That some of the world’s biggest brands trust OpTier with their most critical (and customer-facing) applications is something we don’t take lightly. But we don’t stop there. Not even close. Working intimately with all of our clients to ensure they’re deriving the most value from our technology is another responsibility we cherish. That’s exactly why we offer free ongoing training for all our current customers with OpTier Expert Online Training!

Dubbed OpTier 101, these invaluable training sessions afford us another opportunity to connect directly with our customers. More importantly, they allow us to train new (and refresh older) users on how OpTier can be used to drastically improve application performance throughout the enterprise. There’s even something for everyone: the free, online training covers topics suited for the novice user all the way to the power-user. The best part? In case you miss the live training, sessions are available for replay on-demand via our customer portal at portal.optier.com!

Standing Room Only 

This past weeks OpTier session was a smashing success! More than 200 eager customers attended the training and demand was so overwhelming that we had to schedule another session to accommodate additional customers and field live questions. In the most recent OpTier 101, we provided an overview of:

• The end-to-end transaction tracing portion of our APM solution 
• Use cases of rapidly diagnosing application problems from user transactions to problematic SQL or code related issues
• OpTier analytics and multi-dimensionsal OLAP capabilities that combine user, transaction, code, database, topology, and IT Operations Analytics (ITOA) in the end-to-end transaction context
• The basics of the user interface including time-frames, filters, analysis dimensions, drill downs, charts, etc.
• Out-of-the-box Service Optimization features including chatty and heavy (CPU consuming) transactions

Engaged customers didn’t miss an opportunity to pepper us with questions about the dynamic capabilities OpTier APM can offer. We will continue to acknowledge the tireless dedication and limitless ambition of our customers. Our success, as always, hinges on theirs.

To view the most recent OpTier 101 sessions, visit the OpTier portal at portal.optier.com. To learn more about our new, cloud-based APM solution, click here.

Let’s start with data collection. 

Modern infrastructure and technology has ensured that most companies are collecting critical data from customer interactions across the business. However, that data is being collected in disparate silos and preventing businesses from gaining a holistic understanding of customer data across the enterprise. The inability to “piece together the puzzle” is a legitimate obstacle for companies seeking to extract real value out of Big Data projects.

And move on to data integration. 

When your data is distributed across multiple sources and applications, you’re relegated to spending copious amounts of time manufacturing context between those data source so you can actually make sense of the information. Worse yet, you may even have to reconfigure your customer facing apps to create synthetic context that may or may not be a true indicator of your authentic data. Not to mention this process is expensive and lengthy.

Now transition to data analysis. 

Data analytics, a fundamentally important step in the Big Data process, consists of applying advanced and sophisticated data techniques to uncover patterns, trends, and insights from heaps of enterprise data. This is where legitimate big data value can be uncovered and exploited. However, there are formidable challenges that lie here as well. Mediocre data quality (see data integration above) can compromise effective data analytics. So too can traditional and outdated analytics techniques that produce a delay from capture to analysis.

Real-Time, Transactional Big Data

Fortunately, the Big Data challenges outlined above are not insurmountable. Even better, there is an out-of-the-box solution that can create a holistic, timely, and trustworthy data source that can help you manage customers, performance and revenue more effectively. Welcome to OpTier Big Data Analytics.

In a brand new webinar on May 21st titled “Fast-Track Big Data Answers with Real-time Transactional Data,” we’ll explore how to:

• Circumvent traditional data preparation with end-to-end transaction tracing
• Elevate the value of big data with appropriate context
• Dramatically accelerate the data-to-insight conversion process
• Exploit a singular, real-time data set

Real-time transactional data is the key. Register for the webinar today and learn how transactional data with context can make Big Data fast and easy.

DevOps, a union between development and operations, has emerged as a modern-day software development approach that attempts to fuse software engineers who develop new products with the IT operations professionals who deploy and manage said products.

It’s no surprise today’s businesses rely on web and mobile apps to form their product portfolios. More specifically, they rely on these apps to connect with customers and grow their business. As a result, DevOps has been catapult into the spotlight and tasked with modernizing today’s application delivery systems.

Agile. Iterative. Cross-functional. This is the new DevOps.

DevOps for a New Millennium

Analyst firm Enterprise Management Associates (EMA) recently published a new DevOps report titled “DevOps for a New Millennium: A Lifecycle Perspective Supporting Business Growth in an Altered Economy.”

Authored by EMA’s Research Director for Application Management, Julie Craig, the April 2013 report combines extensive market research, vendor profiles, and customer case studies to explore how the rise of agile development and iterative deployment is redefining how businesses take applications and services to market. The evolution of DevOps and its increasing importance in product development circles is a result of contemporary market demands that place a premium on accelerated time-to-market cycles.

According to the report, “EMA has been researching the growth of both Agile and DevOps practices for several years and found that DevOps has evolved as a far more cross-functional, collaborative, and lifecycle-focused activity than traditionally understood. It now spans the application lifecycle versus being confined to a ‘point in time’ handover of responsibility at deployment.”

Across the DevOps Life Cycle

The DevOps life cycle spans six phases including Assess, Design, Develop, Test, Deploy and Manage.

The report evaluated 12 vendors across the six DevOps different phases and found that only four provided value propositions that span the entire application lifecycle. OpTier was one of the four recognized who delivered capabilities across the full DevOps spectrum and was lauded for its innovative Application Performance Management (APM) and Big Data Analytics (BDA) solutions.

“One of OpTier’s key differentiators is its value proposition across the application lifecycle,” said Julie Craig. “Because of the application visibility delivered by the APM and BDA solutions, OpTier showed very well in EMA’s recent DevOps research report.”

Arming both development and operations teams with in-depth application visibility, both in test and production environments, will help accelerate product development cycles and ensure that businesses can keep pace with rapidly evolving customer demands. Moreover, it will transform siloed product development teams into nimble and responsive product strategists.

Download the free DevOps report here or contact us to speak to a DevOps specialist.

• When the ticket was purchased (2 weeks in-advance, or no advance)
• The class of service (economy, business, or first-class)
• Special seat and flight accommodations

Relying on sophisticated analytics and data-driven tactics, airlines (and other data-reliant businesses) are now using revenue optimization in order to boost top-line growth.

Airlines engage heavily in revenue optimization because their costs are mostly fixed (i.e. operating costs, crew salaries, and even fuel consumption to some extent) and their key revenue driver is a full plane. This notion has led many airlines to invest in big data solutions that allow them to capitalize on advanced data techniques to gain a more complete understanding of their customer and offer value-driven solutions that align with customer expectations and demands.

Airlines can now tailor their flight offerings based on customer tendencies, preferences and perceptions. Examples include discounts for early bookings, auctioning off empty seats, bundled promotions, and even allowing customers to upgrade to another class of services (that would otherwise go unused) so that they can maximize revenue on any given flight.

I recently wrote a blog post titled “Put Your Big Data to Work” and referenced a SaaS company we’re working with that is using Big Data Analytics to optimize customer SLAs. I wanted to take a quick peek into how this global HR firm is leveraging Big Data Analytics to support their revenue optimization efforts and how other SaaS-based firms can capitalize on Big Data Analytics to drive revenue optimization.

If you’ll recall from my previous post, this company developed a comprehensive suite of SaaS-based HR tools. As part of this suite, they offer multiple tiers of service levels to their customers (Silver, Gold, & Platinum) which are each based on different SLA delivery metrics (2, 4, or 6 seconds for example). Not surprisingly, this company’s SaaS product and corresponding application infrastructure represents a considerable capital investment. As a result, in order to recoup costs associated with their expensive application environment, they – not unlike the airlines we discussed above – need to engage in revenue optimization to derive maximum value (i.e. revenue) from their investment (servers, physical network, storage, maintenance, overhead, etc.)

Relying on OpTier’s Big Data Analytics platform, this company is seeking to optimize the distribution of customers on their various SLA tiers to drive additional revenue — all without incurring additional costs. To optimize revenue generated from their “stack,” the company is going to leverage advanced data analytics and contextual big data — provided by OpTier’s industry leading APM platform  will be able – to:

• Optimize product availability and price to drive revenue
• Anticipate customers’ perception of value and extract the maximum amount
• Sell the right product and the right price at the right time
• Predict – to some extent – customer behavior and cater offerings accordingly

How would this look in action you ask? Well, this company would examine the SLAs of their current customers and attempt to incentive customer to upgrade tiers (while maintaining current SLAs) using relevant, timely and customized offers. Alternatively, if they have to spin up a new stack, they may be able to offer personalized discounts to customers to manage demand for the new stack and reduce costs accordingly.

For example, in the screenshot above you can see that the 20+ customers in the SILVER segment are consuming 4000+ transactions – almost ALL within SLA.  We could optimize the utilization of our “GOLD” and/or “PLATINUM” technology stacks (thereby increasing revenue) by offering a “SPECIAL UPGRADE” to 1 or 2 SILVER members – UP to the “GOLD” level.

By capturing and exploiting contextual big data, this global HR can now fundamentally understand customers’ perception of product value and accurately align price/placement/availability for each segment to drive revenue. Capitalizing on OpTier’s Big Data Analytics platform, they can effectively engage in revenue optimization and drive a significant return on their investment.

To learn more about OpTier’s Big Data Analytics solutions, check out our Big Data Solutions Brief or Contact Us.

Some trigger-shy executives may be tempted to simply wait it out and see what’s left after the dust settles. Unfortunately, their jobs and companies can’t afford inaction in today’s hyper-competitive marketplace. Big data analytics adoption is an example of this effect.

Pre-cloud and post-cloud
According to InfoWorld contributor David Linthicum, cloud computing has divided the big data world. While executives within the field, like Amazon CTO Werner Vogels, see the cloud as a powerful big data enabler, others are not yet ready to embrace the cloud as a big data facilitator. Within five years, according to Linthicum, the cloud and big data will be a formidable duo but, for now, companies can enable their systems more effectively if they simply push forward with on-site versions.  Linthicum also noted that a majority of the resources currently needed for effective analytics processes  are already housed within the traditional data center.

So, if the pieces are in place for big data analytics, organizations can dive in locally now and worry about the cloud later. Linthicum recommended that companies base their first generation projects in on-site architecture. He also made a compelling case for launching these efforts now, as the price of hardware and software are relatively low. Moreover, there are fairly well-understood security and regulatory compliance processes for local deployments. The cloud may be intriguing, but the current status quo has its own immediate advantages.

The data is here
Businesses worrying that they may not have the raw data needed for effective analytics can breathe a sigh of relief. Modern infrastructure and progressive technology has ensured that most businesses are collecting critical data every day — whether they know it or not. Digitally tracking customer interactions — the customer journey — gives businesses powerful ways to better serve their customers. New, advanced big data solutions are making this possible and finally empowering businesses to truly understand their customers.

The current generation of big data analytics is based on context. Understanding the who, what, when, where, and why of business transactions can produce enormous insight. More importantly, being able to seamlessly integrate these transactions from different parts of the business can arm business leaders with critical intelligence they need to make smart investments and decisions.

This is not the future. This is now.

To learn more about OpTier’s Big Data Analytics solutions, check out our Big Data Solutions Brief or Contact Us.

While it’s been half-a-year since Sandy made catastrophic landfall on New York, the fallout is far from over. Preliminary estimates assess damage at nearly $75 billion, a total surpassed only by Hurricane Katrina. As members in the local business community, we know the reality. We know it because we’ve been in constant communication with customers, partners and friends about the toll Sandy took on their respective businesses and families.

While many businesses have made full recoveries, others have not. Some we’ve talked to even lost their data centers during the super storm and are now running out of back-up data centers while they relocate/rebuild their primary data centers. As a result of the sobering stories we’ve heard and the brutal challenges still facing many of our neighboring businesses, we’re launching a program aimed at assisting these companies with a FREE Application Performance Assessment.

Free. No surcharges. No string attached. No obligation. Register Now!

What would this assessment consist of and how would we conduct it? Well, we’d come on-site to your location and conduct a thorough (and did we mention FREE) evaluation of your application environment and capacity planning. As you migrate, consolidate, virtualize or rebuild your data center, this free Application Performance Assessment will help you understand the costs and risks so that you will be armed with valuable information designed to assist your recovery efforts. You will have critical application performance baseline information which can be used before, during and after your recovery efforts.

If you’re interested in learning more in the program and signing up, please visit our website to register for your free Application Performance Assessment. We wish you the best of luck with your continued recovery and look forward to helping you get back to “business as usual.”

Thanks,
The OpTier Family

The article uses an example of a person logging into their bank account via a smartphone to make a payment.  Upon successful login, the mobile app not only guides them to make a payment but also suggests which credit card to use in order to maximize reward points. This experience is called the “Mobile Wallet.”

Mobile Wallets are not exclusive to the retail banking industry. Companies like Google and PayPal are also utilizing this concept to enhance the user experience for their respective customers. The Mobile Wallet is designed to promote efficiency and help users naturally make smarter payment decisions. Recognizing the potential of such powerful yet nimble technology has even spurred a well-known entrepreneur to get in on the action. Jack Dorsey, co-founder of Twitter, recently started a mobile wallet company called Square that was highlighted on an episode of 60 minutes this past month.

The takeaway?  Mobile isn’t going anywhere and mobile payment technologies, such as the Mobile Wallet, may also be here to stay.

The Mobile Market is Exploding 

Everywhere you turn you hear about the “mobile” movement. Companies and industries from diverse and non-tech backgrounds are even beginning to capitalize on the ubiquity of smart phones and mobile apps. Gartner predicts over the next few years tablets will be the primary personal device used by consumers versus a laptop or desktop.

It’s not hugely surprising that mobile’s staying power seems untouchable. Consumers want to be able to conduct their business when it’s convenient for them while at the same time receiving a more personalized, richer experience.

In 2012, IDC stated there were 659.8 million smartphones shipments Worldwide. In 2016, IDC predicts this figure will grow at CAGR (compound annual growth rate) of 18.6 percent! Everybody’s getting in on the action. Ovum predicts that North American retail banks will increase overall spending on IT to $45.7 billion, with:

• Mobile at 57%
• Security at 52%
• Online at 46%

Mobile App Growth = Mobile APM Growth 

The increasing number of mobile app deployments will require a similar effort on the app management front. As demand for mobile user experiences grow, so will the importance of well-planned and executed mobile applications. To support those efforts, global businesses will need to invest in mobile application performance management (APM) solutions that allow them to seamlessly assess the performance and availability of enterprise apps.

OpTier is uniquely positioned to provide the retail banking and financial services industries with the mobile APM technologies they need to accelerate growth and drive customer loyalty. We provide a complete Mobile APM solution that allows enterprises to monitor, measure, and improve application performance in order to craft memorable and engaging user experiences.

In addition, OpTier’s Mobile APM solution offers advanced business analytics. The detailed data which OpTier collects about the user, device, transactions, and performance, automatically populates a transaction-centric database that can be used to conduct sophisticated analytics for mobile applications to help companies answer questions like:

• What percentage of customers use 3G vs. 4G?
• Does our web app, iPhone app, and Android app have vastly different performance metrics and conversion rates?
• What percentage of our key customers use the mobile app as opposed to our website, and what devices are they using?

The continued growth of mobile apps and mobile payment technologies, such as the Mobile Wallet, further underscores the need for sophisticated APM technology that provides true, end-to-end coverage.  To learn more about OpTier’s Mobile APM solution and how to deliver exceptional user experiences via mobile, check out our Mobile APM solution brief or contact us today.

One of the elements that makes big data “big” is the constant influx of new information into corporate systems. Endless streams of business and customer data pouring in. And, for that data to be useful to the business, it must be processed, analyzed and exploited in a timely manner.

While traditional business intelligence techniques were concerned with methodical, deep-dive analysis, that is no longer the case. Conducting real-time analysis of incoming data can dramatically help accelerate the data-to-insight conversion process. Businesses that understand the need for speed and embrace this paradigm shift can proactively identify market inclinations and adapt accordingly. The first-mover advantage has never been more important.

Speed is data’s greatest asset
Wired Innovation Assets contributor Alissa Lorentz recently described the need for faster data analytics. She suggested that many in the business world are understandably feeling fatigued over the big data deluge, and recommended that these executives focus their attention on a few specific use cases.

By this token, she noted, they can begin thinking of “fast data” and “smart data” instead of simply “big data.” This intelligence angle is easy enough to understand – Lorentz posited that smart data is the byproduct of analytical algorithms rather than the raw material fed to them.

According to Lorentz, if companies can make their moment-to-moment decisions with analytics-backed intel, then they can begin leveraging “fast data.” Antiquated and time-consuming data processes will no longer be viable. Fast, quick, and immediate will become the day’s new order. And while some data does serve valuable in a historical context, real-time insights win out every time.

No obstructions
When on the quest for faster data, business leaders must ask a few critical questions. They should investigate what may be keeping their resources from being available at high speed in the first place. In many cases, the answer lies in the extract, transform and load processes employed in traditional business intelligence. Companies need to find ways to link their information and uncover valuable insights. However, the classic methods of doing so are cumbersome and can jeopardize the value of the data. Big Data Analytics solutions with real-time context can help.

Modern application performance management (APM) solutions can identify and tag data the minute a transaction occurs. These tags, similar to FedEx tracking codes, remain with the data throughout its journey in the business’ IT systems. Real-time tagging and tracking is incredibly conducive to generating and utilizing “fast data.” Accelerated time-to-value is key for today’s business executives looking for immediate benefits, and real-time analytics will help support that effort.  Making business decisions is never easy. Having access to reliable, real-time data can help arm decision makers with a legitimate competitive advantage. What are you waiting for? The clock is ticking…

To learn more about OpTier’s Big Data Analytics solutions, check out our Big Data Solutions Brief or Contact Us.

As a leader in the OpTier sales organization, I talk to customers and prospects every day about the challenges and pain points that keep them up at night. Some of the specific things I hear include the “extreme cost of ownership” and “extended time-to-value.” Legitimate concerns may I add. So what can I tell these people? Fortunately, I have the perfect answer: OpTier SaaS.

OpTier SaaS is a lightweight, cloud-based version of our award-winning on-premise APM and IT Operations Analytics (ITOA) solution. OpTier SaaS combines the best of both worlds: all of the APM and ITOA features you would ever want, but in an easy-to-stand-up (about 10 minutes) version, with no care and feeding necessary.

Don’t believe me, download it here for free!

Put simply. OpTier SaaS is smart, simple, and scalable.

• Smart - Patented transaction tracing reveals your true application topology.

• Simple - Minutes to deploy and learn. No server setup or maintenance.

• Scalable - Runs 24/7 in production with very low overhead.

Most SaaS tools in today’s marketplace are really only looking at synthetics or exception based monitoring.  Exception-based meaning an issue has to arise before the tools begins monitoring.  OpTier SaaS allows you to monitor your applications when there are no issues, so you can take preventative measures to avert future issues. Would you rather monitor how well an airplane is flying or measure the size of the crater after it crashes?  Yeah, that’s what I thought. With OpTier SaaS, you can:

• Know the level of service you are providing to your users and view locations
• Manage end-to-end transaction flow through your complex applications
• Simplify problem isolation with 2-click triage
• Identify granular root cause analysis down to the system, code and SQL level
• Quickly search for any transaction and view the transaction details
• Analyze bottlenecks and optimize the performance of your application
• Quickly monitor Java and .NET applications (including GWT and REST out 
of the box)

Did I mention the interface is gorgeous and incredibly easy to use?

Some other APM solutions have overhead so high that you can’t instrument all of your code or run it in production environments. What’s the point of that?  That’s like locking the door once the burglar is inside already.

Use OpTier SaaS to provide superior service to your users and improve top line (and bottom line) business growth. Do so by solving application problems such as:

• Transaction bottlenecks (performance segmentation)
• Problematic code / SQL
• Unequal flow distribution (load balancing)
• Complex or chatty transactions
• Resource hogging transactions

Other APM solutions use a sampling technique. They arbitrarily monitor transactions on a periodic basis.  If the transactions are working properly, then all is well.  Or is it? What if all of the other transactions were failing and the help desk/call centers were getting flooded with service requests. But, if the application is working for that small period of time when the periodic monitoring took place, it would appear that everything is fine. It’s like having a burglar alarm on your front door, but not the side or back doors.

OpTier SaaS combines our patented Active Context Tracking (ACT) transaction tracing with ITOA in a plug-and-play solution that provides instant value. With OpTier SaaS, you can quickly identify and isolate problems before they become widespread and prevent them from reoccurring.

Whether you are a developer, application owner, in QA or operations, use OpTier SaaS to gain new insights into customer experience, business operations, and application performance. OpTier is truly the best of all worlds. It’s like having your cake and eating it too! What are you waiting for?

Sign up today for your free trial of OpTier SaaS!

• Application
• User Group
• Location
• Business Processes
• Business Transaction Type

For the purposes of this blog entry, we will deal with only the first three axes; business processes will be examined in a later article and we addressed Business Transaction Types in the previous post.  If you require additional axes, OpTier Big Data Analytics allows for *any* attribute to be used as a classification axis.

Application is an obvious axis.  Usually APM servers (and occasionally tiers) will handle more than one application, and the user will want to be able to differentiate business transactions between applications.  So, each business transaction can also be classified by application [note: it’s possible for a business transaction type (*type* not instance!) to belong to multiple applications – for example, you might classify the application based upon the incoming URL or IP locations which could be different].

For example, let’s say I’m running an environment with multiple applications such as Ordering and Shipping and I see the following two (greatly simplified) transactions.

http://www.acme.com/ordering/validateAddress

http://www.acme.com/shipping/validateAddress

Both URLs will invoke the validateAddress transaction and we would classify this as Business Transaction Type “validateAddress.”  But the first instance we’d set the application classification to “Ordering” and the second to “Shipping.”  Thus, I can now look at this two ways – from the application view, I can see how my checkout transactions only are performing for my application only.  But from the Business Transaction view, I can view how checkout transactions are performing across all applications.

Business Types across all Applications

Business Types for the SharePoint Application only

User Group and Location classification opens up even more possibilities for interesting analytics.  Remember in the previous article we discussed how multiple individual transactions were grouped into a single Business Type. It is the same concept for User Groups and Locations where individual users and locations are amalgamated into higher-level abstractions.

For example, for Location, I could classify by subnet, or even by region (Washington, DC, Paris, Beijing, etc).  For user groups, classification could be by customer, internal vs. external users, administrators – or perhaps by tier of customer (Platinum, Gold, Silver, Bronze) which is a use case I often see.  User Groups can be whatever we want them to be – once I was asked to classify user groups by the browser used.

The point of this is that I am able to view my data in multiple ways – similar to looking at a cube from different directions – which allows me to find issues that might not have been clear otherwise.  It’s easier to understand if we look at some examples:

Locations across all Applications – specific locations are having issues.

User Groups across all Applications – the critical Business and Corporate groups are breaching their SLAs

You also can drill down within a particular selection. For example, here I want to see which transactions are used by the Consumer Gold user group.

Transactions by User Group

And here which Locations are involved with the Corporate User Group which allows me to see if the issue is specific to a location (in this case not!)

Locations by User Group

Finally I wish to touch on an aspect of implicit classification which is one of the most useful capabilities within OpTier.    When a transaction is classified, its various work units (work units are component parts of the transaction, each work unit corresponding to a specific tier in the transaction) are implicitly tagged with the tier they are associated with.  This is useful in two ways – first we are able to access statistical information on the tiers in the environment, but more importantly, we have a mapping of tiers to a transaction.

Thus, if a transaction has issues, one of the areas we can look at is the associated tiers to discern if the issue is confined to a specific tier.  Correspondingly, if a tier has issues, we can “spin the OLAP cube” and view which transaction types have passed through the tier. This provides quick business impact of issues and could sometimes indicate which transaction was responsible for the issue.

Looking at the tiers for the Payment Service transaction type (JBoss-2 likely problem tier)

            And looking from the Tier axis, we see the Payment Service transaction is the only problematic transaction

Now that we have reviewed the methods by which OpTier can classify data, we’ll move into how this is actually accomplished in APM – which is the topic for the next blog posting. To learn more about OpTier’s Application Performance Management solutions, please download our Transaction-driven Application Performance Management report or Contact Us.