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Lovelytics

Data, AI, and analytics consulting

Overview

Lovelytics helps large enterprises manage and use data as a data, AI, and analytics consultant. It designs modern data platforms, builds data pipelines, governance, migrations, visualizations, and applies generative AI and machine learning to solve real business problems. It differentiates itself with deep expertise in the Databricks ecosystem and strong technology partnerships (Databricks, Azure, Google Cloud, Tableau), plus growth through acquisitions to expand global reach. Its goal is to modernize clients’ data infrastructure and deploy AI in practical, measurable ways to deliver tangible business outcomes.

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About Lovelytics

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Industries

Data & AnalyticsConsultingEnterprise SoftwareAI & Machine Learning

Company Size

501-1,000

Company Stage

Acquired

Total Funding

N/A

Headquarters

Arlington County, VA

Founded

2017

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Funding

Total Funding

N/A

N/A

Industry Average

Funded Over

1 Rounds

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Benefits

  • Remote Work Options

Company News

CDW
Sep 25th, 2026
CDW Completes Acquisition of Lovelytics

Addition of leading data and AI services firm deepens CDW’s capabilities, helping organizations build modern data foundations and accelerate AI adoption CDW (Nasdaq: CDW), the strategic end-to-end technology partner that turns customer ambition into outcomes, today announced that it has completed its previously announced acquisition of Lovelytics, a leading data and AI services firm and a member of the Databricks Brickbuilder Partner Network. The acquisition expands CDW's Services Solutions portfolio and will bring the Lovelytics capabilities to a broader set of customers. Lovelytics helps organizations modernize, govern and use their data to improve performance and deploy AI at scale, combining deep Databricks expertise with experience across energy, manufacturing, retail, healthcare, financial services and media. It brings a team of more than 600 across the United States, Canada, Argentina and Colombia who work exclusively in data and AI. This strategic investment strengthens

Infor Capital
Sep 4th, 2026
M&A acceleration continues: 152 deals close as tech and energy lead the charge.

M&A acceleration continues: 152 deals close as tech and energy lead the charge. Strategic buyers dominate September's deal environment - a single day shows the breadth of M&A in 2026 One hundred fifty-two M&A deals closed on September 4th - a volume that underscores the accelerating pace of dealmaking across sectors, company sizes, and geographies. This is not a single mega-deal story. It is a multi-dimensional surge: tech platforms consolidating, energy firms repositioning, financial services adapting, and consumer companies optimizing portfolios all happened on the same day. The signal is clear: M&A activity has moved from headline-driven (waiting for the next mega-deal) to velocity-driven (deals are the baseline, mega-deals are the bonus). Sellers are meeting buyers. Capital is flowing. Valuations are being reset across industries. The mega-deals: AI and energy reshape strategy. Nvidia's $13 billion acquisition of Hugging Face stands as the day's marquee transaction. The deal signals a dramatic shift in AI strategy: instead of building AI infrastructure in-house, Nvidia is acquiring it - specifically, the talent, user base, and model libraries that Hugging Face represents. This is not a typical infrastructure play; it is a talent and platform acquisition. Shell's $16.5 billion acquisition of ARC Resources represents the opposite strategy: energy majors are buying reserves and production capability to expand their own footprint. In this case, Shell is deploying capital to access ARC's oil and gas assets and operational teams. The deal reflects confidence in energy demand and a willingness to pay up for proven reserves and capable management. These two transactions - one in AI services, one in energy - capture the range of strategic thinking driving September's M&A. Technology buyers are hunting for hosted solutions and user bases. Energy buyers are hunting for reserves and operations. Neither strategy is dependent on the other; both are rational responses to their respective market dynamics. A third major deal, Keurig Dr Pepper's decision to facilitate a stake sale in Chobani, reflects portfolio rebalancing. Chobani (the Greek yogurt brand) was acquired by KDP years ago and had become a minority holding. Selling or restructuring positions in mature brands to redeploy capital into higher-growth areas is a form of M&A-adjacent activity that drives valuations and signals seller appetite. Tech consolidation: breadth over depth. Fifty-one of 152 deals (34%) involved technology, software, or gaming companies - far ahead of any other sector. But this category masks important sub-trends. Cybersecurity was the hot zone: NetSPI and Synack merged to create a 200-million-dollar-revenue offensive security platform, and Tusker acquired Fortress SRM to expand its own cybersecurity services. These are not mega-deals, but they reflect the continued roll-up of fragmented cyber markets. CDW's $525 million acquisition of Lovelytics shows how traditional IT service providers are bolt-on buying to add data and AI capabilities without building from scratch. The pattern is consistent across tech: consolidators prefer to acquire complementary teams and platforms rather than develop them internally. Speed to market and customer access matter more than homegrown solutions. Financial services and fintech came in second with 39 deals - driven by wealth management consolidation, payments infrastructure deals, and DeFi platform activity. Equity Bancshares and Lincoln Bancorp's $123.8 million merger exemplifies regional bank consolidation, a slow-burn trend that continues as smaller financial institutions seek scale. Qapture Investments' acquisitions in DeFi strategies signal that even alternative asset managers are consolidating in this space. Business services (33 deals), industrials (23 deals), and consumer (19 deals) all participated actively. The breadth indicates that no single sector is "hot" - instead, buyers and sellers are actively transacting across the entire economy. Geography: US concentration with global spread. The United States accounted for 85 of 152 deals (56%), a share consistent with global dealmaking patterns. Spain ranked second with 16 deals, followed by India (12), and the United Kingdom (10). This distribution reflects both reporting density (US deals get more press coverage) and the reality of capital pools (US firms have access to the deepest capital markets). What is noteworthy is the activity outside the US. Spain's 16 deals suggest active regional consolidation in financial services, tech, and infrastructure. India's 12 deals, including Unacademy's $206 million sale to upGrad, reflect the ongoing consolidation of the Indian edtech market. These regional dynamics rarely dominate global headlines, but they represent significant capital deployment and strategic repositioning in emerging and developed markets alike. Cross-border activity - deals that span multiple countries - also featured prominently. ITP Media Group's acquisition of Heart Media Group to expand across Asia-Pacific signals strategic repositioning for regional dominance. These deals typically involve more complex due diligence and regulatory review than domestic transactions, but they continue to occur at steady rates, especially when strategic rationale is clear. Sector rotation: AI, energy, and healthcare converge. Energy infrastructure deals numbered 13 on September 4th, including the Shell-ARC Resources megadeal and Diversified Energy's $1.8 billion acquisition of Birch (backed by Carlyle), indicating serious capital deployment into energy assets. This sector is seeing both traditional and renewable energy transactions, signaling that capital is willing to back both established reserves and clean energy infrastructure. Healthcare and medtech saw 19 M&A announcements - consistent with the trend of healthcare consolidation driven by regulatory pressures, cost containment mandates, and the rise of specialized service platforms. Consumer (19 deals) and artificial intelligence (17 deals) both showed strong activity. The AI category is particularly interesting: while some deals are explicitly tagged as AI (e.g., CDW-Lovelytics), many others involve AI as a secondary component - acquisition of data capabilities, automation platforms, or software that includes AI features. This multi-sector participation suggests that 2026 M&A is settling into a new normal: no single theme dominates. Instead, multiple drivers - AI infrastructure, energy transition, healthcare consolidation, financial services adaptation, and consumer portfolio optimization - are all pushing deals forward simultaneously. Capital is available, valuations are settling, and buyers are confident enough to transact. Deal mechanics: strategic buyers lead. The vast majority of September 4th deals were strategic acquisitions - buyers purchasing companies to expand their own operations, capabilities, or customer base. Private equity and financial sponsors played a supporting role. This contrasts with years past, when LBO syndicates and financial buyers drove headline volume. Today, corporates and strategics are the engine of M&A. Bolt-on acquisitions and add-on purchases dominated over large-scale breakups or contested transactions. This indicates an orderly market: sellers and buyers are finding common ground without drawn-out negotiations or activist pressure. The speed and relative civility of deals suggest confidence on both sides and an absence of distressed selling. Deal sizes ranged widely - from sub-100-million deals (Tusker-Fortress SRM, various regional roll-ups) to mega-deals (Nvidia-Hugging Face, Shell-ARC Resources). This distribution is healthy: it indicates that M&A is happening at every scale, not just at the top end. Smaller companies have buyers. Mid-market firms are consolidating. Large enterprises are repositioning. Forward look: what September 4th signals. If September 4th is representative of the broader deal environment - and it likely is - then Q3 and Q4 2026 will continue to see robust M&A activity. One hundred fifty-two deals per day extrapolates to several thousand per month and tens of thousands per quarter across all sectors and geographies tracked. The types of transactions evident on September 4th - bolt-on acquisitions, strategic repositioning, consolidation plays, and occasional mega-deals - will likely persist. Buyers with strong balance sheets and clear acquisition strategies (Nvidia, Shell, larger service roll-ups) will continue to move aggressively. Sellers in fragmented industries (cybersecurity, regional banking, edtech) will face sustained pressure from consolidators. For investors and analysts, the implication is that consolidation multiples, synergy capture, and roll-up valuations will remain central to deal and stock market dynamics through the remainder of 2026. For founders and CEOs, it signals that buyers are active, capital is available, and exit windows are open - especially in fragmented sectors where consolidation logic is clear. The era of waiting for "the next mega-deal" is over. InforCapital, partnership is living in the era of distributed, sector-wide, constant dealmaking. September 4th proved it.

NewsnReleases
Sep 3rd, 2026
CDW Corp. to acquire Lovelytics for $525 million to bolster AI and data capabilities.

CDW Corp. to acquire Lovelytics for $525 million to bolster AI and data capabilities. Deal aims to help customers bridge gap between AI experimentation and business value NEW YORK: CDW Corp., a leading technology solutions provider, said Tuesday it has agreed to acquire Lovelytics, a data and artificial intelligence services firm, for approximately $525 million in a move to expand its data analytics practice and accelerate customer adoption of AI. The acquisition combines Lovelytics' expertise in data, analytics and AI with CDW's broad technology capabilities and deep customer relationships. The deal comes as organizations increasingly seek to move beyond AI pilots and turn experimentation into tangible business outcomes, with data readiness remaining a critical barrier, according to industry research. "There is no AI strategy without a data strategy," said Christine A. Leahy, chair and chief executive officer of CDW. "As our customers move from AI pilots to execution, that journey runs directly through their data. Together, Lovelytics and CDW will help organizations turn data into a strategic asset, accelerating AI adoption, improving decisions and driving stronger business and mission outcomes." Only 12% of enterprises feel fully prepared on data readiness to support AI, according to Gartner research. Lovelytics, founded in 2017 and headquartered in Arlington, Virginia, is a member of the Databricks Brickbuilder Partner Network and was the first consulting partner backed by Databricks Ventures. The firm employs more than 600 people across the United States, Canada, Argentina and Colombia and has been named a Databricks Partner of the Year multiple times since 2022, including the Energy & Utilities Partner of the Year and Brickbuilder Partner of the Year in 2026. "We built Lovelytics around the idea of putting together a great team of people who could solve the hardest data and AI challenges for companies," said Scott Love, founder and chief executive officer of Lovelytics. "Becoming part of CDW is the next chapter of that story, and I couldn't be more excited for that team to help CDW's customers move faster on data and AI and to take on even bigger challenges together." Lovelytics specializes in modernizing, governing and deploying data to improve performance and deploy AI at scale across sectors including energy, manufacturing, retail, healthcare, financial services and media. The acquisition will bring those capabilities to more customers, supported by CDW's scale and broad technology portfolio, the company said. "The AI era demands a data and AI foundation that is governed, open and built for agents at enterprise scale," said Andy Kofoid, president of global field operations at Databricks. "Lovelytics recognized this early, bet on Databricks as that foundation, and turned it into a market advantage that attracted customers from around the world. CDW's acquisition of Lovelytics validates what we've believed from the start: Companies that build the right data and AI foundation today will lead their industries tomorrow." The transaction is a deliberate investment in CDW's growth strategy, expanding its Services & Solutions portfolio in modern cloud, data and AI. It is not expected to have a material impact on CDW's financial results in 2026, according to the company. The acquisition is subject to customary closing conditions and is expected to close in the third quarter. CDW Corp., based in Vernon Hills, Illinois, provides end-to-end technology solutions for business, government, education and healthcare sectors in the United States, the United Kingdom and Canada. Lovelytics, now in its tenth year, has been named to the Inc. 5000 list of fastest-growing U.S. companies three times and helps enterprises across North America and Latin America move from AI pilots to production.

MarketScreener
Sep 2nd, 2026
CDW acquires data and AI firm Lovelytics for $525M to accelerate enterprise AI adoption

CDW announced plans to acquire Lovelytics, a data and AI services firm, for approximately $525 million. The deal expands CDW's Data & Analytics Practice, helping customers build data foundations to accelerate AI adoption. Lovelytics, founded in 2017 and headquartered in Arlington, Virginia, employs over 600 people across the US, Canada, Argentina, and Colombia. The firm specialises in modernising data estates and deploying AI at scale, with expertise across energy, manufacturing, retail, healthcare, financial services, and media sectors. According to Gartner research, only 12% of enterprises feel fully prepared on data readiness to support AI. CDW chief executive Christine Leahy emphasised that AI strategy requires a solid data strategy first. The transaction is expected to close in the third quarter and is not anticipated to materially impact CDW's 2026 financial results.

StartupHub AI
Aug 27th, 2026
Databricks links QSR data to profit.

Databricks links QSR data to profit. Databricks and Lovelytics launch a QSR performance tool that connects data signals to root causes and financial impact, enabling faster, informed decisions. Visual TL;DR QSR performance issuesDriver Unified data viewEffect Lovelytics QSR Control TowerCore Faster, informed decisionsOutcome Quick-service restaurant (QSR) leaders often grapple with performance metrics that signal a problem without revealing its origin, a challenge addressed by a new solution from Lovelytics built on the Databricks Lakehouse Platform. Traditional reporting might indicate a promotion missed its sales target, but falls short of explaining if the culprit was low guest demand, poor franchise participation, ingredient shortages, or flawed restaurant execution. The core issue, as highlighted by the Databricks blog, is that crucial data often remains siloed across different departments, systems, and franchise groups. This fragmentation delays the understanding of 'why' a metric changed, by which time the business cycle has already moved on to the next promotion or planning period. Connecting scattered signals. Lovelytics' QSR Executive Performance Control Tower aims to bridge this gap by creating a unified view. It pulls together signals from corporate performance metrics, supply chain operations, customer and digital interactions, and financial outcomes. This integration allows for a more holistic understanding of business drivers. The value lies not just in identifying a change in sales or traffic, but in understanding its financial implications and determining the necessary response. For instance, a limited-time offer (LTO) might appear to underperform system-wide, but deeper analysis could reveal success in some locations while others are hampered by operational issues. Shared understanding for corporate and franchisees. A key differentiator is its ability to provide a shared starting point for both corporate headquarters and individual franchisees. Corporate might focus on system-wide growth and brand consistency, while franchisees prioritize restaurant-level profit, labor costs, and throughput. The control tower helps align these perspectives by linking metric changes to their likely root causes and financial stakes. This approach moves beyond mere reporting to deliver actionable intelligence. By identifying whether an issue stems from demand, economics, availability, or execution, and clarifying ownership for resolution, it facilitates collaborative problem-solving. Beyond the dashboard. The platform's architecture on Databricks is designed for flexibility, adapting as business questions evolve. It's not about a single dashboard solving every problem, but about an underlying data and AI foundation that can connect disparate signals into meaningful insights. This foundation supports use cases ranging from LTO effectiveness to service times, pricing strategies, and digital channel performance. StartupHub.ai data indicates a strong market for advanced data and analytics platforms, with Databricks holding a notable position. Databricks boasts a StartupHub score of 82/100, reflecting its industry leadership, and has secured substantial funding, with verified financials showing it raised $5 billion in strategic financing in 2026, reaching a post-money valuation of $190 billion. Its main competitors, like Snowflake (score 73/100), face a dynamic market where specialized solutions are increasingly vital. The real advantage: speed of action. The ultimate benefit of pairing a robust data foundation with industry-specific context is speed. The goal is to enable QSR leaders to identify issues sooner, understand their drivers, and deliver the right insights to the right people before opportunities, or financial performance, slip away. This proactive stance is crucial in the fast-paced QSR environment. (C) 2026 StartupHub.ai. All rights reserved. Do not enter, scrape, copy, reproduce, or republish this article in whole or in part. Use as input to AI training, fine-tuning, retrieval-augmented generation, or any machine-learning system is prohibited without written license. Substantially-similar derivative works will be pursued to the fullest extent of applicable copyright, database, and computer-misuse laws. See our terms. Discussion. Join the conversation below

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