TBR Perspective: Nokia’s Fixed Networks Business Unit

Nokia is the largest vendor of fixed network access infrastructure by revenue in the Western economic bloc, a position of strength that exposes the vendor to a range of opportunities that arise in the market. While Nokia remains focused on its fiber-based platform, the vendor is also supporting fixed-wireless access (FWA), which is a rapidly growing service offering in the telecom industry.
Though revenue in Nokia’s Fixed Networks business unit has been uneven over the past few years (primarily due to the disruptions caused by the COVID-19 pandemic), the unit is poised to be one of the biggest beneficiaries of government-supported broadband programs and ongoing internet service provider (ISP) investment in high-speed broadband access technologies, driving a positive revenue trend over at least the next three to five years.
 
Nokia is focused on expanding access to broadband (through fiber and/or FWA) and introducing a future-proof platform for ISPs to build upon. The company is trying to be everything to everyone in this domain by providing a near complete portfolio (only DOCSIS is missing).
 
Despite Nokia’s favorable market position and government-induced tailwinds for the broadband infrastructure domain, TBR notes that the supply-and-demand dynamics as well as the timing of investments are prone to be disjointed, lengthening the time required to meet infrastructure deployment objectives compared to what was originally expected by the government and the telecom industry.
 
Additionally, TBR remains steadfast in its belief that building fiber out to every household is not economically feasible (despite what the government and stakeholders in the market say they want) and that alternative broadband access technologies (such as FWA and satellite) are going to increase in the global mix to connect the unconnected and underserved peoples of the world.

Impact and Opportunities for Nokia

BEAD Program Will Likely Stretch to the Mid-2030s due to Challenges and Delays

Broadband Equity, Access, and Deployment (BEAD) Program-supported projects are now slated to begin deployments in 2025, more than a year later than originally planned. There is a long list of reasons (most of which are related to mapping integrity and political processes) why the program has been delayed thus far, and there is a growing list of reasons that suggest it will take longer for the program to fully ramp up and complete its objective (i.e., spend all of the $42.5 billion allocated to the program).
Among the biggest challenges that lie ahead for the BEAD Program is a shortage of skilled labor (e.g., fiber splicers and trenching machine operators) and industrial equipment, such as boring machines, that will be required to deploy fiber to an estimated 5.5 million households across the U.S. Shortages of products that meet the Build America Buy America (BABA) requirements associated with the BEAD Program could also cause a timing and supply issue.
 
Taken together, TBR now believes the deployments tied to the BEAD Program will begin next year and it could take as long as the mid-2030s for all the program’s funding to be disbursed, more than five years longer than the government and market ecosystem originally anticipated. Nokia is doing as much as it can to mitigate and alleviate these potential challenges in the market.
 
For example, Nokia is proactively educating stakeholders in the ecosystem and working with its partners to better match supply with demand for products and resources. This orchestration of the ecosystem will help align stakeholders and enable the industry to put its best foot forward in carrying out this infrastructure build-out program as well as position Nokia to maintain and grow its leading share in the broadband infrastructure market.

Do Not Forget About Non-BEAD Government Programs for Broadband

Though the telecom industry likes to focus on the BEAD Program (likely because it is the largest program by dollar amount in the broadband ecosystem in the U.S. market), there are a variety of other government-supported programs that also deal with broadband, including the American Rescue Plan Act (ARPA), the Rural Digital Opportunity Fund (RDOF), the U.S. Department of the Treasury’s Capital Projects Fund, the Tribal Broadband Connectivity Program, and the U.S. Department of Agriculture’s ReConnect Loan and Grant Program.
 
In aggregate, TBR estimates there is more than $80 billion in direct and indirect government stimulus allocated for broadband-related projects in the U.S. market alone, all of which is slated to be spent by the mid-2030s. There are also a few hundred billion dollars in aggregate in similar broadband-implicated programs in other regions, most notably in China, the European Union, the U.K. and Australia.

Fiber Access Technology Capabilities Exceed Usability, Creating a Conundrum for Vendors

Technological innovations pertaining to fiber access have become so advanced and the bandwidth available through fiber access so massive that the capabilities of the technology far exceed what most end customers could possibly need or use. This disconnect creates a conundrum for vendors such as Nokia that supply the broadband infrastructure market.
 
Though fiber broadband infrastructure is, and will remain, in high demand, most ISPs will be loath to adopt the most cutting-edge technologies because they far exceed what customers would need and put unnecessary additional cost burden on the operator.
 
There are exceptions, such as what Google Fiber and Frontier Communications are deploying (specifically 50G and 100G connections, respectively), but TBR believes most ISPs will focus on 10G or lower connections, which is more than enough bandwidth for the vast majority of households and businesses and are likely to be future-proof for many years to come.

Overbuilding and One-upmanship Risks New Price War for High-speed Internet Service

The government funding boost, coupled with technological advancements and new entrants into the ISP domain, is creating a situation that is ripe for a price war for broadband services. Specifically, many more markets across the U.S. are likely to have three or more (in some cases up to seven) providers of high-speed broadband service in a given area, including xDSL, FTTx, HFC (via DOCSIS) as well as FWA and satellite (mostly delivered via low Earth orbit [LEO] satellites).
 
Given that a provider typically needs to have more than 30% market share in a given area to achieve profitability in the broadband services market, an increasing number of options puts more power into the hands of end users, which historically suggests the pricing environment will be extremely competitive.
 
In response to the hotter competitive environment, providers that are multiservice-oriented are trying to attract and lock in market share by offering converged (aka bundled) solutions, usually giving end users a discount as an incentive to sign up and stay.
 
Additionally, TBR notes that ISPs are increasingly engaging in one-upmanship (which is also a symptom of the existence of too many options in a given market), meaning ISPs are marketing ever higher broadband speeds to customers to position their offerings as better than the competition while attempting to incrementally increase average revenue per user.
 
Though this strategy has been effective in years past, it is likely to lose efficacy after speeds surpass the level at which the benefits of faster speeds become imperceptible to end users. Therefore, in aggregate, TBR expects the pricing environment in the U.S. for broadband service to be increasingly competitive through at least the remainder of this decade.

Private Equity Comes into the Fixed Broadband Market

Private equity firms are entering the telecom infrastructure market in a big way, gobbling up assets and forging joint ventures with telcos that want to (or need to) raise capital and hedge their risks. Some private equity-sponsored entities are also now building out their own greenfield fiber-based networks (such as Brookfield Infrastructure Partners’ Intrepid Fiber Networks) and are even moving the market toward wholesale, shared and other forms of open-access models.
 
Though the inclusion of private equity into the broadband infrastructure domain is bringing large pools of fresh capital into the market, this trend also risks fueling overinvestment, price compression and disruption of incumbent ISPs’ business models. Regardless, expect private equity to remain attracted to assets that offer consistent cash flow over a long duration, and their inclusion in the telecom ecosystem is likely a net positive for overall market development and evolution.

Existing Government Stimulus May Still Not be Enough for FTTP; Alternatives Will Likely be Called on at Scale to Fill in the Gaps

Though governments (and most of the stakeholders in the telecom ecosystem) across the world want full fiber to each premises, this is still not economically feasible. For example, it is not uncommon for some locations in the U.S. to cost upward of $1 million per premise to connect with fiber, a price that will be politically difficult to justify and that is not supported by normal market conditions. In these extreme situations, it is highly likely that governments will allow and embrace alternatives, such as FWA and satellite-based connectivity.
 
TBR notes that FWA and LEO constellations can easily deliver sustained speeds in excess of 100Mbps at a fraction of what it would cost to deploy fiber to the premises (FTTP). With that said, of the estimated 5.5 million households that the government has identified as needing broadband connection in the U.S., TBR would not be surprised if up to 25% of that number of households is ultimately connected via FWA or satellite (enhancements to DOCSIS and xDSL are also potential options to close the underserved gap). In other countries, that percentage could be even higher.

New Business Models Hold Promise to Connect Low-income Households in Emerging Markets

Upstart ISPs, such as fibertime and Vulacoin in South Africa, have established innovative solutions to cost-effectively provide high-speed broadband services to low-income areas. The architecture of the network emphasizes leveraging FWA and Wi-Fi with a relatively low amount of fiber and the business model is focused on selling units of time (in minutes), which is more affordable for lower-income end users.
 
TBR notes this model requires scale and high time of use to achieve profitability, meaning it is best suited for dense areas, especially impoverished neighborhoods. TBR also notes that obtaining access to high-speed internet is a key avenue in which areas can strengthen their local economies and help reduce levels of poverty.
 
In addition to South Africa, Brazil is also exploring the use of this model. This approach is also likely to be leveraged in other parts of Africa as well as in parts of India and Southeast Asia.

Conclusion

Government and private equity involvement in the broadband market may prove to be a mixed blessing. Though there are concerning indicators suggesting there are too many broadband providers in some key markets (especially the U.S.) and that broadband access businesses are becoming overvalued, these market dynamics actually represent tailwinds for Nokia, which is best positioned to garner a disproportionate amount of investment slated for broadband infrastructure in the Western economic bloc, which includes North America, Europe, developed APAC and select developing markets such as India.
 
Nokia’s outsized and unique position in the broadband infrastructure ecosystem enables the company to play a key role in orchestrating partners and customers to achieve their objectives in the most optimal way possible. Fiber will remain the coveted access medium for high-speed broadband, but the world will also employ other broadband access mediums to a large extent.
 
New ISP and hyperscaler business models, coupled with sustained investments by incumbent ISPs and supported by government stimulus, create an environment ripe for moving the world closer to full broadband coverage for all people.

GenAI requires massive investment now for a chance at massive long-term returns

For most new technologies and trends in the IT space, actual business momentum and revenue generation typically take years to develop. In fact, in many cases, particularly with new technologies available to consumers, monetization may never develop, as the expectation of free trials or advertising-led revenue streams never leads to sustainable business models.

 

The history around monetizing new technologies is what makes the rise of generative AI (GenAI) over the past 18 months so notable. In such a short period of time, we have tangible evidence from some of the largest IT vendors that billions of dollars in revenue have already been generated in the space, with the expectation that even more opportunity will develop in the coming years.

 

AI and GenAI revenue streams have not come without investment, however, as the infrastructure required to enable the new technology has been significant. The three major hyperscale cloud providers have borne the brunt of this required investment, outlaying billions of dollars to build out data centers, upgrade networking and install high-performance GPU-based servers. Amazon Web Services (AWS), Microsoft, Google and other cloud platform providers were already spending tens of billions annually to maintain and expand their cloud service offerings, and GenAI adds significantly to that investment burden.

 

The early revenue growth resulting from GenAI offerings has been promising, but put in the context of the increased investment required, it becomes clear that the business impacts of the technology will play out over an extended time period. Most public companies execute quarterly, plan annually and, as a stretch, project their expectations out over three to five years.
 
The impact of GenAI extends even further, as Microsoft CFO Amy Hood stated on the company’s fiscal 4Q24 earnings call: “Cloud and AI-related spend represents nearly all of our total capital expenditures. Within that, roughly half is for infrastructure needs where we continue to build and lease data centers that will support monetization over the next 15 years and beyond.” That means not only that Microsoft spent $19 billion on capital expenditures during a single quarter to support cloud and AI but also that the time horizon for the returns on that investment stretches beyond a decade.

 

Microsoft is, in this way, representative of all cloud platform peers, investing huge sums of capital expenditures now to realize modest new streams of revenue in the short term and anticipating significant revenue opportunity over the next 20 years.

AI-related revenue is already considerable, with growth expected to persist

TBR estimates the four leading cloud platform vendors generated more than $12 billion in revenue from AI and GenAI services in 2023, which is in and of itself a sizable market. On top of that, we expect revenue from those four vendors to increase by 71% during 2024.

 

Below are examples from some of the largest monetizers of GenAI so far, with estimates on the current size of their respective businesses and the strategies they use. A market of that scale and growth trajectory is notable in an IT environment where much more modest growth is the norm. While we expect growth to gradually slow and normalize over the coming years, the AI and GenAI markets remain attractive nonetheless. Insights follow about how some of the current leaders in this space are monetizing.

 

Microsoft (estimated $1 0 billion in GenAI revenue annually): While Microsoft did not quite meet Wall Street’s lofty expectations for AI-related revenue growth, the company posted a solid quarter in 2Q24. In TBR’s opinion, Microsoft’s GenAI strategy is on the right track, and its financial results align closely with our expectations. In 2Q24 Azure AI services contributed 8% of Azure’s 29% year-to-year growth, while Copilot was cited as a growth driver for Office 365.

 

Nevertheless, with Office 365 revenue growth decelerating compared to past quarters, it is clear the monetization of GenAI will take time to materialize. Still, given Microsoft’s current capex spend and capex forecast, the company is committed to its AI strategy. Management stated nearly all $19 billion of capital expenditures this quarter was focused on the cloud business, with roughly half going toward data center construction and the other half used to procure infrastructure components like GPUs.

 

This hefty commitment indicates that GenAI will remain at the forefront of Microsoft’s product development, go-to-market and partner strategies for years to come as the company looks to turn an early lead into an established position atop the AI and GenAI market.

 

AWS (estimated $2.5 billion in GenAI revenue annually): During AWS’ New York City Summit event in July, Matt Wood, the company’s VP of AI Products, noted that GenAI had already become a multibillion-dollar business for the company. Amazon CEO Andy Jassy has also spoken confidently about the future of AI, publicly proclaiming the company’s belief that GenAI would grow to generate tens of billions in revenue in the coming years.

 

The fact that AWS has been playing in AI infrastructure, with custom chip lines for both training and inference, well before the GenAI hype cycle is notable. Customers are not likely to go through the daunting task of moving off industry standard hardware, so these custom offerings can still be a more cost-effective source for net-new workloads, which is one of the reasons they signify a lot of potential for GenAI.

 

AWS’ custom offerings, coupled with tools that customers use to build and fine-tune models, such as Bedrock and SageMaker, will continue to spin the EC2 meter. AWS does have other GenAI monetization plans with a two-tiered pricing model for Amazon Q Business and Q Developer. However, it is still early days for these offerings, and Microsoft Copilot entering the mix, at least from the line-of-business (LOB) perspective, clearly indicates AWS faces an uphill battle.

 

Google Cloud (estimated $2 billion in GenAI revenue annually): Unlike some of its peers in the industry, Alphabet has not clearly quantified the impact that GenAI is having on Google Cloud’s top line. However, on Alphabet’s recent earnings call, executives said that GenAI solutions have generated billions of dollars year to date and are used by “the majority” of Google Cloud’s top 100 customers.

 

These results, coupled with a 40-basis-point acceleration in Google Cloud’s 2Q24 revenue growth rate, to 28.8%, signal that while GenAI is having an impact on Google Cloud Platform (GCP) revenue growth, it is very early days. Steps Google Cloud is taking to boost developer mindshare — with over 2 million developers using its GenAI solutions — and align with global systems integrator (GSI) partners to unlock new use cases, leave us confident Google Cloud can more aggressively vie for GenAI spend through 2025.

 

ServiceNow (less than $100 million in GenAI revenue annually): With Now Assist net-new annual contract value (NNACV) doubling from last quarter, ServiceNow’s steady momentum selling GenAI to the enterprise continues. Now Assist was included in 11 deals over $1 million in annual contract value (ACV) in 2Q24, showing positive early signs that the strategy of packaging premium digital workflow products based on domain-specific large language models (LLMs) is resonating.

 

At 45%, ServiceNow’s Pro SKU penetration rate, which represents the percentage of customer accounts on Pro or Enterprise editions of IT Service Management (ITSM), HR Service Delivery (HRSD) and CSM products, is already very strong. Upgrading these already premium customers to Pro Plus SKUs with GenAI, for which ServiceNow has already realized a 30% price uplift, could signify an opportunity for ServiceNow valued at well over $1 billion. Naturally, a big focus is expanding the availability of Pro Plus outside the core workflow products.

 

IBM (less than $2 billion in GenAI revenue annually): Approximately 75% of IBM’s reported $2 billion in GenAI book of business to date stems from services signings, and IBM lands nearly all watsonx deals thorough Consulting. Companies need help getting started with GenAI in the cloud, and IBM’s ability to lead with Consulting and go to market as both a technology and consulting organization will continue to prove unique in the GenAI wave.

 

On the software side, overcoming challenges with the Watson brand and deciding how much it wants to compete with peers have been obstacles, but IBM is now strategically pivoting around the middleware layer, hoping to act as a GenAI orchestrator that helps customers build and run AI models in a hybrid fashion. This pivot has resulted in a series of close-to-the-box investments, including Red Hat’s InstructLab project, which allows customers to fine-tune and customize Granite models, and IBM Concert for application management.

 

According to IBM, these types of GenAI assets have contributed roughly $0.5 billion to IBM’s AI book of business. By adopting a strategy to embed its AI infrastructure software into the cloud ecosystem of GenAI tools and copilots already widely accepted by customers, IBM ensures it stays relevant with these cutting-edge workloads.

 

Oracle (less than $100 million in GenAI revenue annually): With the Oracle Cloud Infrastructure (OCI) GenAI Service hitting general availability in January and a code assist tool only recently launched into preview, Oracle has been late to the GenAI game. But the company has highlighted several multibillion-dollar contracts for AI training on OCI, which speaks to its tight relationship with NVIDIA and ample supply of GPUs.

 

As an API-based service providing out-of-the-box access to LLMs for generic use cases, the OCI GenAI Service on its own does not necessarily differ from what other hyperscalers are doing. What does stand out is that Oracle offers the entire SaaS suite. Given that all Fusion SaaS instances are hosted on OCI, where the GenAI service was built, Oracle can deliver GenAI capabilities to SaaS customers at no added cost.

 

This means Oracle’s GenAI monetization will be purely from an infrastructure perspective. GPU supply and the cost efficacy of OCI will help Oracle bring new workloads into the pipeline, and we will see a bigger impact to growth in 2025. For context, Oracle’s remaining performance obligations balance (though some includes Cerner) is $98 billion.
 

Dive Into the Future of GenAI with TBR Analysts Patrick Heffernan, Bozhidar Hristov and Kelly Lesiczka

Beyond revenue generation, cost savings is part of the value proposition for cloud vendors and customers alike

Many of the leading IT vendors’ GenAI strategies have centered on investing in solutions for customers. However, vendors have also been serving as customer zero for the technology by implementing it internally. The results from their early implementations seem very much like end-customer use cases, which focus on cost savings and efficiency as the easiest benefits to realize. While many IT vendors have seen operating expenses and headcount level off over the past couple of quarters, implying that AI has had some impact on company efficiency, IBM and SAP have both explicitly stated AI’s impact on their operating models.

 

IBM was one of the earliest vocal proponents for the labor-saving benefits AI could bring to its business. In mid-2023 CEO Arvind Krishna announced a hiring freeze and shared an expectation that AI would replace 8,000 jobs. IBM remains focused on driving productivity gains, which it is largely doing by lowering the internal cost of IT and rebalancing the global workforce. This includes using AI to automate back-office functions. Such efforts have IBM on track to deliver a minimum of $3 billion in annual run-rate savings by the end of 2024.

 

Meanwhile, SAP’s decision to increase its planned FTE reallocation from a previous target of 8,000 to a new range of between 9,000 and 10,000 FTEs shows the company is committed to improving operating efficiency. While the bulk of the restructuring will consist of reallocating FTEs into lower-cost geographies and strategically important business units, taking a customer-zero approach with GenAI is also a component. SAP is leveraging business AI tools focused on areas like finance & accounting and human resources to reduce the labor intensity within the respective business units.

Just like end customers, vendors are investing significantly now in hopes of generating long-term GenAI returns

As seen in TBR’s Cloud Customer Research streams, customers have been investing in GenAI solutions with some haste, forgoing clear ROI measurements or typical budgeting procedures. Customers, as well as the major vendors we cover, have a sense of urgency around GenAI and share the feeling that if they do not embrace these new solutions now, it could place them at a long-term competitive disadvantage. If customers are not making full use of GenAI capabilities, their competitors will be more efficient and productive and capture more growth opportunities. For vendors, the ability to not only deliver GenAI capabilities but also do so at scale will be a competitive necessity for decades to come.

 

In this regard, customers and vendors find themselves in a similar situation, investing in GenAI now just for the possibility of a future advantage, but the scale of investments required are quite different. Customers have the good fortune of leveraging scalable, subscription-based services for many of these GenAI technologies. Customers are still extending their IT budgets and paying more to incorporate GenAI, but they do not have large fixed costs and long-term commitments at this point.

 

Vendors, on the other hand, need to make significant investments, even beyond the already huge levels of investment to support cloud services, to capitalize on the GenAI opportunity. The scale of investment cannot be understated for the largest cloud platform providers like AWS, Microsoft, Google and Oracle. All of these vendors were already investing tens of billions of dollars annually to support data center and infrastructure build-outs.

 

The unique data center and infrastructure requirements to deliver GenAI solutions, including the GPU-based systems, are driving double-digit to triple-digit increases in capex spending for leading vendors. Not only is the level of spending noticeable, the time periods for the returns are also lengthy. In communicating those increased expenses to investors and Wall Street analysts, vendors like Microsoft messaged the returns from these investments playing out over the next 15 years, a time horizon seldom mentioned previously.

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.

SoftwareOne’s Strategy for SAP Clients in DACH

In a June 2024 discussion with SoftwareOne’s DACH (Germany, Austria and Switzerland) leadership, TBR came away with three observations on what might make SoftwareOne a potentially unique player in the SAP ecosystem. At a minimum, SoftwareOne in DACH appears to be taking a different approach to the market opportunities created by the confusion around RISE with SAP, GROW with SAP, migrations to S/4HANA, and the 2027 deadline for the end of ECC support. From the presentation by and discussion with SoftwareOne’s Stephan Timme, president DACH; Vincenzo Boesch, sales leader for SAP Services; and Oliver Berchtold, service director DACH, TBR noted that:

 

• SoftwareOne remains focused on customers’ current SAP environments and helping those customers get to S/4HANA and to the cloud. SoftwareOne is decidedly not focused on business processes. TBR believes that distinction, while subtle, matters because almost every other IT services company and consultancy in the SAP ecosystem of SoftwareOne’s scale and larger starts with identifying business problems and processes that need to be fixed and then declaring, “Hey, look at that, SAP RISE is a perfect solution to fixing these problems!” SoftwareOne does not dance around the business issues but instead gets straight to what it excels at: solving the SAP challenges of licensing, migrating and maintaining.
• SoftwareOne recognizes SAP customers have been struggling with the confusion and costs around S/4HANA and RISE, along with all the other changes wrought by the end of support for ECC coming at the same time as the rise of generative AI (GenAI). During the SoftwareOne analyst event that TBR attended in Austin, Texas, in April, PF Grillet, SoftwareOne’s global SAP leader, told us there has “never been a more complex time for decision-making around SAP.” SoftwareOne’s answer to the confusion is a value proposition rooted in four promises: fast, easy, agile/flexible and cost-effective. TBR would summarize that value proposition and SoftwareOne DACH’s overall approach to clients in one word: And TBR suspects DACH clients value pragmatism.
• When TBR noted the absence in SoftwareOne’s presentation of the SAP catchphrase “clean core,” Vincenzo Boesch explained that for him and his colleagues, SoftwareOne’s readiness services provided a comprehensive approach to what SAP calls clean core, emphasizing the need to prepare for migration, migrate only what is needed, and then maintain the benefits of a clean core throughout the transition and ongoing functioning of SAP within the client’s environment. SoftwareOne aims to remove complexity and streamline a customer’s environment, modernize and prepare customers for innovation, and then adopt an iterative transformation approach, more focused and agile, to continue the move to full clean core over the time frame best suited to the customer’s needs and capabilities. SoftwareOne recognizes few customers will have the means and the appetite to do it all in one go. In short, SoftwareOne is honest about the challenges, does only what is necessary and sets the client up for sustained success.

 

On the last point, TBR believes this approach to clean core likely fits the needs of small and midsize businesses with less complex IT environments. In follow-on discussions with TBR, SoftwareOne explained that the company is not targeting all customers but rather focusing on those customers that have limited need for business transformation, already have efficient business processes, and are driven to modernize their ERP and remove the mainly technological limitations of ECC. With enterprise clients migrating complex and highly customized SAP instances, SoftwareOne’s focus purely on SAP runs the risk of discounting business process change management cost and potentially pushes complex and problematic processes into the future, setting clients up for aggravation, not success. Failure seems unlikely given SoftwareOne’s overall track record of success with SAP. A more likely outcome for SoftwareOne would be selling additional consulting services related to complex migrations.

Stepping Back to Look at the Bigger SAP Picture

Just as there has “never been a more complex time for decision making around SAP,” as Grillet said in April, there also has never been a better time for a services firm to take a pragmatic approach to helping small to midsize businesses migrate to S/4HANA. As of 4Q23, GROW with SAP had amassed only 700 customers, which pales in comparison to the estimated 24,000 customers on ECC, 14,600 on S/4HANA and 8,800 in S/4HANA Cloud. Furthermore, GROW’s install base skews toward new logos, suggesting many legacy SMB customers have been comparatively slow to adopt the offering.
 
SAP will look to change this trend, primarily through its ecosystem, which will present opportunities for partners focusing on the SMB segment. The partners that succeed will recognize the oversized impact that cost and complexity have on SMBs, which are more likely to be resource-constrained in technical staff relative to larger enterprises. These customers are more apt to pursue the simplest path to the cloud possible, aligning closely with how SoftwareOne is positioning its services.
 
The contrast between SoftwareOne’s pragmatic approach and the strategies of much of the rest of SAP’s services ecosystem is becoming more stark. While RISE and GROW still hold S/4HANA migration at their core, SAP has become vocal about the offerings’ ability to drive multiproduct sales motions. In 1Q24, SAP released new add-on packages for RISE that bundle line-of-business (LOB) suites in finance and supply chain, and the company will look to partners to lead the charge in their adoption.
 
Yet, cross-selling initiatives risk adding to the complexity of an already challenging migration and implementation process. In the coming years, efforts to encourage business AI adoption will add to the noise for customers simply looking to bring their ERP deployments to the cloud. Many customers, especially SMBs, will appreciate a service provider whose value proposition is a fast, easy, agile/flexible and cost-effective migration. This will be a key differentiator for SoftwareOne as the vendor positions as a services provider capable of cutting through the noise and guiding customers toward the path of least resistance.

Minding the Minefields Before Stepping in Them

During the early days of robotic process automation, some IT services companies and consultancies advised clients to automate their processes before evaluating the efficacies and benefits, betting that automating even less-than-optimal processes would generate cost savings. Predictably, that bet did not always pay off.
 
Two considerations for SoftwareOne: First, the company should ensure that its message and what it delivers reinforces the pragmatism of speed, flexibility and value. In other words, promote this really well to ensure SoftwareOne’s approach and value proposition stand out in a large and noisy market. Second, in TBR’s view, while Boesch and his colleagues presented a compelling value proposition for SoftwareOne’s DACH clients, this approach will not necessarily apply to the customers that need a broader business process transformation.
 
For more than a decade, many consultancies and IT services companies have been stressing a business-first and technology-second mindset, but TBR believes SoftwareOne’s approach will appeal to clients – maybe especially in but certainly also outside of DACH – that are similarly focused on pragmatic, technology-first outcomes.

Disruptive technology is anything but stagnant. From the hyperscalers pushing the parameters of their latest large language models (LLMs) to other firms trying to provide their own models in an arguable race to the bottom, the lines of the generative AI (GenAI) stack are blurring.

 

In many ways, this activity is good, elevating innovation, competition and the role of the partner ecosystem, which will increasingly define how cloud vendors go to market in years to come. But at the same time, it is fueling the proliferation of unstructured data and adding to the already complex and redundant IT estate with yet another LLM, another vector database, and more third-party AI orchestration and integration tools.

 

Informatica is on a mission to break through this added complexity, honing the metadata system of record that defines Informatica Data Management Cloud (IDMC) to account for the latest models, databases and integrations that must play friendly with customers’ existing applications.

 

Of course, the challenge remains getting customers to think about data ahead of GenAI amid all the hype. At TBR, we often talk about how GenAI models are only as good as the amount and quality of data that are fed into them. Garbage-in, garbage-out should be self-explanatory when it comes to data’s role in LLMs and GenAI, and yet effective data management is still often overlooked or deployed too late within the enterprise.

 

Corporate culture, executive pushback and integrating point solutions remain among the top obstacles, and according to TBR’s 2H23 Cloud Infrastructure & Platforms Customer Research, 35% of respondents are taking a best-of-breed approach to data management, resulting in fractured data silos. That is why the Informatica World 2024 opening keynote theme — “Everybody’s ready for AI except your data” — rang true and speaks to an opportunity for Informatica, and increasingly its consulting partners, to address as customers try to navigate GenAI’s complexities and ultimately take advantage of GenAI’s opportunities.
 

TBR’s Leading Analysts Discuss Use Cases, Risk and Governance, Commercial Strategies, and Resource Management for Cloud and Software Applications in GenAI: Watch the Full Video Below Now

“CLAIRE GPT is the Chat GPT for Enterprise Data”

IDMC is the culmination of a five-year, $1 billion R&D investment, and executives have clearly articulated the pace of innovation will only accelerate with a meticulous focus on improving the native functionality in each of IDMC’s seven core modules.

 

At the event, product leaders for each of these components (e.g., Data Governance, Integration PaaS [iPaaS], MDM [Master Data Management] & 360 Apps) got on stage to showcase their innovations over the past year. They discussed not only core innovation, which could be anything from improving the user experience to adding new prebuilt connectors and extensions, but also a set of innovations specific to CLAIRE — for example, an automated assessment of metadata in MDM.

 

All these new added capabilities highlight Informatica’s goal of infusing GenAI into every component of the IDMC platform. Perhaps the best example of this strategy, though, is CLARIE GPT, which was first announced at last year’s Informatica World conference and is now generally available. The year 2023 was largely one of execution, as Informatica worked to put the AI engine, trained on vast amounts of intelligent metadata already sitting in CLAIRE, through preview, solicit customer feedback, and prepare for its release to the general public with a fresh message: “CLAIRE GPT is the ChatGPT for enterprise data.”

 

To see this message in action, we were given a hypothetical scenario in which a marketing operations analyst tasked with retaining disengaged customers uses natural language prompts in CLAIRE to find the right data sets and ask questions about that data. For example, the analyst can ask CLAIRE GPT to list data sets for marketing campaign analysis, and CLAIRE will pull the appropriate data, including those sitting in multiple systems like data warehouses and BI tools.

 

But Informatica actually delivering capabilities like CLAIRE GPT with IDMC for customers’ GenAI projects is only one piece of the puzzle. Informatica is also using GenAI to improve the underlying platform capabilities and deliver a unified natural language experience in IDMC. In a move that is now table stakes among SaaS, PaaS and IaaS vendors, Informatica is creating its own Copilot — CLAIRE Copilot — to auto classify and enrich master data.

IDMC Modernization Initiatives Well Under Way

Aside from offering new IDMC capabilities and releasing CLAIRE GPT, 2023 was a major year for modernization, which was a big theme at this year’s event. With license revenue down 68% year-to-year in 2023 and cloud subscription revenue up roughly 40% over the same period, Informatica is executing on its strategy of ceasing new license sales and enhancing programs to help customers move to IDMC in the cloud. This includes strengthening the existing modernization packages for legacy PowerCenter, MDM and Governance workloads with fresh automated tooling, including CLAIRE Copilot for PowerCenter (available in 2H24); increasing partner certifications; and growing these programs within larger accounts.

 

One of the more compelling customer modernization success stories from the event was Takeda Pharmaceuticals, which in the span of 18 months was able to consolidate the 12 data centers still running PowerCenter down to three.

 

Chief Data Officer Barbara Latulippe told a story of how Takeda wanted to become GenAI ready but recognized that modernization was the initial step and that migrating the low-risk and simple applications first was the recipe for success. The second phase for Takeda was consolidating 6 different MDM environments into one, which ultimately paved the way for the company to adopt other IDMC capabilities, including the Informatica Cloud Data Marketplace, on which the company is now live today. As was the case with Takeda, customers that go through the modernization process will ultimately ask how GenAI can be used to create value.

 

We expect data marketplaces to play an increasingly important role here; if a customer needs to automate a certain business process and thus locate the right LLM for that process, as well as the right data to contextualize said LLM, one-click experiences will go a long way, further democratizing GenAI and making it fairly seamless for customers to turn a regular application or process into one that is GenAI-enabled.

Informatica’s Relationships With All Hyperscalers Are Maturing, but Work with Microsoft Fabric Is a Big Win for Both Companies

For any cloud platform company, integrating with all the major hyperscalers is no longer a unique strategy but a prerequisite. As we continue to follow Informatica, however, there are some strategic ecosystem moves the company is making in both its product and go-to-market strategies to reach new customers and protect its positioning as the most neutral data management provider.

 

One key example is Informatica’s fast-growing relationship with Oracle, which Informatica formerly recognized as a hyperscaler partner two years ago. While other platform companies are closely aligned with Amazon Web Services (AWS), Azure and Google Cloud Platform (GCP), many have yet to account for OCI (Oracle Cloud Infrastructure), which could be due to a lack of maturity, including how Oracle cosells with ISVs in the cloud.

 

But as a key member of the legacy Oracle Database ecosystem for some time, Informatica has been able to quickly elevate its relationship with Oracle around OCI and now serves thousands of Oracle customers in North America alone. Informatica recently made Data Governance & Catalog available natively on OCI — ahead of GCP, which will support this capability later this year — and is expanding to more OCI regions.

 

But the most transformative ecosystem development highlighted at the event was Informatica’s work with Microsoft, specifically how it is filling whitespace within Fabric, Microsoft’s own answer to data management in its broader pursuit of GenAI dominance. Specifically, Informatica is coming in with its Cloud Data Quality solution native to Fabric, which means that as customers ingest data into Microsoft Fabric’s OneLake repository, Informatica ensures the data can be automatically profiled and assessed in real time.

 

Unlike many other partners that are joining Microsoft’s partner program as ISVs building and selling applications on top of Fabric, Informatica has signed on as a design partner, an exclusive invite-only track that allows partners to create entirely new workloads consumed natively within the Fabric solution. This distinction is important, as it essentially embeds Informatica’s Data Quality tooling directly within the customers’ analytics workflow and ensures Informatica can offer Microsoft’s roughly 11,000 Fabric customers access to IDMC in the back end and increase the likelihood they explore other aspects of Informatica’s platform.

 

Though Fabric is touted as an end-to-end SaaS solution serving the entire data life cycle, Microsoft understands its gaps and recognizes the trust customers place in Informatica when it comes to data integration, quality and governance. Letting Informatica fill whitespace around Fabric, an integrated platform that is underpinned by a data lake architecture, is a strategic move for Microsoft and should be noted by Azure competitors that similarly partner with Informatica but in some ways lack Microsoft’s platform mentality; to us, the Fabric-Informatica integrations only reinforce how aligned Azure and Informatica are as two platform-centric companies understanding the symbiotic relationship between data and GenAI.

Final Take

In many ways 2023 was an execution year for Informatica. Since last year’s event, when Informatica showcased CLAIRE GPT, the company has been adamantly focused on bringing the GenAI engine into general availability, all the while delivering a broader and deeper set of AI-rich features natively within IDMC.

 

Meanwhile, with momentum for PowerCenter migrations, which in 1Q24 accounted for 80% of all Informatica’s modernization deals, and early success of the MDM program, modernization has been a key initiative for the company, driving cross-selling opportunities and pushing Informatica closer to the $1 billion cloud annualized recurring revenue mark.

 

Rallying a cohesive ecosystem — made up of hyperscaler and global systems integration partners that are expanding their certifications as part of these modernization programs — around IDMC will remain key to Informatica’s success, ultimately allowing the company to cross-sell more services and ensure IDMC becomes the de facto way customers get their data ready for GenAI.