How a modern Edge Data Solution can build value for the Instrumentation Industry
With the increasing emphasis on data, it is crucial to ensure you have the right assets and processes in place to optimize your business functions to stay competitive. The domain of analytical instrumentation is no exception to this, with a steady increase in the need for sophisticated lab instruments being observed in businesses across verticals.
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However instrument manufacturers continue to face one key challenge - how can they wield maximum visibility to deliver and evaluate instrument performance?
Having visibility over instrument performance is immensely valuable to instrument manufacturers and their customers. It can help significantly improve instrument utilization as well as identify or prevent issues. For a manufacturer, this visibility can be a rich source of insights into their customer’s instrument usage. Providing visibility and business insights to their customers can be a lucrative source of value-added and recurring revenue for them as well.
But how do they gain this visibility? The answer lies in two words - Edge Data.
The Benefits and Challenges of using Edge Data for visibility
Edge data, quite simply put, is data that is produced, located or processed “at the edge.” This can include equipment sensors, remote cameras, machine components, and more. This type of data can provide real-time analysis and insights into instrument performance, presenting both instrument manufacturers and their customers a variety of benefits through visibility.
However, unlike enterprise data which is structured and organized, edge data rides on the other side of the spectrum. This very characteristic is what makes edge data challenging to work with. The sheer complexity of edge data presents businesses across the globe with an array of challenges, that can be broadly condensed into 7 types:
Absence of a well-defined project roadmap and budget with clear-cut objectives
Requirements for a massive deployment team to implement complex and customized solutions
Lack of freedom as edge data includes a delicate balance of public, private, and hybrid cloud activities
Sourcing parts from vendors
Processing and acting on large quantities of data
Application development left up to the end-user causing businesses to miss out on key capitalizing opportunities
Risk of being outdated due to quick advancements in edge computing solutions
As a consequence of these challenges, the current success rate of edge data projects is markedly low, with 65% of projects failing. Even among businesses with successful edge deployment, a mere 12% have been able to experience the full range of benefits. However, this does not mean it is impossible to achieve success with edge data projects. In fact, it is essential to use edge data to stay competitive.
The future: Modern Edge Data stack
So how does Prescient look to combat these challenges? The answer lies in proper implementation of a Modern Edge Data Stack (MEDS). With the ability to derive important operational and business insights, MEDS helps companies bring structure and consistency to their processes. Unstructured and uncontextualized data is brought from the edge to produce high-quality data, run analytics, and transform it into a Common Data Format for BI analysis.
Prescient’s solution has already delivered results - improving installed base visibility for a leading global manufacturer, and ensuring optimal data-driven insights. This has enabled them to improve their instrument utilization by 39%, make more informed business decisions and significantly add value to their customers and to their own bottom line. You can read more about it here.
To Sum Up
Big data is imperative for growth in this day and age. While organizations spend voluminous sums of money on acquiring it, they sometimes fail to acknowledge the importance of managing it. However, in order to develop a robust and scalable edge data solution, it is essential to build on the right foundation. Prescient’s Modern Edge Data Stack provides precisely that - Enabling businesses to effectively leverage data to maximize value creation.