posted on Jan 11, 2018 by NelsonHall Analyst
Tags: Movate, Network Management
As 2018 begins, the RPA sector is starting to produce more segment specialists from within its vendor base. Whereas just two years ago the sector was still finding its footing in addressing common back- and front-office application automation, enterprise customers today have the luxury of building best-of-breed solutions that often incorporate two or more vendors working in concert to automate a broader spectrum of tasks.
CSS Corp’s Contelli is a relatively new automation platform, but one that is gaining attention for its capability set in a complex and high-value enterprise support area – namely, automated network management. Contelli received an elevated role at CSS in the wake of the company’s late 2016 reorganizaton, which saw CSS' board elect to change the direction of the firm. As part of this strategic direction change (one that saw an influx of new management talent take place in the executive suite), the company transitioned from a corporate focus heavy on legacy IT services to one centered on customer engagement and digital transformation. That transition also included an elevated role for CSS' automation platform, which was rebranded from AIMS (Automated Infrastructure Management Solution) to Contelli.
The product continuously analyzes client IT operations and uses network traffic data, paired with algorithmic analysis of historical data, to predict downtime, reconfigure traffic for improved efficiency, dynamically provision and de-provision IT assets, and resolve repetitive support tasks. CSS estimates ~30-40% improvements in operational efficiency in IT operations, and ~45% to ~65% reduction in FTEs, in typical deployments of Contelli IT Management Engine.
Although Contelli’s brand name may be a new one in the market, the platform has already achieved success. For a leading managed network services provider with 450k network devices under management, Contelli software provided the client with a 25% improvement in average handle time for open ticket calls, a 22% improvement in case closure rate, and, perhaps most importantly, a 100% success rate in case audits performed on work Contelli automated.
Three factors make Contelli an appealing offering for organizations seeking to reduce their network management costs:
- It touches a broad range of KPIs. Network optimization isn’t always realized by identifying a few significant sources of cost savings and quality improvement potential; often, the task involves incremental improvement of multiple KPIs, from throughput and traffic efficiency to asset provisioning speed, to support ticket resolution turnaround cycle. Contelli’s position within the network management stack enables the product to offer a broad array of improvements in KPIs across multiple task areas
- It learns continuously from network data. Automating a fluid process is among the steepest challenges in intelligent automation today. As variables change within the task area to be automated, the RPA platform of choice must not only be able to adapt on the fly, but learn entirely new sets of events and exceptions as topologies and assets evolve. Contelli’s development team has invested considerable time and resources in the product’s machine learning layer to enable dynamic network management automation
- It is a focus area for CSS’ Innovation Labs. Contelli is a mature offering today, but CSS has significant plans to improve and upgrade the product’s machine learning capabilities in the company’s Innovaton Labs, an R&D environment for continuous improvement of the platform. CEO Manish Tandon has circled Innovation Labs in red as a key strategic plank for the company’s evolution, and Contelli is slated for considerable time ‘up on the lift.’
Contelli isn’t a ‘one stop shop’ for front- and back-office enterprise automation, but for organizations seeking to self-fund a larger-scale RPA initiative with a broad slate of KPI improvements in a critical business task area, it’s an appealing choice for network management administrators.