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Key Considerations for Effective Big Data Architecture - Neural Technologies
Neural Technologies4 min read

Key Considerations for Effective Big Data Architecture

Whether we’re talking about the Empire State Building, or the framework for an effective big data ecosystem, reliable architecture is the key to success. 

But while a physical structure is largely defined by its first iteration, truly effective data architecture in our modern world should be able to flex and adapt to meet changing needs in a landscape defined by complex data integration and increasing volumes of data streaming.

Enterprises in industries such as telecoms are capturing huge volumes of data from a wide variety of data sources, creating pressure to transform into actionable business insight. Effective data architecture is about creating a system for that data to flow seamlessly across systems, providing access to data as and when it is required. 

The sheer volume of data and number of sources generating it means today’s data architecture is increasingly complex. The right system has to be able to adapt to meet the needs of an expanding big data ecosystem, enabling seamless data integration that can meet the need of legacy infrastructure and modern, real-time data streaming. Here are five key areas to consider

Big data integration 

Flexible, scalable architecture that enables big data integration is vital in the modern landscape. That means embedding data architecture which can adapt to meet growing data demands. 

Effective big data integration means a solution that can integrate both structured and unstructured data, with 95% of businesses noting that integrating unstructured data is a major problem for their businesses. The hugely diverse data landscape means integration requirements continue to change, requiring enterprises to not only consider the data of today, but the data demands of tomorrow too.

Neural Technologies’ Big Data Integration solution offers a powerful solution to capture, curate, analyze, search, store, transfer, and present complex data in a simple, user-friendly way. It allows users to benefit from a wide range of interfacing mechanisms, and is designed to meet the extensive data modeling and data processing needs of the modern enterprise landscape. Simple integration enables enterprises to quickly adopt, embed, and benefit from this solution with a rapid return on investment as they unlock the value of deepening data insight for their business.

AI/ML model ready

Artificial intelligence (AI) and machine learning (ML) are critical parts of a future-proof data strategy, and a key consideration for data architecture on the modern landscape. These sophisticated algorithms enable enterprises to assess and automate responses to data flows in real-time and near-real-time, providing key functionality for an ever-expanding data sphere. 

Adoption of AI by enterprises has more than doubled since 2017 according to research by McKinsey, although that growth has plateaued in recent years at ~50%-60% of companies, demonstrating a need for enterprises to more deeply embrace AI and ML as part of an effective data architecture strategy. Neural Technologies’ pioneering AI and ML expertise puts these advanced technologies at the heart of our Data Integration suite of solutions, providing a platform for success in even the most data-heavy industry landscape.

Seamless systems integration

Data architecture needs to be able to integrate a wide range of systems, both legacy and novel technologies, to ensure that all data sources are integrated into the wider big data ecosystem. This is vital in a rapidly evolving technology space, where new innovation is opening up new revenue opportunities at a remarkable pace. Enterprises with rigid, inflexible data architecture will find themselves with data blindspots, losing out on valuable data insight and potentially exposing themselves to risks such as fraud, revenue loss, or money laundering.

Neural Technologies’ Optimus platform takes a data-agnostic approach, integrating a wide range of data types from both legacy and new data sources. This ensures that the architecture can adapt as needed to embrace new hardware and software without compromising holistic data systems. 

Supporting streaming analytics

Data no longer emerges in big batches on a regular cadence, ready to be plugged into analysis systems designed for big data dumps on a predictable schedule. The data of today flows in huge rivers of information, providing time-sensitive data that can, and often does, lose value the longer it goes unprocessed.

Effective streaming analytics are key to unlocking the value of this data in real-time, not simply capturing it as it is generated, but providing rapid analysis that empowers both automated systems and manual business decision making. Our Data Mediation solution provides integration of legacy and state-of-the-art data platforms, including high-throughput data streaming needs. This flexible solution integrates proprietary, legacy infrastructure with innovative, modern streaming infrastructures.

Modular and adaptive

Data architecture must be able to adapt. The huge pace of data evolution over the last decade clearly shows how flexibility is key to future-proof data solutions. The innovations over the next decade are likely to provide an increasingly impressive transformation of our data landscape, meaning modular, adaptive technologies will be key to an effective and evolving data architecture.

Neural Technologies’ modular approach enables enterprises to adopt and adapt flexible, scalable solutions that can help power up their data-driven processes, providing simple integration that rapidly unlocks value for your business.

Get in touch for a free consultation, and see how we can unlock the value of data for your business