Big Data. Big Data Analytics. Data Mining. Are you aware of these technological terms and their implications in the business world?
Big Data Analytics is a topic that covers various techniques and technologies that bring competitive advantages to companies and to the most diverse type of entities.
With the practice of Big Data Analytics you can gain visions of market behavior, improve internal work processes, and make faster, more informed decisions, among other advantages.
But let’s go in parts. If you are not yet aware of the Big Data Analytics activity, we first start with the basics, the Big Data concept.
Meaning of Big Data
To explain Big Data, a term that has changed the way we do business, we can talk about collecting, integrating, storing and processing massive amounts of data to extract useful information through analytical mechanisms (e.g., dashboards).
Further explaining its meaning, the Big Data concept is mainly based on the storage of huge volumes of data in distributed computing infrastructures, forming a cluster of computers interconnected in On-premises environments (inside the premises of the companies ) or in the Cloud.
And how do you collect this data? What are the data sources? And what are its main characteristics?
Organizations collect data from a variety of sources, from social networks to purchasing, from sensors to machine-to-machine transmissions, from filling forms to running a specific machine.
Examples of everyday data sources are e-mail, text documents, videos, audio files, purchases made on the internet, etc. Depending on the context, these data sources will have different characteristics and can be divided into three main characteristics:
- Volume – the data sets present in Big Data projects can range from a few gigabytes of data to terabytes or petabytes of data;
- Variety – with Big Data techniques and technologies, we are able to integrate data of different types, including structured (eg, relational databases, spreadsheets), semistructured (eg, XML, JSON) or unstructured (eg, text, image , video, audio);
- Speed – often the Holy Grail of a Big Data project is the collection and integration of data in real time, decreasing the time of data access, ie, as soon as a phenomenon happens (eg, a post is placed on a social network), this phenomenon is used to refresh existing analyzes and indicators (eg, how many posts were made on a particular subject on a given day).
Big Data Analytics
All the work of data collection ends up having its purpose. In the business context, careful data analysis will reduce costs, increase revenue, streamline business processes, increase productivity, and streamline business processes.
The world’s largest companies use descriptive analysis (eg, dashboards, reports, KPIs) and machine learning methods to evaluate and make more informed and therefore more assertive decisions to improve the services they offer or the products they sell. Due to the constant increase of data sets available to the various organizations, these methods are now enabled by Big Data techniques, technologies and infrastructures, which guarantee the collection, storage, processing and analysis of these new types of data.
Use of Big Data in Industry
In Industry, the stoppages on the production line are equivalent to business and profits. The use of Industry 4.0 technology trends, such as Big Data techniques and technologies, or the Internet of Things, can reduce equipment downtime and downtime by measuring their overall effectiveness and repair needs.
The use of Big Data in Industry also allows for a much more detailed view of the effectiveness of the organization and its processes, leading to more informed, effective and efficient (real-time) decision-making processes.
Here we can talk about the Business Intelligence Platform for Data Integration, developed in a partnership that includes EPMQ, Bosch Car Multimedia Portugal and the University of Minho.
This project includes an integrated data system that allows, through an iterative process, the development of the Organizational Big Data Warehouse, increasing the quality of the operations of the factory, in terms of efficiency in access and quality of critical information, necessary for the decision-making and stakeholder involvement.
Big data is thus already a common expression of the CCG vocabulary. It is being developed in the field of applied research EPMQ, under the scientific coordination of Maribel Santos.