Legacy systems are far more likely to get hacked. This is especially true for companies that become involved in private equity transactions, such as mergers, acquisitions, and divestitures. These transactions often result in IT system changes and large movements of data and financial capital which leave organizations acutely vulnerable. With details of these transactions being publicized or publicly accessible, threat actors can specifically target companies likely to be involved in such deals. We have seen two primary trends throughout 2023: Threat groups are closely following news cycles, enabling them to quickly target entire portfolios with zero-day attacks designed to upend aging technologies; disrupting businesses and their supply chains; Corporate espionage cases are also on the rise as threat actors embrace longer dwell times and employ greater calculation in methods of monetizing attacks. Together, this means the number of strategically calculated attacks — which are more insidious than hasty smash-and-grabs — are on the rise. 

To combat technical debt, developers — even frontend developers — must see their work as a part of a greater whole, rather than in isolation, Purighalla advised. “It is important for developers to think about what they are programming as a part of a larger system, rather than just that particular part,” he said. “There’s an engineering principle, ‘Excessive focus on perfection of art compromises the integrity of the whole.’” That means developers have to think like full-stack developers, even if they’re not actually full-stack developers. For the frontend, that specifically means understanding the data that underlies your site or web application, Purighalla explained. “The system starts with obviously the frontend, which end users touch and feel, and interface with the application through, and then that talks to maybe an orchestration layer of some sort, of APIs, which then talks to a backend infrastructure, which then talks to maybe a database,” he said. “That orchestration and the frontend has to be done very, very carefully.” Frontend developers should take responsibility for the data their applications rely on, he said.

While the benefits of modernization are clear, companies don’t need to be cutting edge everywhere, but they do need to apply the appropriate architectural patterns to the appropriate business processes. For example, Amazon Prime recently moved away from a microservices-based architecture for streaming media. In considering the additional complexity of service-oriented architectures, the company decided that a "modular monolith” would deliver most of the benefits for much less cost. Companies that make a successful transition to modern enterprise architectures get a few things right. ... Enterprise technology architecture isn’t something that most business leaders have had to think about, but they can’t afford to ignore it any longer. Together with the leaders of the technology function, they need to ask whether they have the right architecture to help them succeed. Building a modern architecture requires ongoing experimentation and a commitment to investment over the long term.

As we step into this transformative era, the concept of “no-collar jobs” takes center stage. Paul introduced this idea in his book “Human + Machine,” where new roles are expected to emerge that don’t fit into the traditional white-collar or blue-collar jobs; instead, it’s giving rise to what he called ‘no-collar jobs.’ These roles defy conventional categories, relying increasingly on digital technologies, AI, and automation to enhance human capabilities. In this emergence of new roles, the only threat is to those “who don’t learn to use the new tools, approaches and technologies in their work.” While this new future involves a transformation of tasks and roles, it does not necessitate jobs disappearing. ... Just as AI has become an integral part of enterprise software today, GenAI will follow suit. In the coming year, we can expect to see established software companies integrating GenAI capabilities into their products. “It will become more common for companies to use generative AI capabilities like Microsoft Dynamics Copilot, Einstein GPT from Salesforce or, GenAI capabilities from ServiceNow or other capabilities that will become natural in how they do things.”

In a monolithic data management approach, technology drives ownership. A single data engineering team typically owns all the data storage, pipelines, testing, and analytics for multiple teams—such as Finance, Sales, etc. In a data Mesh architecture, business function drives ownership. The data engineering team still owns a centralized data platform that offers services such as storage, ingestion, analytics, security, and governance. But teams such as Finance and Sales would each own their data and its full lifecycle (e.g. making code changes and maintaining code in production). Moving to a data mesh architecture brings numerous benefits:It removes roadblocks to innovation by creating a self-service model for teams to create new data products: It democratizes data while retaining centralized governance and security controls; It decreases data project development cycles, saving money and time that can be driven back into the business. Because it’s evolved from previous approaches to data management, data mesh uses many of the same tools and systems that monolithic approaches use, yet exposes these tools in a self-service model combining agility, team ownership, and organizational oversight.

Some modern data warehouse solutions, including Snowflake, allow data providers to seamlessly share data with users by making it available as a feed. This does away with the need for pipelines, as live data is shared in real-time without having to move the data. In this scenario, providers do not have to create APIs or FTPs to share data and there is no need for consumers to create data pipelines to import it. This is especially useful for activities such as data monetisation or company mergers, as well as for sectors such as the supply chain. ... Organisations that use data lakes to store large sets of structured and semi-structured data are now tending to create traditional data warehouses on top of them, thus generating more value. Known as a data lakehouse, this single platform combines the benefits of data lakes and warehouses. It is able to store unstructured data while providing the functionality of a data warehouse, to create a strategic data storage/management system. In addition to providing a data structure optimised for reporting, the data lakehouse provides a governance and administration layer and captures specific domain-related business rules.

Digital transformation without a clear vision and roadmap is identified as a big reason for failure. Several businesses may adopt change because of emerging trends and rapid innovation without evaluating their existing systems or business requirements. To avoid such failure, every tech leader must develop a clear vision, and comprehensive roadmap aligned with organizational goals, ensuring each step of the transformation contributes to the overarching vision. ... The rapid pace of technological change often outpaces the availability of skilled professionals. In the meantime, tech leaders may struggle to find individuals with the right expertise to drive the transformation forward. To address this, businesses should focus on strategic upskilling using IT value propositions and hiring business-minded technologists. Furthermore, investing in individual workforce development can bridge this gap effectively. ... Many organizations grapple with legacy systems and outdated infrastructure that may not seamlessly integrate with modern digital solutions. 

What sets the very best testers apart from the pack is that they never lose sight of why they’re conducting testing in the first place, and that means putting user interest first. These testers understand that testing best practices aren’t necessarily things to check off a list, but rather steps to take to help deliver a better end product to users. The very best testers never lose sight of why they’re conducting testing in the first place, and that means putting user interest first. To become such a tester, you need to always consider software from the user’s perspective and take into account how the software needs to work in order to deliver on the promise of helping users do something better, faster and easier in their daily lives. ... In order to keep an eye on the bigger picture and test with the user experience in mind, you need to ask questions and lots of them. Testers have a reputation for asking questions, and it often comes across as them trying to prove something, but there’s actually an important reason why the best testers ask so many questions.

Most businesses with large volumes of data use a data lake as their central repository to store and manage data from multiple sources. However, the growing volume and varied nature of data in data lakes makes data management challenging, particularly for businesses operating with various domains. This is where a data mesh approach can tie in to your data management efforts. The data mesh is a microservice, distributed approach to data management whereby extensive organizational data is split into smaller, multiple domains and managed by domain experts. The value provided by implementing a data mesh for your organization includes simpler management and faster access to your domain data. By building a data ecosystem that implements a data lake with data mesh thinking in mind, you can grant every domain operating within your business its product-specific data lake. This product-specific data lake helps provide cost-effective and scalable storage for housing your data and serving your needs. Additionally, with proper management by domain experts like data product owners and engineers, your business can serve independent but interoperable data products.

Maintaining legacy tech can prove to be every bit as expensive as a digital upgrade. This is because IT staff have to spend time and money to keep the obsolete software functioning. This wastes valuable staff hours that could be channeled into improving products, services, or company systems. A report from Dell estimates that organizations currently allocate 60-80% of their IT budget to maintaining existing on-site hardware and legacy apps, which leaves only 20-40% of the budget for everything else. ...  No company can defer upgrading its tech indefinitely: sooner or later, the business will fail as its rivals outpace it. Despite this urgency, many business leaders mistakenly believe that they can afford to defer their tech improvements and rely on dated systems in the meantime. However, this is a misapprehension and can lead to ‘technical debt.’ ‘Technical debt' describes the phenomenon in which the use of legacy systems defers short-term costs in favor of long-term losses that are incurred when reworking the systems later on.