At this point, the value of big data and analytics in solving today’s business problems is widely accepted. But with the onset of IoT devices like sensors, beacons and monitors being brought into the enterprise, it’s proving more difficult to capture and harness that data, too. And without a system in place that facilitates the capturing, mining, slicing and dicing of said data, a company will be lost in the dark trying to decipher what story the data is actually telling and using that knowledge to revamp business operations. Luckily, a new tool is emerging to help with transforming and revolutionizing the way business gets done—enter the Analytics of Things (AoT).
The Analytics of Things enables a company to mirror, predict and prognosticate based on data, manage sensor and other third-party data and, ultimately, provide intelligent feedback and analytics-based rules that can lead to more strategic actions and improved business operations.
At its core, AoT can help a company contextualize their existing data for more informed decision-making. Though it hasn’t gotten the press or buzz that big brother IoT has, AoT is starting to ride a cresting wave into mainstream consciousness—the global AoT market is expected to surpass $57 billion by 2025. Besides, any company utilizing IoT and trying to get the most out of these connected devices and all of their data needs to be able to analyze them.
Data is a veritable gold mine for many industries, becoming more diverse and complex as its use in solving business problems continues to take hold. In order for companies to make the most of data and actually change the way that they do business for the better, they need AoT systems in place to intelligently manage, compress, process, stage and harness it. Though a company’s data may be wholly diverse, establishing one unified analytics ecosystem that integrates data from all IoT and edge devices will make for a successful foray into AoT.
In order to extract the most value and revenue from big data investments and implementations, a company first needs to adopt an Analytics as a Service (AaaS) mindset. In doing so, one’s big data architecture will become more compact and manageable, leading to instant, real-time analytics that offer greater and more accurate insights for businesses to latch on to. On the flip-side, any company collecting outside data through remote IoT devices should be incorporating it into their overall data bank to tap into edge analytics. Syncing these edge devices with centralized platforms paints a more comprehensive data picture where the exchange and analysis of this data can create actionable business strategies and lead to monetization.
Next, developing an AoT and big data strategy that accounts for architectures, transformation and analytics needs will ensure a company gets the most bang for its analytic buck. Investing in platforms, assets and accelerators that complement existing data and analytics setups will only bolster this strategy and yield greater results. Once this interconnected system is in place and data is feeding into all of it, AoT can help develop a governance framework for transforming people, process and technology at a given company.
How so? Analytics stemming from big data in combination with blockchain are slowly being deployed to create more intelligent automation systems, including chatbots, digital assistants and even automated analysts to do all the AoT analyzing themselves. As has been proven time and time again, intelligent automation and leveraging of IoT leads to higher quality business operations and sparks greater digital transformation for enterprises.
What’s more, the side effects of this are more innovative employees, thanks to automation freeing them up to focus on more mindful tasks like improving customer experience. Not to mention a sizeable bump in bottom line for most companies, too. When looking at the value of AoT at a high-level, know that all the creativity and business savvy in the world can only do so much to improve operations and take care of business without driving analytics forward, first.
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