We live in a hyper-connected world where every interaction generates data. From our voices captured by virtual assistants to images and videos shared on social networks, the constant flow of information feeds the so-called “era of” data hype it is talking about AI (Generative or not), unfortunately I see that there is little clarity about some basic concepts essential to extract the full value of this type of innovative technology.
According to a report by IDC, the overall volume of data should exceed 175 Zettabytes by the end of 2025exponential growth driven by the Internet of Things (IoT), Artificial Intelligence (AI) and digital services.
This explosion of data brought with it the need to understand, store and, above all, use information strategically.This is where fundamental concepts such as data warehouses, data lakes and big datathey have transformed the way companies make decisions and shape their strategies.
Data, in order to be useful, needs to be organized and accessible. This starts with the storage, Performed on frameworks ranging from traditional relational databases to modern platforms such as data warehouses (organized repositories optimized for queries) and data lakes (where raw, structured, and unstructured data is stored without a defined schema).
The 5Vs of Big Data
The concept of Big Data is often described by 5Vs:
- Volume: the massive amount of continuously generated data.
- Speed: how quickly this data is produced and processed.
- Variety: the diversity of formats, from text to videos to social media data to IoT sensors.
- Veracity : the quality and reliability of the data.
- Value: the potential for insights that data can offer.
Companies that can integrate these elements into their operations turn data into strategic assetsusing them to innovate, optimize processes and predict trends.
Data-driven strategies: informed and optimized decisions
Data analysis has become essential in the context of the 4th Industrial Revolutionwhere automation, connectivity and AI have redefined business competitiveness, organizations now combine executive intuition com predictive analyticscompanies like Amazon, Netflix and General Electric illustrate how strategic use of data can transform businesses across different industries.
Amazon, for example, is a classic case of data-driven decisions, using real-time analytics to recommend products, optimize inventory, and deliver a personalized customer experience.
Netflix stands out for its ability to collect and analyze viewing data to decide which series and movies to produce, avoiding investments in projects with little popular appeal and saving millions of dollars.
In the industrial sector, General Electric (GE) employs IoT sensors to monitor machine performance, predict failures and reduce operating costs, demonstrating how integrating Big Data with AI can bring efficiency and innovation
on an industrial scale.
Use of AI in data quality
To harness the potential of data, many companies turn to AI. Advanced algorithms enable complex pattern identification, scenario prediction, and decision automation.
However, data quality is key. Studies show that inconsistent or inaccurate data can cause financial loss, as in the case of companies that have spent millions on marketing campaigns based on incorrect information veracity data is as essential as investing in analytics technologies.
In recent years, data analysis has gone from being a technical topic to becoming a strategic agenda in boards of directors. According to the MIT Sloan Management Review report 87% from business leaders they state that data analysis is essential to achieve organizational objectives.In addition, the Generative AI and tools like the ChatGPT they are being used to create simulations and explore hypothetical scenarios in executive meetings.
Moving to the 5th Industrial Revolution
As we move forward to the the 5th Industrial Revolution , the balance between automation and human customization becomes a priority. data analyses with more intuitive approaches, creating an environment where decisions are grounded by numbers but enriched by human experience.
The future of data analytics points to trends that promise to further transform the business landscape.One of them is Data as a Service (DaaS), where companies monetize their data and provide it as a service to other businesses, creating new revenue opportunities.
In parallel, privacy and regulation gain importance with legislation such as the General Data Protection Regulation (GDPR) and the General Data Protection Law (LGPD), which highlight the need for robust and responsible data governance.In addition, the growing demand for immediate insights has driven the advancement of data streaming technologies, allowing real-time analysis and more agile decisions.
Therefore, data collection and analysis in times of Generative AI are no longer just competitive advantages; they have become strategic needscompanies that master these technologies thrive in an increasingly dynamic and challenging market.
The integration of data with technology and human expertise promises to shape the future of business decisions and usher in the new era of innovation and growth, powered by the amazement with which every week some AI-generated novelty gives us.
