The Great Enterprise Intelligence Arms Race
Joe Williams and Tom Krazit
Protocol
September 27, 2021
Breaking down the garden walls
For the past two decades, software-as-a-service pioneers like Salesforce and Workday eagerly built walled gardens around their systems in hopes of making their services as easy to use as possible for cloud newcomers.
That strategy is now causing a huge headache for enterprises that are increasingly looking to break down the data silos and compile information from various sources to run advanced analytics. Their goal? To predict the future.
"SaaS, for all the benefits it's provided, has completely fragmented enterprise data," Pure Storage CEO Charles Giancarlo told Protocol. "SaaS made it a lot easier for an individual department to immediately get access to an application … but that was at the expense of that data not being available to be matched with other data in my corporate environment."Instead of relying on Salesforce data to figure out the day's sales tally, for example, executives want to combine that information with data from Gong to find customers thinking about leaving.That tectonic shift underway is spurring a race between so-called "systems of record' vendors and startups like DataRobot, Databricks, Dataiku and Scuba Analytics.Those startups all promise to break down those data silos and combine information from various sources to try to answer more forward-looking questions — or what some have labeled the "systems of intelligence."
It's clear there's investor enthusiasm for this concept: Databricks just reached a valuation of $38 billion. DataRobot sits at $6.3 billion, while Dataiku's recent $400 million funding round propelled it up to $4.6 billion. Snowflake commands a market cap of $94 billion.
Legacy vendors "were counting on a lock-in of their data … and they aren't able to do that because customers are demanding that Salesforce and Microsoft be open. It's ushering in that next generation of company that can be more nimble," Emergence Capital founder and general partner Gordon Ritter, an early Salesforce investor, told Protocol.
The market leaders have been working for years to build prediction features into their existing products. But the results have been mixed.
In 2007, Oracle began to popularize the category with the launch of its "BI Applications" suite — a product line that would go on to become a multibillion-dollar business. On the other hand, IBM failed spectacularly to turn Watson into an all-purpose AI engine.More recently, Google Cloud announced a new product that integrates information from various sources — like an ERP platform from SAP — to provide deeper insights into corporate supply chains."Intelligence needs to be leveraged much better," said Hans Thalbauer, managing director at Google Cloud. "We are focused on the aspect of data and making data available and accessible."
Salesforce, however, is probably the most prominent example of a big enterprise software vendor embracing intelligent systems.The company released its own AI engine called Einstein in 2016, but the product has struggled to find its footing.
Salesforce claims it's running 100 billion "predictions" every day, and the company has lofty ambitions to merge Einstein more deeply into its data analytics tool Tableau, suggesting that it sees a bigger role for the tool despite one of the product's leaders previously conceding that customers aren't "going to do everything" with it."Tableau is a general-purpose data-analytics platform," said Gartner analyst Jason Wong. It's "not quite machine learning yet, but the Einstein analytics team is part of Tableau now. So there is a vision for Salesforce via Tableau to get involved in this area."
So unless Salesforce can significantly improve Einstein's abilities, it's unlikely to keep pace with the innovation happening outside CEO Marc Benioff's fiefdom.
Einstein is great at analyzing Salesforce data. But companies rarely stay within one software ecosystem and instead are increasingly using a variety of applications from multiple vendors.In other words, Einstein is great when you want to generate a real-time sales forecast using CRM data but becomes less useful when you want to know whether that forecast is within the typical range for that week of that quarter.
Salesforce has tried to address its challenges in multiple ways, including with Hyperforce, its tool that enables the company's products to work with all the major public clouds.
It also recently launched a big partnership with AWS that enables information to flow more freely across the two systems. And, of course, it has MuleSoft, the API provider it acquired for $6.5 billion in 2018."MuleSoft is a platform that you can use to integrate every system at your company: Those back-office systems, those legacy systems you like to pretend don't exist but aren't going anywhere, your supply chain, your ERP," said Bret Taylor, Salesforce operating chief and apparent heir to Benioff, at Dreamforce last week.
But managing those connections on your own could be a much more difficult endeavor than using a packaged solution from a third-party vendor. With engineering resources in short supply, CIOs may be hesitant to add yet another burden to an overworked IT department.
It's ultimately why the shift Taylor is talking about is a challenging one. Linking together a hodgepodge of legacy systems and new, digitally-native applications in hopes of developing AI-based models to predict future behavior or outcomes is no easy task.The market leaders are at "a little bit of a disadvantage because they haven't thought about that openness," said Wong. "That is a key part [of the problem] for these megavendors. Their own core platform architecture, is it in a state where they can implement some system of intelligence to really maximize it?"
There's a tried-and-true solution that Salesforce and others can turn to: Acquire one of the many startups gaining traction in this sector of the industry. That's likely to be very expensive. But for the software giants, it's very likely going to be necessary.
"The market is going to be about intelligence," said Scuba Analytics CEO Tony Ayaz. And companies like Salesforce "need to gather better intelligence to not only run their own business, but serve their customers better. Because everyone is looking for actionable intelligence."
—Joe Williams
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