Big data EN

Data analysis: why is it important for my business?

Throughout our blog, we have discussed the importance of being able to collect data and make decisions based on the analysis of the data presented. 

But, what is?

Is a process that consists of analyzing information to extract meaningful data from a certain set. This analysis technique is carried out with big data in most cases, although it can be applied to any data set.

The main objective of data analysis is to help people and organizations make decisions based on patterns, behaviors, trends or preferences, thanks to a collection of data.

A clear example would be that companies can use analytics to identify their customers’ preferences, purchasing habits and market trends and then build from those learnings, create strategies to address them and manage changing market conditions.

Now, you’re probably wondering: What are the different types of analytics? 

According to this article, there are multiple analysis methods and techniques for data analysis, but there are four types that apply to any data set:

1.Descriptive: Refers to understanding what happened in the data set. As a starting point in any analysis process, descriptive analysis will help users understand what happened in the past.

2.Diagnostic: A descriptive analysis based to understand the “why” something specific happened. It enables users to gain insight into the exact information of the root causes of past events, patterns, etc.

3.Prophetic: Predictive analytics that will predict what will happen in the future. This will combine descriptive and diagnostic analysis data and use ML ( Machine Learning)  and AI ( Artificial Intelligence) techniques to predict trends, patterns, problems, and more.

4.Preceptive: Taking predictions from analytics and that go one step further by exploring how the assumptions will happen. This can be considered the most important type of analysis as it allows users to understand future events and adapt strategies to handle any predictions effectively.

It’s clear that data is the future and easily accessible data is a key component for any industry. Big data, data analysis and data science all help decision makers understand and extract essential information.

Want to learn about how LISA handles data? Click here

Big data EN

What will the future of data mining look like?

The future for data mining looks very promising at the moment and the data is only increasing. Did you know that during 2020, accumulated digital data grew by around 44 zettabytes and 1.7 megabytes per second for each person around the world?

Just as mining techniques have evolved, so have those technologies that are responsible for extracting valuable information. Not so long ago, only organizations like NASA could use their supercomputers to analyze data; the cost of storing and computing data was too high for most entities.  

Today, companies are doing all sorts of cool things with machine learning, artificial intelligence, and deep learning with cloud-based data lakes. This is the case with LISA Insurtech!

 Machine learning can generate unlimited knowledge about people and organizations. This is how companies can collect, store and analyze a large amount of information.

Cloud based technologies make it more convenient and cost-effective for organizations to access big data and computing resources. Thus, enabling companies to quickly collect data from sales, marketing, the Internet, inventory and production systems, and other sources; and act accordingly to improve results.

One may wonder, how do you choose a technology  that will get your business  the most value from data mining? Look for a platform that:

  • Incorporates best practices for the industry or type of project. Healthcare organizations, for example, have different needs than insurance companies.
  • Manage the entire data mining lifecycle, from data exploration to production.
  • It can be aligned with business applications, including BI, CRM, ERP, financial systems, and other business software that you must interoperate with for maximum return on investment.
  • It integrates with leading open source languages, providing developers and data scientists with the flexibility and collaboration tools to create innovative applications.
  • Meets the needs of IT, data scientists, and analysts while serving the reporting and visualization needs of business users 
Why is so important to the insurance industry?

One of the most important assets for the insurance industry is your information and everything we can do with it.

We can analyze, understand and carry out plans to offer better products, services and personalized attention to each of our clients.

Understanding that industries are changing with the current technological avalanche, forces us to move quickly in order to be up to what our old and new customers want.

LISA Insurtech has the necessary experience to accompany traditional insurance companies in their digital transformation

If you want to know more, click here

Big data EN

Data mining: What is it and why is it so relevant?

Technology has served as a springboard for many companies to get out of their comfort zone, undergoing a valuable metamorphosis that takes them to a competitive level and in this case, we want to talk about Data Mining and its importance.

Are you ready? Let’s get to the topic😊

What is Data Mining?

According to an article published by IBM, this is a process for finding patterns and valuable information from large data sets. This is made possible by the tremendous growth of big data and storage technologies.

The technology is constantly evolving to handle large-scale data, but leaders still face challenges with scalability and automation.

Did you know that the concept of “Data Mining” is not something from the Digital Age? This concept has been around for over a century, being most famous in the 1930s.

One of the first cases of Data Mining occurred in 1936, when Alan Turing introduced the idea of a universal machine that could perform calculations similar to those of modern computers.

On the other hand, Data Mining has greatly improved the decision making of organizations through the analysis of fully detailed data. In the same vein, we find two main purposes behind detailed analysis:

1.They can describe the target data set.

2.They can predict results through the use of machine learning algorithms.

Both methods are used to filter and organize data, displaying the most relevant information, from fraud detection to user behaviors, security breaches and bottlenecks.

These are the advantages of Data Mining

We can use data mining to solve almost any business problem involving data, including:

  • Revenue growth.
  • Understand customer segments and preferences.
  • Acquire new customers.
  • Improve cross-selling and up-selling.
  • Customer retention and loyalty.
  • Increasing the ROI of marketing campaigns.
  • Detecting fraud.
  • Identifying credit risks.
  • Tracking operational performance.
To summarize…

Data mining is not a new thing, but has been around since the 1930s and the first Turing tests. It is also very important for companies as it offers a lot of benefits from the analysis of a huge avalanche of captured data.

At LISA Insurtech we constantly equip ourselves with cutting-edge technology in order to revolutionize the insurance industry with faster and safer processes. Thanks to Data Mining we are able to detect fraud, segment the preference of the insured, among many other things.

If you want to know how we do it, please visit the following link.

Big data EN

Big Data: How you improve the capacity of the healthcare sector today?

Since its inception, technology has been one of the most important innovations to make people’s lives easier and more comfortable. The same happens within the health sector, since the study not only advances in medical treatments, but also new technologies play a fundamental role in achieving part of this continuous improvement.

An example of the above is how Big Data or data analysis is a fundamental foundation in recent research that has been carried out.

Alzheimer’s support

One of the fields where it has been working is in the early detection of Alzheimer’s and dementia, diseases that by 2050 will be affecting more than 150 million people around the world.

An example of this is the initiative proposed and launched by the European Union and the European Federation of Pharmaceutical Industry Associations (Efpia), through the IMI (Innovative Medicines Initiative) consortium. It is working on a system that seeks early diagnosis.

Mopead (Models of patient Engagement for Alzheimer’s Disease), has been designed by the GMV company to enhance citizen participation in early detection through a model in which an online questionnaire is filled out, where they participate in a medical examination and are subject to primary and tertiary care tests.

Likewise, researchers from the Universitat Politècnica de València (UPV) are working on the development of Artificial Intelligence (AI) libraries, which will aim to help the clinical diagnosis not only of Alzheimer’s but also of depression and some types of Cancer. This technology will be supported by Big data and supercomputing, which provides a great capacity for analyzing all types of information.

Big data against Coronavirus

As we have already mentioned, the massive analysis of data will be essential for the fight against various types of diseases, since the large amount of information becomes the key to better prevention. At the health level, work has been carried out to face a pandemic such as the Coronavirus.

In this context, the use that China made to contain the spread of the virus has become clear, even a Canadian startup, BlueDot, came to foresee the pandemic last December as a result of data collected and analyzed from social networks.

Currently, various projects are underway, such as geolocation-based tools, which will help control possible outbreaks of Covid-19 in hospitals. In Spain we find the case of Radar Covid, the most downloaded Coronavirus tracking tool.

There is no doubt that technology is not focused on a single place, but is present in various sectors in order to make its evolution easier through an infinity of tools.

At LISA we use technologies to provide the insurance industry with mechanisms to accelerate settlement processes, save on costs and prevent fraud. All this in order to guarantee a unique and innovative experience to maintain the satisfaction of the insured.

You want to know more? Click here.