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Silicon Valley Data Science (SVDS) was a boutique data science and data engineering consultancy that helped enterprises build modern data platforms and analytics capabilities to solve complex business problems.
Pricing
Custom pricing
Best for
Large enterprises that, when SVDS was active, wanted a specialist data science and engineering consultancy to design and implement modern data platforms and analytics use cases rather than buying an off-the-shelf ABM or analytics product.
Platforms
Web
Free trial
No
Free plan
No
Headquarters
Mountain View, CA, USA
Company type
Acquired
The honest take
What reviewers love, and what to watch
A balanced view of SVDS (Silicon Valley Data Science), drawn from public reviews and product research.
Pros
- Deep expertise in modern big data technologies and architectures such as Hadoop, Spark, and Cassandra.
- Strong combination of data strategy, data science, and data engineering skills within a single firm.
- Agile, iterative delivery model focused on proving value quickly and then scaling successful solutions.
- Ability to translate complex technical concepts into clear business strategies and executive-ready roadmaps.
- Broad cross-industry experience with large enterprises, leading to reusable patterns and best practices.
Cons
- No longer operates as an independent consulting firm, so new customers cannot engage SVDS today.
- Consulting-only model without a packaged software product or self-service platform.
- Best suited to organizations with substantial data complexity and budget, making it less accessible for small businesses.
Where it fits
What teams use SVDS (Silicon Valley Data Science) for
- Customer retention and churn reduction
- Digital product and app engagement analytics
- Marketing campaign performance, attribution, and ROI measurement
- Fraud detection and risk analytics
- Patient engagement and outcomes optimization
- Designing and launching revenue-generating data products
- Operational optimization and resource allocation
- Modern data platform and cloud migration initiatives
- Data governance, compliance, and data quality management
- B2B account and customer segmentation to support ABM and targeting strategies
Key strengths
- High-caliber technical team with experience building multi-petabyte data platforms and advanced analytics.
- Proven ability to deliver end-to-end: from data strategy and architecture through engineering and data science.
- Industry-agnostic methodology that could be adapted to retail, financial services, healthcare, technology, and more.
- Collaborative, agile engagement style that emphasized rapid prototyping and measurable business outcomes.
Compare your options
SVDS (Silicon Valley Data Science) alternatives
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Accenture Applied IntelligenceDeloitte AnalyticsMu SigmaFractal Analytics
Questions, answered
Frequently asked about SVDS (Silicon Valley Data Science)
The short version is on the surface. Open any question to go deeper.
SVDS (Silicon Valley Data Science) was a boutique consulting firm founded in 2013 that specialized in data strategy, data engineering, and data science for large enterprises. It helped organizations design and build modern data platforms, develop advanced analytics and machine learning solutions, and create data-driven strategies to address challenges such as customer retention, digital engagement, fraud, and operational efficiency. The company operated until late 2017, when its core technical team was hired by Apple and the business was wound down.
SVDS did not offer a standardized SaaS product or public price list. Instead, it worked on custom consulting engagements where pricing depended on project scope, duration, and required expertise. Typical work involved multi-week or multi-month projects for mid-market and enterprise clients, with fees negotiated directly in a statement of work. Today, the firm is no longer active, so there is no current pricing available.
SVDS's core offerings centered on data strategy, modern data platforms, and advanced analytics. Key capabilities included defining data strategies and roadmaps; architecting and implementing data lakes and warehouses on technologies like Hadoop and Spark; building scalable data pipelines; developing predictive models and machine learning solutions; performing customer and marketing analytics; advising on data governance and data quality; planning and executing cloud migration for analytics workloads; and training and enabling client teams through workshops and seminars.
During its operating years, SVDS competed with both large global consultancies and specialized analytics firms. Comparable alternatives included groups such as Accenture Applied Intelligence, Deloitte Analytics, Mu Sigma, and Fractal Analytics, as well as data-focused practices within other technology consultancies. These firms similarly offer data strategy, data engineering, and advanced analytics services for mid-market and enterprise organizations.
SVDS was primarily geared toward mid-market and enterprise organizations with complex data environments, significant data volumes, and budgets to support multi-week or multi-month consulting engagements. While its expertise could theoretically help smaller companies, its consulting model and focus made it a better fit for organizations with dedicated data or IT teams and strategic transformation initiatives. Small businesses looking for an off-the-shelf analytics or ABM platform would generally be better served by SaaS tools rather than a boutique consulting firm like SVDS.
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