A growing demand for smart services that utilise cloud computing has given rise to the ‘as a Service’ model. And it’s no different when it comes to data. In this piece, smrtr’s GM commercial Steven Millward explains how the Data as a Service model has come to prominence…
One of the most transformative forces in the technology industry in recent years has without doubt been the ‘as a Service’ model.
Perhaps best-known in the form of Software as a Service (SaaS), the ‘as a Service’ model has largely been enabled by the rise of cloud computing, which means services and products can be delivered over a network, rather than on-site.
Popular SaaS options now include HubSpot, Shopify, Dropbox, Google Workspace (formerly G Suite) and even Netflix.
The reason for the popularity is simple. Users now want flexible solutions that can be interchanged easily and require little to no installation. The widespread uptake of the as a Service model also means those that do not start delivering these agile solutions risk being left behind.
What is Data as a Service?
Given the state of the industry, it has been little surprise to see the rise of Data as a Service (DaaS) as an option for businesses looking for data-driven solutions and insights. Similar to SaaS, DaaS gives businesses the ability to bundle data and analytics products that enable more targeted marketing and decision making – for a fixed or transaction based monthly cost.
By mining through a variety of Big Data sources, DaaS can offer customers customised datasets that are relevant for the business. These customised datasets can be combined with the company’s first-party data to further improve results.
Last year, Gartner placed Data as a Service ‘at the peak’ as part of the research firm’s Software as a Service ‘Hype Cycle’ for 2019. This means we should now be at the stage where high-growth adoption will start kicking in and DaaS will break into the mainstream.
One of the major challenges for companies in becoming data-driven, is the data engineering and integration components – making data available when and where it is needed. It was often the case that there was a large cost and delay in making data available to users, and this was multiplied several times to put data to different usages.
DaaS enables companies to switch on useful data where it is required. For example, insights on consumers may be required in:
- Internal systems for analytics and insights
- CRM systems for email decisioning
- Content management for serving differentiated web pages
- DMP and CMP for customer management
- Buying platforms for digital advertising
smrtr has listened to its clients and strives to make third party data insights and targeting available across all of these platforms in a consistent and easy way. That enables internal teams to conduct their best analytics work whilst making the result easily deployable by marketers in their chosen endpoints. First party data can also be combined to create an even clearer picture of a business’s target segments.
While smrtr have a number of successful case studies here in Australia, DaaS is proving remarkably effective across the world. A study by eMarketer found 41 percent of businesses were able to improve customer experience using DaaS, while 35 percent were able to increase revenue.
With DaaS continuing its ascendency, at smrtr, we understand how this approach can benefit our partners. We also take a slightly different approach to the DaaS model, focussing on creating statistically validated solutions specific for each industry that are based on real transaction data.
To find out more visit smrtr.com.au or get in touch and we’ll get back to you within one business day.
