Strategic BPA and RPA Layers: Necessities for a Smart City Data Center

Summary

By Jack Wilson, Process Engineer, Optimum Consultancy Services

Abstract

The implementation of Smart Cities promises the realization of benefits to the city’s government, private sector, and its residents. The perpetual integration between the government’s public services and the private sector’s innovation, coupled with the collection of real-time data from the city’s residents and strategically placed data collectors could result in robust assessment of the city’s current state as well as dynamic forecasting of its future state. And while this outlook is exciting and much-anticipated, the lack of strategically implemented Business Process Automation (BPA) and Robotic Process Automation (RPA) within the Smart City’s Data Centers could result in the city’s inundation by the data wave following a Smart City transformation.

Introduction

Harnessing the capabilities of the IoT to enable a city’s resilience, infrastructure sustainability, and public services comes at the costs of not just providing the functionality to aggregate and analyze incoming and legacy data, but also knowing that data and streamlining the underlying workflows required to compile, vet, and store the data, and socialize its findings.

The concept of BPA encompasses the optimization (either through non-technology process enhancements or technology-assisted automation) of a single process or an enterprise-wide system of processes and subprocesses, while RPA utilizes technology that mimics human behavior along a spectrum of functionality ranging from data extraction and transfer to cognitive behavior through machine learning and artificial intelligence.

Strategic Placement of BPA and RPA in Smart City Data Centers

Data Centers[1] are the key to compiling, aggregating, vetting, analyzing, and socializing the Big Data collected throughout the Smart City[2]. And though future characteristics of these data centers are still evolving[3], they will need both BPA and RPA functionality in order to ensure the Smart City’s realization of Big Data-related benefits. The following content details the sequential layers of this data center automation, allowing for intake, analysis, and publication of data from and to Smart City Sensors:

Primary Automation Layer – Data Intake and Analysis

As data is relayed to the Smart City’s Data Center from strategically-placed sensors throughout the city’s infrastructure (utilities, transportation, residential and commercial assets, etc.), the Data Center engages its RPA functionality to categorically extract the data. This categorization allows for the data’s assignment to the appropriate archival environment (repository) for future retrieval and analysis.

Based on pre-designed parameters, this incoming data also undergoes a vetting process through which faulty, redundant, or anomalous data is flagged, published for further analysis by either robots with higher cognitive functionality (i.e. machine learning and artificial intelligence), or human analysts, and removed from its original incoming dataset. If the original receiving robot has baseline integrated cognitive functionality, its machine learning capabilities will allow for more precise data vetting once decisions from the advanced robots or humans have been published back to its environment.

Once this data has been fully vetted, it is analyzed through cognitive contextualization and machine learning, allowing for raw interpretation of the Smart City’s current state, as well as robust forecasting functionality once RPA draws from archived legacy data and performs detailed comparison against the new data received.

Secondary Automation Layer – Data Decisioning through Intervention

The analyzed data triggers a BPA suite of functionality, including automated alert and notification systems, that engage the human intervention cycle through intervention points (analysts) and relay metrics and KPIs that correspond to thresholds of concern for which the intervention is required. 

Depending upon the level of this required intervention, communiques are pushed across multiple systems and user environments. Utilizing digital collaborative workspaces, choices are made, and approval processes are triggered to capture formal decisions by high-level stakeholders. These decisions are recorded as digital records and, based upon predetermined parameters, assigned appropriate retention policies and repositories for archival.

As the process of the human intervention cycle unfolds, workflows and stages are monitored, producing metrics that allow for their iterative tailoring and streamlining, producing more powerful and efficient integration of the human intervention cycle within an automation sequence.

Tertiary Automation Layer – Data Decisioning Analysis and Publication

Following the decisioning of the analyzed data relayed from Smart City Sensors, BPA functionality concedes to RPA functionality where those robots with more advanced cognitive behavioral elements analyze the decisions made through the human intervention cycle. These analyzed decisions are archived for future lessons learned publication once the new dataset is relayed that incorporates the decisioned data. Following this analysis, the decisioned data is contextualized with the decisions’ impacts, which are then published to the receiving Smart City Sensors to act upon the data.

Conclusion

Data Centers are critical elements to a Smart City and their ability to perform Big Data intake and seamless handoff to the human intervention cycle is vital for analysis and forecasting. For this to occur as needed, Smart City designers must understand the spectrum of functionality available through Business Process Automation and Robotic Process Automation and integrate them in a manner that drives the Smart City to realize its many benefits!

Jack Wilson is a Process Engineer with Optimum Consultancy Services, a software consulting firm specializing in business optimization through advisory services and modern software solutions.