APMdigest asked the top minds in the industry what they think AIOps can do for IT Operations. Part 5 is all about data.

Start with What Can AIOps Do For IT Ops? - Part 1

Start with What Can AIOps Do For IT Ops? - Part 2

Start with What Can AIOps Do For IT Ops? - Part 3

Start with What Can AIOps Do For IT Ops? - Part 4

DATA-DRIVEN ITOPS

AIOps is not a product. It's about the mental shift we saw in DevOps with developers using tools from operations and vice versa. Add AI to the mix and you'll see the DevOps persona using data science tools, like Jupyter Notebooks, and data-scientists implementing DevOps tooling, like operators. AIOps is culture — it can help Operations to become even more data-driven.
Marcel Hild
Manager AIOps, Office of the CTO, Red Hat

AIOps can help ITOps to become a data-driven organization by integrating independent, distributed, siloed teams and processes through the lens of data flow in the context of customer impact and value alignment. It can significantly improve the process of issue identification, knowledge, and resolution thereby improving customer and employee experience across multiple domains of IT operation management. It improves cost and value of business as it applies contextual data to drive proactive insightful actions to improve ROI and customer satisfaction.
Bhanu Singh
VP Product Development and Cloud Operations, OpsRamp

GAINING VALUE FROM BIG DATA

IT Operations teams play a crucial role in maintaining business' applications and end users' digital experiences. These teams take on the responsibility of monitoring all of the data pertaining to the apps and quickly identify and address any hiccups that could impact customers. Incorporating AIOps into a full-stack observability platform supports digital assets and teams can automate many responsibilities as well as handle a larger data set across the IT stack. AIOps will handle the tedious tasks of keeping track of the data and give IT Ops teams an overview of what's important and where they should focus to ultimately impact their bottom line.
Joe Byrne
Regional CTO, Cisco AppDynamics

IT architectures generate a significant amount of data that is often bypassed and discarded without detailed analysis while monitoring. This data, with the assistance of AIOps, can help fill the performance visibility gaps and predict anomalies. AIOps takes the structured and unstructured data and processes it into meaningful information that helps preempt any probable future events that may impact availability and performance. By leveraging this information, it also helps avoid future outages and delays that businesses may face by formulating complex automated decisions based on various learning techniques.
Arun Balachandran
Sr. Marketing Manager, ManageEngine

The true power of AIOps lies in the ability to consume and analyze the ever-increasing data generated by IT —and present it in a practical, actionable way. Whether it's looking at infrastructure and application data, IT service management (ITSM) data or business system data, AIOps helps IT operations teams go beyond the manual processes of sorting through deep arrays of data to find meaningful information. AIOps allows IT Operations teams to cut through the noise by quickly surfacing information that helps minimize downtime and maximize performance.
Ranjan Goel
VP, Product Management, LogicMonitor

MAKING DATA ACTIONABLE

IT organizations are under continuous pressure to keep applications running, manage various infrastructure components, and deliver faster results at lower cost. While businesses are undergoing digital transformation, IT operation teams need to outpace the demand by adopting AIOps. The real value of AIOps is the ability to take events and metrics from various systems, correlate, reduce, and identify "needles in the haystack". There is a large volume of data produced, the key is to analyze and present it in a way that is actionable. These actions are a combination of automated and manual tasks that should be managed via a service management (ITSM) tool with the appropriate change controls. AIOps platforms reduce the amount of human involvement needed for the data analysis, surfacing insights that allow IT operations to make faster decisions.
Randy Randhawa
SVP of Engineering, Virtana

IMPROVING DATA QUALITY

AI augmented intelligence in data preparation can improve data quality by surfacing and automatically correcting anomalies in data feeds.
David P. Mariani
CTO and Founder, AtScale

BUILDING BETTER MODELS

AI can assist data engineers in building better models by suggesting table relationships and producing histograms that show frequency distributions for field values. IT leaders that embrace AIOps can completely transform how their organizations make decisions.
David P. Mariani
CTO and Founder, AtScale

AIOps allows for real-time, continuous data acquisition, providing outcome data for model updates and insights as part of an ongoing feedback loop. By triggering events that enable data scientists to easily update and deploy new models, AIOps creates a ripple effect throughout the application ecosystem and enterprise at large. The ripple effect results in greater agility and reliability in response to the volatility, uncertainty, complexity, and ambiguity of digital transformation.
Alan Young
CPO, InRule

FAST QUERY RESPONSE

Nevermind robots writing code. One AIOps dimension that can get overlooked is how AI can be used to prepare data for analysis and data science algorithms by automating some data engineering tasks. More and more developers are tasked with creating "data apps" and data engineers that do this work are in short supply. AI can automatically find the best strategy to optimize data storage by indexing, aggregating, and querying to ensure sub-second query response times on very large datasets. Developers can't really call their creations successful if they slow to a crawl as soon as data volumes rise. And they are certain to rise.
Li Kang
VP, North America, Kyligence

CONNECTING DATA SILOS

IT operations departments can often struggle with manual processes and heavily siloed tools, creating tedious and fragmented workflows. The power of AIOps lies in its ability to connect these siloes by accessing various types data from multiple sources (e.g., metrics, logs & traces) as well as other contextual information (incidents, changes, application maps, users). AIOps combs through large amounts of this data to identify patterns and anomalies and predict when issues are going to occur before they impact users. IT operations departments resolve issues more quickly and accurately, stopping them before they snowball into enterprise-wide disruptions.
Jeff Hausman
SVP & GM Operations Management (ITOM, ITAM, Security), ServiceNow

HOLISTIC BUSINESS VIEW

Operations teams have become overloaded with data from rapidly expanding modern IT infrastructure. They're also dealing with shrinking budgets and increased number of devices that make it harder to keep things running smoothly. AIOps allows organizations to gather all their data in one place and build machine learning models that understand, alert, and act when needed. For example, when AIOps is paired with IT operations, a more holistic business view is established to help analyze the available telemetry, report potential issues, and provide remediation steps operators can review and implement on the spot.
Eric Thiel
Director, Developer Experience, Cisco

UNDERSTANDING HOW CHANGE IMPACTS BUSINESS

AIOps enables a big data analytics approach for IT operations, DevOps and Developers. The adoption of AIOps enables IT and business operations with a more proactive way of working by predicting and remediating performance or other bottlenecks across applications and deployments before they might negatively impact business and customers. Critical business services which are automated through key applications must be monitored through data that is produced during key tasks within these business services. Understanding different patterns or clustering data allows business and IT to understand the relationships and anomalies and act upon them. What this means: Applying big data analytics to transaction and customer data makes it easier to monitor how changes within the environment affect the business operations. Discussions and plans around application modifications, upgrades, or technology changes will be more effective and efficient as the impact will be known before choosing the path forward.
Eveline Oehrlich
Chief Research Officer, DevOps Institute

Go to What Can AIOps Do For IT Ops? - Part 6