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Big Data, Business Intelligence and Data Science

It is clear that in a world where digital transformation is here to stay, every organization must have the ability to collect, select, organize, analyze and interpret a large volume of data. In this sense, the proper management and knowledge of data are key and strategic in any business sector. For this reason, more and more companies are deciding to invest in data analytics solutions based on the premise that information is power (and that data is the currency of the digital economy).

In the current context, there are a number of methodologies that seek in some way to facilitate data-driven decision making. Companies that take on the challenge of knowing and interpreting their data benefit in a variety of ways, either by saving costs, gaining speed, improving customer service, anticipating the competition, improving the operational management of the business, and identifying new business opportunities. In short, obtaining these benefits will depend on the right choice of the most suitable data analysis processes for the company.

Concepts such as Big Data, Business Intelligence and Data Science have in common the intention of extracting value from information, although they do so in a different and complementary way.

Big Data refers to the storage of large volumes of data and the procedures used to find repetitive patterns within this data. Big Data focuses on the capture and processing of data, working with a large amount of complex data (structured and unstructured) coming from various sources, such as sensors, smart devices, websites and social networks, among others. The amount and complexity of this data makes it difficult to analyze and manage if the appropriate tools are not used.
Business Intelligence is responsible for data management, data organization and production of information from data. It is applied in organizations to fundamentally improve decision-making capabilities by performing data mining tasks, analyzing business information, and generating reports. Business intelligence is predominantly used for the analysis of stored historical data, impacting business performance, but without being able to predict future data. In this sense, it is oriented to the past, studying the historical evolution of the company to understand its development by finding analytical patterns. Business intelligence is the set of applications, methodologies and technologies capable of transforming data into valuable and structured information to be used for business purposes. It focuses specifically on internal company and industry data. Some examples of data analyzed through Business Intelligence are those related to marketing, customer service, sales, or data from the company’s human resources. It is precisely through Business Intelligence that the analysis of data obtained from Big Data can be carried out.
Data Science could be considered as an evolution of Business Intelligence. Its objective is the generation of value from the collection, classification, visualization and corresponding interpretation of data. This more complex data analysis helps the company to generate new knowledge by discovering and answering new questions. To do so, it uses a range of techniques involving statistics, computer science, predictive analytics, and machine learning. In this way, Data Science makes it possible to analyze massive data sets, seeking solutions to problems that have not yet been thought of. The data being analyzed are both internal and external, for example, videos, emails and social media content. Data Science experts can predict potential trends by exploring seemingly unconnected data sources, finding better ways to analyze the information.

This type of solutions based on data analysis to make better business decisions, are no longer seen as tools intended only for large companies, but are increasingly SMEs interested in these technologies and methodologies, working in an integrated way to get the most out of the growing volume of data.

At Macrotest we have the #DataLab division, seeking to help companies through comprehensive solutions for data management, data analysis and implementation of artificial intelligence for prediction and personalization of services.

We are at your disposal to solve all your data analysis needs!

Technology trends

Technology is advancing continuously, at an unprecedented pace, and its application is taking place in the most diverse sectors. Agriculture and health are two of the most dynamic sectors where technology is constantly advancing. Below, we review some of the most relevant developments in terms of technological trends.

In the agricultural sector in particular, technological innovations are of such magnitude that they have led to the emergence of new concepts such as “green revolution” or “agriculture 4.0”. An example can be environmental monitoring through electronic sensors installed on crops and the possibility of automating irrigation systems. Another of the most frequent technological applications is the use of drones that, through remote sensing, make it possible to obtain images and videos remotely. The application of artificial intelligence to the management of a large volume of data from agricultural machinery, for example, is also key, as is the Internet of Things (IoT), which allows real-time information to be obtained and facilitates traceability through the installation of devices such as chips. It also highlights the automation of machines and the use of GPS in tractors, allowing to know the exact measurements and distances in crops. The application of Big Data also entails a series of important benefits in the agricultural sector, such as obtaining information on weather conditions, the state of the soil, the crops planted, the amount of inputs needed according to the environment, and identifying anomalies in crops, such as pests or diseases.

In short, the application of these different technologies in agriculture allows not only to improve yields or increase efficiency in the use of resources, but also to anticipate and identify certain risks. For example, at Macrotest we have the Agroint solution for intelligent crop management, minimizing the impact of pests, diseases and weeds through monitoring and geolocated records from cell phones.

The health sector is not lagging behind in terms of new technological trends. In recent years, technology applied to healthcare has not only focused its efforts on saving lives, but also on making daily life more comfortable.

Among the main technologies is the use of virtual reality in medical training, involving realistic scenarios such as surgery or emergencies. Once again, the application of artificial intelligence appears as fundamental, whether to monitor patients, assist surgeries by means of automated devices, or even predict the situation of high-risk patients.

Another trend is access to remote healthcare, for example through online chat platforms available 24 hours a day through chatbots that apply artificial intelligence, or even medical consultations by video call. There are also implantable and ingestible sensors that facilitate the diagnosis, treatment and monitoring of certain diseases through the Internet of Things.

Mobile applications can also be mentioned, which contribute to improving the healthcare system through the prevention, control and treatment of numerous diseases, including monitoring the follow-up of patients with diseases such as diabetes or epilepsy. Meanwhile, Cloud Computing applied to the healthcare sector makes it possible to speed up access to patients’ clinical data, as well as to improve communication between healthcare centers. These trends as a whole currently imply benefits both for healthcare professionals and for the users of the healthcare system.

In this line in Macrotest we have developed a solution for patients who suffered a stroke which allows the realization of their rehabilitation from home, based on years of research, development and innovation.

We are waiting for you to deepen these applications that promote the development of the agro and health sectors.