In the early days of the digital era, data centers were mysterious buildings shrouded in secrecy. They were huge, stark buildings billowing steam and looming in the distance. And the secrecy is understandable when you consider that for the enterprises using them, their IT operations within these buildings were the “crown jewels” supporting their businesses.
While some enterprises chose to build and manage their own data centers, others have chosen to outsource their IT environment to third-party, multi-tenant colocation data centers for their shared resources and economies of scale. Those early centers were considered quite simple. They provided the secure data center space, power and connectivity (ping, power and pipe) for companies to outsource their IT environment.
From there, data centers have evolved to support new technology advancements and related increasing complexities. Many have become trusted partners helping enterprises leverage the business benefits of efficiency, good economics and risk mitigation.
Data digitization – also known as digital transformation – took off in its current form about 10 years ago and has accelerated steadily ever since. According to Statista, “the amount of data created, captured, copied, and consumed worldwide is expected to grow from around 59 zettabytes (ZB) in 2020 to around 149 ZB in 2024.”
For the enterprise, an unforeseen byproduct is an even greater urgency for IT agility, adaptability and transformation. Business models are being disrupted while the digitization of the economy is accelerating as new technologies serve a reshaped consumer and workforce. Advancements in compute, storage and connectivity in particular have been increasing the sophistication and scale of enterprise IT requirements.
Data: The Life Blood of Artificial Intelligence
As digital transformation accelerates and a global digital economy takes shape, third-party data centers are evolving and responding by incorporating technologies such as artificial intelligence (AI) and machine learning (ML).
While ML is based on the idea that machines should be able to learn and adapt through experience, AI refers to a broader idea where machines can smartly execute tasks. AI applies ML, deep learning and other techniques to solve actual problems. Both depend on vast amounts of data to make good on their ability to analyze it and execute tasks based on that analysis.
AI-Driven Commerce Platforms
The ability to apply AL, ML, speech recognition, location services, and speed and identity tracking in real time is enabling new applications and services once thought to be beyond the reach of computing technology. We have become intertwined with digital technology to a degree that we could not have imagined. Consider the stunning success of hyperscale AI-driven commerce platforms such as Amazon Prime, Twitter, Uber and Netflix that are now mainstays in our daily lives and exemplify the new global digital economy. These hyperscale companies typically build their own dedicated data centers to accommodate their significant requirements for speed to market, scale, location, economics and logistics. And the pandemic has further increased these requirements.
According to IDC, instead of hindering growth, COVID-19 is accelerating data growth due to abrupt increases in work-from-home employees, a changing mix of richer data sets, and a surge in video-based content consumption. Digitization is increasing complexity and the cost associated with running on-premises data centers. At the same time, modern third-party data centers have been developing and enhancing their services with digitized delivery models that leverage AI and ML.
According to an IDC survey, 72% of CIOs said they plan to increase their usage of data center colocation over the next 12 months. Also, according to Gartner, end-user spending on global data center infrastructure will reach $200 billion in 2021, an increase of 6% from 2020.
The Digital Transformation of the Data Center
In response, leading multi-tenant data centers are becoming fully digitized and instrumented for advanced technologies. Advancements in AI and ML are improving system reliability, energy efficiency and security while simplifying and reducing operating costs. Customers are interacting with their data and services to achieve real-time visibility, access and dynamic control of critical metrics across compute and storage environments from a single platform and even from a mobile device.
AI and ML is enabling new, smart data center services such as facial recognition to identify badge holders and detect and index objects and anomalies. These technologies enable the ability to build accurate forecasts of a customers’ power consumption so they contract only for what they need, while virtual assistants run continuously in the background collecting data and performing automated data analysis. Many third-party data centers also offer environmental sensors that analyze and provide customers with on-demand visibility into rack-level temperature and humidity metrics to align with their sustainability requirements.
Business and IT leaders responsible for managing and optimizing their IT environments need to understand these technologies. These new digitized services (accessed from anywhere globally) are driving business decisions that have enterprises realizing a lower cost profile and cost of ownership compared to conventional, non-digitized data centers. By increasing scalability, flexibility and infrastructure visibility, enterprises can now optimize every capital and operating expense dollar spent while reducing business risk. This is rather new and may represent uncharted territory for many business leaders.
Scale, Connectivity and Sustainability
AI, ML and the new digital services are cool, but more important for the enterprise is the fact that they can help enable enterprises to rapidly scale their business, control costs and attain more advanced connectivity.
As you’re looking for the right technologies and data centers, consider whether they can rapidly scale up or scale down space and power as required for your IT environment. Do they offer cloud and hybrid colocation options for any services you use, like AWS, MS Azure and Google clouds? Do you require in-building access to on-net carriers, multiple fiber routes, internet peering exchanges and/or sub-sea cables?
Enterprises seeking to outsource their IT and their customer-facing digital business to a third-party provider — or thinking about changing data center providers — should consider that it’s important to understand the provider’s ability to support business’ growth trajectory, provide real-time data and manage the level of connectivity required to effectively deliver services to customers.
The best providers will start the conversation with questions focused on these areas and will be proactive about developing solutions mapped to them.
Evaluate providers from an organizational perspective, as well as the specific capabilities of their data centers. Service and capabilities will often vary from data center to data center and region to region.
Align your long-term company objectives with key criteria when evaluating providers’ strengths, weaknesses and long-term visions. Moving to a colocation facility is often expensive and potentially disruptive, and is a long-term commitment.