Published on the 15/03/2017 | Written by Steve Singer
Subscription business models are driving continuous innovation, writes Steve Singer…
Software offered as a subscription is becoming the new standard. In fact, as early as 2015, Gartner estimated that by 2020, 80 percent of vendors would adopt a subscription model. This change in the way companies use software reflects user demand for flexibility; companies are looking to prioritise variability in their spending based on usage and to ensure they benefit from the value of the software before making a long-term commitment, it is affecting more than business models – it is also driving a faster pace of technological development. Bringing innovation closer to the market For business users, cloud solutions provide quick access to required resources to get the job done. By comparison, perpetual license models also allow for periodic software updates – but the rhythm of these updates and the frequency at which they are available to users cannot be compared with the ongoing agility and innovation offered by providers of subscription services. This is not related to how their software is marketed, but rather to the vendor’s ability to establish a continuous cycle of innovation for its products, the rewards of which transfer to their customers. Big data and cloud: continuous innovation is the model It is essential for integration, processing and operating software vendors responsible for these massive volumes of data to get as close to the market as possible, which means complying with key standards such as Hadoop, Spakr and Apache BEAM and aligning with the open source communities defining them. In practical terms, in order to be successful in a highly competitive, data-driven economy a company needs to anticipate technology changes and create a product roadmap that aligns and embraces them. Open source technologies – which are backed by the collaboration of a technically adept developer community and various partners – are particularly well suited to a continuous innovation model. Previously dominant or legacy software models, marked by “proprietary” software solutions and perpetual licensing, took 18 to 24 months to deliver new features. If you want your business to keep pace with the advances in machine learning, IoT, real-time data streaming analysis capabilities, depending on a model that consists of delivering new versions every 18 months is simply not viable for businesses. Supporting the emergence of new data uses In the past, a technology feature could last for years without risk of becoming obsolete (e.g. SQL). Today, the speed at which data platforms become obsolete is mind blowing – for example, MySQL is taken over by Hadoop, which is supplanted by Apache Spark, and who could say what comes next. Competition is fierce between companies using digital transformation as a strategic lever for performance and competitiveness. The result: Users of these technologies need the ability to easily adapt from one standard to another practically overnight. That’s why it’s so vital to select vendors which are in line with the times. It’s necessary because your business needs to move at the speed of ‘what’s coming next’. Steve Singer is ANZ Country Manager, Talend.
Forget for a moment how software is billed and consider the value it provides. This is where the real challenge lies: The ability to provide frequent releases that encapsulate current technology innovation and customer demands, while also being easy to manage and scale.
The continued growth in the use of big data and cloud technologies is in and of itself, a compelling proposition for continuous innovation. The speed at which these technologies advance requires users to adapt at an unprecedented rate – some have become obsolete in as little as 12-18 months.
Modern solutions for data integration must be at the frontlines of big data and cloud technology innovation. Not only to address customers various and rapidly evolving challenges – including customer intimacy, business sustainability, agility and economies of scale – but also to encourage the emergence of new data uses like streaming, real-time insights and self-service.