Challenge - What to Do With All that Data?
Without the right tools and know-how, extracting usable insights from what may be vast amounts of data can be an overwhelming task. Additionally, the exercise may prove unproductive if companies are unable to transform that data into value–in the form of improving revenue, business agility, and positive customer experiences, while reducing costs, developing new products, and the like.
Solution - Giving Data a Purpose
We help drive our clients’ competitive advantages by facilitating their transformation from a traditional to a data-driven organization. Our Data Science experts:
- Extract value from data of all sizes–structured, unstructured, or semi-structured–as actionable business insights
- Employ a wide range of tools, including SQL, Python, R, Java, and open source projects like Hive, Hadoop, Spark and TensorFlow
- Perform comprehensive data-related tasks, ranging from extracting and cleaning data, to subjecting data to algorithmic analysis via statistical methods or machine learning
- Are experienced at enhancing all business value chains: talent management, customer service, marketing, and logistics, to create a data-driven culture
Building flexible and cost-effective solutions for high-demand systems requires leveraging the cloud. To this end, for the past decade we’ve partnered with Amazon Web Services, Microsoft Azure, and Google Cloud Platform.
Our data scientists resolve problems related to classification, recommendations, prediction, optimization, anomaly detection (fraud), and more.
Using machine learning, genetic algorithms, and deep learning statistical methods, we assist across the full range of business needs:
- Insights Discovery and Features Correlation. We use customer insights to personalize services, conduct churn analyses and market research, as well as other applications.
- Classification and Clustering. Our team segments customers and classifies issues to extract meaning from large volumes of data.
- Prediction. We make reliable predictions and forecasting based on data, as well as personalizing simulations for customers, and conducting new product tests.
- Recommendation and Ranking. These are developed using behavior-based next best sell, robo-advisors, search engines, and cross-selling techniques.
- Anomaly Detection. We are skilled in fraud detection, error handling, and security risk-detection.
Our scientists design and implement solutions for processing mass sets of data using flexible and automated infrastructures.
We design data engineering strategies and software development for scalable and data-intensive systems. Our team implements architectures capable of managing historical data and content that require real time processing.
Structured and Unstructured Data
Using the latest technology, we are experienced in analyzing structured data, such as transactional data, and unstructured data, including emails, social media posts, and audio and visual archives. With unstructured data analytics, we can, for example, leverage NLP data to enhance customer service by building AI-powered chatbots.
Using CRISP-DM methodology, we take a wholistic approach that evaluates business context when applying results. It follows an iterative cycle in which our technical team works closely with the business team, interpreting datasets and evaluating the application of extracted insights.
We transform knowledge extracted from data, ensuring it is easily understood by decision makers. Our emphasis is on identifying trends, as well as the distribution of information in time, space, and, sometimes, together with exogenous variables that shed light on variances in information.