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Moving to the cloud can be accompanied automation and ai at the same time
The herculean lift: pulling your data stack from servers to the cloud
Artificial Intelligence is the key to the consolidation of cloud capabilities as well as the driver of cloud infrastructure; potentially determining organizational structure and the design of business processes.
Classic computing algorithms required knowledge of parameters sufficient to allow hard coding of a finite set of defined decision nodes. Machine learning refers to processes that modify their own algorithms according to patterns detected in data inputs. It is particularly suited to dynamic processes with poorly-defined rules or relationships.
Deep learning is an extension of machine learning in which multi-layered artificial neural networks are created, data is not preprocessed, and data inputs are used to refine decisions about previous data inputs. Deep learning is particularly powerful in the areas of natural language processing and image recognition.
Natural language processing may be based on statistical methods, machine learning, or deep learning using artificial neural nets (ANN). ANN have great promise in areas such as requirements elicitation, customer touch point interfaces, and customer service. These processes have the ability to “fill in the blanks”, making them particularly suitable for NLP.
AIOps provide real-time decision making in areas such as procurement, resource allocation, and other optimization adjustments far exceeding the capacity of any manual controller. For example, upstream procurement controlled by real-time monitors and cloud processing reduces the bullwhip effect compared to a manual controller.
AI based data analytic mechanisms allow for rapid detection of faulty application behavior from subtle anomalies in operational data. These anomalies may span multiple microservices, files, and metrics. The scale and complexity of cloud-based systems preclude manual control. AIOps allow for early failure detection and/or prediction which minimizes system downtime.
Detection and/or prediction of failure modes may not clearly specify the underlying process responsible, leaving site reliability engineers with a daunting and time-consuming task. Current AI based root cause analysis instruments interpret multiple sources of data in a Bayesian framework to identify the primary defect causing system failure even when it is a new type of failure.
"We've build in-depth and complex automation systems with tier-1 automation software for some of the largest fintech firms in the country in their category."
Jignesh Shah, Sr. Automation Consultant, Product Perfect
"We saw the diagrams take shape and quickly realized they had a great handle on the complexity. Our data project would not have succeeded without their involvement."
Angela Ruthenberg, Automotive Data Analyst, Confidential Client
"Product Perfect consultants have been deeply positioned as a part of our team. We've seen substantial benefits from their involvement."
Daniella Serquen, President, BalanceGenics
The use of software robots to automate repetitive tasks which often span multiple processes and require low levels of cognition. There are three main types and four primary characteristics attached to RPA
Data-related robots transmit, transform, and/or analyze data.
Integration-related robots have input from at least two applications, services (including Cloud services), and/or input devices.
Process-related robots respond to triggers to control a predetermined sequence of functions.
Automates the processes performed by humans
Sequences performance and control modules
Improves architecture and efficiency to the system of record
Software robots are modular, flexible, scalable, and modifiable
Published by Dell, the Digital Transformation Index showcases the breakdown of digital transformation within organizations. Each organization is grouped into a bucket and plotted along an innovation adoption curve. The results are telling.
Carving out a customized cloud approach to meet every organizational objective.
Helping clients take full advantage of ubiquitous cloud-based computing platforms
Working with clients and partners to manage the costs and configurations of their ever-increasing cloud footprints
Moving to the cloud can be accompanied automation and ai at the same time
Spending on digital transformation technologies is expected to reach $1.8 trillion in 2022.
2020 Statistica, " Spending on Digital Transformation Technologies and Services Worldwide from 2017 to 2025."
Post-pandemic disruption and uncertainty in the marketplace underscore the importance of rapid data acquisition. Then, the opportunity exists to enable teams to feed that data directly into powerful hyper-automation routines to revolutionize primitive or uninformed business processes.
The disruptive effect of COVID-19 includes model drift. Pre-pandemic datasets and heuristics may not accurately reflect new realities in the global value chain. The use of automated bots to ingest current data from a broad array of new sources provides for rapid adaptation to new realities in the marketplace.
Rapid response to a dynamic environment requires data analysis with low system latency. Digital transformation driven by cloud capabilities informs both IT architecture and organizational structure. Hybrid systems optimize speed and efficiency, using embedded analytics to improve performance and to guide model recalibration.
Digital transformation, (and hyperautomation within it), is driven by the development and deployment of AI capabilities. These new and expansive tools ingest, analyze, and utilize big data in their computational and predictive models to drive decisions and steer direction. The technology of artificial intelligence can not be avoided, ignored, or buried. In literally every industry, it's only a matter of time until competitors garner the power of the tools and rise to compete at new levels not previously seen.
Digital transformation is not optional. Disruptive innovation in big data analytics, artificial intelligence, robotic process automation, and the self-supporting enhancement provided by AIOps mandates that business processes themselves be redesigned to conform to these capabilities. The cloud infrastructure is the natural framework for digital transformation with the goal of a harmonious relationship between business processes and cloud-based functionalities.
Event-driven processes - This is an extension of active database systems in which processes are built by defining actions as responses to events. Triggering events may be external or internal across various cloud function modules.
Functions vs. applications - No longer do we simply build applications. We now encourage and foster development teams to break-down their applications into supportive microservices and individual functions. These functions are then hosted as mini-api's that service the needs of the entire organization.
Container management - An extension of event-driven design to a function-centric architecture consisting of containers. It provides advantages in scalability and run-time.
IoT and Cloud/Fog interactions - Function centricity and run-time performance make serverless computing a natural framework to support complex systems for smart manufacturing and logistics. These systems rely on the low latency associated with “fog” computing.
Senior consultants with previous experience with digital transformation can set the stage for a well-framed engagement.
A focused session on your specific software applications, platforms, or projects. Typically this includes technical resources from both sides.