Data on demand: Exploring Data-as-a-Service and energy

What is an ‘as-a-Service’ model?

The ‘as-a-Service’ business model first emerged in the late 90s, driven initially by the technology sector and Software-as-a-Service. It represented a paradigm shift in how businesses answered the needs of their customers, transforming from the traditional product offering to full-service delivery delivered on demand based on user personalised preference. Customer experience lies at the very heart of this new model, and it is largely facilitated by the innovative use of data and technology. If you want to learn more about the evolution of the ‘as-a-service’ model and in particular Energy-as-a-Service, read our recent blog here.

 

How does Data-as-a-Service fit into this mould?

In today’s day and age, data is prolific. There is now far greater recognition of the value of data, particularly real-time data, and its pivotal role in digital transformation across almost every industry you look at. However, as big data use grows, organisations are increasingly finding that the traditional data architecture they have in place is simply no longer adequate. As a result, a need has emerged over the last few years for a technological solution which overcomes existing infrastructure barriers and enables the full exploitation of data in such as a way as to drive collaboration, innovation and ultimately business growth. This solution is Data-as-a-Service. Data-as-a-Service provides full bespoke big data service delivery, from storage and integration to processing and analytics, to customers via the cloud. Much like Energy-as-a-Service, Data-as-a-Service is a relatively nascent concept and has only come to the fore in recent years as giant strides have been made in cloud and big data technology.

 

Isn’t Data-as-a-Service the same as data hub aggregation?

 

There are a number of different data management approaches such as data lakes and data hub aggregation, and while there is plenty of overlap, they shouldn’t be confused with Data-as-a-Service. Let’s start with data lakes. They are central repositories for raw data, created solely for data storage with no real facility for processing, analytics or similar. In other words, there is no option to operationalise or share data stored in data lakes but they can be a low cost storage solution. Data hubs on the other hand take data management much further –they use a hub and spoke architecture to enable data storage and sharing between disparate systems in a far more agile approach. Data hub aggregators are systems which are engineered specifically to facilitate data sharing, integration and processes such as data standardisation.

So how is Data-as-a-Service different? Data-as-a-Service streamlines the entire data service journey from capture and storage through to integration, analytics and end user management in one bespoke operation and on one tech platform. It has less of a focus on storage and concentrates instead on two distinct elements which particularly stand it apart from data hub aggregators. These are:

 

  • Integration – the application of API technology makes it easy to connect to multiple data providers at scale, and solves the problem of how to integrate big data with outdated systems
  • Management of external relationships with the end user, including contract management and user onboarding

 

What are the benefits of Data-as-a-Service?

The benefits of Data-as-a-Service are far ranging. The streamlined customer-centric operation cuts out the need for multiple disparate service layers, such as data resellers, and removes the complexities of data integration.

 

Key benefits include:

  • Operational cost savings – the use of cloud storage technology reduces the need for physical data silos while a streamlined service makes data use far more cost and time-efficient
  • Lower risk – as with other ‘as-a-Service’ models, the fully managed service provided offloads the risk and challenges of big data management from individual IT departments to the Data-as-a-Service provider
  • Agility and innovation – API integration allows for data sharing between platforms at scale and at a rapid pace, enabling collaboration and innovation

 

Why is Data-as-a-Service so important to the energy industry in particular?

Energy has traditionally lagged behind other industries on the digitalisation curve. While prolific, energy data tends to still be locked in proprietary silos and legacy IT infrastructure has, to date, hampered the development of systems needed for complex data analytics. Data-as-a-Service offers the opportunity to uncover and unlock operational and behavioural energy data for third party systems more quickly and at lower cost. In doing so, it opens up the industry to more rapid product and service development, faster innovation and an altogether better customer experience. In fact, Data-as-a-Service could well be seen as a facilitator of Energy-as-a-Service by putting data directly in the hands of an energy industry ripe for digital transformation.

 

If you’re looking for near real-time data, why not speak to us about re.alto Connect, our new suite of fully managed Data-as-a-Service solutions which gives access to energy data from sectors such as e-mobility, metering and renewables. Get in touch to find out more

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