Past several years have been a boon for digitalization across the industries. Innovation and adoption of new technology has witnessed an unprecedented growth curve. In addition, the diversity of growth is nothing short of remarkable seen in all domains including data engineering, machine learning, productivity software, financial services, workflow management, blockchain technology, healthcare, and edutech. Technology has made it possible to do new things differently and more efficiently in all these areas. However, the road to continued growth calls for consistent and continuous evolution.
Platform-ization boom: The story so far
A growing trend in this technology boom has been the platform-ization of software and services. Gone are the days when people built, sold, and adopted software as an end-to-end solution for a specific use. Technology is now increasingly used as a platform. Modern business solutions are designed as a set of components and building blocks that can be arranged, rearranged, adapted, and extended in myriad ways. For example, organizations rejected single-flavored, end-to-end payment solutions in favor of customizable solutions (such as white labeling, adding custom payment and discount types, special data processing constraints) and other workable solutions (one may use a vendor for fraud detection but use the payment solution for processing). Similarly, one doesn’t necessarily have a single observability solution anymore. An organization may use a combination of solutions that interact with each other to profitably leverage their individual strengths (one solution is great at real-time data gathering but another provides better network level insights while an inhouse plugin adds custom anomaly detection). So, while two companies may use the same technology providers and building blocks, their effective final solution may be very diverse, with significant performance, operational and capability differences.
In most of these examples, the biggest advantages have often come from just leveraging new and innovative technology. For a bank, running a legacy monthly batch processing pipeline to find specific customer insights, the move to a stream processing system that feeds into modern data analysis engines creates a big win in terms of the freshness (leading to reactiveness) and quality of insights. Similarly, for a bank relying mostly on classic regression models, a move to CNN/DNN-based prediction models backed by modern data pipelines can provide a technology win that would have taken several years to secure organically. The multiple available solutions have made it possible to plug together the relevant ones offering wins far bigger than the evolutionary in-house technology roadmaps. Legacy systems with technology roadmaps limited to in-house development plans can now choose from a wide range of cutting-edge solution providers and assemble them in several ways to modernize their stacks and systems.
Change is the only constant
While the current trend is set to continue, the competitive edge several organizations had from moving to the SaaS and Cloud (IaaS, PaaS) universe is likely to level over time. The competitive boost from the exposure to a wide range of new technical blocks has also motivated the competitors to follow a similar path. So, while technology adoption is still a big advantage for organizations (given several legacy systems are awaiting modernization), it will cease to be the key differentiator in the market for a business going forward. At a time when even the tiniest competitive edge can create a big difference in influencing the end customers, the normalization of the advantage gained from technology adoption is likely giving way to the resurgence of technology experts.
While the rate of digitization and service-fication of platforms, and technology-spurred technical sophistication has provided organizations a competitive edge, it is set to slow down over time as those lagging embark on similar journeys. When that happens, it isn't the adoption of new technology blocks but the ways these blocks are arranged and integrated, and who drives the use of these blocks will be the key differentiators. For example, it would have been a big gain for the data models of a bank leveraging CNN/DNN-based forecasting technologies. However, over time, the margin of gain would shrink with most of their competitors migrating to similar technologies, making it critical to not just leverage new technology but to use it appropriately and with expertise. The same bank will now need the help of experts to invest in model shapes, sizes, and better feature engineering, determine the right hardware setup to make model handling cost- and time-efficient, and bridge the gap between domain and technical areas with unique insights.
Onwards and upwards
The trends of platformization, digitization, and cloudification of technology have helped turbo-charge the technology roadmap of several organizations in the recent years. Moving legacy systems to a new era of technical innovation helped organizations not only modernize their stacks but also leverage the collective innovation of a much broader nature. One can imagine this trend speeding up the progression of technical sophistication in companies and taking them from being early adopters to becoming sophisticated users. As this space gets populated fast, the competitive differentiation is set to shift from just technology adoption to working with the right experts who can steer the technology roadmaps of these evolved systems to take enterprises to the next level of growth, success, and innovation. There is much excitement ahead.