Over 15 years of dedication to Data and Analytics
Sngular Data & Analytics is the result of a long-standing history in data mining and analytics. Daedalus S.A. was founded in 1998 as a spin-off of two academic research groups from the Polytechnic University and the Autonomous University of Madrid (Spain) with the aim of providing advanced solutions in the areas of language technology, data mining, web technology, and business intelligence.
In 2015, Daedalus became Sngular, a company with international presence and offices in the United States, Mexico, and Spain.
Since then, we have become Sngular Data & Analytics, the division dedicated to extracting value from structured and unstructured data.
Sngular has provided solutions to first-rate customers in various industries such as media, telecommunications, government and financial services. We have also participated in advanced international R&D projects, gaining a high reputation as an innovative company committed to the success of its customers.
We have developed both an advanced technology for data analytics (see MeaningCloud) and a highly experienced team that includes data scientists, data engineers, data managers, software engineers, and computational linguists.
Best-in-class at analyzing structured and unstructured data
Structured data is information displayed in titled columns and rows which can easily be ordered and processed by data mining tools. Typically, structured data comes from ERP or CRM-like sources. Most analytics companies are likely to be proficient in analyzing this type of data. Structured data is regularly the strongest part of most analytics teams. We have a lot of experience in that, too.
But we are witnessing the explosion of unstructured data. Examples of unstructured data include: emails, social media posts, text documents and audio/video files.
All industries need to exploit both types of data. Unlike many other Big Data companies, Sngular is highly experienced in both structured and unstructured data. The benefits of analyzing both types of data are countless: from managing internal knowledge to preventing fraud, as well as carrying out a 360-degree analysis of our customers.
As for unstructured data, MeaningCloud is one of the most powerful ways to extract meaning from unstructured content, social conversations and internal documents.
Combining Big Data and Data Science
Big Data and Data Science are two different yet complementary fields. We have specialists in both.
When we talk about Big Data we are referring to the data engineering strategies for designing and implementing data-intensive scalable systems. It focuses on the development of software that is capable of handling large amounts of data (volume), data that needs to be processed in real time (velocity), and data in a variety of formats. In short, the goal of Big Data is to build the infrastructure that supports horizontal scalability and a response time appropriate for each project.
Data Scientists will build analytical data processes upon these architectures to extract value from data.
We work on Big Data projects whose goal is to make scalable systems. In other projects we leverage Data Science techniques to focus on analyzing and extracting value from data sets.
We endeavor to find the right balance between analytics, business needs, and operational constraints. We call this the “Common-sense-driven approach.”
Many years in the field have taught us to have a rather cautious approach towards data solutions: “being possible is not enough.”
Rather often, a number of data scientists select an analytic technique to achieve a miraculous solution. Unfortunately, the solution is not always possible or simply economically unwise, once the various constraints are properly weighted.
We always balance the results we promise with business constraints (including sensitivity to time, money and people). Engineering constraints are also important: the solution might turn out to be so expensive due to its complexity that the team has to reject it.