Driving Business Outcomes with Modern Analytics
With our Data Science Services
We help you prioritize and plan your implementation, while considering business impact, existing data, technology and skills.
Our technology best practices and pre-built framework components work with a variety of platforms and tools that accelerate time to value.
With open source tools at the core, we help organizations build big data infrastructures that can easily adapt and scale to meet your future needs.
Our Data Scientists excel at identifying opportunities and insights from unstructured and non-traditional data sources.
Our managed services support big data platforms and applications. Our experts use well-defined processes to deliver continuous improvements.
Our certified experts provide a variety of courses in Hadoop, Spark, NoSQL, Cassandra, Cascading and Big Data Concepts.
“Think Big’s breadth of knowledge of big data technologies made them an obvious choice, along with the extensive real-world experience of their consultants. The latter is critical – we need to know what works in practice rather than in a white paper”
Jamie Turner, Chief Technology Officer, PCA Predict
Big data dominates the headlines, yet most organizations have not figured out how to turn exponentially growing volumes of data into business value. Taking the right approach to big data, including implementing the right technologies, processes and analytics to achieve desired business outcomes, is critical.
Big data is no longer a choice – it’s a must. With an increasing number of large and small companies alike jumping on the bandwagon, the market has witnessed a skills shortage – whether it is data scientists, engineers, project managers, developers – the list goes on and on. Having worked in the big data …
In the race to adopt data science capabilities, it is common for companies and services to focus on the ‘operationalization’ of data science. However, this focus on operationalization can leave data science groups taking ‘operational analytics’ for granted. Not surprisingly, the similarity of these terms causes confusion. Let’s first focus on the differences between operational …