Silicon Valley Data Science (SVDS) is a big data and data science consulting company that specializes in agile and business-focused solutions. The team’s experience in building products, engineering systems and strategic consulting has led the company to develop a distinctive methodology that blends the best of agile product development and iterative consulting techniques. SVDS works with clients to iterate towards big data applications that create meaningful business impact.
Silicon Valley Data Science works in the rapidly-changing big data industry. Currently, it’s difficult for companies to reap the full business advantage that big data offers. The key to unlocking big data is building skilled and proficient data teams, yet this expertise comes at a premium and can be hard to find. Silicon Valley Data Science is the solution by bringing in data teams who are talented, know how to meet business needs with technology, and balance data science and engineering.
Silicon Valley Data Science’s founders identified the need for a new approach to providing data science, big data and engineering services―inspired by the technology and product development techniques that power Silicon Valley’s success. The company has assembled a team of scientists, engineers, architects, consultants and entrepreneurs who have built data-driven applications, engineered large-scale data systems, driven deep analytical insights, interpreted data for business users, and translated insight into action. The management team has a wealth of experience from companies large and small, including Accenture, @WalmartLabs, O’Reilly Media, LiveOps, Joyent, Tivo, SPSS (IBM), Yahoo!, Sun Microsystems (Oracle) and Chicago Board Options Exchange.
SVDS’s consulting engagements range from advisory and strategy services, providing education and investigation, through proofs of concept and pilot implementations, to full scale production systems supporting data-driven business processes. Successful investments in data begin with business needs in mind: defining a data strategy and development roadmap pays dividends and helps realize value quickly. At every stage, from planning through to production, SVDS works across both data engineering and data science. While different projects will use each in different proportion, both are vital.
The pattern of big data technology adoption by business parallels many of the characteristics of earlier technology transformations: rapid advances in technology, extensive changes in infrastructure and middleware, and a lack of skills in the marketplace to help businesses to adapt and thrive. SVDS aims to capture the opportunity at the intersection of a growing amount of data and companies’ goal and needs of becoming more intelligent via this continuing data growth. IDC predicts annual spending on big data technology and services will reach $48.6 billion in 2019.
SVDS has the potential to become a leader in the big data analytics and business intelligence industry.
The management team brings decades of experience with consulting projects and big data analytics. SVDS has been able to engage with a number of clients from the company’s inception and has built a strong customer pipeline. The company’s business model can be profitably managed by scaling in a smart way to capture new revenue opportunities without excess spending on consultants and new technologies. Over time, the company also will improve processes and cross-apply learnings to new project which will improve outcomes for customers and increase employee work efficiency.
The company works in a highly technical and rapidly changing field. Employees need to be retrained regularly to stay current with the technologies and tools relevant to big data consulting. Employee retention, business execution risk, and client acquisition are also other applicable risk factors.
GSVC percentage figures are based upon the fair value of each holding as of the quarter ended September 30, 2016, or the cost basis of the holding (exclusive of transaction costs) if the investment closed subsequent to September 30, 2016. In either case, these values are divided by the fair value of total portfolio investments of GSV Capital as of September 30, 2016.