Latest news with #JagadishGokavarapu
Forbes
6 hours ago
- Business
- Forbes
Data Architecture: How Tech Leaders Can Future-Proof Their Analytics Strategy
Jagadish Gokavarapu, VP of Technology at Wissen, 25+ years in IT and Services, specializing in AI and digital transformation. In an era where data is a strategic asset, organizations often falter not because they lack data—but because their architecture doesn't scale with their needs. Leaders must design data ecosystems that align with business strategy while balancing cost, complexity and agility. This article explores common architectural pitfalls and outlines how executives can make smarter, outcome-driven decisions when it comes to data and analytics infrastructure. 1. Let Business Use Cases Drive Architectural Choices Too often, leaders adopt trending architectures—real-time pipelines, serverless functions—without fully evaluating their business need. The result? Over-provisioned systems and inflated costs. Real-time pipelines are powerful, but they're not always necessary. A company reporting weekly sales doesn't benefit from a high-frequency event-driven architecture—it just inherits its cost and complexity. Instead, aligning technical architecture with actual business latency requirements (real-time, near-real-time, batch) ensures both operational efficiency and long-term maintainability. 2. Serverless Isn't A Strategy—It's A Tool Serverless computing reduces operational overhead and scales with demand. However, it's not universally applicable. Workloads involving long-running or compute-intensive tasks can be more efficiently handled using containerized or server-based deployments. Executives should treat serverless as part of a larger strategy, not the foundation of one. Use it where agility and cost-efficiency are paramount—for example, for infrequent data collection tasks or on-demand analytics. But when stability, control or debugging depth matter, a hybrid model is often better. 3. Build A Tech Stack That's Modular, Not Monolithic No single tool or platform fits all analytics scenarios. A modular architecture lets you evolve components independently based on changing needs. For example: Apache Kafka or AWS Kinesis for high-throughput, real-time streaming. Apache Airflow or Azure Data Factory for orchestrated batch workflows. dbt or Snowflake for SQL-based transformations and modeling. Selecting tools by workload type ensures the system remains maintainable and avoids the bloat of unnecessary tools or duplicated functions. 4. Observability Begins With Logging—Not Dashboards Observability isn't just about metrics and dashboards. It starts with structured, consistent logging across the stack. Without proper logs and audit trails, even the best monitoring tools offer limited value. In several architecture redesigns I've led, simply introducing a logging strategy improved incident response time and enabled better capacity planning. Logging not only supports compliance and debugging—it's critical for performance tuning and long-term optimization. Treat it as a core architectural requirement, not an afterthought. 5. Leverage AI And Low-Code To Address The Skills Gap As technologies evolve faster than teams can upskill, AI and low-code platforms provide a scalable solution. AI-powered monitoring tools can detect anomalies, automate remediation and optimize infrastructure dynamically. Low-code tools enable faster pipeline development, especially for business-driven use cases. This dual approach doesn't replace engineers—it amplifies their impact. I've implemented low-code solutions that reduced onboarding time for new analysts, freeing up senior engineers to focus on innovation. Make Architecture A Strategic Lever, Not A Technical Silo Your data architecture should serve as a growth engine, not a cost center. By making intentional choices—based on business goals, not hype—you can architect a system that is scalable, efficient and adaptable. The leaders who invest in observability, modularity and AI/low-code enablement today will be the ones driving tomorrow's data-first strategies. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
Forbes
5 days ago
- Business
- Forbes
Tech Retirement Crisis: Vulnerable Industries And What They Must Do
As seasoned tech professionals with decades of institutional knowledge approach retirement, many industries risk losing the expertise required to maintain aging systems and tools. These legacy technologies often power critical operations but are unfamiliar to younger tech professionals, creating a widening skills gap. The retirement of veteran tech experts is a wake-up call for industries still tethered to outdated systems—the challenge isn't just replacing those systems, but also preserving the knowledge that keeps them running. Below, members of Forbes Technology Council highlight the industries most at risk and share what leaders can do now to transfer essential know-how and prepare the next generation to step in with confidence. 1. Ports Ports still rely on terminal operating systems (TOS) and EDI standards from the 1980s. Young engineers face a steep learning curve with cryptic interfaces and minimal documentation. To future-proof, leaders must digitize maritime workflows using AI-integrated APIs while creating simulation labs for cross-training younger talent on legacy systems—before the sea of knowledge ebbs away. - Jagadish Gokavarapu, Wissen Infotech 2. Financial Services Financial services is one industry that relies on legacy systems younger tech professionals can't maintain. To ensure continuity as technology leaders retire, organizations must urgently modernize by migrating to the cloud, unlocking AI and attracting top talent for future growth. - Chetan Mathur, Next Pathway Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify? 3. Automotive; Heavy Machinery Industries like automotive and heavy machinery rely on proprietary software and specialized control systems. As experienced engineers retire, there is a risk of knowledge gaps emerging. Leaders should implement mentorship programs in which older professionals share their expertise with their younger counterparts. Investing in intuitive training materials can also help bridge the generational divide. - Neel Sendas, Amazon 4. OT And Manufacturing Operational technology and manufacturing often run on legacy operating systems and hardware and rarely incorporate AI into critical workflows. While these sectors require deterministic outcomes, pairing AI models with strict guardrails—such as limiting outputs to a predefined set of approved results—can still boost efficiency and innovation without introducing undue risk to sensitive environments. - Keren Katz, Tenable 5. Corporate Banking Corporate banking risks falling behind as legacy systems outlast the talent trained to maintain them. To stay competitive, banks must modernize with interoperable cloud, SaaS and AI solutions; ensure structured knowledge transfer between generations; and foster a culture of innovation to future-proof operations, enhance compliance and meet rising customer expectations. - Alex Ford, Encompass Corporation 6. Professional Services Many professional services firms still rely on outdated tools—spreadsheets, siloed systems and tribal knowledge held by a few. Younger talent isn't trained on or drawn to these ways of working. As experts retire, firms risk losing both continuity and edge. Leaders must modernize workflows and capture knowledge in connected, accessible systems to future-proof their teams and retain their advantage. - Sarah Edwards, Kantata 7. Energy The energy sector still leans heavily on legacy SCADA systems and custom PLC setups that younger professionals rarely encounter. As veteran engineers retire, leaders should invest in cross-generational training programs and gradually modernize infrastructure—bridging knowledge gaps before they become operational risks. - Kirill Sagitov, COYTX GLOBAL LLC 9. Healthcare The healthcare industry continues to rely on legacy technology, such as EHR systems built with platforms like MUMPS, a language that is now unfamiliar to most new professionals. As experienced staff retire, younger pros lack the skills to maintain them. Leaders should prioritize mentorship, detailed documentation and phased modernization to ensure continuity and prepare for a tech-forward future. - Tannu Jiwnani, Microsoft 10. Aerospace The aerospace industry still relies on legacy systems, such as Fortran-based simulations and outdated telemetry tools. As seasoned experts retire, leaders must document key knowledge, modernize tools and build mentorship pipelines. Just as NASA blends Apollo-era wisdom with Artemis-era innovation, bridging generations ensures mission continuity and tech evolution without losing institutional memory. - Shelli Brunswick, SB Global LLC 11. Retail Retail is a prime example of an industry still dependent on legacy systems, siloed data and the deep expertise of long-tenured employees. As these experts retire, leaders should accelerate the shift to unified commerce platforms, modernize tools for frontline staff, and leverage AI for both onboarding and customer engagement to future-proof their operations. - Zornitza Stefanova, BSPK 12. Global Logistics Global logistics runs on fragile legacy EDI pipes, which younger pros rarely touch. Instead of patching, leaders should build a blockchain-backed trade graph. Pair retiring EDI experts with AI agents to translate workflows, compress the past and future-proof the industry. - Akhilesh Sharma, A3Logics Inc. 13. Utilities The utilities industry still runs on SCADA and COBOL—tools young tech talent rarely encounters. Leaders must build simulation labs for training, capture tacit knowledge digitally and layer modern APIs over core systems to ensure continuity before the grid's human backbone retires. - Mark Mahle, NetActuate, Inc. 14. Broadcast Media The broadcast media industry still runs on decades-old video playout servers, SDI hardware and proprietary codecs. As tech veterans retire, leaders must digitize tribal knowledge, modernize with IP-based workflows and train new talent through virtual production labs—before the signal fades on this critical yet aging infrastructure. - Roman Vinogradov, Improvado 15. Life Insurance The life insurance industry uses policy administration systems that need to be maintained for the lifetime of the insured (which is usually up to 40 or 50 years). The older books of business need to be migrated to modern systems to mitigate the risk of a loss of support due to retiring professionals. - Arnab Mukhopadhyay, VNS Health 16. Government Agencies Too many government agencies run COBOL systems, managing payments on half-century-old infrastructure. We can prioritize 'bridge' roles, pairing retiring experts with younger staff to transfer knowledge of both technical systems and the legal frameworks behind them. Leaders must modernize while preserving privacy protections through gradual adoption of new technologies. - Nick Hart, Data Foundation 17. Airlines The airline industry is deeply reliant on legacy reservation and operations systems built in languages like Fortran and TPF, which are unfamiliar to modern developers. Leaders should launch dual-track modernization: Pair retiring experts with junior engineers on active systems while building cloud-native replacements. Preserve tribal knowledge now or risk turbulence tomorrow. - Sandipan Biswas
