Low Code/No Code

Organizations may already possess the talent needed to extract additional value from the data they collect, according to a report released last week by Forrester.

Many employees now use analytics in their jobs, even if data science is not their full-time role. This includes business analysts and professionals in finance, marketing and engineering departments, the tech research and advisory firm said.

AI-enabled tools that automate away the more complicated aspects of data science, combined with machine learning algorithms and low-code applications give these professionals, dubbed “citizen data scientists” by Forrester, the ability to produce sophisticated business insights and predictive models.

Demand for data skills continues to outstrip the supply of available talent. As companies compete for IT workers against a backdrop of historically low unemployment, identifying previously untapped analytics capabilities within an organization can provide relief.

Individuals with knowledge of correlation, statistical reasoning and regression analysis may be found in a variety of non-data science roles. 

With additional training and guidance, workers with these skills can begin by collecting and preprocessing data. From there, they can move on to more complex functions, such as identifying trends, patterns and anomalies in datasets and — with help from automated machine learning — uncover otherwise hidden insights with predictive value.

Accessing these skills requires a systematic approach as well as technological assets, according to Forrester.

Deploying low-code tools with ML and AI capabilities can be risky without clear governance and process guardrails to ensure accuracy. Standardizing data across the organization to ensure there is consistency in results is also important, the report said.

Talent-strapped organizations can find analytics skills among entry-level job applicants, too, even among candidates without traditional technologist resumes. A recent study by Handshake found that 4 in 5 non-tech majors graduating in the class of 2023 possessed skills related to data analytics and IT systems.

Top engineering leaders are prioritizing generalists over technologists with more specialized skill sets, according to a Harris Poll survey on behalf of Karat of 789 decision-makers.

Nearly half of engineering leaders identified as top performers in the study prioritize hiring software generalists, compared to 26% of lower performers. More than one-third of top performers sought out full-stack developers, compared to 20% of lower performers. 

Top performers also take less time to fill open positions. On average, engineering leaders take 26 days to fill an open role, while top performers take just 17 days. 

The need for more generalized skills cropped up at the beginning of the year, as the long-term effects of high attrition and a drained talent pool blocked enterprise modernization efforts. 

Even when the tech industry was riddled with layoffs for months, departing tech workers were able to quickly find their next roles.

The appetite for technology workers across industries can be attributed to a number of factors, including the increased dependence on technology throughout departments. Software touches workflows in finance, supply chain, back-office and everything in between.

Software engineers are so valuable to an organization that 55% of engineering leaders in 2023 say a strong engineer is worth at least three times their total compensation, according to the report. That statistic is up 10 percentage points since 2020. 

“Organizations that are prioritizing being able to move quickly and rapidly deploy resources will look to hire and cross-skill their employees to be able to deliver products and services quickly without relying on other groups,” Forrester Principal Analyst Fiona Mark said via email. “Leading organizations will look therefore to hire and develop T-shaped workers who have a broad range of skills but are able to be specialists in one or more domain, and the ability to acquire new skills.”

As companies continue to experiment with generative AI, having the foundational elements of technology infrastructure and architecture in tip-top shape becomes critical. But hiring for generalized skills isn’t always the solution, Mark said. Organizations need domain specialists with deep expertise to provide a competitive advantage, according to Mark.

The signs of poor quality software can be obvious: bugs, errors, complaining customers, and the inability to execute projects because tools aren’t up to the task.

But the financial costs of these flaws can quietly add up. Software quality issues cost U.S. businesses $2.41 trillion in 2022, according to a report from Synopsys and CISQ. That’s nearly double the cost of the U.S. budget deficit.

These costs come from cyberattacks that target existing vulnerabilities, software supply chain issues, and the growing impact of accumulating technical debt. 

“This is not just about having better functioning software. It’s about having software that actually works the way it’s supposed to,” said Anita D'Amico, vice president of cross-portfolio solutions and strategy for the Synopsys Software Integrity Group.

Fixing software can be an expensive undertaking, but one that will only get worse if ignored. That can cost enterprises in terms of lost opportunities and — potentially — customers. But it’s possible, starting with reconfiguring the development process, and tackling the biggest issues first on the road to software redemption.

Rethinking software, from development up

Poor software quality can be a consequence of relegating security and quality assurance to the last stages of the development process.

Both criteria should be considered before the software is designed and developed. They are part of architectural analysis and threat modeling, said D’Amico. “It would be like designing a house without thinking whether or not you’re going to need air conditioning.”

That strategy might require a paradigm shift, especially since software quality and security are not required courses in college, she said. Plus, “people are in a rush to get code out,” with efficiency, performance and scalability taking precedent.

Problems with software quality can also become evident once a product is released before being tested at real-world scale, said Brian Alletto, a director in the technology practice at West Monroe.

“Things may work at a very small proof of concept stage, but then when you scale up to a larger enterprise, to even internet scale, you find the technical decisions you made for the small scale system just don’t work,” he said. 

Considering issues like reliability, security, and scalability during the development process can address these issues before they run amuck.

Testing and quality tools should be “put into the developers’ hands as part of their development process,” said D’Amico. “Once you put it into the pipeline, you can break a build because [of] security, quality or compliance issues."

Lost opportunity costs add up

Fixing poor quality software can be expensive, not just in having to repair issues, but in the time and effort required for those repairs — which could have been spent on something else. 

“All that time that your developers are doing the workarounds for this poorly designed system is time that they could have been working on a new feature of a new project,” said D’Amico. 

Constant bug-hunting can take a toll on developers. More than 2 in 5 developers said fixing bugs is their biggest pain point, according to Rollbar’s 2021 State of the Software Code report. The report also found 2 in 5 respondents spent 25% of their time fixing bugs. Even more alarming, 88% of respondents said that bugs and errors were first found by customers. 

Problems being obvious to customers is why poor software quality is also an immediate business risk for CIOs, D’Amico added. “If that system is a must-have and it really isn’t functioning properly … the customer might not be happy,” she said.

Finding and fixing software

A good place to start is by doing inventory on underlying platforms and technology components. 

That approach can help CIOs identify products running on older versions of commercial or open source software, said Alletto.

“You can start finding things, just from an age perspective, that are pretty strong indicators of technical debt,” he said. CIOs can also look for trends in operational history, and look for an increase in errors and indications of customer dissatisfaction.

Finding where poor quality software lives can help create a road map to prioritize “what are we going to tackle, what is really impacting innovation, what is impacting services?” said Alletto. 

While the best time to fix poor software was during development, that’s not going to help CIOs stuck with poor quality software now, said D’Amico. 

Once there's a clear picture of what technical debt looks like, the next step is to prioritize the issues which could cascade and have the biggest impact, she said. That way, CIOs can address technical debt by “paying it down faster than it’s accumulating.” 

Companies spend an average of 40% of their IT budget maintaining and updating existing software, according to a new Forrester report commissioned by no-code PaaS company Unqork.

Many enterprises are adopting low-code technology platforms to reduce the time and money spent servicing technical debt, according to the report, which surveyed 312 application development and delivery decision-makers. Nearly half use a combination of declarative low-code tooling and hand coding, but 42% still rely exclusively on custom coding.

Despite the efficiency advantages of limiting new code, nearly two-thirds of respondents said their companies can’t meet development needs without hand coding, and 62% are too busy updating legacy software to adopt low-code alternatives.

Technical debt accumulates as today’s shiny new software innovations age into tomorrow’s legacy applications, in need of security patches and updates.

No-code solutions — modular application development platforms that eliminate the need for hand coding — promise to reduce technical debt, while also opening the creation process to staff outside of IT.

Reducing custom coding drove 3 in 5 respondents to adopt codeless solutions. Freeing coding professionals to focus on higher-priority projects and ensuring nontechnical employees can develop and maintain apps were also high on the list.

While respondents’ companies haven’t yet made the transition to codeless architecture, 70% say it’s critical to do so in the future.

The market for low-code technologies is on a steep growth trajectory, according to Gartner. It is projected to total $27 billion in 2023, up almost 20% from last year. Low-code application platforms, which include no-code solutions that require only declarative text instructions for app building, account for nearly one-third of the total market and are expected to generate 25% more revenue than last year.

While commonly grouped together, the distinction between low-code and codeless technologies is significant, according to Forrester analyst Jessica D’Oliveira.

“No-code is within low-code from an enterprise perspective,” D’Oliveira said. “But no-code refers to systems where you do not inject code.”

Most no-code platforms allow for some hand-tooled coding. But each new line of code adds technical debt.

Codeless platforms provide users with prebuilt components, widgets and services that can be configured without scripting to create an app,” Jason Wong, distinguished VP analyst at Gartner, told CIO Dive.

“Anytime you add customizations via scripting and programming, you introduce technical debt into a low-code or no-code platform,” Wong said.

Four in 5 executives say modernizing legacy apps and data stand in the way of potential AI benefits, according to a Rackspace Technology survey published Tuesday. The company surveyed 1,420 IT leaders.

Limited resources, a lack of change culture and integration challenges constitute the top three obstacles to modernization, according to survey respondents. Other issues include the lack of a clear roadmap and communication gaps between stakeholders.

While companies cite costs and security as two major obstacles toward modernizing apps and data, they also find lower costs and greater security as two key benefits of modernization, "indicating that many organizations are stuck in a Catch-22," said Jeff DeVerter, chief technology evangelist at Rackspace Technology, in a release. 

Vendors have spent months baking generative AI into every piece of the enterprise tool kit. But the basic underpinnings of enterprise tech still need some work to maximize the effectiveness of these solutions.

The CRM category, easily among the most business-critical applications to the enterprise, has been the subject of a vendor race to include generative AI features. Microsoft and Salesforce, two major providers in the space, both announced the addition of generative AI capabilities in their products.

ERPs, CRMs and HR platforms were listed by survey respondents as the top apps in need of upgrades, according to Rackspace. 

Most employees are detecting the slower pace of AI adoption in the workplace, according to an EY report. Part of the delay in implementation stems from out-of-date computing infrastructure powering these critical tools.

"If you are running an old school data center without a high degree of virtualization or cloud-like models, it's just slower and more expensive for you to adopt more emerging technologies, like next-gen AI, or digital twin," said Matt Barrington, EY Americas emerging technology leader, previously told CIO Dive.

But another key factor in the slow rate of modernization stems from leadership, and how oversight of major IT projects is structured. 

Modernization falls mostly on IT departments or members of the executive team, according to Rackspace. Just one-quarter of respondents say their companies rely on a cross-functional team to carry out projects.

More than one-third of IT professionals expect they'll continue to struggle to recruit skilled developers in 2023, according to a report from software company Infragistics. The firm surveyed 2,000 IT leaders and software developers in December and January.

Atop the list of hardest-to-fill roles are DevOps engineer, data analytics developer and IT security engineer, according to the company. 

More than three-quarters of respondents said their organizations have turned to low-code or no-code solutions as a response to the skill shortage.

Layoffs in big tech have not yet translated to an oversupply of technologists, as in-demand open roles continue to outnumber workers available to fill them.

"Even though we've seen a ton of tech layoffs, in the hundreds of thousands in the last three to four months … we're still seeing it's difficult to hire folks, especially if you're not in Silicon Valley," said Jason Beres, SVP, developer tools at Infragistics. 

HR leaders, tasked with filling open roles, say they lack confidence in their ability to meet hiring targets, according to research from General Assembly. 

The gradual but steady drumbeat of a return to the office at most large organizations is also tipping the scale in favor of the jobseeker, even as economic indicators sour.

"What ends up happening is that really good talent will still leave a company and go elsewhere because they can get what they want," said Beres.

Still, some signs of a rationalization in the tech workforce are emerging. Average technology salaries, after a significant upward run in recent years, grew just 2.3% last year, one-third of the growth rate recorded in 2021, according to Dice analysis.

More than half — or 56% — of data and analytics leaders say their teams are ineffective at providing value to their organizations, according to a Gartner survey published Tuesday. The analyst firm surveyed more than 550 data leaders at the end of 2022. 

Data and analytics leaders blame skills and staff shortages for blocking their efforts, the top obstacle for 1 in 5 respondents. Lack of resources or funding and culture challenges are also top roadblocks.

Despite the barriers to skill building, as well as the broader challenges to finding technology talent, 44% of D&A leaders say their teams grew in the last year. 

With more digital systems surrounding operations, companies have more data than they can handle. 

Cloud migrations are partially to blame. With a more cloud-based technology stack, nearly three-quarters of organizations are struggling with data management, a Coleman Parkes survey found in September.

With data skills as a key driver to project execution, leaders can look inward to develop the talent they need. But hiring or upskilling is just the start.

"Becoming a data-driven enterprise requires explicit and persistent organizational change management to achieve measurable business outcomes," said Alan Duncan, distinguished VP analyst at Gartner, in an email.

Chief data and analytics officers need to tackle data literacy while building culture at their companies, according to Duncan.

For success in change management, Duncan said chief data and analytics officers can: 

• Sell the desired business change by communicating aspirational and inspirational SMART — specific, measurable, actionable, realistic and time-bound — benefits for the organization and individual stakeholders.
• Educate stakeholders by providing specific and targeted data literacy training in the capabilities that the workforce needs to be successful.
• Deliver the changed business state by executing programs of data and analytics, while adapting the messaging for stakeholders according to their cultural readiness to engage.

Despite the skills shortages, organizations are tasking their chief data and analytics officers with a broad range of responsibilities, the survey found. These C-suite leaders must define and implement data strategies, create data and analytics governance and manage a data-driven culture change.