Today’s commercial landscape is shifting rapidly towards the Internet of Things (IoT) and digitisation.
Yet when it comes to these innovations, all too often we focus narrowly on the destination while failing to adequately consider the nature of the journey there.
Yet it’s the decisions that are made on the transformation journey that will determine the profitability of the original strategy.
Thus, although change can be transformative for the utility, the fact that it doesn’t come with a new ‘plug and play’ infrastructure is problematic.
Utilities today, when it comes to IoT and digitisation strategies would do well to focus on the means rather than the end.
First and foremost, they should be asking themselves questions about how they integrate and manage data. Ignore the underlying data issues (about which, more to follow) and the impact of strategic innovation will be severely compromised.
You don’t have to look far to find companies that have built seemingly strong digital and IoT platforms only to then fail to leverage their full potential because underlying data issues haven’t been addressed.
Why is this the case? And why is data so important in the digital age?
The clue may be in the name. Digitisation means the transformation of analogue processes and mechanisms into to bits and bytes.
Connectivity is one important component of this, but that is all.
Yes, in the new world, machines are connected to machines and network components are smarter but connectivity is simply the means of communication; it is the pipe.
The language of communication in the digital/IoT world is data. Addressing connectivity and then ignoring issues of collecting and using raw data is an example of the proverbial saying ‘Suppose they threw a party and no one came’.
Successful IoT and digital strategies are data driven. It’s only useful to have new, fast, and flexible ways to run your business if you know what you actually need to do. Without data, you’re flying blind.
Knowing what you need to do requires being aware of the information contained in the data generated by your customer’s relationship and activities with your business (for instance, what your customers are purchasing and what experience they are getting on your network).
Adding a data management layer
This being the case, how you address data integration and management is mission critical to the success of any digital transformation.
If you’re already deploying smart meters, connecting devices and moving into the digital world but you’ve overlooked the data issues, it’s not too late.
Data and integration and management layers are available that enhance existing technology investments, they don’t replace them. And they increase return on investment across the board.
What utilities require is a data integration and management application (or layer) that sits between their network (ideally, as close to its edge as possible) and the applications that are situated on “top” of it, those used to manage the business like systems for billing, customer relationship management, analytics, network performance optimisation, meter management, etc.
The latter group requires information from the former in order to perform effectively (for instance, a billing system can’t generate a bill without information relating to the customer’s activities on the network) so it stands to reason that the better the quality of that information and the faster it becomes available, the better the performance of the business.
What is ‘under the hood’ of a data integration and management application? What should the utility expect it to do? How should you expect it to work? What value will you accrue from using it?
Here is a checklist of features and functions that energy companies should consider table stakes in how they can use data to drive their business to digital success:
- The collection of data in any format or type from any source in your network (grid, smart meter, or otherwise).
- Enrichment of usage data from any additional external database or application.
- The guiding of usage data to accounts with product reference and account information from any source.
- Assignment of amounts or values to the usage if that is desirable/appropriate.
There is more. It’s critical to have the ability to audit and report data-related processing, ensuring in the process that there’s no duplicate usage.
Correlation, consolidation and aggregation of usage of different types from any source, based on configurable criteria is also vital as is normalisation of master data.
Data management applications should also be able to help consolidate multiple data integration platforms and or other applications while still ensuring vendor independence, in so far as the user requires it.
Plus, they should address data quality and governance and confer a degree of application/infrastructure future proofing to protect and extend RoI on existing investments.
Data management - best practice functions
Four other characteristics are particularly important too.
1. The ability to leverage data via either batch or real-time processing as required by your business.
2. A system that is quick to implement and easy to configure meaning the ability to dynamically support and even drive your ability to respond to changing market conditions or even (see the next article in this series) changes in the weather!
3. Support for complex data needs is another consideration. Analytics always benefit from as much data as can be delivered. The right data management application offers the capability to collect and process complex data types, even binary machine logs via simple configuration.
4. And a final point is future proofing. Your business will likely expand over time to include more data sources which often brings complicated data collection into the enterprise analytics project scope.
Ignoring the IoT and digital transformation would be suicidal for the modern utility, concludes DigitalRoute’s Brody. The same can be said of ignoring fundamental questions of data integration and management.