manage fault, leveraging renewable supply and responding to increasing consumer demands. These improvements enable the utility operators to generate benefits for themselves and their customers.
Big data – how to make it bigger and better
The popularity of the term ‘big data’ could create the false belief that utilities have already figured out the best methods to optimize data– and given its status as an industry ‘buzzword’, you’d expect the major players in utilities to have already figured out how to gain the maximum benefit from its analysis. However, utilities generate data through many diverse systems and processes which can be complex to dissect, therefore making it difficult to establish patterns that require immediate action. The main objective of a data-driven operation however is to create 'data ecosystems'; here generation and utilization of data occurs within a continuous, automated lifecycle.
First and foremost, utilities understand that data is an evolving asset to be maximized. The value of this asset is maximized by aligning, processes, systems, and organizational structures around the data. The organizational change required to optimize data as an asset is perhaps the most foundational aspect of the data transformation model. Clearly, it would be easy for a utility to be content with their existing use of data, and many organizations limit the application of data to existing process and structure within the business. Data-driven utilities however have shown that by placing data at the centre of the organization, they can improve performance in challenging areas such as operational costs, grid control, trouble response, and customer service. By aligning data with key business objectives, network operators are afforded the right information at the right time, enabling a more effective business response.
Secondly, utilities need to acknowledge that systems should enable the generation and use of as-operated data. In creating a data ecosystem, data-driven utilities are transitioning their existing operations process from the legacy focus of ‘as designed’ to today’s capability of an ‘as operated’ focus. By doing this, the data produced reflects the contemporary performance of the grid as it is operated, rather than how it was designed. This is incredibly important with the constant evolution of grids, particularly given the commercial impact of Distributed Energy Resources (DERs). This means utilities companies can use greater system interoperability and advanced utilization techniques to operate smarter and more efficiently.
These methods will naturally help to improve performance across the grid, but, it’s also vital that utilities consider the ways in which this data is used in day-to-day work. Integrating the data within the existing system footprint, for instance, creates a much more interactive user experience than using a separate system and screen. Moreover, a multitude of spreadsheets, disparate data and multiple systems each reduce the value of the data asset.
Why an as-operated paradigm enables smart grid investments
As previously mentioned, many organizations remain tied to an ‘as designed’ data paradigm. This view ignores the constant environmental and system changes that occur within networks. But for smart grids, the impact of persisting with such a practice is two-fold.
Firstly, an as-designed paradigm forces utilities to restrain the intelligence of the grid. Moving toward smart, two-way interoperability (between systems and applications), provides a much stronger basis from which to draw operational, financial or customer service insights. This data can then not only be more effectively applied and utilized but also accelerated within core utility systems. In turn, this enables utilities to expand the breadth and depth of operational information they are supplied with, and in doing so permits them to continue to advance towards operating a 'smarter grid.' Persisting with an ‘as designed approach’ within its organization and system footprint prevents the utility from capitalizing isolated smart device systems such as network monitoring equipment. Creating an as-operated paradigm within the organization and system portfolio assures that smart grid technology is rendered more effective.
Secondly, by aligning processes, systems and organizations around the role of data within the utility, organizations can take advantage of the data produced by smart grid systems. This data, when supported by performance-based insights, can help identify beneficial operational patterns within an organization, and in doing so will streamline a variety of processes within a utility, whether they be normal, emergency or regulatory operations. With this in mind, it’s vital that utilities consider aligning smart grid investments holistically, within the context of existing operations and organizational structures.
Be smart to get smart
Data analysis and big data have been in existence for many years, to the point that the majority of players within the industry will already be utilizing data to derive performance insights and drive increased optimization within their networks, particularly in relation to smart grids. However, isolated hardware or system investments aren’t sufficient to realize the promise of 'big data.' Of course, it is vital that there is always the availability of data – but it is the utilization and application of that data which is of utmost importance. In order to derive both technical and operational performance insights, it’s essential that enriched, relevant data is mined, visualized and deployed in a manner that defines a utility operator as a 'smart grid operator,' and means that the network can be viewed not only as a whole but from an ‘as operated’ point of view. These utilities will then be able to truly define themselves as data-driven.