2020 Application Performance Management Predictions - Part 3
December 16, 2019
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Industry experts offer thoughtful, insightful, and often controversial predictions on how APM and related technologies will evolve and impact business in 2020. Part 3 covers more about AIOps, AI and Machine Learning (ML).

Start with 2020 Application Performance Management Predictions - Part 1

Start with 2020 Application Performance Management Predictions - Part 2


I believe that AIOps will develop as an umbrella capability that will integrate DataOps, ModelOps and DevOps together. These processes will enable IT to better support the entire continuum of data pipelines, model development, performance testing, model deployment, model monitoring and model refinement at the same pace that DevOps has been operating in the recent past. Eventually it will lead to semi or fully automated systems that enable self-learning and continuous improvement.
Radu Miclaus
Director of Product, AI, and Cloud, Lucidworks


2020 will see the birth of "next-generation" automation products using higher-level model-based approaches combined with AI and Analytics to optimize testing, understand root causes, and link to business outcomes.
Antony Edwards
COO, Eggplant

In 2020, we will begin to see the rise of hyper automation, which is the meeting point of intelligence driven by AI and ML with autonomy driven by robotic and cognitive process automation. Hyper automation will help support dynamic and complex business processes including loan processing, insurance claims, warehouse dispatch, and others. This will provide the unique advantage of mimicking user actions on terminals like carrying out transactions and generating dynamic content contextually to deliver on speed, accuracy, reliability and reduced costs.
Rajesh Ganesan
VP, ManageEngine


Operations analytics will increasingly see more and more from AIOps as we begin to make the first headway into AIOps, but I don't think that most of AIOps will be able to have a major impact of driving automated change.
Thomas Hatch
CTO and Co-Founder, SaltStack


In 2020, we will begin to see the arrival of next-generation bots which will be able to not only make recommendations to users and customers but ask more advanced questions in support of an extended dialogue. This more sophisticated implementation of AI will be delivered in response to increasing demand to heighten user satisfaction and customer engagement. Through more advanced and insightful dialogue, these more sophisticated bots will have the intelligence to have the deeper interaction users and customers want to better solve their issues or improve support.
Kevin J. Smith
SVP, Ivanti


Root cause analysis is ready for takeoff. In 2019, we saw customers start to automate incident management, but in 2020, companies will increasingly place emphasis on leveraging automated Root Cause Analysis for faster and more effective remediation. It will be critical for enterprises to address how they are measuring their KPIs (in addition to typical IT stats) on uptime, so that they can benchmark the success of automated Root Cause Analysis over the next decade.
Gregg Ostrowski
RCTO, AppDynamics


Beyond monitoring and business rules: The application of machine learning and optimization to system design and planning, will allow IT to build systems that autoscale in a predictive fashion, designed to meet SLAs with the most efficient cost.
Radu Miclaus
Director of Product, AI, and Cloud, Lucidworks


Democratization of data has opened up analytics usage to departments that have traditionally not employed analytics for decision-making — such as IT. This means that there are now new and different sources of data that need to be standardized and checked for quality before they can be used for analysis. Getting from data to insight takes far less time when data from various sources are structured to fit a common schema or format, otherwise known as data standardization. To accommodate this, next year is going to see a rise in the demand for ETL (extract, transform, load) tools, which help cut down the time it takes to standardize data. Analysts have to begin familiarizing themselves with newer sources of data and employ ETL tools, when necessary.
Rajesh Ganesan
VP, ManageEngine


With an increasing number of systems, use cases and the sheer volume of data, data pipelines will be top of mind for organizations in 2020 and beyond. Businesses will continue to pursue more advanced data, analytics, and AI initiatives across their organization, which will necessitate DataOps sophistication to keep pace with the accelerating data development lifecycle. DataOps is by no means a new term or methodology, but increasingly, businesses will begin adopting DataOps practices to be able to scale and deliver on their investments in data, analytics and machine learning applications.
Sean Knapp
Founder and CEO, Ascend

As organizations begin to scale in 2020 and beyond — and as their analytic ambitions grow — DataOps will be recognized as a concrete practice for overcoming the speed, fragmentation and pace of change associated with analyzing modern data. Already, the number of searches on Gartner for "DataOps" has tripled in 2019. Vendors are entering the space with DataOps offerings, and a number of vendors are acquiring smaller companies to build out a discipline around data management. Finally, we're seeing a number of DataOps job postings starting to pop up. All point to an emerging understanding of DataOps and recognition of its nomenclature, leading to the practice becoming something that data-driven organizations refer to by name.
Kirit Basu
VP, Products, StreamSets

Go to 2020 Application Performance Management Predictions - Part 4, covering End User Experience.

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