The use of digital technologies has turned out to be a real gold mine for the production of a valuable resource: data generation.
Devices that are always connected (IoT) and easy to use, increasingly intelligent production processes, ever wider digital transformation combined with a reduction in costs, have lowered the threshold of access to these technologies, making them available to all users. At the same time, the development of analytical platforms and algorithms based on machine learning and artificial intelligence have made it possible to transform this mass of information, for example consumer habits and behaviors, into valuable and useful data for companies.
The development of the IoT and IIoT (Industrial Internet of Things), which uses sensors to improve people’s lives and business processes, is also data-driven.
The Digital transformation allows you to transform every touch point, digital contact, whether it is a totem in a railway station or a website or a newsletter or high in a valuable source of information, a real mine of data.
The large amount of data, structured and above all unstructured, generated by digital technologies are Big Data. Unstructured data because video, text, audio and any other digital track are currently widely used and form the company’s big data.
Mature companies already exploit this new resource to improve the offer of their products and services, optimize costs and resources, reduce waste, devise new marketing strategies, increase customer retention, predict new market trends and exploit them to your advantage.
What are Big Data used for?
Big Data are therefore the raw material needed to generate new knowledge, useful for making more informed decisions and implementing the best actions/ strategies.
By integrating these data with the basic information assets (most of the times little used, or used only to make descriptive analyses on past events) companies can carry out advanced analyses with predictive and/or prescriptive purposes, with a significant impact on multiple business processes (Advanced Analytics).
What are the sources of Big Data?
The sources of Big Data are numerous and diverse. Among them, we find:
- Social Media Data: Platforms like Facebook, Twitter, Instagram, and LinkedIn generate vast amounts of data daily in the form of posts, comments, likes, shares, and interactions.
- Websites and Search Engines: Every time a user visits a website, performs a search, or clicks on a link, data is generated that can be analyzed to understand online behavior and user preferences.
- Transactional Data: Financial transactions (online purchases, credit card payments) produce data that can be used for market analysis, sales monitoring, and fraud detection.
- Sensors and IoT (Internet of Things) Devices: Connected devices (surveillance cameras, smart home devices, industrial sensors) generate real-time data on various parameters such as temperature, humidity, movement, etc.
- Mobile Device Data: Smartphones and tablets generate data through apps, geolocation, network usage, and other mobile activities.
- Healthcare Data: Electronic medical records, medical devices, and fitness applications generate patient data that can be used to improve treatments and monitor public health.
- Public and Open Data: Governments and non-governmental organizations often release public data that can be used for social, economic, and environmental analysis.
These sources generate data in various forms (structured, semi-structured, and unstructured) and volumes, requiring advanced tools to extract meaningful insights.
How to extract value from Big Data?
To get new value from the analysis of Big Data you need:
- the right technology infrastructure such as data warehouse, data lake, cloud systems rather than on premise;
- proper management of business processes in a “data-driven” environment;
- introduction of Business Intelligence & Analytics in systems capable of integrating analysis models and algorithms of Machine Learning and Artificial Intelligence;
- business analytics and data science skills to develop the project.
At this point the data integration and data visualization tools (e.g. SAS, Qlik, Dataiku, Azure Machine Learning, etc.) will do the rest, providing the user manager with simple and intuitive analysis dashboards, to allow him to do in-depth analysis without any IT expertise, focusing exclusively on the aspects important to his business.
What does this mean? Immediately obtain all the information to decide how to improve an aspect of your business or a specific business area (sales&marketing, finance, production, logistics, etc.) basing your decisions on reliable data and algorithms and concrete scenarios.
The 5v of Big Data
To date, big data capable of generating value are catalogued according to the 5v model:
- Veracity – reliable data.
- Volume – high mole (constantly growing).
- Velocity – data is generated and captured in real time.
- Variety – data from different sources, in different formats, even unstructured.
- Variability – their meaning changes depending on the context.
To the 5 Vs, a new V has recently been added:
- Value – data is transformed into valuable insights.
In order for big data to express its potential, it is necessary that it is managed in a global project, where the various business departments are no longer analyzed in compartments, but share their information in a single system capable of creating connections, to allow deep data exploration, to bring out unpublished information, which would otherwise remain hidden in an insignificant amount of data. An appropriate data lake or data hub on which to base the consolidated concept of datawarehouse or specialized data mart.
How can companies increase their profits with Big Data?
Thanks to Big Data and a Business Intelligence & Advanced Analytics project, any company, even small, can achieve its business objectives and see its profits grow.
Cloud Computing and innovation allow today to bring these technologies to the market for small and medium enterprises. According to the Big Data and Business Analytics Observatory of the Politecnico di Milano, 44% of Italian SMEs have increased awareness of the need to analyze and exploit the data collected. To manage the Digital Divide as a possible cause of a deep selection on the market, topic that we will deepen in another article.
Examples of Big Data applications in different sectors
Big Data in Marketing
A deep understanding of customers, their purchasing habits, product usage patterns, and additional data such as movement and consumption preferences enables more precise audience profiling. This allows businesses to target key drivers that enhance customer satisfaction, increase engagement, and improve customer retention.
Similarly, integrating company data with market forecasts and behavioral trends can provide valuable insights to boost sales, expand product/service offerings, and even identify new market opportunities.
Big Data in the Manufacturing Sector
Another effective use of Big Data is cost reduction through predictive maintenance strategies. While manufacturing industries were among the first to adopt Advanced Analytics to extend the lifespan of machinery and maximize return on investment, today predictive maintenance is also widely applied in logistics, transportation, luxury, fashion, and hospitality sectors.
Moreover, the Internet of Things (IoT) is extensively used in manufacturing, leveraging Big Data from interconnected sensors and devices to optimize energy efficiency, reduce costs, and minimize waste.
Big Data in the Healthcare Sector
By analyzing vast amounts of data from electronic health records, medical devices, wearable sensors, and monitoring applications, Big Data helps improve diagnostics, personalize treatments, and predict epidemics.
Additionally, Big Data enables the identification of public health trends, enhances hospital resource management, and accelerates clinical research by facilitating drug discovery. Predictive analytics based on Big Data can also aid in disease prevention, improving healthcare quality, and reducing overall medical costs.
Big Data in the Hospitality Sector
In the hospitality sector, for example, the use of Big Data is already a reality thanks to Business Intelligence projects that enable the generation of a dynamic pricing strategy in real time. This involves taking into account demand, supply, and availability while integrating analyses with additional data from external sources, such as major OTAs, traffic data, and tourist flows.
Conclusion: the Future of Big Data
The real challenge of the future will no longer be the ability to provide answers to business questions (Augmented Analytics systems will simplify this process), but rather to ask the right questions, explore uncharted aspects, and uncover new and hidden insights, perhaps before others do.
Do you want to discover how to implement Big Data in your company? Contact us for a free consultation!
We realize Business Intelligence & Advanced Analytics solutions to transform simple data into information of freat strategic value.
