Hybrid, Proximity and Ecosystems Are Elevating the Importance of Colocation

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A Multitude of Secular Trends Are Reinforcing Colocation’s Value Proposition

Market trends over the past few years have made several things clear about the IT strategy of most enterprise customers, all of which reinforce the value proposition offered by colocation providers:

  • Hybrid environments will persist — Whether due to existing legacy investments, divisional or regional nuances, or acquisition and divestiture activity, heterogeneity will remain in most IT environments. At one point, the benefits of public cloud made organizations consider a homogeneous, fully cloud-based IT delivery strategy, but those visions have faded for most. The challenge — and goal — is to embrace the hybrid heterogeneous approach and find the best way to integrate, manage and optimize services across these diverse sets of delivery methods and assets. Colocation data centers play a critical role for customers, offering a hybrid approach to facilities and in the interconnection of cloud and on-premises services.
  • Location and proximity matter — The importance of delivery locations is driven by not only the hybrid nature of the environment but also the latency requirements of many workloads. Edge workloads are the clearest example, but there are other cases where latency is critical or where regulations determine the location of data.
  • Investment costs and opportunity costs are important — While organizations are still looking to control and minimize IT costs where possible, there has been a shift toward selective investment. This started when IT became one of the few levers companies could control as they shifted their business models to adapt to changes wrought by the COVID-19 pandemic. Most recently, the onset of generative AI (GenAI) convinced organizations that IT could be a competitive advantage as well, prompting investment in new solutions and technologies to keep pace with the market and key competitors. In this way, organizations are still closely controlling investment costs in new solutions but also can be swayed to spend due to the fear of lost opportunities. Colocation provides an emerging value proposition with GenAI and AI workloads, offering prebuilt facilities and interconnection services without requiring large retrofits or new capital expenditures.
  • Ecosystems equal innovation — Though hyperscalers have become the center of the IT ecosystem over the past decade, the network of infrastructure providers, ISVs, systems integrators (SIs), colocation providers, consultancies and managed services providers remains intact. With the hybrid approach that most customers are embracing, combined with the digital transformations being deployed and then amplified by the onset of new AI and GenAI technology, numerous vendors are part of most enterprise IT solutions. The orchestration of those multiple vendors is critical and most often handled by a trusted SI partner.

Colocation Is a Relied-upon Option for the Vast Majority of Enterprises

According to TBR’s 2Q24 Infrastructure Strategy Customer Research, a significant portion of enterprises report colocation as some part of their overall IT delivery strategy. Most have less than 25% of their workloads deployed in colocation facilities, which is a reflection of the two predominant delivery strategies: their own centralized data centers and cloud-based environments. Colocation is even more of a consideration for new workloads, however, as 72% of surveyed respondents expect to deploy between 25% and 50% of net-new workloads using colocation providers.
 
We believe this trend is due to two factors. First, enterprises are reluctant to build their own data center facilities for workloads that perform best outside the cloud or that have location and latency requirements. Most organizations want to reduce their data center capacity at this point, not add to it at. Second, for many new workloads, data center requirements are more challenging to provide. With the need for increased density, more power requirements and unique GPU-based AI services, a modern data center is required. The challenges of technology, facilities and staffing highlight the value of a ready-to-use colocation facility.

Digital Realty and Equinix Stand Out in a Tightly Packed Colocation Market

Recent trends around hybrid deployment models, latency-sensitive workloads, data residency and AI-reliant solutions have highlighted the sometimes-overlooked benefits of colocation providers. Especially for large enterprise customers, the scale of colocation facilities, strength of alliances and ability to invest in supporting new technologies make a difference in the value of their services. TBR research shows Digital Realty and Equinix are head and shoulders above the rest of their peers in terms of the ability to meet enterprise requirements. From a purely data center location perspective, Digital Realty is the market leader worldwide, with 309 data centers, including those from unconsolidated joint ventures, effective 1Q24.

 

The current revenue perspective is one component when it comes to colocation spending, but enterprises also want to know their solution providers will be able to scale as demands grow. Especially after the supply constraints over the last couple of years and the ongoing shortage of key components for next-gen workloads like GenAI, customers are not always secure in their ability to access resources on a timely basis. While the supply of colocation capacity remains tight, investing now to guarantee expanded capacity is another differentiator. Here again there are advantages to scale, as Digital Realty actually outpaced all covered vendors in level of capital expenditures in 2023. This commitment to current investment is a signal to customers that they can continue to grow with Digital Realty moving forward.

Digital Realty Is Well Positioned to Address Hyperscaler Demand, Both Financially and in its Go-to-market Approach

Though adamant about vying for mindshare among both enterprises and hyperscalers, Digital Realty has always been better known for its play in wholesale colocation. Over the past several quarters, Digital Realty has employed an aggressive joint venture strategy, allying with private equity firms to build multibillion-dollar hyperscale data centers in both Tier 1 and Tier 2 markets. As such, much of Digital Realty’s financial makeup and recent performance have stemmed from this customer base, with over 80% of annualized base rent from new leases in the >1MW category (effective 1Q24). The retail colocation market will undoubtedly continue to grow, led by robust demand for hybrid deployments and cloud adjacency for reasons highlighted earlier in this piece.
 
But many sources continue to suggest a rampant demand surge in the wholesale market as hyperscalers rush to satisfy their own customers’ AI and GenAI deployments. There are several ways Digital Realty is addressing this demand. Some are financial, including the ability to recycle capital by selling off nonscalable, single-tenant facilities to reinvest in strategic markets and maintaining a conservative capital structure; for context, Digital Realty owns nearly all of its facilities, in stark contrast to competitor Equinix, which is still leasing roughly 40% of its data centers. But the other aspect is Digital Realty’s go-to-market approach and how the vendor is nurturing relationships with the hyperscalers and their own partner ecosystems.

Digital Realty and Oracle Have a Strong Customer-partner Relationship: Other Hyperscalers Should Take Note

Digital Realty has always had a strong relationship with Oracle, which is now Digital Realty’s third-largest customer, deploying in 38 locations and spending $170 million in annualized recurring revenue (ARR). It is hard to dispute Oracle’s success pivoting from a traditional software incumbent and SaaS provider to an IaaS challenger with OCI (Oracle Cloud Infrastructure), which is on track to become Oracle’s largest cloud business in the coming years. Digital Realty astutely took notice of OCI’s role in the market, not to mention Oracle’s tight relationship with NVIDIA, which supplied Oracle with GPUs early in the AI wave.
 
Recent developments like connecting to Oracle’s EU Sovereign Cloud and offering NVIDIA-based OCI instances in its high-traffic Northern Virginia data center only reinforce Digital Realty’s role in powering OCI’s expansion. It is one of the reasons Oracle can not only boast more rapid footprint expansion over peers but also deliver on the “distributed cloud” message that nearly all hyperscalers are eager to convey. For perspective, Oracle holds only a single-digit share percentage in the IaaS market, but Oracle’s ability to leverage Digital Realty to expand ahead of peers is notable and something that other hyperscalers that are adamant about building their own data centers should recognize as they fight to capture net-new AI workloads.

SIs and Consultancies Pull It All Together at Scale for Enterprises

For IT services companies and consultancies, two needs mentioned above — orchestration and scale — illustrate how the colocation piece of the enterprise IT ecosystem can provide competitive opportunities.

Orchestration Is Critical and Most Often Handled by a Trusted SI Partner

Companies like Deloitte, Accenture and Infosys have the most complete view of an enterprise’s IT environment, positioning them best to coordinate vendors that provide disparate technologies. Most consultancies and SIs stay in well-defined swim lanes, delivering their added value while facilitating cloud, software and even hardware solutions from an enterprise’s suppliers. In TBR’s research, the market-leading consultancies and SIs use their industry knowledge, influence and reach within a client as the basis for orchestrating a full spectrum of technology providers, calibrated specifically to an enterprise’s IT needs.
 
As described above, colocation continues to be a pressing need, creating an opening for IT services companies and consultancies that have traditionally shied away from alliances that are too far removed from their core competencies. Just as alliances have formed around cloud computing and enterprise software, with IT services companies and consultancies delivering value through innovation, cost containment and managed services, partnerships with colocation specialists could add a compelling component to an IT services company’s orchestration value proposition. Consultancies’ and SIs’ business models depend on retaining clients and expanding footprint within clients. If colocation can become another differentiating factor and improve enterprise clients’ overall IT environments, SIs and consultancies will willingly seek partnerships.

Enterprises Want to Know That Their Solution Providers Can Scale as Demands Grow

If client retention remains critical to SIs’ and consultancies’ business models, scale increasingly marks the difference between average performance and market-leading results. No SI or consultancy can out-scale the largest players, but TBR’s research shows that companies that manage their alliances well can leverage their ecosystem for scale unattainable on their own. In short, no other company can be Accenture, but an SI or consultancy can replicate Accenture’s reach with the combined forces of a well-oiled tech and services ecosystem.
 
Colocation providers already play within the technology ecosystem but have not traditionally been considered a means for consultancies and SIs to increase scale. As AI and GenAI increase compute power demands and enterprises turn to their consultancies and ask, “How can I take advantage of all this new technology without exploding my IT budget?” and “How can I take this GenAI-enabled solution from pilot to my entire enterprise,” colocation can become a critical component.

The SI and Consulting Tech Evolution: From ERP to Cloud to GenAI to Colocation

In TBR’s view, SIs and consultancies will never become adept at selling those components of the technology ecosystem that are the furthest from their core competencies, but the market leaders have become exceptional at managing and expanding their ecosystems. TBR’s research around the relationships between the largest cloud providers and the largest SIs demonstrates how much revenue can be driven through alliances. As SIs and consultancies mature their partnering practices, colocation will become another element orchestrated by the likes of Deloitte, Accenture and Infosys. Quite possibly some smaller SIs and consultancies will use colocation as a critical component to scaling themselves into competitive positions against those larger players. As GenAI drives new demands — on compute power, budgets and expertise — TBR will closely watch these relationships between SIs and colocation providers.

Databricks Pivots Around Data Intelligence to Address GenAI Use Cases

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Just like it did with the data lakehouse five years ago, Databricks is establishing another paradigm with data intelligence, which has the data lakehouse architecture at its core but is infused with generative AI (GenAI). Data intelligence was a key theme throughout Databricks Data & AI Summit and signals Databricks’ intentions to further democratize AI and ultimately help every company become an AI company.

A Brief Databricks Backstory

Founded by the creators of Apache Spark, Databricks is known as a trailblazer for launching new concepts in the world of data, such as Delta Lake, the open table format with over 1 billion yearly downloads, and the “lakehouse” architecture, which reflects Databricks’ effort to combine the best of what the data lake and data warehouse offer. Launched in 2020, the lakehouse architecture can handle both structured and unstructured data, and addresses the data engineer and business analyst personas in a single platform.

 

Delta Lake and Unity Catalog, which governs the unstructured data stored in these Delta tables, serve as the basis for the lakehouse architecture and are part of Databricks’ longtime strategy of simplifying the data estate and, by default, AI. But with the advent of GenAI, which is causing the amount of unstructured data to proliferate, Databricks has spearheaded yet another market paradigm, pushing the company beyond its core areas of data ingestion and governance into data intelligence.

 

At the heart of data intelligence is the lakehouse architecture and also Mosaic AI, the rebranded result of last year’s MosaicML acquisition that equipped Databricks with the tools to help customers train, build and fine-tune large language models (LLMs). These also happen to be the same technologies Databricks used to build its own open-source LLM ― DBRX ― sending a compelling message to customers that they, too, can build their own models and use the Mosaic AI capabilities to contextualize that data and tailor it to their business, thus achieving true data intelligence.

What Is Data Intelligence?

Databricks’ executives and product managers largely communicated the definition of data intelligence through demonstrations. One of the more compelling demos showed how Mosaic AI can be used to create an agent that will build a social media campaign, including an image and caption for that campaign, to boost sales.

 

The demo depicted how a user can use transaction data as a tool to supplement a base model, such as Meta’s Llama 3. This demo was key to highlighting one of Databricks’ product announcements, the Shutterstock ImageAI model, which is built on Databricks in partnership with Shutterstock and marks Databricks’ foray into the multimodal model space.

 

The exercise created an image for the fictional social media campaign that included a company’s bestselling product — chosen through transaction data — and a catchy slogan. But to convey the contrast between data intelligence and general intelligence, the demonstrator removed the “intelligence” ― all the data-enabled tools that exist in Unity Catalog ― and generated the image again. This time, the image did not include the bestselling product and was accompanied by a much more generic logan.

 

This demo reinforced the importance of contextualized data in GenAI and the role of Unity Catalog, which helps govern the data being used, and Mosaic AI, which allows developers to use enterprise data as tools for creating agents (e.g., customer support bots).

 

Data intelligence is about not only the context behind the data but also making that context a reality for the enterprise. For instance, in the above scenario, the demonstrator was able to put the image and slogan into Slack and share it with the marketing team through a single prompt. In this example, it is clear how a customer with Databricks skills could use GenAI in their business.

Databricks’ Acquisition of Tabular Is a Blow to Snowflake and a Surefire Way to Stay Relevant in the Microsoft Ecosystem

As a company born on the values of openness and reducing lock-in, Databricks pioneered Delta Lake to ensure any engine can access the data sitting in a data lake. Delta Lake remains the most widely adopted lakehouse format today, handling over 90% of the data processed in Databricks, and is supported by other companies, as 66% of contributions to the open-source software come from outside Databricks.

 

But over the past few years, we have seen Apache Iceberg gain traction as a notable alternative, garnering significant investment from data cloud platforms, including Snowflake. When Databricks announced its acquisition of Tabular ― created by the founders of Apache Iceberg ― days before the Data & AI Summit, it signified a strategic shift that will help Databricks target a new set of prospects who are all in on Iceberg, including many digital natives.

 

The general availability of Databricks’ Delta Universal Format (UniForm), which helps unify tables from different formats, indicates the company’s intention to make Delta and Iceberg more interoperable and, over time, potentially reduce the nuances between both formats, though this may be a longer-term vision.

 

The Tabular acquisition in some ways also marginalizes Snowflake’s steps to become more relevant as a Microsoft Fabric partner. Available through Azure as a first-party native service, Databricks has always had a unique relationship with Microsoft, and Delta serves as the basis for Microsoft Fabric. But Microsoft’s recent announcement to support Iceberg tables with Snowflake in a push for more interoperability was notable, and now with Tabular, Databricks can ensure it remains competitive in the Microsoft Fabric ecosystem.

It Is All About Governance

First announced three years ago, Unity Catalog has emerged as one of Databricks’ more popular products, allowing customers to govern not just their tables but also their AI models, an increasingly important component in GenAI.

 

At the event, Databricks announced it will open source Unity Catalog, which we watched happen during the Day 2 keynote, when Unity Catalog was uploaded to GitHub. Despite Unity Catalog’s mounting success, this announcement is not surprising and only reinforces the company’s commitment to fostering the most open and interoperable data estate.

 

It is very early days, but open sourcing Unity Catalog could help drive adoption, especially as governance of GenAI technologies remains among the top adoption barriers.

Databricks SQL Is Gaining Momentum

It is no secret that Databricks and Snowflake have been moving into one another’s territories. Databricks, with its expertise in AI and machine learning (ML), has been progressing down the stack, trying to capture data warehouse workloads. Snowflake, with its expertise in data warehousing, is looking to get in on the AI opportunity and address the core Databricks audience of data scientists and engineers.

 

Snowflake’s early lead in the data warehouse and strong relationship with Amazon Web Services (AWS) could be making it more difficult for Databricks to attract workloads. Combined with the enormity of the market, there may never be a scenario in which Databricks becomes a “standard” in enterprise accounts for data warehousing. But Databricks’ messaging of “the best data warehouse is a lakehouse” certainly seems to be working.

 

Traditionally, customers have come to Databricks for jobs like Spark processing and ETL (Extract, Transform, Load), but customers are increasingly looking to Databricks for their data warehouse. These customers fall into two groups. In the first group, customers on legacy systems, such as Oracle, are fed up with the licensing and are looking to modernize. In the second group, existing cloud customers are looking for a self-contained environment with less lock-in, compared to vendors like Snowflake, or are seeking to avoid challenges with system management and scale after having worked with hyperscalers.

 

As highlighted by Databricks Co-founder and Chief Architect Reynold Xin, Databricks SQL is the company’s fastest-growing product, with over 7,000 customers, or roughly 60% of Databricks’ total customer base. During his keynote, Xin touted improved startup time with Databricks SQL Serverless to five seconds and automatic optimizations for BI workloads to be four times faster compared to two years ago. Provided Databricks can continue to enhance performance while pushing the boundaries on ease of use to better compete with Snowflake and other vendors in attracting less technical business personas, we expect this momentum will continue and will challenge competitors to raise the bar for their own systems.

Databricks Is Bringing an Added Layer of Value to the BI Stack

Databricks AI/BI is a new service available to all Databricks SQL customers that allows them to ask questions using natural language (Genie) and perform analytics (Dashboards). In a demo, we saw the two user interfaces (UIs) in action: BI offers common features like no-code drag and drop and cross-filtering, and AI includes the conversational experience where customers can ask questions about their data.

 

Databricks AI/BI may lack some of the complex features of incumbent BI tools, but ultimately these are not the goals of the offering. The true value is in the agents that can understand the question the business analyst is asking and hoping to visualize. Databricks’ approach exposes the challenges of bolting on generic LLMs to a BI tool. But the company is not interested in keeping this value confined to its own BI capabilities. Staying true to its culture of openness, Databricks announced at the event that it will open up its API to partners, ensuring PowerBI, Tableau and Google Looker customers can take advantage of data intelligence in these BI environments.

Conclusion

With its lakehouse architecture, which was founded on the principles of open-source software and reduced lock-in, Databricks is well positioned to help customers achieve data intelligence and deploy GenAI. The core lakehouse architecture will remain Databricks’ secret sauce, but acquisitions, including those of MosaicML and Tabular, are allowing Databricks to broaden the scope of its platform to tap into new customer bases and serve new use cases.

 

If Databricks can continue to lower the skills barrier for its technology and sell the partner ecosystem around its platform, the company will no doubt strengthen its hold on the data cloud market and make competitors, including the hyperscalers in certain instances, increasingly nervous.

Blending Industry Expertise with Cybersecurity Credibility: Insights From PwC’s EMEA Financial Services Team

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Compelling Cybersecurity Needs Meet PwC’s Capabilities

A July 1, 2024, briefing by PwC’s EMEA Financial Services (FS) team provided TBR with a closer look at PwC’s largest industry practice by revenue and the ways the firm has blended industry expertise with cybersecurity managed services experience and credibility. Julian Wakeham, UK EMEA Consulting Financial Services leader; Moritz Anders, Digital Identity lead, Cyber Security & Privacy, Germany; and Joshua Khosa, Service lead, Cyber Managed Services, Germany, steered the discussion for PwC.

 

Anders said FS clients’ three compelling cybersecurity needs — compliance, cost optimization and talent — shaped PwC’s approach to cybersecurity managed services and, in TBR’s view, will be consistent revenue drivers for PwC as those needs will be perpetual. The challenges around recruiting and retaining highly specialized cybersecurity experts, for example, remain outside the core functions of most enterprises, yet the cybersecurity risks continue evolving, necessitating that consultancies step into that role. A significant part of PwC’s value, therefore, comes from assembling and deploying experts in both cybersecurity and the underpinning enterprise technologies.

 

Critically, according to Anders, PwC has approached cybersecurity managed services not as an IT play, where it can simply throw technology and people at the problems, but as an ongoing business challenge best tackled through a highly automated architecture and a sustained focus on business outcomes.

 

Echoing the three compelling cybersecurity needs highlighted above, Anders and Khosa provided details about a use case with a Europe-based bank that delivered three clear business outcomes: “compliance and audit readiness, operational efficiency, and enhanced security,” with the last relying, in part, on PwC using its alliance partners to keep emerging technologies and updates flowing to the client.

 

In TBR’s view, the compliance and audit-readiness components reflect PwC’s legacy strengths and brand around governance, risk, and compliance, and the operational efficiency outcomes build on the firm’s decades-old emphasis on and experience with operations consulting. In short, PwC continues playing to its strengths.

 

At the end of the briefing, the PwC team was asked why this particular Europe-based bank chose PwC for a complicated, multiyear cybersecurity managed services engagement. Anders said PwC remained direct and humble throughout the selection process, informing the client, without marketing spin, what PwC could and could not do well.

 

Among the strengths PwC brought to the table, according to Anders, was Europe-based talent at scale, in contrast to competitors, which relied on offshore resources. Wakeham noted PwC’s flexibility, focus on business problems (and not just selling technology solutions), PwC’s Industry Edge+ as a key enabler for business model reinvention, and the “deep trust” PwC’s clients have in the firm. 

Watch Now: TBR Vice President Dan Demers and TBR Principal Analyst Patrick M. Heffernan discuss trends expected to shape the market in 2024, including GenAI’s impact on ecosystem alliances and how clients use TBR’s research and analysis to add context to strategic questions and address challenges around alliance enablement

Business Model Reinvention, Ecosystem Strategy and Expansive Capabilities

Reflecting on the EMEA FS briefing and previous discussions with PwC across topics and capabilities as diverse as people advisory services, IoT and generative AI, TBR made a few observations. First, PwC’s focus on “business model reinvention” was mentioned at the beginning and end of the discussion, with Wakeham acknowledging that the firm did not create that term or idea but explaining that PwC’s own market research indicated the importance to CEOs of that strategic focus. TBR reported earlier this year on PwC’s ideas around business model reinvention and notes that while previous strategic shifts have taken time to gain traction across the PwC member firms, business model reinvention appears to have considerable momentum and heft.

 

Second, PwC’s alliances strategy appears to be evolving as both the competitive and ecosystem landscapes change, with increased expectations that technology partners will bring business to PwC. In contrast to the usual equivocation and lack of details around how ecosystem partners can play a role in PwC’s go-to-market strategy, the EMEA FS team provided both a direct answer to TBR’s question about whether partners bring PwC into client projects and an explanation for the underlying reasons why software vendors would introduce PwC into an engagement.

 

PwC maintains a vast array of technology partnerships across cybersecurity, enterprise platforms, cloud, IoT and more, necessitating a well-managed ecosystem effort and providing extensive opportunities to gain new clients and expanded opportunities within existing accounts. Continually refining the ecosystem playbook will be vital to PwC’s continued success.

 

Lastly, PwC’s EMEA FS team provided another example of the breadth of the firm’s capabilities, an element of PwC’s value proposition that can sometimes be forgotten when focusing too intently on one piece of the overall firm. For example, in cybersecurity managed services, PwC brings expertise and capabilities in cyber incident response, smart cyber defense, cybersecurity upskilling, identity and access management, and OT & IT security, to name a few.

 

TBR believes the extent of PwC’s capabilities and offerings, while not unique, can sometimes be lost on clients and ecosystem partners that are focused on the immediate services the firm is bringing to their engagement. If PwC remains focused on business model reinvention and continues evolving its ecosystem strategy, the breadth of the firm’s capabilities will become the underlying strength that sustains PwC’s success.