2018 Application Performance Management Predictions - Part 6
January 04, 2018
Share this

Industry experts — from analysts and consultants to users and the top vendors — offer thoughtful, insightful, and often controversial predictions on how APM and related technologies will evolve and impact business in 2018. Part 6 covers ITOA and data.

Start with 2018 Application Performance Management Predictions - Part 1

Start with 2018 Application Performance Management Predictions - Part 2

Start with 2018 Application Performance Management Predictions - Part 3

Start with 2018 Application Performance Management Predictions - Part 4

Start with 2018 Application Performance Management Predictions - Part 5

DATA GROWTH

The amount of data facing ITOps practitioners is only going to grow in the coming year and teams will be faced with the increased challenge of finding the signal in the noise — and fast — to resolve incidents. As a result, it will be necessary for ITOps to reexamine previous assumptions around automation and responsibility.
Eric Sigler
Head of DevOps, PagerDuty

SMART DATA

By utilizing smart data, which distills the essence of the traffic flows that traverse the service delivery infrastructure in a distributed fashion, close to the source, and compresses it into metadata, businesses can ensure they only store the information that holds real value. This information can then be used to gain meaningful and actionable insights, helping organizations to gain a competitive edge while driving efficiencies by enabling data to be rapidly compressed, and substantially reducing the volume of data stored by an order of magnitude or more. Smart data is already used to power a range of service, operations and business analytics across different industries including automotive, manufacturing and healthcare, and we expect its usage to increase dramatically in 2018. With the proliferation of IoT sensors, mobile devices and digital services creating an abundance of data used by the various applications and services that rely on hybrid cloud infrastructure, having the ability to convert smart data into meaningful and actionable IT and business insights, will help corporations to thrive in 2018 and beyond.
Michael Segal
Area VP, Strategy, NetScout

APM CONVERGED DATA STORES

Today's APM tools and monitoring, in general, have discreet silos of data for time-series, transactions, and logs. In 2018 We will begin to see the first converged data stores, which will unlock the ability to answer questions significantly more easily than today's tools.
Jonah Kowall
VP of Market Development and Insights, AppDynamics

Read Jonah Kowall's Blog: Looking Back at 2017 APM Predictions - Did They Come True?

APPLICATION-CENTRIC APPROACH TO BIG DATA

In the past, people were focused on learning the various big data technologies. It took time for users to understand, differentiate, and ultimately deploy them. There was a lot of debate and plenty of hype. Now that organizations have cut through the noise and figured all that out, they're concerned about actually putting their data to use. The enterprise doesn't really care about the technology being used. It's not important which distribution or database or analytics they're using, what matters is the result. The enterprise has realized this and we can expect to see an increased adoption of an application-centric approach to big data in the coming year.
Kunal Agarwal
CEO, Unravel Data

NEW PERFORMANCE METRICS

We'll see new patterns of backend performance problems stemming from the broader adoption of containers and microservices architecture. Our performance palette will be expanded to include new measurements like micro pause delays, herd effects, cold start time etc.
Peco Karayanev
Sr. Product Manager, Riverbed

DBA TAKES ON IT OPS ROLE

The New Job Description: DBAs Take on New Responsibilities. One of the most significant changes we will see in 2018 will be toward a more collaborative relationship between IT infrastructure managers and database administrators (DBAs). As more applications are run in the cloud, senior DBA managers will be able to take more of a central role in troubleshooting problems and improving efficiency. DBAs will be looking beyond just the application and database to find and fix issues. Both IT and application teams will need tools that look deeply into the cloud infrastructure to identify causes of performance and availability issues and provide accurate recommendations for addressing them.
Jerry Melnick
President & CEO, SIOS Technology

DISTRIBUTED ANALYSIS

2018 will see a de-emphasis on centralized cloud-based management and traditional data-lakes, and a shift towards distributed analysis. This will be driven not only by explosive growth in IoT, but also by the support of edge computing by major cloud vendors
Richard Whitehead
Evangelist-in-Chief, Moogsoft

RESTFUL API DRIVES ITOA

The advent of efficient RESTful APIs on many services and applications coupled with the maturation of time-series databases such as OpenTSDB and InfluxDB will drive IT operations analytics to use more quantitative approaches, and lead to advances in root cause analysis. This is due to the high storage efficiency of the time series databases, and the speed with which the optimize-on-write approaches they use can accept data. It is now increasingly practical to track large quantitative data volumes. RESTful API endpoints from applications and cloud services are rich in metrics, and the same types of APIs are efficient at accepting such metrics in data streams. With these large volumes of contemporaneous, high-cardinality time series data sources, operations analysis will become possible at a higher scale than previously possible. Cross-correlation will yield forensic insight into failures. In contrast, predictive time series analysis based on auto-regressive/moving average models, while mathematically practical, will fail to lead to any significantly valuable results on operations data, with rare exceptions.
Thomas Stocking
Director of Sales Engineering, GroundWork

IOT INFLECTION POINT

IoT apps need to get out of the hype cycle and deal with real world pain points. Their devices are still a hassle to set up and they solve too few real world cases. Alexa has shown promise, but IoT platforms as a whole are too fragmented for developers to invest in learning them. Hardware manufacturers will agree on a common set of protocols and open up APIs for devices to work seamlessly.
Abhinav Asthana
CEO, Postman

In my opinion, the biggest occurrence in 2017 was that IoT reached peak hype, giving way to new hype-cycles for machine learning (actually, an offshoot of IoT) and for Artificial Intelligence (a familiar topic area, and one that requires IoT data as fuel for its intelligence). We saw a combination of several companies making very large investments in IoT, while others are scaling back or reorganizing their IoT teams. This combination of investment push and pull means that we're at an inflection point. For IoT, this means we're now at a point where projects have to deliver results. IoT vendors invested ahead of demand, with all sorts of claims of IoT one-stop shopping. With more capacity in the industry than there is demand, I expect we will see players drop off or shift focus.
James Kirkland
Chief Architect, IoT, Red Hat

SELF TEACHING

We've now passed the point where we know that the human brain can no longer cope with the complexity of modern applications. Meanwhile, businesses have never relied on digital services more than they do today. Today, every company is a digital company and every critical IT issue has become a business issue. Therefore, APM solutions must evolve from just being early performance-issue detection tools to providing much more insights into the other phases of the resolution process. This includes not only root cause diagnostic and identification capabilities, but also self-teaching capabilities that leverage big data and AI-based algorithms and require very limited initial configuration to deliver actionable insights and recommended remediation actions. This will allow DevOps teams to make the best possible decisions to resolve performance issues.
Vincent Geffray
Senior Director of Product Marketing, IT Response Automation, Everbridge

Read 2018 Network Performance Management Predictions, the final installment.

Share this

The Latest

October 10, 2019

The requirements of an APM tool are now much more complex than they've ever been. Not only do they need to trace a user transaction across numerous microservices on the same system, but they also need to happen pretty fast ...

October 09, 2019

Performance monitoring is an old problem. As technology has advanced, we've had to evolve how we monitor applications. Initially, performance monitoring largely involved sending ICMP messages to start troubleshooting a down or slow application. Applications have gotten much more complex, so this is no longer enough. Now we need to know not just whether an application is broken, but why it broke. So APM has had to evolve over the years for us to get there. But how did this evolution take place, and what happens next? Let's find out ...

October 08, 2019

There are some IT organizations that are using DevOps methodology but are wary of getting bogged down in ITSM procedures. But without at least some ITSM controls in place, organizations lose their focus on systematic customer engagement, making it harder for them to scale ...

October 07, 2019
OK, I admit it. "Service modeling" is an awkward term, especially when you're trying to frame three rather controversial acronyms in the same overall place: CMDB, CMS and DDM. Nevertheless, that's exactly what we did in EMA's most recent research: <span style="font-style: italic;">Service Modeling in the Age of Cloud and Containers</span>. The goal was to establish a more holistic context for looking at the synergies and differences across all these areas ...
October 03, 2019

If you have deployed a Java application in production, you've probably encountered a situation where the application suddenly starts to take up a large amount of CPU. When this happens, application response becomes sluggish and users begin to complain about slow response. Often the solution to this problem is to restart the application and, lo and behold, the problem goes away — only to reappear a few days later. A key question then is: how to troubleshoot high CPU usage of a Java application? ...

October 02, 2019

Operations are no longer tethered tightly to a main office, as the headquarters-centric model has been retired in favor of a more decentralized enterprise structure. Rather than focus the business around a single location, enterprises are now comprised of a web of remote offices and individuals, where network connectivity has broken down the geographic barriers that in the past limited the availability of talent and resources. Key to the success of the decentralized enterprise model is a new generation of collaboration and communication tools ...

October 01, 2019

To better understand the AI maturity of businesses, Dotscience conducted a survey of 500 industry professionals. Research findings indicate that although enterprises are dedicating significant time and resources towards their AI deployments, many data science and ML teams don't have the adequate tools needed to properly collaborate on, build and deploy AI models efficiently ...

September 30, 2019

Digital transformation, migration to the enterprise cloud and increasing customer demands are creating a surge in IT complexity and the associated costs of managing it. Technical leaders around the world are concerned about the effect this has on IT performance and ultimately, their business according to a new report from Dynatrace, based on an independent global survey of 800 CIOs, Top Challenges for CIOs in a Software-Driven, Hybrid, Multi-Cloud World ...

September 26, 2019

APM tools are your window into your application's performance — its capacity and levels of service. However, traditional APM tools are now struggling due to the mismatch between their specifications and expectations. Modern application architectures are multi-faceted; they contain hybrid components across a variety of on-premise and cloud applications. Modern enterprises often generate data in silos with each outflow having its own data structure. This data comes from several tools over different periods of time. Such diversity in sources, structure, and formats present unique challenges for traditional enterprise tools ...

September 25, 2019

Today's organizations clearly understand the value of digital transformation and its ability to spark innovation. It's surprising that fewer than half of organizations have undertaken a digital transformation project. Workfront has identified five of the top challenges that IT teams face in digital transformation — and how to overcome them ...