Why high-quality IoT data and advanced analytics are two sides of the same coin in energy trading
Ask most members of the public what role the Internet of Things (IoT) plays in their lives, and you’ll probably get a blank look. But tell them it enables connected devices like Amazon Echo, Ring video doorbells and locational data on a Deliveroo order, and they’ll immediately grasp its importance.
At root, IoT is the use of technology to create a network of smart assets, vehicles, appliances and other ‘things’ that can send and receive data through the internet. But while its impacts for consumers are growing by the day, IoT’s potential on an industrial scale is simply colossal.
Why? Because it enables industrial organisations to establish two-way real-time data connectivity with tens or hundreds of thousands of devices and assets across multiple geographies—thereby providing a level of insight and control they could only have dreamed of in the past.
Turning IoT data into insight
However, to derive real business value from this connectivity, you need more than a flow of IoT data from the connected things. You also need to be able to interpret and understand that data to turn it into actionable business insights. And for that, you need sophisticated data analytics.
Combining IoT with analytics can transform how organisations optimise assets and provide services to customers. It does this by equipping them with data-driven insights that can be presented in visualised ways to support faster, better-informed decisions, help to reduce costs and ultimately boost profitability.
It’s an exciting opportunity—and the convergence of three technology trends means there’s never been a better time for energy businesses to seize it. First, the explosion in asset data from connected sensors. Second, new big data technologies that handle vast amounts of data. And third, advances in analytics, enabling assets and supply chains to ‘think’ in real time. What’s more, costs are falling rapidly across all three areas.
Think data quality—then visualisation
Little surprise then that we’re seeing more and more energy clients companies invest in combining IoT and analytics. But as the urgency of implementing analytic capabilities increases, we’re also detecting a shift in how companies approach these projects.
What’s the change? Well, to deliver its full potential, analytics needs to be connected to every other data source in the organisation—especially IoT. And for the insights from the analytics to support good decisions, the underlying data being fed into it must be of the highest possible quality.
This is where some organisations have had to do a rethink. In recent years, we’ve seen a lot of companies focus on their data mainly from a visualisation point-of-view, concentrating on what their data platform looks like and how accessible it is. However, this isn’t enough. It’s also vital to focus on where that data is coming from and how timely and accurate it is.
Getting the visualisation front-end right is actually easier than sorting out the data. But it’s imperative to do both. This need applies particularly to energy companies with an integrated supply and trading model, given the fantastically rich array of data sources they can draw on to drive value—from connected assets to smart devices worn by engineers to live market and trading data.
Use cases on the rise
As integrated energy companies’ investments in IoT and analytics continue to rise, more and more use cases are emerging that capitalise on the resulting insights. These already include real-time traceability and optimisation of physical assets or inventory, enhanced compliance and trade surveillance, and better pre-trade analytics to identify arbitrage opportunities. The list is growing by the day.
Other market players such as hedge funds are also moving to realise the potential of combining accurate, timely IoT data with analytics. They’re increasingly seeing energy markets as a place where fundamentals can be modelled from diverse data sources like weather and news feeds, generation units, storage capacity and even the REMIT insider information feed.
But whatever the use cases for IoT/analytics, and whatever kind of business is applying them, the message is clear. To get the optimal outcomes from data analytics and visualisation, the underlying data needs to be valued and managed like an asset. So, when creating your IoT/analytics strategy, remember that the starting-point—and vital enabler of success—is mindset.
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