Consumer Data Rights have driven faster adoption of self-serve technologies for consumers to access their own data, freeing up the time of the service provider’s customer care team. In turn, consumers are becoming more receptive to exchanging their data with service providers, knowing they retain ownership, the exchange is secure, and they are deriving a material benefit from how this data is being used.
An AFR article published late 2021, noted that a study conducted by Quill, Studioworks and Open Data Australia found 90% of 56 surveyed companies intended to use CDR data to build new digital products. At that time, Regional Australia Bank was already using CDR data to streamline expense verification and affordability assessments for loans, and Beyond Bank was developing apps to manage personal finances.
The Next Opportunity
As CDR is rolled out in the energy sector, we are sure to see a continued focus on reducing the cost to serve customers and an ongoing focus on self-help. As utility data experts, the team at Ignite need to look beyond immediate implementations and plan for future horizons where CDR solutions are part of the foundational platform from which data becomes a core value gain business opportunity. That next phase is extending the success of self-serve access in the business-to-consumer domain to strategic data sharing as the springboard for self-serve access in the business-to-business domain.
Our team recently deployed self-serve data capabilities with an Australian-based energy company, enabling its business customers to access granular data at will, which they, use to deliver benefits to their customers (the end user).
“There was a lot of business time taken up with answering ad-hoc customer requests for data.” says Mitch, Ignite Solution Architect. “By giving the customers access to their own information, our client was able to free up time to focus on creating deeper value for their customers, improving processes and outcomes, rather than serving information that the customer wanted to access themselves.”
In the process of this project we uncovered some important lessons relevant to enabling self-serve, and opportunities for extending to strategic data sharing.
As discussed in our previous perspective on data sharing, Barb Wixom’s strategic data sharing model requires four key practices for enablement:
- Investment in data management practices, to identify and make data assets useable (anonymised), available and combinable.
- Reducing friction between data sharers, through the control of data assets, service platform design and automating repeatable sharing control processes.
- Enabling trust and collaboration between partners, through fair policies and processes that are win-win.
- Being proactive in the management of data sharing projects, so that the focus is on value realisation, not an exercise in looking busy.
Self-serve data portals follow the same key practises as strategic data sharing, which is why we encourage organisations to think of them in the same way.
Reducing friction between data sharers – it’s all about good architecture
Reducing friction is key to enabling data sharing between organisations and between data providers. The first step to achieving this is ensuring data is liquid – easy to access and transfer between different architectures.
Invest in data management practises that increase data liquidity
Removing manual interventions in your data flows is key in enabling robust data sharing. It’s important to understand where, when, and how human intervention occurs in the data management process, and then automate it.
Be resistant to change
It is also important to have an architecture that is resistant to breakdown and robust enough to handle changes in incoming data sources. As an example, the data sharing architecture for an energy provider needs to seamlessly handle the switch in core applications like billing systems.
Don’t forget the consumers’ needs
In the case of our self-serve project, the team was met with a dilemma – is this project for accessing pre-defined information or for analysis of defined data? Data platforms are typically strong in one or the other, not both. Businesses, whose consumers have analytical needs typically require a SQL database for investigation relationships. For businesses providing access to large sets of pre-defined information, such as consumption and billing information, across a large pool of consumers concurrently, NoSQL is the database of choice.
Keep it simple and intuitive
A successful self-serve solution requires simple and intuitive access. It needs to be able to pull back millions of rows of data in a point of a second. Depending on your analytical needs, your architecture will require drastic change in order to enable such access.
Consumers and data partners want their information immediately and quickly. Pulling back a couple of million rows of data within 200 milliseconds isn’t the main focus for a more internal-facing SQL-based solution aimed to support analysts and reporting.
Self-serve increases risk
A self-serve solution increases risk to any business, as does any data sharing practice. Predefine and interrogate the data that is to be used, putting in the required protocols to protect that data. A self-serve solution is something that can be accessed by thousands of customers at any one time, and data sharing involves an exchange between various parties with varying uses. Making sure your data is protected from any data partner’s misfortunes is imperative.
Prove the value of data sharing
If you’ve already deployed a consumer-facing self-serve data solution and are looking to derive value from strategic data sharing, then some of the hard work has probably already been done. Trust has been enabled between you and the consumer, your data asset is both protected and liquid.
If you’re looking at engaging in data sharing, follow the research where leading organisations “proactively manage innovation initiatives as value-realising projects”. This means proving the value of what you want to achieve with data sharing before shifting from an analysis to shared-focused use of data.