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
AIOPS INTEGRATES DATAOPS, MODELOPS AND DEVOPS
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.
Director of Product, AI, and Cloud, Lucidworks
AI ENABLES HYPER AUTOMATION
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.
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.
AI DOES NOT IMPACT AUTOMATION
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.
CTO and Co-Founder, SaltStack
AI BOTS SOLVE USER ISSUES
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
AUTOMATED ROOT CAUSE ANALYSIS
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.
ML OPTIMIZES SYSTEM DESIGN AND PLANNING
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.
Director of Product, AI, and Cloud, Lucidworks
EXTRACT, TRANSFORM, LOAD
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.
DATAOPS WILL REIGN
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.
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.
VP, Products, StreamSets
Go to 2020 Application Performance Management Predictions - Part 4, covering End User Experience.