Sngular Data & Analytics is the result of a long-standing history in data mining and analytics. As far back as 1998 as a spin-off from the Polytechnic University and the Autonomous University of Madrid (Spain) we were already providing advanced solutions in the areas of language technology, data mining, web technology, and business intelligence.
In these almost 20 years, Sngular has provided solutions to first-rate customers in various industries such as:
The challenge banking companies are currently facing comes from the volume of data available. Like so many other sectors, banking needs to figure out how to translate data into something useful, something that can be used in the company’s decision-making process.
The banking domain has the added difficulty of providing an extremely wide array of products and services, each one of them with very specific subcategories. Data arrives through completely different channels.
As for unstructured content, in this digital age feedback is ever-growing and not just limited to the periodic surveys sent to clients. Word-of-mouth has gone digital and has become more relevant than ever: everyone with a Twitter or Facebook account has an opinion, and more often than not, it’s about the products and services they consume.
Voice of the Customer combines two key needs of information extraction: knowing in detail what the customer is talking about and correctly interpreting his feelings about it. The former provides a quantitative view of the feedback obtained while the latter provides a more qualitative analysis, measuring what clients think a company is doing right or wrong.
Insurance companies collect huge volumes of data on a daily basis and through multiple channels (structured data, customer care centers, emails, social networks, the web in general). The information collected includes policies, expert and health reports, claims and complaints, results of surveys, relevant interactions between customers and non-customers in social networks, etc.
Insurance companies also have to cope also with the challenge of combining the results of the analysis of these textual contents with structured data (stored in conventional databases) to improve decision-making. In this sense, industry analysts consider it essential to use multiple technologies based on Artificial Intelligence (intelligent systems), Machine Learning (data mining), and Natural Language Processing (both statistical and symbolic or semantic).
Media companies collect enormous amounts of business data every minute: advertising data, sales, anonymous readers and registered users, data from subscribers of paper editions and online users, data on the content users create, and many more.
Data plays a key role in predicting which readers are more likely to unsubscribe and can allow sales and marketing teams to try and retain these subscribers who are at risk of leaving the company.
So far, the business model in the online world has been focused on advertising: mainly banners and some incursion into native advertising and branded content. Basically, the more ads users see, the higher the benefit to the company. In this task of retaining users, data tells us what content is more likely to engage each individual user, driving them to visit more pages and spend more time on our site.
Personalizing both advertising and content according to each user's interests and purchase history certainly helps improve profitability.
On the other hand, semantic technology allows us to "understand” the structure and the meaning of digital content, which facilitates search, integration, production, and publication tasks.
Most Telecom companies are focusing on using internal data to conduct analytics programs that enable them to measure the actual use of their resources in order to optimize their network.
On the other hand, they are interested in getting to know customer behavior and make key business decisions that allow them to transform big data sources into valuable information and business action. Typical areas of Big Data Analytics for the Telecom sector include fidelity enhancement (+ churn rate optimization), lifetime value segmentation and cross-selling and upselling.
Life Sciences and Pharma
Escalating costs in the sector of the Life Sciences industry is forcing it to rely on data to make critical business decisions. As a result, Pharmaceutical industry organizations are analyzing big data to reduce costs, address problems related to variability in healthcare quality, and escalate healthcare spending. Specifically, by studying hospital readmissions, data mining is helping the sector evaluate which treatments are most effective, among other important information.
The trend of digitalizing and decentralizing clinical practice has brought about the dispersal and disintegration of health information. The technology behind MeaningCloud helps to automatically extract the concepts in any type of multimedia health content and classify them by taxonomy, find and retrieve information by using professional health language (as opposed to the technical one), and integrate dispersed files according to their content.
However, people enjoy sharing information through social media, including healthcare data. Conversations around drugs, symptoms, conditions, and diseases can be analyzed to learn more about them. For example, using social media we can see how some people buy and sell certain drugs, perhaps illegally.
Retailers are leveraging structured and unstructured data about their customers’ behavior at every stage of the retail process: forecasting demand, predicting trends, and optimizing prices.
Retailers are obviously very interested in uncovering the associations and connections between specific products in their stores (both physical and online). In Basket Analysis you look to see if there are combinations of products that frequently co-occur in transactions. It is often applied to the layout of the store by putting co-occurring products close to one another.
On the other hand, we can forecast trends by analyzing social media posts and web browsing habits to work out what’s causing a buzz. Sentiment analysis, along with machine learning algorithms, is being used to predict what the top-selling products will be.
Energy efficiency is a global challenge. Energy Intelligence, in terms of demand prediction and consumption control, is one of the keys to achieve it.
Sngular works with energy producers and consumers, bringing them prediction and optimization tools that help them to adjust production to the demand and manage an intelligent energy consumption.
Smart cities are likely to improve our quality of life at many levels. Big data can help reduce emissions and lower pollution. Parking problems can be managed better. Sngular also works with energy producers and consumers, bringing them prediction and optimization tools that help them adjust production to the demand and manage an intelligent energy consumption.
For example, the CIUDAD2020 project, in which we participated actively, aims to develop a new model of smart city that is both ecologically and economically sustainable in which analyzing the citizens' actual demand and communicating the opportunities offered by the Internet and the growing number of devices connected are the foundation for delivering public services tailored to the needs of the citizens.