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  • What Will DevOps Look Like in 2024?

    What Will DevOps Look Like in 2024?

    The last two years have been marked by significant downsizing in technology companies. As large layoffs at companies like Amazon, Meta and Google make major headlines, there is a general shift away from the “spending cash and growing fast” mindset that free capital and strong growth during the pandemic launched in the tech industry. The name of the game is to be more conservative with spending, and that mindset will drive a lot of investments in the next year. While these cost pressures may feel challenging to technology organizations, there are a few key opportunities as well. Many trends are starting, sustaining and ending, so let’s focus on the top six likely to drive changes in the industry in 2024 and beyond.

    1. AI—Specifically Generative AI—Move Right on the Hype Cycle

    While AI is not new, OpenAI and ChatGPT have created an amazingly accessible and powerful tool that is positioned to drive a lot of value. The hype cycle is still in full swing as dozens of new uses of the technology hit the market daily, and if you’re not trying to build a generative AI-enabled product, you’re getting asked when you will start. In 2024, we will likely see LLMs move into the trough of disillusionment as inflated expectations and projects don’t all play out. However, those organizations with generative AI products that truly deliver value to users should expect to see massive growth. Technology organizations will need to make hard decisions about AI products. You will either find traction in the next year and see huge growth or quickly find it doesn’t work and shift focus back to non-AI products.

    2. Developer Experience Gains Momentum

    While DX has been talked about for years, we’re seeing new investments in tools that empower developers. This trend is partially born out of a need to reduce tool and system sprawl for engineering teams, to empower teams and allow them to move faster and to improve efficiency. While you may not build or buy a DX platform, you will likely start a DX team or committee that better represents the folks building your software. This will lead to some great internal changes and key efficiencies getting attention that may have been previously overlooked. The wonderful thing about this trend is that it both brings efficiencies to the organization and makes developers’ lives easier. It’s a win-win!

    3. Increased Pressure to Reduce Costs

    Even for organizations that have avoided layoffs, most are seeing pressure to shift into a leaner existence. This often means critically looking at all sources of spending and taking on new projects to optimize them. Maybe a cloud re-platform can reduce hosting spend by 20%, or maybe you start looking to remove or combine SaaS tools to reduce seats. You may even see hiring freezes for critical teams. Organizations will have to embrace other trends to increase the efficiencies of their teams to meet business demands here. Don’t be surprised to hear “do more with less” become a mantra around the office.

    4. Increased Accountability Through Data

    While data platforms for technology organizations continue to grow, teams are finding that it’s not enough to just measure a few metrics. Organizations will be required increasingly to use the data they have to show they are delivering value. Partially due to increased pressures mentioned above, teams will need to become more predictable, and better at identifying and handling risk at every stage of development. It’s no longer enough to just build a great development engine, teams must ensure that the engine can power them to their destination.

    5. ESG in Technology

    Focus on DEI in technology continues to require significant investment, however, organizations are starting to consider a broader umbrella of social responsibility. This can range from considering the environmental impacts or carbon production of your servers to changing your software supply chain to avoid supporting particular organizations. These types of requirements are appearing sooner and more overtly in the software development process, and may significantly impact how teams choose to build or implement software.

    6. DevSecOps Continues to Grow

    There has been a renewed focus on security in recent years after some critical attacks exposed vulnerabilities in the software supply chain. It’s not surprising that security is now written into the fabric of how we build and deliver software. Teams are now expected to consider security at all stages of development from product planning to operation in production, and require additional experts, training and resources to both monitor and ensure compliance. Requirements for SaaS products continue to evolve as the last remnants of on-premises solutions evaporate, and teams should be prepared to move quickly as new threats are discovered and need to be patched.

    As the technology industry prepares for 2024, it’s clear that innovation and adaptation will be the keys to success. From the continued evolution of AI and the growing focus on the developer experience to the imperative to reduce costs, increase accountability through data, embrace ESG practices  and fortify security through DevSecOps, technology organizations face an exciting future.

  • Leaders in Resilience See ROI From Observability

    Leaders in Resilience See ROI From Observability

    Observability is maturing throughout the software industry. Observability is often defined as merging metrics, logs and traces to get a fuller picture of an application’s state. This can help pinpoint disruptions or security incidents and offer a clearer path to remediation. Observability is touted to advance the application monitoring practices of yesteryear, but are investments paying off?

    According to recent findings, many organizations are using observability tools and seeing a positive outcome. A full 73% of organizations reported using observability tools for over a year. And impressively, 86% of observability leaders said the ROI on their observability tools has exceeded their expectations. The practice appears to be paying dividends for organizations that have gone “all-in” with mature observability processes.

    The State of Observability 2023 report released by Splunk demonstrated how observability leaders are reaping a positive return from observability tools. According to the report, observability is helping to meet performance expectations, avoid downtime and enhance resilience in general. However, not all organizations are on the observability maturity bandwagon. Below, we’ll examine some of the key trends from the report and consider the benefits of refining your observability efforts.

    Now that observability is more commonplace, dedicated roles are emerging to support these initiatives. In fact, 87% of respondents now employ specialists who work exclusively on observability projects. Site reliability engineers likely make up a portion of these experts, as observability data can be used to advance efforts around meeting service-level objectives (SLOs).

    But although observability might be more common than a few years ago, not every observability practice is mature or standardized. Just 40% have a formal approach to resilience that has been institutionalized organization-wide. Because of this, only 43% are confident in their team’s ability to meet its application reliability and performance objectives. It should be noted that this number increases significantly for those with more mature observability adoption, which we’ll expand on below.

    Furthermore, not all applications are cloud-native. Organizations reported operating and maintaining, on average, 165 internally developed business applications, half of which are hosted in the public cloud and half of which are on-premises. Due to the persistence of legacy modalities, observability tools must continue to support a hybrid architecture if they are to be comprehensive and useful.

    And although we’ve previously covered how many organizations are drowning in a sea of observability data, observability leaders now say their ROI on observability tools far exceeded their expectations. Perhaps this indicates that, with more open standards, analysis centralization and fewer false positives, teams are finding ways to avoid observabiliy tooling sprawl.

    Lastly, the report unveiled desires to loop AI and ML into the observability pipeline. In fact, 66% of observability teams already use AI/ML, while 26% are in the process of deploying the technology. And 91% said AIOps is an important enabler of their observability goals. Certainly, AI is becoming a pervasive instrument to accelerate many areas, including observability.

    Benefits of Observability

    An impressive 64% reported that their investment into observability has exceeded their expectations. So, what are some of the top benefits of observability? Well, the report found the topmost benefits to be areas like decreasing problem detection and resolution times, identifying “true positive” incidents, and enhancing application security.

    Observability is being deployed as a tool to evolve traditional security efforts, as it uncovers a deeper understanding of application behaviors. This can help security teams correlate errors to specific diagnoses and spot potential threats in a quick manner. As such, 59% said observability helps them uncover security issues thanks to the intelligence capabilities of observability solutions. And 55% say they can uncover more security vulnerabilities thanks to the visibility afforded by observability.

    Observability can also be used to improve visibility into hybrid architectures. A full 86% said it’s important to have flexible observability solutions that cover hybrid architectures, though half identify this as an area for improvement. Although we like to think that cloud-native development is the status quo, many enterprises still oversee a mixture of on-premises, private cloud and multi-cloud environments. A unified, agnostic observability layer is thus essential to peek into all these disparate environments to grapple with disparate data outputs.

    The Leaders of the Observability Pack Win

    Service interruptions can have negative consequences for organizations, including lower customer satisfaction, loss of revenue and loss of customers. In fact, 76% of respondents reported downtime can cost up to $500,000 per hour.

    Thankfully, observability has emerged to help improve overall resilience and avoid these negative repercussions. But, to truly reap the benefits, you must have a mature practice in place. As defined by the report, “observability leaders” have a more formal approach to resilience and have standardized this across their organization. And 89% of those leaders are completely confident in their ability to meet their application availability and performance requirements. The catch-22 with observability is that you must invest more energy into formalization and knowing when the effort is worthwhile is a moving target.

    A solid observability practice is supported by standardized metrics, logs and traces across the organization. Furthermore, to make observability behave correctly, you will need flexible tooling that can leverage data from multiple sources, such as the network, infrastructure, servers and databases.

    The State of Observability 2023 report surveyed 1,750 observability practitioners from around the globe, making it the most extensive study on observability to date. For more insights, you can pick up a copy of the report behind an email gate here.

  • Don’t Make Big Tech’s Mistakes: Build Leaner IT Teams Instead

    Don’t Make Big Tech’s Mistakes: Build Leaner IT Teams Instead

    One of the biggest narratives in the U.S. concerning the current wave of tech layoffs is the fact that ‘traditional’ sectors have a prime opportunity to snap up new tech teams and IT talent. This has always been a significant challenge for sectors such as banks, insurance and manufacturing that compete with big tech for talent. In 2022, a majority of CEOs (71%) identified skills and labor shortages to be that year’s biggest business disrupter.

    And they were right to be worried. According to Salesforce, three in four workers do not have the digital skills needed by businesses to prepare for the future of work. The temptation in this environment for many traditional organizations is to enter the labor market to build larger tech teams to help drive their modernization programs. However, this may be a mistake.

    Building large, expensive, bloated tech and IT teams is not always the answer. It’s critical that traditional companies learn this lesson from big tech.

    Instead, traditional organizations should consider other avenues to drive their digital transformation ambitions with their lean resources. What does this look like?

    What is the Real Challenge you are Trying to Solve?

    Since COVID-19 hit, U.S. companies have been forced to reevaluate how they work, with remote working becoming commonplace–even for companies who couldn’t imagine themselves working remotely.

    And the challenges have grown as U.S. businesses seek growth to recover lost gains from the last few years. Evanta’s 2022 CIO Leadership Perspectives study found that CIOs rate data and analytics as their second top priority within the IT function. It is clear that business leaders see advancing organizational use of data, analytics and automation as key to reaching enterprise objectives.

    For many organizations, the biggest challenge they face is the unprecedented amount of data generated. Critically, more than 80% of all new data is unstructured and messy. This makes it difficult for data-driven companies to accurately review, gather, analyze and rely on what is mission-critical information.

    Traditional industries such as health care, banking, insurance or the public sector, have even larger concerns because data is increasingly critical to how they build value and deliver services for customers. So, it is important for their IT teams to create a single source of truth and data governance framework across their organizations to ensure the right people and senior leaders have the most accurate information to make smarter decisions.

    For instance, in the manufacturing industry, no-code database platforms can be leveraged to structure daily operational data and visualize insights through digital dashboards for quality assurance, continuous improvement and health safety.

    Additionally, at Baserow, we work with treasury customers at some of the biggest international banks that use no-code platforms to keep track of local legislation around the world by centralizing information in real-time databases available to relevant teams, including compliance.

    Modern organizations depend on their IT teams to accomplish a lot of their goals. So, it is important to provide them with the tools to focus on more complex development projects. No-code tools free your non-technical teams to address their pressing needs themselves without having to wait for budget or resource availability in the IT department.

    Have you Also Considered Upskilling Your Current Team?

    We know from the Salesforce survey that the vast majority of U.S. workers lack the digital skills to support their organizations’ ambitions and needs. This suggests that there is a wider issue within most companies at large. Do businesses need to prioritize upskilling their current team instead?

    A U.S. Gallup report found that 72% of workers are overwhelmingly interested in pursuing additional digital training to prepare them for an increasingly digitilized workplace. Is there room for no-code technologies to supplement the digital capabilities within companies?

    Enterprises are already expanding the responsibilities of application development outside their IT teams as well as increasing the productivity of their existing IT resources by automating and abstracting repetitive developer tasks. Gartner predicted that by 2025, 70% of apps will employ low-code/no-code technology. This trend aligns neatly with the business needs, such as the development of organization-specific applications and tools, because every business’s requirements are different.

    Furthermore, no-code/low-code technology enables companies to drive innovation and shorten the idea-to-market cycle times. The technology’s pre-built components make it easier for end-users and workers to intuitively assemble the templates that solve their complex needs.

    In Conclusion

    U.S. companies that need to fill the IT skills gap should consider how technology can be leveraged to boost productivity and accelerate the speed of innovation while accepting that new technical resources will continue to be expensive and scarce.

    These organizations are better off taking the lead in the democratization of IT within their teams. Only then can they keep up with the speed and agility required in today´s competitive environment.

  • Four Technologies Transforming Data and Driving Change

    Four Technologies Transforming Data and Driving Change

    Data is getting a facelift in 2023 courtesy of event-driven architecture (EDA). These four technologies are set to transform DevOps and IT operations over the next year–whether it be how companies make use of data or the customer experience they offer.

    1. Applied Observability: A Powerful Source of Decision-Making

    Applied observability has been recognized by Gartner as a key 2023 strategic tech trend at its most recent Orlando Symposium. It ensures that the right data is delivered at the right time for rapid action based on confirmed stakeholder actions rather than intentions. “Observable” data include key digitized artifacts that appear when any stakeholder takes any kind of action. Applied observability feeds these observable artifacts back in a highly orchestrated and integrated approach to accelerate organizational decision-making and allows the business owner to track how long an action took to fully process at every point in the workflow.

    But unearthing these insights requires an event-driven approach to software architecture. Distributed tracing, for example, is a method of tracking application requests as they flow from front-end devices to back-end services and databases. By definition, and in contrast to traditional tracing, distributed tracing can be visualized to show a searchable, graphical picture of when, where and how a single event flowed through an enterprise, regardless of the number of hops the workflow took to fully process.

    Consider the potential traceability and end-to-end observability benefits across a payment ecosystem underpinned by EDA. Embedding distributed tracing into an event mesh emits trace events in OpenTelemetry format so banks can collect, visualize and analyze them in any compatible tool. This empowers them to not only confirm that a given message was published but easily understand exactly when and by whom, where it went, down to individual hops, who received it and when … or why not.

    2. Hyper and Intelligent Automation: Taking Automation to the Next Level

    Gartner and IDC identified both hyper and intelligent automation in their yearly technology trends for 2023. Hyperautomation moves automation up a level, adding more intelligence to automation and using a broader set of tools so that previously un-automatable tasks can be automated. Hyperautomation initiatives come in many different shapes and sizes and are being seen across a wide range of industries, from banking and insurance to manufacturing and health care.

    But hyperautomation requires an organization to be underpinned by an EDA and–more specifically, an event mesh–to maximize success. An event mesh is an interconnected network of event brokers that allows data to be pushed in real-time to parts of the organization where the data is needed. It does this dynamically, meaning that new event types can be added at any time and interest in events can be registered to allow a seamless interchange of data for the applications that are interested in using it.

    Take the aviation industry as an example. An event mesh streams information such as flight routes, delays, cancellations and mileage accruals between applications, connected devices and people anywhere in the world, instantly. With an event mesh, information about events can be continuously streamed to multiple systems; filtered so each system only receives the data it needs. This ultimately enhances the customer experience as passengers, pilots and crews are notified in real-time when something relevant occurs on each and every flight.

    3. The Acceleration of the Event-Driven API Economy

    The API economy has exploded. Research from MIT Initiative on the Digital Economy showed that over a four-year period, businesses using APIs saw 12.7% more growth in market capitalization compared to those that did not adopt APIs.

    But the API economy is changing as event-driven architecture and asynchronous event-driven APIs become increasingly important to companies striving to operate in real-time. Using both synchronous and asynchronous methods can result in an application environment in which system resources are most effectively used.

    AsyncAPI and RESTful APIs will be the next evolution of the API economy. API management platforms will need to adapt quickly to this new reality. As a prime example, Forrester recently spotlighted a global biotech company that is blazing a trail in the API-driven area. As the case study demonstrates, the organization understood that EDA is a complement to RESTful APIs–another tool in their digital business toolbox, filling in the gaps of a REST-only approach.

    As a result, they planned a unified event + REST approach from the outset. Both APIs and events are managed as enabling digital products, with the governance and life cycle of both unified as one process. A platform team builds a unified API + event platform for app dev teams rather than building each as a separate siloed platform. The results include faster speed to market and new business opportunities. The company also embraces FAIR data principles: Data that is findable, accessible, interoperable and reusable. A digital marketplace consisting of both events and APIs makes the data findable and machine-readable via Open API Spec (OAS) and AsyncAPI metadata. Data can then be accessed on demand via APIs and events push data in real-time.

    4. The Metaverse Continues at Pace

    Driven by technologies including digital twins, augmented reality and virtual reality, McKinsey estimates the metaverse will be worth $5 trillion by 2030. Data in real-time will be the vital common denominator to link the digital and physical divide and optimize the Metaverse. McKinsey lists real-time data as a vital element to facilitate adoption of the metaverse in several key industries including retail, banking/financial services and transportation.

    The metaverse cannot be static. It needs to be in real-time, in motion. It needs an EDA and a real-time event mesh to support it.

    For example, if an avatar visits a retailer in the metaverse, the retailer will need to keep track of countless user actions such as how long someone spent looking at a particular product, which products got the most interest, etc. The metaverse will produce a significant amount of data that companies will be interested in analyzing in real-time. This data can be used to make decisions from real-time adjustments to inventory to incentivizing commerce through real-time offers/discounts/coupons and even dynamically pricing products.

    Designing a Data-Driven Future With new Tech

    Together, these four predictions have the potential to revolutionize company operations, communications and decision-making–but to take advantage of their full potential and reap success, these four trends will need EDA by their side.

  • Don’t Hire for Product Expertise

    Don’t Hire for Product Expertise

    We all know the jokes about talent recruitment that “requires a century of experience in AI/ML.” With the number of layoffs, no doubt this type of approach will get worse. But that is not generally a tech problem; it is an HR problem–the problem of having people hire in tech that aren’t active in or knowledgeable about tech.

    The real concern that is very much a tech problem is hiring for product and/or technology. I will say outright: Stop doing that except in very special circumstances. Hire for the ability to find information. One of the other things we all joke about is increasingly convoluted interviews where the hiring company wants you to solve their problems in a fraction of the time it would actually take them to do it. Again, if you’re a hiring company doing this, knock it off.

    IT is an ever-evolving field, more so once Agile and DevOps are taken into account. Indeed, we would not be far off if we renamed the field Continuous Evolution, because tomorrow is guaranteed to hold different solutions, no matter what. The idea that requiring experience in Grafana or mastery of Python is an indicator of long-term success is downright farcical. There are positions where this is tolerable–if a position is for maintenance of a traditional app that is not going anywhere and is not getting massive updates, then a list of what tools are desired for the role is easy to build and will be lasting. But I would argue that hiring specifically for that application’s needs is not in the best interest of the organization as a whole, because that person is not portable and adaptable. For modern development, particularly of new products that are just now being architected, the needs are going to change in the process, so claiming you want X years of experience in Spring (when it may use a different framework by deployment time) is not productive.

    As we move into belt-tightening yet again, employees who can do the job and know how to look up new information will be worth their weight in gold. Having a person who can work on existing applications and drive innovation in future applications all in one body is important. Giving them a chance to exercise that drive to learn is also important, but is a separate issue. None of the areas I cover–applications, operations and security–are standing still or even coming close to standing still. The need for people who can go find out what is needed is huge.

    And while the internet is a horrendous mix of good and bad, the ability to find information—and even solutions to problems—is undeniably better than it ever has been. Having people skilled at using that information resource is the key to moving forward.

    So, find people that can solve problems given a modern context. Hiring for technology will solve today’s problems, but problems are not restricted to today, so solve ongoing problems. Gate for basic tech knowledge and skills, then evaluate for the ability to find answers. A far better use of everyone’s time during interviews would be—using email as an example—“Hosted Exchange just went down. Walk me through your troubleshooting steps. You have a couple minutes.” Or—using development as an example—“We just discovered that an OSS library we use has unacceptable vulnerabilities. Walk me through how you would find instances of it and what steps you would take to find replacements. Compliance is breathing down our neck, you have a couple of minutes to show us.” You get to see how they approach likely problems, they get to ask about the environment. Far better than “Show us all the steps to get BGP routing configured,” or “Develop an entire B-Tree while we watch,” questions. Certainly better than any question that ends with “And don’t use the internet.” Are you kidding? Can we talk about the field and what year it is?

    As I’ve said off and on over the years, you want the best coworkers you can get or the best employees you can get. Either way, hiring for expertise with a given tech product is not the best route to that goal. You rock—so get people with you who do, too. It’s not going to get less complex, so have peeps that can keep up and carry a load, no matter what tomorrow’s technology looks like.

  • Navigating the MLOps Revolution: Three Missteps to Avoid

    Navigating the MLOps Revolution: Three Missteps to Avoid

    With nearly 60% of businesses set to adopt MLOps by 2024, this function has quickly established itself as an indispensable part of the enterprise technology world. And with computing needs rapidly expanding as digital transformation efforts continue, the growth of the MLOps market is unlikely to stop anytime soon.

    That said, as with any emerging technology–particularly something as powerful as MLOps–there is no shortage of potential pitfalls that businesses and their tech teams can fall into if they move too quickly. More importantly, these missteps can have significant impacts on a company’s ability to generate revenue and optimize performance.

    With that in mind, below are three of the biggest mistakes that companies tend to make when adopting MLOps.

    Jumping in Without Testing the Waters

    If you doubt your MLOps maturity, you should probably wait. One of the biggest factors that undermine MLOps adoption and long-term success is that companies jump in before they are actually ready to adopt the technology. This not only leads to MLOps feeling like a burden but also often devolves the customer-vendor relationship into a transactional sales relationship rather than what it should be: a partnership. Onboarding technology needs to be a give-and-take process with ongoing dialogue between the customer and the vendor. If not, vendors – even the most ethical ones – have to rely on partial situational intelligence and guesswork to offer solutions that may or may not address the problems and deliver results. The best way to overcome this is for companies to map out exactly what MLOps challenges they are up against, what barriers are present, and also the outcomes they are looking for. With these key components understood and agreed to in advance, not only can companies make sure they are getting the best vendor partner, but the vendor can then provide much more tailored guidance.

    Misunderstanding What MLOps Actually Is

    There is a tendency among new adopters to distill MLOps down to the idea that it is made up of two components: Storage and compute. Yes, these are two significant elements of the MLOps equation, but this view is a massive oversimplification. Also, if it is not clarified at the outset, this misperception can lead organizations to feel overwhelmed by MLOps and make adoption less “sticky” as a result.

    The fact is, MLOps is composed of numerous pieces–from experimentation to deployment–that each come with their own specific nuances. Each of these parts is interconnected to the next and can derail MLOps results if not properly planned for and maintained. In addition, making sure that every necessary component is functioning the way it should in order to meet the needs and expectations of each client takes time. So businesses and their broader DevOps teams need to be prepared to exercise patience if they have any hopes of successfully adopting MLOps in a way that is geared towards long-term sustainable results.

    Not Realizing the Amount of Iteration MLOps Takes

    When looking at projects from a traditional DevOps perspective, there is a common belief that if a project is stable enough to get it to DevOps, it is ready for production. With MLOps projects, however, this simply isn’t the case. In comparison to DevOps, MLOps is prone to evolution – meaning that iteration and reiteration are incredibly common. For example, in the midst of the MLOps process, companies will want to iterate over the existing process, sometimes at a much more frequent cadence than their software release cadence. This means that there is greater ongoing contact and collaboration between MLOps teams and other teams. This interaction stands in stark contrast to traditional DevOps projects where once production is complete, developers hand over the keys and are essentially hands-off. Businesses and their traditional DevOps teams need to become comfortable with this new way of doing business and build in the flexibility and leeway to make sure that while their MLOps is able to function correctly and grow, that it does so in a way that works for them.

    Businesses are right to be excited about the prospect of adopting MLOps. However, to hit the ground running and deliver impact over the short, medium, and long term, it is essential that companies deploy a comprehensive strategy to ease onboarding headaches and make the process as seamless as possible. And by keeping these key missteps in mind, businesses can do just that.

  • Break Open the Black Box of Software Development

    Break Open the Black Box of Software Development

    If you asked an airline executive what their biggest expense was just a few decades ago, they would’ve easily answered, “Jet fuel.” These days, you’d be hard-pressed to find an executive in any industry that wouldn’t answer with, “Software.” But here’s the difference: With jet fuel, that executive would have told you that they can fly x number of seat miles per gallon/liter of fuel (or pounds of fuel, if we’re being technical). With software development, the expense is becoming more material–and far more variable–but the return on that investment is rarely clear. Over the past decade or more, software development has readily assumed its position as the fastest-growing (and often largest) expense line item in virtually every industry. All companies are adding software experiences to or around their products, and they need the people and tools to build, support and continuously optimize those experiences. And that costs money–a lot of money.

    The problem is that the language, the metrics and the world in which software development exists do not translate into operational or financial terms that business leaders can readily understand.

    “We invested X million dollars into software development last year,” a CFO might say. “And what do we have to show for it?”

    Meanwhile, a technology leader might respond with, “We’re doing X deploys a day!” or, “We wrote X thousand lines of code!”

    They might as well be speaking different languages because, in practical terms, they are.

    And the problem is, that you can’t manage what you can’t measure. To continue our airline theme, software development continues to be a ‘black box’—this hidden-in-plain-sight source of information—that financial executives can’t seem to peek into.

    Why the Black Box?

    It’s not likely that technology leaders are being purposefully elusive toward business leaders–it’s more likely that they simply are not aligned on what, fundamentally, they need to do to speak the same language.

    The question at hand is, actually, “Are these investments we’re making turning into our desired business outcomes?” But the question technology leaders feel compelled to answer is, “What do your people do all day?”

    These are questions that Agile and DevOps don’t solve–translating Agile or DevOps metrics (also known as DORA metrics) into actual, actionable metrics for business leaders to understand. Although these metrics are tremendously useful for teams, using them to steer the business is a mistake.

    Misaligned in purpose and using different language, business leaders and technology leaders talk past each other as software development costs continue to rise.

    But the cost of software development is too high for business leaders to continue to tolerate this ‘black box’ of ever-rising costs. It’s the fiduciary responsibility of financial leaders to understand the value of software investments and figure out how they are affecting business outcomes–or their jobs could be on the line.

    In case this seems like a distant threat, it isn’t: I saw a headline recently about a prominent automobile executive who lost their job because they weren’t able to gain the necessary insight into, and business outcomes out of, their significant software development costs and that was affecting the competitiveness of their entire company.

    It’s happening. And it’s on both business and technology leaders to break that black box open and start speaking the same language.

    The next question, of course, is, “How?”

    How to Bridge the Communication Gap Between Technology and Business Leaders

    For far too long, business leaders have made do with the activity metrics provided by technology leaders–they’ve tolerated this lack of visibility and found other ‘strategic’ ways to justify the rising software development costs. Perhaps they’ve been questioning how A leads to B–how an increase in funding for software development might result in greater customer retention or app usage, for example–but they have not had a tangible way to prove to themselves, much less to investors, whether this is actually happening.

    In order to understand and quantify software development costs, we have to start demanding more from our technology leaders: Working with them to establish realistic objectives and key results (OKRs), and holding them accountable to business metrics that actually relate to outcomes. That means insisting on business results–and not accepting activity metrics in their stead.

    Stable-Fund Products, Not Projects

    Here’s a hard pill for finance to swallow: When it comes to delivering software-enabled customer experiences, it’s not about optimizing costs. It’s much more nuanced than X dollars in equals X dollars out. Funding by projects might help make the numbers neat, but it doesn’t reflect the reality of the business.

    Project-based funding also tends to keep project leaders in a cost-center mentality: It makes it seem as though all resources are fungible; as if the work and the people performing it are interchangeable.

    One of the goals of Agile is to maximize value for the customer. But when we fund by project, we often stop funding a piece of work–declaring it done–before it actually starts generating value. We have this false end date that doesn’t reflect the actual ending of the value that we funded to create.

    Instead, finance organizations should move to the stable funding of products: Funding an outcome and giving teams the ability to stop, change or extend their work to achieve the business outcomes of the product.

    This is a shift that one prominent UK bank has been going through in recent years–from traditional portfolio management to lean portfolio management, including the shift to stable-funding projects. You can learn more about how NatWest Group (formerly Royal Bank of Scotland) has made this shift in this webinar.

    Visualize the Flow of Value with Flow Metrics

    Finally, we have to stop treating the collection of technology metrics and business metrics as two separate, unrelated efforts. Agile and DevOps are great at accelerating the throughput of your software development … but vanity metrics around how quickly teams are delivering are not enough to justify the growing and material investment in software development.

    Finance leaders have the opportunity to be the Babelfish between technology leaders and business leaders. As mentioned above, one way to encourage clearer communication between teams is to use OKRs to naturally tie software development objectives to tangible business results. The more we practice drawing connections between technology and business metrics–the closer we get to finally breaking open that elusive black box.

    A key part of this is communicating operational metrics differently: Developing a core set of metrics for tracking the flow of business value in software delivery, and providing a mechanism that correlates the investment in flow for each product’s value stream with the business results for that value stream.

    Visualizing the flow of value in a software development process may not be as easy as watching value flow on a manufacturing floor–but that doesn’t mean it’s impossible. Thanks to the vast number of data and visualization tools at our disposal, it is possible to make software products and their value streams just as visible as production lines.

    The Flow Framework, created by Dr. Mik Kersten, addresses the pivotal challenge of breaking open the black box of software development and bridging the gap between business and technology to optimize the flow of business value to customers.

    Break Open the Black Box with Value Stream Management

    The black box of software development is, at its core, a communication issue. It’s on both business and technology leaders to bridge this communication gap. Business leaders need to demand more from technology leaders and insist on translating activity into business metrics. Technology leaders need to practice tying their efforts to tangible business outcomes to help justify their growing expenses.

    Of course, it’s helpful to have tools purpose-built to facilitate this visibility and communication.

  • Fear Factor

    Fear Factor

  • Techstrong TV: Closing the Diversity Gap in Tech Careers

    Techstrong TV: Closing the Diversity Gap in Tech Careers

    Charlene and Liliana discuss closing the gap in diversity in careers in tech. Learning to code can be a beneficial skill for traditionally underrepresented demographics, leading to more employment opportunities. The video and a transcript of the conversation are below.



    Recorded Voice:         This is Digital Anarchist.

     

    Charlene O’Hanlon:   Hey everybody, welcome back to Techstrong TV. I’m Charlene O’Hanlon and I’m here now with Liliana Monge, who is the cofounder and CEO of Sabio Coding Bootcamp. Liliana, thank you so much for joining me today, I really do appreciate it.

     

    Liliana Monge:           Thank you so much for having me. I’m super excited to have this conversation.

     

    O’Hanlon:   Excellent, excellent. Well we are happy to have you here, especially because I, I really do appreciate the efforts that you guys are doing through your coding bootcamp and really want to give you guys a little boost up if you will by a little bit of free promotion.

     

                                     But let’s talk about what you guys are doing. What, tell me a little bit about Sabio Boot, ah, Coding Bootcamp.

     

    Monge:       Yeah, so we started in the fall of 2013 in Los Angeles. Back then we had a very singular focus to make sure that the LA ecosystem was really going to be diverse. There was you know Los Angeles kind of at one point in time really lived in the shadows of San Francisco, of Silicon Valley and you know I would say in 2010, 2011 things really started to get heated up, more venture capitalists started to come into the state, a lot more companies started going public, such as Cornerstone OnDemand. So we knew what was going to happen in terms of all of this amazing wealth creation and jobs.

     

                                     So my partner and I, my cofounder, Gregorio and I, decided that we wanted to make sure that the ecosystem in California and Los Angeles was going to be diverse. So we set up a really innovative program that allows any adult who is smart and motivated to become a software developer within a very short period of time. So it’s very practical training. We were a hundred percent dedicated to helping women, more Latinos, more Black people, people who are underrepresented in tech like military veterans join this innovation economy.

     

    O’Hanlon:   That’s great, that’s great. And I’ve spoken with folks in other cities who basically have the same idea to, to really bring up the, you know rise up the traditionally underrepresented communities when it comes to technology. And it really surprises me how many start with these coding bootcamps or these coding classes. It seems like that is kind of the cornerstone of so many IT jobs these days. So I mean do you see a lot of folks entering the tech space using that particular avenue, the coding or, or is it do you think that it is kind of fundamental these days?

     

    Monge:       So I do think it’s definitely fundamental and it is a solution that can be applied pretty much throughout the United States or the world at this time, especially because we see the cost of hardware has come down significantly. That is a barrier to entry, right? You have to kind of have your own machine which is you know that, that needs to run VC software. As we all know software engineers run quite VC software. So you have to start with a price point of somewhere around you know $1500.00 to $2000.00 so that every person has the equipment they need, so that can be a barrier.

     

                                     However, once if you find a way to overcome the hardware barrier, you also have to make sure that there’s reliable internet, right, because all the information is on the internet, you’re posting to AWS Cloud probably your code and things like that. So there are two barriers.

     

                                     But then the last component, which is curriculum and practical training to become a software engineer I would say that is probably the most prolific. There is so much curriculum out there. So we decided to build our own custom curriculum. However, there are other organizations that release curriculum that’s available. There’s also a bunch of free stuff. Vendors many times have free resources. So it is a very wonderful solution to help any population.

     

                                     You know there’s so much going on right now. Like let’s say in Appalachia where the economy is changing and so people are like, “Is this a possible solution?” Miami is going through a massive transformation, right? They were known for hospitality, for travel, for leisure and now it’s just got this massive tech injection. So you can see how you can really take a coding bootcamp and kind of pluck it anywhere and if there is an ecosystem that’s going to support it, it can definitely create an amazing tech workforce anywhere in the country.

     

    O’Hanlon:   Yeah, I, I find it especially in, in urban areas that these coding bootcamps are, are cropping up. And I wonder if that’s also kind of I don’t want to call it a “substitute,” but maybe another avenue for especially kids coming out of high school that don’t really have maybe the means to go to a four-year college and then there are some people who don’t see the value in a four-year college, they just want to do the tech school route and get, get a basic understanding of what they need to do and then, and then go out and enter the workforce.

     

                                     So do you see these, ah, the important or the, the popularity of these coding bootcamps do you see them as more of a kind of a just a step for or these transitional and especially traditional under-represented communities a way for them to, to get the resources, to get the education they need without having to spend four years in college? Or do you see it more as they’re looking at it as, “I don’t want to go to college, but I do want to learn a trade and I want to…you know I see that there is value in coding because tech is the future.” So where, where are you kind of seeing people joining the bootcamps from?

     

    Monge:       Yeah, so that’s a fantastic question. When we began we thought we were going to be able to take students who graduated from high school and enroll in the program. However, we have found that 18-19-year-olds are a little bit too young to think about, “In three months I’m going to be a professional.”

     

    O’Hanlon:   Okay.

     

    Monge:       So we will enroll people who are that young and there’s always like 1 out of you know 40 that are 18 or 19, however, across the county and coding bootcamps you’re right did start in all the major metros had their own coding bootcamps.

     

                                     We see people who are more about 21 to about 25. So it’s individuals that have some type of life experience, maybe they’ve worked a little bit, maybe they did some community college, or maybe they got a degree that didn’t allow them to really participate in the economy in a meaningful way. So we’ve met so many music majors, so many psychology majors, even people who have you know a general business degree you know might have a hard time finding their footing in the economy.

     

                                     So we find that people who have some type of life experience, whether you’ve worked in retail and now are a little more mature you know and you’re really thinking about,” Okay, I want a career that’s going to offer me a lot of opportunities,” those are really the people that we’ve seen across the country decide to participate in the coding bootcamp. I would venture to guess that it’s the space that’s a little more mature, it might move a little bit younger, but right now we haven’t seen a massive demand from the 18- or 19-year-olds.

     

    O’Hanlon:   Okay. So I know when you got started it was really a focus on getting the underrepresented communities, the Latina community and you know just addressing a need that was going to exist in the LA area, but what, have, have you seen that demographic changing with your bootcamps as you’ve progressed? Has, has it been less about making sure that you know these traditionally underrepresented communities and, and, but it’s become more of a kind of a general population that have been attending the bootcamps?

     

    Monge:       Yes, definitely. So we did see that big shift and especially you know in 2020 when COVID hit Sabio had to go into a hundred present live remote environment, right? So starting in April and May we really were able enroll a person who was in Texas, a person who was in Chicago, because all you needed was your laptop and strong access to the internet. So we have seen that the demographics of our coding bootcamp have become you know they’re very diverse. Where we will have you know Latinos, African Americans, we have a massive women in tech scholarship, we do a lot to try to encourage ladies to come to the program and support them post-graduation.

     

                                     But one of the things that we weren’t aware of when we started was that there’s actually a large military veteran population that is very well suited for coding. That we discovered maybe two or three years into the coding bootcamp. So now a good percentage of the people in the program are military veterans. So we’ll have people who actually live around the world, because veterans I don’t know have seen the world and they, they discover beautiful parts. And so it’s become a really diverse community in a unique way that we weren’t really anticipating.

     

                                     But what we want is basically our training room to look like America, right? An America super diverse, super fantastic and that’s who should be participating in the innovation economy. We think everyone should give it a consideration, give it thought, try it a little bit and decide for themselves if it’s something that they want to do professionally.

     

    O’Hanlon:   I, I think – I love that, I think that’s, that’s amazing that you guys are doing that. It’s interesting to think about. I, I have a 12-year-old son and you know he codes every day and he doesn’t even realize that he’s coding it, that he’s doing coding. We’re, you know I’m in awe of what he’s able to do on, on a regular basis. It’s not anything – I mean obviously I could learn how to code at some point, but I think we’re gonna, we’re, we’re kind of reaching this, this point in time where it’s not really going to be a question of, “Do you want to learn how to code,” it’s just you know you’re gonna learn how to code, it’s going to be kind of ingrained like you know like math or English or anything like that.

     

    Monge:       Right.

     

    O’Hanlon:   It’s just going to be part of, you know part of our everyday lives.

     

                                     So do you, do you foresee in the future that you might see kind of a shift in the, in people’s attitude regarding coding and, and you know and then you know maybe have as a result maybe your curriculum is going to be shifting into maybe other areas?

     

    Monge:       Yeah, so I think there’s opportunities on both sides. I mean we’ve seen that you know there’s been a national movement for Hour of Code, right?

     

    O’Hanlon:   Mm-hmm.

     

    Monge:       So in December during Computer Science Week most elementary kids will sit down and actively say like, “I am coding today.” So they’re developing the digital proficiency from an early age, which is fantastic.

     

                                     There are also a lot of after-school curricular activities that are related to coding and robotics. So I think that there has to be like you said a general understanding of what code is, how it operates, have some kind of fluency in it.

     

                                     There are also a lot of tools that help people do low-code automation, right? So there’s that development. But then there’s the development on the other side of the dumbbell which is you’re going to have a lot of AI and machine learning. We’re doing a lot of stuff with autonomous vehicles. I think I’ve seen that they, they showcased the tractor that’s a hundred percent autonomous, right? That the farmer’s controlling just with his phone.

     

    O’Hanlon:   Mm-hmm.

     

    Monge:       So I think there’s really exciting things that are happening across the spectrum, the technical spectrum and so it is going to become more incumbent for people to have some type of digital literacy and I think it just depends on how much…

     

                                     Right, like you and I probably I don’t know how much – like I never finished calculus, I could, I tried it, I could never get there.

     

    O’Hanlon:   [Laughs]

     

    Monge:       And now as an adult running a business I’m like, “What even is it,” right? Like do, who really needs it?

     

    O’Hanlon:   Right, right,

     

    Monge:       Versus, “I need algebra,” you need spreadsheets, you need concepts of finance. So I think it’s going to be something similar. There’s going to be a big spectrum. Does that make sense? Did I answer that?

     

    O’Hanlon:   Yeah, no, no, it, it makes, it makes total sense. You know you brought up AI and machine learning and other areas where we’re going to be heading.

     

                                     You know I was just talking to somebody earlier today about the metaverse and, and you know now that, now that we’ve got these entire you know universes that are being constructed virtually and people can actually interact and, and it’s nothing like Second Life. You know if anybody remembers Second Life 20 years ago people thought that that was going to be the next big thing and that was a big bust. But you know the metaverse and, and now companies are, are talking about using holograms to, to have you know interactions with other people.

     

    Monge:       Yeah.

     

    O’Hanlon:   There’s so many things that are going to be coming that, that just are kind of mind blowing. That we are going to need this next generation of you know the kids today who they are the next generation of coders and they’re the ones who are going to create these new universes for us and all these technologies and capabilities. So I think it’s really, really amazing that, that they’re getting this foundation now so that they can start kind of… They don’t have to think about the actual idea of coding, you know getting over that hurdle, they can just think about all of the possibilities that are, that are open to them and that they can provide to, to, to their customers in the future. So good stuff there.

     

                                     I love the idea that you know we’ve got, we’ve got these coding camps that do really give a nice foundational base to anybody and that’s, that’s an important thing. And anything that we can do to, to really blur the lines between equity and parity when it comes to underrepresented populations in technology I think is a really, really good thing.

     

                                     So Liliana I do want to thank you very much for what you guys are doing. I think it’s a great thing.

     

                                     If folks are interested in finding out more about the Sabio Bootcamps how can they get it? Do you have a website you want to point them to?

     

    Monge:       Yes, certainly. So it’s just our name: S-A-B-I-O-.L-A, because we started in Los Angeles. If you go to the website you’re going to find a bunch of different resources. We have a lot of introductory courses that are absolutely free, so that people can dip their toe in the water and say, “Okay, is this something that might interest me?”

     

                                     From what I’ve learned a lot of people really describe getting a little bug when they wrote something and something happens and they’re just like, “Oh wow.”

     

                                     But I think a lot of people are you know we’re, we’re makers, right? We, we evolve to work with our hands, to have tools and to build things, so this is one of those ways that we can really be creative and build something that other people can use or maybe you just build a solution for yourself that you really need, that no one else has built.

     

    O’Hanlon:   All right, well that’s, that’s such great stuff. I do appreciate it. Again, thanks for joining me today on TechStrong TV. Liliana thanks again for your time.

     

    Monge:       Thank you for having me.

     

    O’Hanlon:   All right everybody please stick around we’ve got lots more TechStrong TV coming up, so stay tuned.

     

    [End of Audio]

     

  • Level-Setting Expectations

    Level-Setting Expectations