Among the topics we discussed:

• What is platform engineering?
• Why does platform engineer have a chance of going mainstream?
• How is Percona addressing the needs of its clients?
• The future of database infrastructure? What are some key milestones we can expect in the years ahead?

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Among the topics we discussed:

What’s your single biggest takeaway from the report?

Okay, let’s discuss some key findings from the report:

• 57% of IT teams are under pressure to reduce SaaS spend. I’m surprised – I thought companies loved SaaS. So SaaS has gone too far?
• 58% of leaders are focused on using automation to drive efficiency. What’s been slowing down the move toward automation?
• 82% of teams reported they are facing higher expectations. So you continue to see a labor shortage in tech?
• How is Workato addressing the automation needs of its clients?

To wrap up: What’s the future of automation in the workplace? What are some key milestones we can expect in the years ahead?

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The post Workato CEO Vijay Tella on Automation Trends appeared first on eWEEK.

Among the topics we covered: 

• What trends are driving the data market now, in terms of retail and customer data? Are companies fully aware of how they’re handling customer data?
• What most needs to change about how customer data is handled? How can customers get better control?
• What role can data brokerages play? How does NCR factor into this?
• The future of how retail data is handled? What do you see in the years ahead?

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The post NCR’s Ismail Amla on Trends in Retail Data Management appeared first on eWEEK.

Among the topics we discussed:

• There is a lot of buzz around the metaverse. What exactly is the metaverse? How will this help us work more collaboratively?
• We now live in a world where “hybrid work” – collaboration between professionals across locations and time zones – is primary and essential. What trends do you see driving this overarching theme?
• What’s the Nvidia advantage when it comes to supporting hybrid work and virtual worlds. Let’s talk about key tools.
• The future of hybrid work? Clearly it will only get more primary. What milestones or major developments do you foresee?

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The post NVIDIA’s Richard Kerris on Hybrid Work and the Omniverse appeared first on eWEEK.

• What are a couple of key trends driving the enterprise software market?
• What’s a typical pain point for companies as they grapple with digital transformation? What advice would you give them?
• Let’s talk about the ServiceNow Impact solution. How does it help companies?
• What do you see for the future of digital transformation? What role will AI play?

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The post ServiceNow’s Aaron Fulkerson on Digital Transformation and AI appeared first on eWEEK.

It costs significantly less to write perfect software than to write software of dubious quality. It may cost more initially in terms of time and resources, but your teams will save several times that cost if they get the code right from the start.

The people who make up your software teams are the linchpin of your projects. That’s why continuous improvement for teams and continuous learning for people are the best investments you can make. When your teams don’t have what they need to function efficiently, success becomes less likely.

These are the top efficiency killers for your software teams:

1: No product market fit

It doesn’t matter how efficient or effective your software organization is if you don’t have product market fit. What you produce is only as valuable as your market makes it.

2: Poor quality

Errors in the code cause not just bad customer experiences but rework for the team, which increases labor costs and takes the team away from further progress in building the system.

And an absence of trustable automated tests makes it expensive and risky to make changes to simplify the code, which makes the team move even slower.

3: Cost of change

Unless you take deliberate steps to reduce complexity, adding to a code base naturally increases that complexity. This makes it more and more time-consuming, and therefore expensive, to make subsequent changes to the code. Pressure from stakeholders or management to move on quickly to the next task drives teams to skip the refactoring steps needed to keep the cost of change low.

The effects of this aren’t always detected when they happen, and the ultimate effect is to make system changes unaffordable, and to rob teams of the bandwidth they need to correct the trouble they’ve gotten into. For a small software company, the ability (or lack thereof) to control runaway cost of change can mean life or death.

4: Bad team dynamics

A dysfunctional team can’t function well – it’s that simple. Hiring for qualities that make people good teammates, giving teams time to get through the “forming-storming-norming-performing” curve, and keeping well-functioning teams together for the long term leads to better-performing organizations.

5: Bottlenecks

Organizations can make the mistake of inadequately staffing one or more roles as they define development processes that involve handoffs from one role to another as code moves toward production.

Quality assurance is a frequent victim here. Some organizations set up their teams in such a way that multiple teams depend on the output of other teams, which may not be able to deliver all the things the downstream teams need in a timely fashion. The whole organization is only as fast as the slowest bottleneck along the value chain.

6: Manual, repetitive work

In software, there’s no such thing as standard work. If your team finds any, it must quickly be automated out of existence. Repetitive manual work is error-prone and expensive, and manual processes don’t scale as your system does.

Deployments, system patches and other manual work can quickly eat up time that a team doesn’t have to spare.

7: Unneeded complexity

When it takes a long time to edit, build, test and deploy software, that slows progress. Slow automated tests, written using end-to-end testing tools, adds to a team’s cycle time. So do manual testing, long deployment processes, and code review processes inserted in the middle of the edit-to-deploy cycle.

These days, it’s all about reducing complexity. But organizations need to remember what weakens or even destroys the efficient development of software. The strength, health and cohesion of your teams are what will make or break your projects. People don’t necessarily need to be 100% qualified according to the specified job requirements to succeed in a role -many of those requirements can be taught along the way.

Thinking in terms of soft skills – communication, collaboration and so on – when hiring will help build stronger teams. Providing ongoing training will also help. Keep the seven efficiency killers discussed above top of mind as you create a more efficient and effective software engineering cohort.

About the Author:

Susan Davis is CTO of Certn

The post Avoid These Seven Software Team Efficiency Killers appeared first on eWEEK.

While this addresses applications that are up and running, it begs the question, is there something that developers can do during the development process to optimize the app for the before it goes into production?  This is where the concept of “full stack testing” needs to be understood.

Testing can no longer be done in silos

Historically, testing of the “stack” has been done in silos with network operations testing their domain, developers testing the app, etc. The problem with this is that each team might give their area of responsibility a clean bill of health but when the app goes live problems occur. This is because the application experience traverses the entire stack and that isn’t tested at all.

Enter full stack testing that looks at the end-to-end app performance. This enables developers to fix issues before the app is rolled out in production. This can remove any “blind spots” that might impact user experience.

Solving this challenge is critically important today as delivering a high-quality user experience is core to attracting top talent and customer experience is now the top brand differentiator so good performance for public facing apps is must. Poor performing apps will drive employees away and cause customers to flip to a competing brand.

Live from the lab event helps navigate full stack testing

I understand that most businesses likely have not implemented full stack testing. To help with this, Keysight Technologies is hosting an event, “Live from the lab: Software test automation” on October 28^th, to help organizations make this shift. In some ways, Keysight epitomizes the concept of full stack testing, as it’s a company that has deep roots in network testing but recently jumped into application testing with the acquisition of Eggplant software, one of the leading software testing automation companies.

Keysight Technologies is the premier network testing vendor, whose products are used by almost every network vendor, telco and many large enterprises. The fact that the company is now jumping into software testing is a strong indicator that full stack testing is the future of testing. This is the only way organizations can understand user experience before moving an application into production.

The Woz talks the future of testing

The virtual event is keynoted by Steve Wozniak of Apple fame where he will discuss the future of software automation. I find the Woz to be an excellent choice of speakers as Apple’s competitive advantage comes from the integration of hardware, software and the network. In some sense, Apple has been doing full stack testing since its inception.

The session after Wozniak’s is one worth watching as Gareth Smith, Keysight’s GM for Software Test Automation, will share his vision for full stack testing and discuss how to simplify testing across end user platforms, applications, and integrated systems by using an AI based “digital twin.”

To date, digital twins have been very popular in manufacturing and engineering organizations but not so much in technology circles. The use of them though can help companies test, run “what if?” scenarios and try new things without disrupting production workstreams.

It’s important to note that the event isn’t all vision as Keysight’s Sejal Patel will do a session on how full stack testing can be applied to the widely adopted Salesforce CRM application. Salesforce is the market leader in its category and many organizations run their business off it, so poor performance can be lost dollars.

As I mentioned before, testing is something most companies do in silos, but businesses are changing. The digitization of everything means applications, systems and networks all need to work together to deliver best in class experiences. Full stack testing is the way forward and the Keysight event can help get things kick started.

The post Keysight Event Highlights The Importance of Full Stack Testing appeared first on eWEEK.

The move to an enterprise-wide digital core standardized more than 80% of Vodafone’s core business processes and boosted automation levels to over 60% across the company, yielding not only new cost synergies and efficiencies for the company. But just as importantly, it freed employees to focus on meeting customer expectations, and on innovating to develop new products and services that emphasize a fuller, value-driven customer experience.

Vodafone, like many companies across the business landscape, is prioritizing digital investments — in 5G, artificial intelligence, machine learning, the Internet of Things (IoT), etc. — to help them deliver the enriched interactions, experiences and outcomes that their consumer and enterprise customers increasingly demand.

Ultimately, whether they compete in telecom, tech or some other industry, companies that successfully couple data intelligence and digital technologies with a relentless focus on the customer will be the ones best equipped to develop the compelling services and consistently outstanding customer experiences that will sustain them in an increasingly experience-focused and outcome-driven world.

Simply put, the companies that are best at collecting, protecting and responding to data from customers, connected devices and partners will be in the best position to delight their customers with experiences.

As difficult as it can be to accurately predict the direction of an industry or a market, it’s clear where my industry, telecom, is heading. I expect that by 2025, telecom revenue from non-communication services will overtake that of basic communication services. Enabled by the aforementioned digital technologies, this transformation from product-centric to customer-experience-centric already is well underway.

Moves like Vodafone’s are designed to eliminate gaps between the back office, customer engagement and network operations, in order to deliver personalized customer experiences across all channels. As Vodafone realized early in its digital transformation, it needed to put an enterprise-wide digital core in place to house and connect these advanced digital capabilities to their back and front offices, and to customers.

To transform themselves from product-focused commodity sellers to customer-focused service providers, we see companies leaning on several digital-forward approaches and strategies, all integrated within or connected to that digital core. One of them is the use of experience management tools to put customer experience and customer value at the center of everything they do.

In the experience economy that is taking shape today, the cycle time to sense, analyze and respond to the customer experience carries increasing competitive weight. Experience management, or XM, synthesizes operational performance data (O-data, which explains what is happening), with experience data (X-data, which explains why it is happening) gathered in the moment from customers, employees and partners.

As telecom companies like Three Ireland are discovering, XM data yields uniquely valuable insight into how operational factors impact the customer and employee experiences, creating a feedback loop that shows companies how to improve and refine the experiences their products and services, and their work environment, provide. Using a digital XM platform, Three Ireland increased its Net Promoter Score by 7% in one year, achieved record-high levels of customer intention to stay, and made customer experience a top-tier metric across the organization.

Ultimately, experience management uncovers pathways for companies to extend their customer value by anticipating and fulfilling customer needs proactively, based on a truly unified 360-degree view of customer experience across all touchpoints and channels.

Because an outstanding customer experience depends on engaged employees, companies are applying the same XM approaches to the employee experience. Likewise, they can apply those XM capabilities to their business relationships, to better understand their partners and stakeholders.

Which leads me to another prediction: By 2025, I expect business relationships will be driving a paradigm shift in the telecom business, whereby companies create multi-industry business ecosystems in which they align interests and combine strengths to create experience-based products and services that add value for customers and provide inroads into markets other than connectivity.

Competing as an ecosystem, they and their partners can deliver context-based intelligent connectivity and digital services at the edge, enabled by a digital platform to which all members of the ecosystem (and their customers, too) are connected. As the launch point for distributed B2B and B2C services, such a platform should enable all participants in an ecosystem to configure, price, quote and settle complex offerings where connectivity, infrastructure, solutions and services are combined.

From 5G-driven automotive telematics-based services to virtualization, the possibilities for telecom businesses to develop new revenue streams and explore new business models are seemingly endless. Companies like Verizon already are going down that road, with 5G-enabled data processing and analytics services at the edge. By 2025, I expect businesses in a wide range of industries, including telecom, to be leveraging customer experience insight and business ecosystems to develop innovative new business models and services as a way to compete with disrupters and new market entrants.

With the right combination of business models, data intelligence and digital capabilities, companies can diversify their revenue streams by transforming themselves into true enablers of digital transformation (for businesses) and a digital life (for consumers) with value-added services that sit on top of, or around, the network.

According to estimates by the World Economic Forum and Accenture, there’s $32 billion in value for telecom companies to unlock through 2025 by redefining customer engagement, and another $940 billion to unlock by extending revenue streams beyond connectivity. That’s reason enough for companies to put their customers at the center of everything they do.

About the Author: 

Carl Kehres is Global Vice President, Telecommunications, for SAP SE. He has spent the past 20+ years helping the largest global telecommunications companies with their transformation projects.

The post Beyond CX: A Digital Playbook for Thriving in a Hyper-Customer-Centric World appeared first on eWEEK.

Creating exceptional user experiences across a plethora of mobile devices and platforms is imperative, but seeing and solving performance issues can be a struggle and time-consuming. While testing an application before it ships is important, many times test environments do not replicate real conditions, and performance regressions may not show.

To catch the most frustrating performance issues, mobile development teams need to explore what’s happening on users’ devices. This requires visibility into how fast the app starts, duration of HTTP requests, number of slow and frozen frames, how fast views are loading, and more.

Although there are a number of metrics to consider, below are the five most important that every mobile team should track to better understand how an app is performing.

1. Crash-Free Users and Sessions

A crash is considered to be an unhandled error. This could happen during the initial load phase of an application or at any time during an application’s use. Tracking the percentage of users that did not experience a crash, as well as the number of sessions that did not end by the crash of the application, are important KPIs for measuring application stability.

Tracking and comparing these numbers from release to release can also surface trends and provide valuable insights about code health.

2. Cold and Warm App Start Times

When a user taps on an app icon, it should start fast. App start time values represent the amount of time elapsed between launching the process and finishing drawing the corresponding activity on the screen.

Apple recommends that iOS apps take—at most—400 ms to render the first frame. The Google Play Console warns Android developers when an app’s startup times are excessive. This includes when a cold start (defined as when an app is launched for the first time or after a reboot or update) takes longer than 5 seconds or a warm start (an app launched at least once and is partially in memory) takes longer than 2 seconds.

No matter the platform, it is crucial that an app starts quickly, as this is a user’s first touch with a company, and slow load times leave a bad first impression. On iOS, Mac Catalyst, tvOS, and Android, companies should track how long their app needs to draw the first frame. This information can help improve the duration of the app start.

3. Slow and Frozen Frames

Unresponsive user interface, animation hitches, and other app jank annoy users and degrade the user experience. Two mobile vitals that measure these issues are slow and frozen frames.

A phone or tablet typically renders with 60 frames per second (fps), though the frame rate can be as high as 120 fps. With 60 fps, if an app takes longer than 16.67 ms for a frame to render, it is considered a slow frame. Frozen frames are UI frames that take longer than 700 ms. An app that is running smoothly should not experience either.

4. User Misery

User Misery is a user-weighted performance metric to assess the relative magnitude of application performance. While developers can examine the ratio of various response time threshold levels, User Misery counts the number of unique users who were frustrated based on four times the satisfactory response time threshold (ms). User Misery highlights transactions that have the highest impact on users.

Resolving Mobile Performance Issues

These mobile vitals can unlock valuable insights into user experience and enable developers to better understand how code changes impact their applications. Decreases in crash-free sessions or users, slow cold and warm starts, and slow or frozen frames are all key indicators that performance is diminishing, and quick action should be taken to determine where the issues lie.

An effective way to resolve mobile performance issues is distributed tracing, defined as a method of recording the connected operations of multiple services. Typically, these operations are initiated by requests from one service to another, where a “request” could be an actual HTTP request, or work invoked through a task queue or some other asynchronous means.

Although this is a standard technology used for understanding what’s going on across distributed services, it is still relatively new for mobile applications. Ultimately, teams looking to manage and optimize mobile performance will find great success when monitoring the right vitals and adopting distributed tracing techniques to more efficiently debug mobile applications.

About the Author: 

Philipp Hofmann, software engineer, Sentry

The post Four Mobile App Performance Vitals Every Company Should Measure appeared first on eWEEK.

Clearly, artificial intelligence will alter the software business at every level: how applications will function, how they’ll evolve, even how they’re sold. But likely the most revolutionary of these changes is how applications are created.

The AI technology driving this change is called various things, but the phrase “AI-Augmented software engineering” is as good as any. You’ll see it perched at the top of Gartner’s chart of emerging technologies:

What is AI-Augmented software development? In short: it’s a system of development tools and platforms with AI built in that enables exponentially faster and better app creation than “hand” coding or traditional dev tools.

Among other advantages, the AI-driven system does the grunt work of laying out code; it can even predict or suggest code frameworks.

AI Will Enable Citizen Developers

Perhaps most significant, AI enables less technically-inclined people to create or upgrade applications. Opening the gates of software creation to non-techies is a big disrupter – they vastly outnumber the slender cohort of skilled devs. While skilled developers will move faster with AI, the large pool of non-devs could provide a generational push to innovation.

Note that Gartner puts AI-Augmented software engineering at the very peak of “inflated expectations.” To be sure, this idea is (mostly) still a hope for the future, and has limits even in best case.

The problem is that writing software is like any upper-end intellectual endeavor: the judgment and nuance of the human mind are required for top work. Writing software is creative, as any good dev will tell you. Just as a song can’t be written by a computer (though “song-like music” can), a complex, new piece of software still can’t be coded by an AI system.

On the other hand, an AI system “learns” prodigiously, so it can suggest paths that might elude the most creative human. An AI-augmented software program takes in a torrent of data; it gains knowledge (or at least data) far faster and more comprehensively than humans. It can’t make the “leaps” of human developers, yet it can lay out patterns and fill in decision trees, or even predict future directions.

Low Code Platforms Begin to Incorporate AI-Augmented Software Development

AI-augmented software development is rising in tandem with the rapidly growing low code / no code market. A low code software platform offers an easy-to-understand visual interface that enables non-techies to build or tweak applications.

Major low code platforms are beginning to incorporate AI, notably Google’s AppSheet and Microsoft’s Power Platform. AppSheet uses natural language processing (NLP) to allow citizen developers to simply speak commands for the app’s development. Although in its infancy, this use of NLP is a futurist’s dream – creating software is as easy as talking to a computer.

AppSheet uses AI and ML to build predictive models into an application using the app’s own store of data. Remarkably, Google claims that this ML-intensive task requires no prior ML experience from the developer.

Similarly, Microsoft’s Power Platform includes Power Automate and Power BI modules to allow a non-tech developer to design and automate analytics systems into the application with relative ease. AI really is opening doors to an entirely new group of citizen developers.

This larger group of “developers” is needed. Adopting AI-Augmented software development is a necessity for companies to remain competitive. Developers are expensive and in short supply: US labor statistics indicate that there were 1.4 million computing science jobs that were unfilled in 2020. Companies routinely face challenges in hiring software developers.

Four Long Term Effects of AI-Augmentation

Clearly, AI-augmented software will dramatically shape the future: When writing software is as accessible as writing a detailed report, the pace of business will change in ways that aren’t fully predictable. Some reasonable assumptions:

1. Data explosion It’s likely that most of the apps created with AI-assisted tools will mine, manipulate, or present data. Any capable staffer will be able to find new ways to use data for competitive advantage; your average sale rep will be altering apps to learn more about prospects. The end result is that data mining will grow even more parabolically than it is today.
2. Security concerns: It’s reasonable to assume that lower level staffers won’t be able to code an application that will allow a major cyber attack; to prevent this, AI-augmented platforms will – we hope – have “guardrails” to block cybersecurity vulnerabilities by rookie devs. Yet with such vastly larger brigades of citizen developers, building so many intricate structures – getting more advanced as AI advances – it’s likely that we’ll see security holes.
3. AI builds AI: In a boost to AI, AI-Augmented development platforms will be used to create more artificial intelligence capability. The process will fuel self-referential exponential growth: a tool that uses AI will create AI products, which in turn allows faster and more advanced building of AI-boosted applications. It is, perhaps, a dizzying prospect. Where the future takes us in this regard is hard to say. But when futurists talk about “the singularity” – when machines gain true independence – then this “AI builds AI” aspect clearly suggests it.
4. Democratization of Tech: Certainly, the greatest effect of AI-augmented software is the democratization of software development and technology overall. Cloud computing allowed small companies (even startups) to rent a data center and so compete with far larger outfits. Similarly, AI-augmented software platforms will allow smaller companies to build out big time competitive infrastructure.

Bottom line: we will soon look back at today’s non-AI based software and wonder, how did we get anything done with these applications?

The post How AI Is Altering Software Development with AI-Augmentation appeared first on eWEEK.