Data Intensity: The Key to a Data-Driven Future

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What does the data-driven future look like?

It will consist of systems that are:

Highly automated and uses data to make reliable, fair and split-second decisions. Personalized and situationally aware to meet user needs. Able to manage data movement, geographic distribution, governance, privacy and security; and Decentralized, process data ownership and work in tandem with centralized systems to enable data sharing for the greater good.

But we don't need to wait for all that.

The data-driven future is already here.

An autonomous vehicle is an intensely data-driven system, sensing its environment in real time and translating it into vehicle operations. At a level below autonomy, assistive technologies are also data-driven, relying on real-time data to produce insights - i.e. the blind spot detection system sends an alert - or to make decisions about when to use anti-lock brakes and crashes. avoidance systems.

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Enabling these applications and use cases to be more data-driven is a journey that requires managing complexity and adopting new approaches that allow you to better manage systems through maturity and sophistication. To assess digital maturity and resilience and improve your data-driven business, think in terms of data intensity.

The intensity of the data is multivariate and changes strongly as you move through multiple dimensions. The data intensity of an application depends on data volume, query complexity, query latency, data ingestion speed, and user concurrency. Additional dimensions may include hybrid workloads (transactional and analytical), multimodal analytics (operational analytics, machine learning, search, batch and real-time processing), elasticity, data movement requirements, etc. .

Data intensity increases

Data intensity is not just about the volume of data, but also what you do with your data. However, as data volumes increase, the intensity increases. Intensity increases exponentially when data also arrives faster, creating the need for an application to handle 10x more users while meeting the same (or better) latency SLAs. The intensity also increases sharply when real-time operational data analysis combines with natural language interaction and recommendations.

We live in a data-intensive era, and the intensity is increasing as businesses rely more on data to better understand their customers and shape experiences. How your organization responds in the data-intensive age can either add more complexity and friction for you and your customers, or provide you with new opportunities for differentiation and growth.

Choosing an approach that leads to greater complexity and friction is clearly counterproductive. Yet historically, many organizations have worked on the assumption that different workloads require different architectures and technologies, and that transactional and analytical workloads should be separated. Managing data intensity in this environment creates inherent complexity, friction, and movement of data that adds latency and works against real-time information.

Fortunately, you now have the opportunity to revisit and challenge traditional assumptions to embrace, enable, and make the most of the data-intensive era. You can take advantage of cloud computing, which offers unprecedented scalability and flexibility and the ability for organizations to innovate and experiment; separate...