Bringing Structured and Unstructured data together to solve business problems

Over 80% of business information comes from unstructured data (email, social media, contact center…). We are best-in-class at solving business problems that have both structured (ERP, CRM…) and unstructured data sources to extract value from. Learn more

Why partner with s|Data&Analytics

s|ngular Data & Analytics is the result of a long­standing history in data mining and analytics. s|ngular has provided solutions to first­rate customers in various industries such as media, telecommunications, government and financial services.

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Customer Analytics: Veni, Vidi, Vici

We show you how Big Data technology enables us to obtain a 360-degree customer view and thus to offer what the market really demands. In this way, we can reduce the risk of abandonment and satisfy and retain customers more easily.

ADAM: Automated Discovery and Analysis Machine

As data scientists, we have to deal with bad formatting data sets with missing or wrong values, and many other problems that hamper our progress. Some studies, such as the Cleaning Big Data: Most Time-Consuming, Least Enjoyable Data Science Task, Survey Says, found that data scientists spend 60% of their time on cleaning and organizing data. Once this process is completed, data analysis phase is performed. In this task, values, histograms, variables distribution, and correlations between them are studied. Most of the time, modeling phase involves repetitive analysis tasks, such as selecting the best algorithm by using automated procedures (for example, GridSearchCV in scikit-learn), or features selection process applying different predefined techniques. In the end, all Data Analytics projects are very similar regarding methodology and techniques applied. ADAM (Automated Discovery and Analysis Machine) system is developed in order to optimize our time and focus more on intellectual labors and techniques to solve the specific problem. ADAM is a framework that helps us to perform an automated analysis of the data set by applying Data Science techniques.

CRISP-DM Phase V: Evaluation

In this post, we continue describing the CRISP-DM (Cross Industry Standard Process for Data Mining) methodology, after our previous post Phase IV: Modeling. In this case, we discuss the fifth phase of the data analysis project, known as Evaluation.

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