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What Xylem's $7.5 Billion Acquisition of Evoqua Tells Us About Water Intelligence

Sep 10, 2024 by Thomas Geiger

What Xylem's $7.5 Billion Acquisition of Evoqua Tells Us About Water Intelligence

In early 2023, Xylem completed its all-stock acquisition of Evoqua Water Technologies at an implied enterprise value of approximately $7.5 billion. The deal created the world's largest pure-play water technology company, combining Xylem's strength in water infrastructure (pumps, analytics, treatment) with Evoqua's dominance in North American industrial water treatment.

The transaction is worth examining not just for what it means for the water sector, but for what it illustrates about how structured market data surfaces insights that narrative analysis alone would miss.

The strategic logic

Xylem had attempted to acquire Evoqua as far back as 2017, when Evoqua's majority owner AEA Investors preferred an IPO and rejected a bid valued at around $2.5 billion. Five years and a tripling of the price later, Xylem got its deal.

Why the persistence? Evoqua's portfolio filled specific gaps. Its industrial water treatment capabilities complemented Xylem's infrastructure focus. Its service network — thousands of professionals maintaining mission-critical water systems for life sciences, microelectronics, and food and beverage companies — provided recurring revenue that Xylem's hardware-heavy model lacked. And Evoqua's subsidiary Magneto Special Anodes offered a strategic entry point into the hydrogen electrolysis market, supplying titanium-based anodes for PEM electrolysis and nickel-based anodes for alkaline systems.

That last point is the kind of connection that only becomes visible when you model supply chain relationships across sectors. A water treatment acquisition that quietly positions the acquirer in the electrolyzer value chain — not through a hydrogen-focused deal, but through an electrode subsidiary buried in a water company — is exactly the type of cross-sector link that structured data reveals.

What the data shows

When we map this transaction in our knowledge graph, several patterns emerge that aren't obvious from the press release.

The combined entity has over $7 billion in revenue and immediately dominates the North American water technology market. But the geographic concentration is notable: 82% of Evoqua's revenue comes from the US market. This creates strategic risk — the combined company's growth outside North America depends almost entirely on Xylem's pre-acquisition international footprint.

The deal's significance for the hydrogen value chain is disproportionate to how it was covered. Magneto Special Anodes is a small part of Evoqua's overall business, but it's one of a limited number of suppliers for critical electrolyzer components. Tracking these second-order supply chain connections — who supplies components to whom, and how ownership changes ripple through the value chain — is where structured data becomes indispensable.

The competitive landscape shifted in ways that extend beyond water. After Veolia's merger with SUEZ in 2021, the Xylem-Evoqua combination represents the second major consolidation in water technology in three years. Grundfos, the next-largest pure-play competitor, now faces a significantly larger rival with a broader portfolio.

Why this matters for market intelligence

Deals like Xylem-Evoqua demonstrate why tracking individual sectors in isolation misses the picture. The water sector, the hydrogen sector, and the industrial chemicals sector are connected through shared technologies, components, and supply chain relationships. An acquisition in one sector creates ripple effects across others.

This is the kind of analysis our knowledge graph is built to support: not just "what happened" but "what does this connection imply for the broader market structure." If your organization tracks water infrastructure, electrolyzer supply chains, or industrial M&A, the relationships between these domains matter as much as the individual data points.

Filed under: Sector Intelligence · Thomas Geiger