IdO de type industriel
3 min readTo illustrate the benefits of a purpose-built IoT analytics solution, I will use the [...]
To illustrate the benefits of a purpose-built IoT analytics solution, I will use the example of a carpenter building a new home. You have the planning, design and carpentry skills and are confident handling the project from start to finish. But even though you will piece the project together, you will likely hire an electrician to ensure that your work respects industry standards and best practises. The same is true for IoT data analytics. Implementing IoT data analytics requires an extremely specialized skillset. It is a niche market and few players have dedicated 100% of their time and expertise to IoT data analytics.
Analytics can be generally defined as the discovery and communication of meaningful patterns and insights in data. Looking at the use of analytics in the web, mobile application and social spaces – it is evident that data-driven decision-making is a crucial factor in determining the competitiveness and relevance of today’s companies. Most websites, mobile applications and social engines – new and existing – have analytics as an integral part of their product and business strategy. Their primary motivator for adopting analytics is real-time, valuable insights for (1) usage feedback to drive better and more-focused development, as well as (2) improvement and optimization of value-added services to spur potentially new monetization avenues.
IoT is no different. Product manufacturers/OEMs must embrace analytics as the cornerstone of their connected product experience and associated service delivery. When applied to the Internet of Things (IoT), analytics focuses on providing competitive differentiation and strategic insights to IoT product manufacturers and service providers on the usage and behaviour of connected products.
Over the past few years, IoT has transitioned from an over-marketed idea to a market reality. Most IoT adopters are increasingly seeing business value in its ability to provide real-time, actionable intelligence as well as compelling insights from their sensor data. And like most (emerging) technologies, the question of either building an analytics platform using toolboxes and libraries or buying one is often asked early on in the process, especially by large organizations with an IT department. When confronted with this choice, organizations often underestimate the time and resources that go into building analytics. Some even think that they can throw all of their data in Hadoop, install ‘R’ and done they are. This cannot be further from the truth.
Designing, developing and deploying analytic insights in the world of IoT/sensor data requires skill sets that are different than most traditional software projects. These skills include (but are not limited to):
Recruiting some or all of these profiles requires at the very least three months. If there is no ramp up period at all (which is unlikely) and considering that software built in-house usually takes at least 9-12 months to get off ground, a very optimistic time-to-insight (i.e. some analytic insights in production) would be between one year to one and a half year. It is hence somewhat obvious that for connected object manufacturers/OEMs to operate as quickly as possible with a feedback loop between their data and relevant insights, pre-built self-service data analytics and data science is essential. It may even determine how quickly these organizations can grow or succeed. Ultimately, subscribing to an analytics platform means that data will swiftly be available to all who need it, allowing more time and resources to be dedicated to improving the actual connected products, delivering new services, onboarding more customers, reducing churn, etc.
mnubo’s SmartObjects solution provides turnkey analytics for IoT manufacturers/OEMs and service providers. Interested in learning more about the SmartObjects solution? Request your free consultation today!