SVDS (Silicon Valley Data Science)

Put Your Data To Work.

ABM Strategy Agencies

2013 Founded

51-200 Employees

Mountain View, CA, USA Headquarters

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Quick Facts

Website

svds.com →

Company Type

acquired

Phone

(650) 397-6570

Address

333 N Michigan Ave # 1320, Chicago, IL 60601 →

Business Type

Software company

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About SVDS (Silicon Valley Data Science)

Silicon Valley Data Science (SVDS) was a big data and data science consulting firm founded in 2013 to bring Silicon Valley–style data-driven innovation to large enterprises. The company specialized in combining data strategy, advanced analytics, and modern data engineering to help organizations use data as a strategic asset. SVDS worked across industries including retail, financial services, healthcare, technology, media, and industrials, building platforms and models that addressed customer retention, digital engagement, fraud, operations, and more.

SVDS’s services were organized around three core pillars: Data Engineering, Data Science, and Advisory Services. On the engineering side, the firm designed and implemented scalable data platforms using technologies such as Hadoop, Spark, Cassandra, and modern cloud infrastructure. Its data science teams built predictive models, machine learning pipelines, and experimentation frameworks to power use cases like customer segmentation, churn prediction, recommendation, and real-time decisioning. Advisory offerings focused on data strategy, data governance, cloud strategy, and data maturity assessments, producing roadmaps that tied technical investments directly to business outcomes.

The firm positioned itself not as a traditional systems integrator but as a high-caliber, agile team that could rapidly prototype solutions, validate technology choices, and then harden those prototypes into production-ready systems. SVDS invested heavily in thought leadership around modern data platforms, Spark, Hadoop, and data governance, speaking at major industry conferences and publishing extensive technical content. It also partnered with GSVlabs to launch Silicon Valley Data Academy, an immersive program to train enterprise-class data engineers and data scientists, further cementing its role in the data ecosystem.

SVDS operated from April 2013 through December 2017, during which it raised approximately $4.5M in venture funding. In late 2017 and early 2018, Apple hired much of SVDS’s core technical team, including key founders and leaders, and the consulting business was wound down. While the company no longer takes on new clients, its website remains online as an archive of blog posts and case studies that continue to be referenced by data professionals.

Key Features

Data strategy development - aligning data initiatives with business goals and defining prioritized roadmaps.

Modern data platform architecture - designing cloud and on-premises data lakes, warehouses, and analytics platforms.

Data engineering - building scalable pipelines for data ingestion, transformation, and integration across disparate sources.

Advanced analytics and data science - developing predictive models, machine learning algorithms, and controlled experiments.

Customer analytics - segmentation, churn prediction, and lifetime value modeling to improve retention and cross-sell/upsell.

Digital product engagement analytics - analyzing app and web behavior to increase engagement and monetization.

Marketing and campaign analytics - measuring and optimizing ROI, attribution, and audience targeting for marketing programs.

Data governance and data quality - frameworks, processes, and tooling for stewardship, lineage, and compliance.

Cloud strategy for data - selecting services, providers, and migration patterns to move analytics workloads to the cloud.

Optimization and operations analytics - using data to match resources to demand, remove bottlenecks, and reduce failures.

Data maturity and capability assessments - evaluating people, processes, and technology to identify gaps and opportunities.

Technology and architecture benchmarking - empirical evaluations of tools and architectures to de-risk major investments.

IoT and device data solutions - ingesting and analyzing data from hardware, sensors, and medical or industrial devices.

Training and enablement - workshops, seminars, and coaching to upskill client data, engineering, and business teams.

Executive advisory - guiding leaders on how to become data-driven organizations and structure data teams and investments.

Best For

Company Size

mid-market enterprise

Industries

Retail & eCommerce Financial Services Healthcare & Life Sciences Technology & Software Media & Entertainment Industrial & Manufacturing

Use Cases

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

FAQ

What is SVDS (Silicon Valley Data Science)?

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.

How much does SVDS (Silicon Valley Data Science) cost?

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.

What are the main features of SVDS (Silicon Valley Data Science)?

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.

Who are SVDS (Silicon Valley Data Science)'s main competitors?

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.

Is SVDS (Silicon Valley Data Science) good for small businesses?

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.