This blog post addresses:
- Enterprise architecture in utility retail is changing
- Innovation is happening elsewhere
- Data is key to differentiation and innovation
- Data first, the rest will follow
ENTERPRISE ARCHITECTURE IN UTILITY RETAIL IS CHANGING
Enterprise architects are focusing increasingly on the technology strategy part of the job.
The enterprise architecture role within a utility retail enterprise is significantly changing, driven by the changes in the strategic challenges of the utility retailers.
The focus for the IT department as a whole and enterprise architecture (EA) in particular has long been to find a balance between:
- digitisation as a means to achieve operational efficiency; and
- the enablement of the continuous transformation of the traditional utility retailer towards an efficient, competitive player in the increasingly competitive utility market.
For the technology strategy part, the EA could historically rely on the large, traditional software suppliers such as Oracle and SAP to aid them in shaping their pan-organisation technology strategy. This allowed them to mainly focus on the operational efficiency part of the job.
With a stronger focus on customer and user experience on one hand and the utility retail market’s competition reaching an all-time high, the enterprise architect’s focus is shifting heavily towards a technology strategist. While operational efficiency may remain the driving force behind projects nowadays, the key role for an enterprise architect is to enable the utility retailer to leverage technology innovation to gain a competitive advantage over its competitors.
INNOVATION IS HAPPENING ELSEWHERE
Fast-moving, innovative business functions should be extracted from slow-moving transactional systems to enable innovation.
That role is becoming increasingly complex, as innovation is no longer (just) happening within the big players. While Oracle, SAP and Microsoft’s footprint still remains significant, specific business functions require more, faster and more innovative approaches.
The utility industry is forced to make a giant leap, from a position in the back of the line of digitised industries to the front, to make sure it’s not eaten by the tech-native companies, such as Tesla or Octopus Energy.
Those tech dinosaurs, as Salesforce founder Marc Benioff once called Oracle, Microsoft and SAP disrespectfully, all have CRM, ERP, MDM, BI and general cloud server offerings tailored to utility retailers. They however still suffer the same problems as before: long implementation and migration times due to large, not-well-integrated monolithic applications that were given a coat of cloud paint. This results in large projects, big budgets, cumbersome change trajectories and slow decision making, which is (still) hurting the retailer’s agility and thus competitiveness towards smaller incumbents in the market.
Everywhere, implementation times reduce, integrations get productized and solutions are moved to the cloud. An example is our partner Vlocity by Salesforce, which is thriving on large and complex utility-specific Salesforce implementation trajectories. And while COVID-19 has its impact on the short-term transformation trajectories, it gives time to think ahead and determine the true differentiators: Gartner predicts enterprise investment in cloud trajectories that was initially predicted for 2023 and 2024 to show up as early as 2022.
A key activity for a modern enterprise architect is to identify which application capabilities and business functions are stable and don’t change often and which ones are volatile (e.g. due to regulations) and are key to your competitive position. If these capabilities are intermingled, then consideration should be given to how to separate them, as this will undoubtedly become an inhibitor in the digital economy.
DATA IS KEY TO DIFFERENTIATION AND INNOVATION
Innovation starts with a data-driven architecture in which data streams, not business functions or processes, are the fundament.
To what degree are your business functions such as pricing, forecasting, settlement, billing and reporting service-based and accessible through rich APIs with dynamic content definition? If core business value functions are locked in monolithic applications and difficult to access, this may become an inhibitor to new or modified business processes required for new products and services.
The answer lies in the data. New insights, new products and better forecasts result in more innovation. Once you stop thinking about those functions being the master, and your data architecture, including your data warehouses or lakes being the minion and turn that equation around, you enter the world of a data-driven enterprise architecture.
Above: A diagram depicting how the Gorilla platform can work with your data and processing needs
IT vendor review checklists around privacy, security and compliance should be amplified with data openness checklists, encouraging vendor solutions to put data centrally, build your fast-moving data processing functions close to it, and expose towards slower-moving, business-enabling transactional systems.
FIRST COMES DATA, THE REST WILL FOLLOW
Don’t select data technologies without having a clear business function context.
All too often, driven by global trend reports praising artificial intelligence, blockchain and machine learning, purposeless innovation trajectories are executed to enable future unknown innovations in those areas, without the core ‘purpose’ question answered. Technology should always be an enabler, never a dictator. While user and customer experience may well be driven by artificial intelligence in the near or far future, it is definitely underpinned by data in the first place.
At Gorilla, we’ve built our platform from the ground up with this sole mission in mind: to empower slow-moving transactional systems with data-heavy business processing functions. Being it bespoke pricing with countless cost components based on cost and consumption curves, long-term bottom-up portfolio forecasting or machine-learning-driven cost forecasting or forward curve generation – it all starts with bringing the data close to those functions and enabling processing at the highest granular data level. Only then true innovation will take off.
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