The Future of Data Infrastructure

- Overview

Data has the potential to drive and scale any business, economy and country, providing direction for valuable strategic decisions. With the advent of enterprise digitalization, the demand for data compromised by modern technologies such as artificial intelligence (AI) and Internet of Things (IoT) has increased more than ever. This requires a well-built data infrastructure where your business data can be maintained, organized and distributed in the form of insights. 

Data infrastructure refers to the hardware, software and network technologies used to support the storage, processing and management of data within an organization. This can include a wide range of technologies such as databases, data warehousing, data lakes, data centers, cloud computing platforms and network equipment. 

An effective data infrastructure is a critical component of a modern data-driven organization and requires careful planning and design, taking into account factors such as data volume, velocity and diversity, as well as security and compliance requirements. It must also be adaptable and flexible, able to grow and expand as the organization's data needs change over time.

- Integrated Data Infrastructure

As an important part of the entire business data center and warehouse, data support infrastructure consists of power, cooling, security, monitoring and measurement systems, designed to help maintain core business data operations. 

You will be surprised to know that even in the midst of the pandemic crisis, the global data center support infrastructure market is still growing at a CAGR of 9.4%. The growth of modern data infrastructure is driven by the rapidly growing volume of data that needs to be stored and managed in an efficient manner.

Furthermore, the continuous demand for cloud data storage, online mobile cloud computing services, data visualization, and big data analytics has led to sustainable growth of the market for data infrastructure solutions. In fact, the development of data storage systems is a major source of integrated data infrastructure.

- HPC, Big Data and Cloud Computing: the way forward to the future of mankind  

Progress for both science and mankind is going to depend more and more on “supercomputer brains” that can process large amounts of data in real time, providing them with a meaning and - subsequently - turning it into actionable knowledge. 

The Internet of Things and the convergence of high performance computing (HPC), big data and cloud computing technologies are enabling the emergence of a wide range of innovations. Building industrial large-scale application test-beds that integrate such technologies and that make best use of currently available HPC and data infrastructures will accelerate the pace of digitization and the innovation potential in key industry sectors (for example, healthcare, manufacturing, energy, finance & insurance, agri-food, space and security).  

- High Performance and Super Computing 

In the Age of Internet Computing, billions of people use the Internet every day. As a result, supercomputer sites and large data centers must provide high-performance computing services to huge numbers of Internet users concurrently. We have to upgrade data centers using fast servers, storage systems, and high-bandwidth networks. The purpose is to advance network-based computing and web services with the emerging new technologies. 

The general computing trend is to leverage shared web resources and massive amounts of data over the Internet. The evolutionary trend towards parallel, distributed, and cloud computing with clusters, MPPS (Massively Parallel Processing), P2P (Peer-to-Peer) networks, grids, clouds, web services, and the Internet of Things. 

"Supercomputer" is a general term for computing systems capable of sustaining high-performance computing applications that require a large number of processors, shared or distributed memory, and multiple disks. Supercomputers are primarily are designed to be used in enterprises and organizations that require massive computing power. A supercomputer incorporates architectural and operational principles from parallel and grid processing, where a process is simultaneously executed on thousands of processors or is distributed among them. 

Performance of a supercomputer is measured in floating-point operations per second (FLOPS) instead of million instructions per second (MIPS). As of today, there are supercomputers which can perform up to nearly a hundred quadrillions of FLOPS, measured in P(eta)FLOPS. As of today, all of the world's fastest 500 supercomputers run Linux-based operating systems.  

[Murren, Switzerland - Christophe Cosset]

- Turning Big Data into Smart Data 

Big data refers to extremely large datasets that are difficult to analyze with traditional tools. It is often boiled down to a few varieties of data generated by machines, people, and organizations. Big data is being generated by everything around us at all times. Every digital process and social media exchange produces it. Systems, sensors and mobile devices transmit it. Big data can be either structured, semi-structured, or unstructured. IDC estimates that 90 percent of big data is unstructured data. 

Big data is arriving from multiple sources at an alarming velocity, volume and variety. To extract meaningful value from big data, you need optimal processing power, analytics capabilities and skills. In most business use cases, any single source of data on its own is not useful. Real value often comes from combining these streams of big data sources with each other and analyzing them to generate new insights. 

Analyzing large data sets, so-called big data, will become a key basis of competition, underpinning new waves of productivity growth, innovation, and consumer surplus. Big data must pass through a series of steps before it generates value. Namely data access, storage, cleaning, and analysis.  

- Future Cloud and Edge Computing

Cloud computing is the delivery of computing services—servers, storage, databases, networking, software, analytics, and more - over the Internet (“the cloud”). Companies offering these computing services are called cloud providers and typically charge for cloud computing services based on usage, similar to how you’re billed for water or electricity at home. 

Most cloud computing services fall into three broad categories: infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (Saas). These are sometimes called the cloud computing stack, because they build on top of one another. There are three different ways to deploy cloud computing resources: public cloud, private cloud, and hybrid cloud. Knowing what they are and how they’re different makes it easier to accomplish your business goals. 

Cloud computing provides a simple way to access servers, storage, databases and a broad set of application services over the Internet. A Cloud services platform such as Amazon Web Services owns and maintains the network-connected hardware required for these application services, while you provision and use what you need via a web application.  

- A Health Data Revolution

The data revolution is a transformative effort to improve the way data is used and produced. It also aims to close data gaps to prevent discrimination.

The very beginning of the bio (big) data revolution is already upon us with the emergence of wearable, constantly connected tech that collects information (data) about our health. 

There’s an overall belief that this could be a great thing for a society, the ability to actually have reams of data that can be applied to create better health care practices. We'll have a lot more information about how people really eat, exercise, and conduct their daily lives, which will allow doctors and researchers to better tailor programs to serve our needs and help us become healthier.

Big data is already common in healthcare. It can be used for:

• Improving outcomes: By using machine learning and big data, healthcare can better understand patient health, predict disease outbreaks, optimize treatment plans and improve patient outcomes.
• Improving services: For example, hospitals can cross-reference patients’ electronic health records with national statistics to identify possible causes of illness.
• Improving procedures: For example, data mining can help medical scientists and experts uncover data patterns, trends, correlations, and other factual correlations.
• Improving healthcare systems: For example, instant data entry can improve patient care, free up more direct care time for nurse practitioners and clinicians, and help hospitals better deploy staff and resources.