Enterprise Data Science Platforms sector
Strategic acquirers, private equity (buyout funds and growth funds) firms, and valuation benchmarks for Enterprise Data Science Platforms
1.1 - About Enterprise Data Science Platforms sector
Companies in this category build enterprise-grade platforms to manage the end‑to‑end data science lifecycle. Enterprise Data Science Platforms unify data ingestion, preparation, experimentation, model training, and governed deployment across big data environments. Vendors provide scalable infrastructure, collaboration and reproducibility features, and integrations with cloud data lakes and warehouses to help customers operationalize machine learning and analytics faster with stronger controls and lower total cost.
Typical offerings include managed notebook environments and collaborative workbenches for Python and R, pipeline orchestration with ETL/ELT connectors into lakehouses and warehouses, and distributed compute engines such as Spark for large-scale training. Many provide AutoML, feature stores, experiment tracking, and model registries. Mature platforms add MLOps tooling for CI/CD, real-time and batch serving, monitoring and drift alerts, lineage, and role‑based governance.
Primary customers are enterprise data science teams, data engineering organizations, and digital product groups building machine learning into applications. These platforms help accelerate model development, improve predictive accuracy, reduce deployment friction, and standardize governance and compliance. Outcomes include faster time‑to‑insight for use cases like churn prediction and demand forecasting, lower infrastructure overhead through managed compute, and more reliable production performance.
2. Buyers in the Enterprise Data Science Platforms sector
2.1 Top strategic acquirers of Enterprise Data Science Platforms companies
Databricks
HQ: United States
Website
- Description: Provider of a unified data intelligence platform that helps organizations manage, analyze and operationalize data with AI; built on Lakehouse architecture and technologies such as Apache Spark, Delta Lake and MLflow, it supports large-scale analytics, data engineering and machine-learning workloads.
- Key Products:
- Data Intelligence Platform: Cloud-based environment enabling governed data sharing, engineering, warehousing, streaming and AI application development, giving unified workspace for analytics and machine learning workloads
- Cybersecurity: Solution protecting data assets in Databricks deployments, providing monitoring, threat detection and compliance controls to secure sensitive information and satisfy regulatory requirements
- Data Migration: Services assisting organizations in transferring datasets and pipelines to Databricks, ensuring minimal disruption through assessment, planning, execution and validation of cloud data moves
- Training and Certification: Programs delivering instructor-led courses, self-paced labs and official certifications that build user proficiency in Databricks tools, accelerating adoption and ensuring best-practice implementation.
- Company type: Private company
- Employees: ●●●●●
- Total funding raised: $●●●m
- Backers: ●●●●●●●●●●
- Acquisitions: ●●
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2.2 - Strategic buyer groups for Enterprise Data Science Platforms sector
M&A buyer group 1: Big Data Analytics
97 companies
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Description: Companies in the Enterprise Big Data Analytics Platforms category build cloud-native environments that unify data engineering, streaming, warehousing, and machine learning into governed analytics workspaces. They enable large organizations to ingest and normalize diverse data, apply AI to extract insights, and operationalize models at scale, reducing time-to-value while improving data security, compliance, and collaboration across business functions. These vendors are common strategic buyers of data infrastructure and analytics assets.
Databricks
- Type: N/A
- Employees: ●●●●●
- Description: Provider of a unified data intelligence platform that helps organizations manage, analyze and operationalize data with AI; built on Lakehouse architecture and technologies such as Apache Spark, Delta Lake and MLflow, it supports large-scale analytics, data engineering and machine-learning workloads.
- Key Products:
- Data Intelligence Platform: Cloud-based environment enabling governed data sharing, engineering, warehousing, streaming and AI application development, giving unified workspace for analytics and machine learning workloads
- Cybersecurity: Solution protecting data assets in Databricks deployments, providing monitoring, threat detection and compliance controls to secure sensitive information and satisfy regulatory requirements
- Data Migration: Services assisting organizations in transferring datasets and pipelines to Databricks, ensuring minimal disruption through assessment, planning, execution and validation of cloud data moves
- Training and Certification: Programs delivering instructor-led courses, self-paced labs and official certifications that build user proficiency in Databricks tools, accelerating adoption and ensuring best-practice implementation.
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Buyer group 2: ████████ ████████
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Key Products: ████████, ████████, ████████, ████████
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Buyer group 3: ████████ ████████
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Key Products: ████████, ████████, ████████, ████████
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3. Investors and private equity firms in Enterprise Data Science Platforms sector
3.1 - Buyout funds in the Enterprise Data Science Platforms sector
2.2 - Strategic buyer groups for Enterprise Data Science Platforms sector
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4 - Top valuation comps for Enterprise Data Science Platforms companies
4.2 - Public trading comparable groups for Enterprise Data Science Platforms sector
Valuation benchmark group 1: Enterprise AI Platform Providers
4 companies
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Description: Companies in this trading comparable group are public software providers delivering enterprise AI platforms and domain-specific applications. They monetize through subscription and usage-based licensing, complemented by professional services, and focus on deploying AI for predictive analytics, voice interfaces, and healthcare decision support. They are grouped together because they offer scalable, cloud-native AI infrastructure and applications to enterprise and healthcare buyers, driving comparable growth profiles and margin structures.
Tempus AI
- Enterprise value: $●●●m
- Market Cap: $●●●m
- EV/Revenue: ●.●x
- EV/EBITDA: ●●.●x
- Description: Provider of technology solutions including genomic sequencing, clinical data structuring, image recognition, biological modeling, and AI-powered platforms for precision medicine and optimized therapeutic decisions. Enables real-time, data-driven treatment personalization and the discovery of novel therapeutic targets.
- Key Products:
- Genomic Sequencing: Comprehensive sequencing for genetic insights
- Data Collaborations: Partnerships for shared clinical data analysis
- Biological Modeling: Advanced modeling for biological processes understanding
- Companion Diagnostics: Tools for pairing treatments with genetic profiles
- Clinical Trials Execution: Support for efficient clinical trial operations
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Revenue growth
Median
15.7%
2025E
16.0%
2026E
EBITDA margin
Median
28.2%
2025E
28.6%
2026E
Cash EBITDA margin
Median
27.6%
2025E
28.0%
2026E
EV/Revenue
Median
8.4x
2025E
6.8x
2026E
EV/EBITDA
Median
28.9x
2025E
24.4x
2026E
EV/Cash EBITDA
Median
29.0x
2025E
25.5x
2026E
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