At Trianz, we believe Data Science holds great promise as the next giant leap in the evolution of business intelligence to convert data into information and knowledge. Integrating large volumes of data with advanced analytics techniques, modern computer technology, and domain expertise within specific business sectors, our Science service offering springs from our clients’ growing demand to look inside the crystal ball of Big Data to predict the future of their enterprise.
Trianz’ data science consulting services are ideal for businesses that want to understand why they are losing ground to competition, or if they want to predict future earnings based on a number of converging factors: past performance, current and forecast economic conditions, social media sentiment, website activity, and customer churn rates, among others. What sets Trianz’ data scientists apart from pure statisticians is our computer programming expertise -- which we use to manage large datasets -- combined with our emphasis on business domain research and knowledge, which we leverage to guide our analyses.
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To manage all this data, our data science specialists use new tools and technologies including Hadoop, MongoDB, Map-Reduce, Natural Language Processing (NLP), columnar databases, NoSQL, Cassandra, Pig, Hive, and Impala, just to name a few. Businesses and organizations now have access to enormous amounts of data that come in as and/ or can be converted into a variety of forms such as key value pairs, documents, object notation, text, free-form language, columnar data, etc.
By combining our business intelligence, data warehousing, and Big Data expertise with advanced statistical techniques, we are able to provide meaningful added value to your analytics initiatives.
Our Science services focus on the following areas:
- Predictive modeling
- Advanced statistical analysis
- Machine learning
- Data mining
- Embedded analytics
- Unstructured Data Management
- Large data volumes
- Complex data integration
- Industry-specific research
- Business processes analysis