But the main and current theme to reach this path has been Artificial Intelligence (AI), which in recent months has brought to light themes about numerous products and tools, such as the GPT chat and its competitors.
However, AI is not just about chat. After all, it is just the beginning of the journey of data projects. In view of this, it is time to monopolize this topic in the content and understand, first of all, the role of AI in a strategy.
MATH TECH - DATA + AI Webinar - Data Journey
Implementing AI in the Future of Data Analytics
If implementation is the question, first of all it is important to know what role artificial intelligence will play in the future of data analysis.
With the growing volume of available data and the complexity of information, AI comes in as a facilitating arm, bringing benefits such as:
Large-scale data processing, interpreting it at higher speed;
Pattern detection and insights, thanks to its machine learning and data mining capabilities that identify these fronts;
Automation of analytical tasks such as data cleaning and pre-processing, segmentation, etc.;
Real-time analysis with support for fraud detection and identification of complex problems;
Improved decision making, for example, with a combination of machine learning algorithms.
By understanding these concepts, it is easy to learn how to implement this functionality in your daily life. Let's understand, in more technical terms, that through acculturation this can become a reality. Let's look at the topics:
Automatic analysis and insights generation
To generate analysis, first of all, you need data literacy. That is, you need to learn how to ask questions of the data and understand what it wants to tell you. One thing is for sure: keep your common sense.
In the end, we will get help in finding what we don't know, and the result is a crossing of information and correlations that we wouldn't always look for.
Translation: understand how data works, ask correctly and know your business.
Support in the data engineering and governance development cycle
In this example, we can think about accelerating ETLs, playing the role of peer review, documenting and analyzing permission matrices, for example, and testing data access hypotheses x profile.
Data cataloging
With the emergence of AI, encoding data means making it qualified. After all, this is the help needed to implement it in a company's day-to-day operations, without losing its effectiveness or becoming a fad.
Ask yourself where the data is located. If it is repeated in more than one place, try to understand whether it can be unified or whether there is a different latency.
AI can help you, but it is important that the cross-referencing is done through structured data. Will this be a means of implementation? Of course!
Types of Artificial Intelligence tools for your data strategy
To test the necessary tools, it is important to have a capable data team involved vietnam phone number example in an analytical culture...that is, one that has undergone literacy, as mentioned previously, and delved deeply into the process of understanding this Data chain and what it has to offer.
Beyond that, AI is a functional model, as it does not replace data analytics professionals. Instead, it empowers them by providing advanced tools and capabilities to handle data at an increasing scale and complexity. Human-AI collaboration is essential to fully harness the potential of data analytics in the future.
Given this, some tool recommendations are
Machine Learning;
NLP - Natural Language Processing for information extraction;
Recommendation systems;
Visual data analysis;
Read more:
- How AI can accelerate the process of hyper-personalization ;
- How generative artificial intelligence impacts life in society;
- Artificial Intelligence Marketing: 6 examples to apply in your business.
Governance challenges
As new technologies emerge, the challenge of data governance also increases, and addressing them today requires a comprehensive and collaborative approach.
After all, we have a permissible environment, which is the internet, and new tools only work as a consequence of this location and the access it offers. Therefore, the more content there is, the richer the AI becomes and, as a result, the more answers there are.
However, with this factor, the risk of data leaks is greater and, consequently, there is a need for greater attention to security and privacy. Not to mention, who is responsible for leaks? Or possible delivery errors, if the chat in question provides incorrect information?
Data governance in this case comes in to define clear roles and responsibilities for the parties involved in the development, deployment and monitoring of AI systems.
The same applies to high-quality data. With governance, we can break down data silos and define specific areas for responses, preventing potential harm in the future.
Privacy and ethics, with the LGPD, also become a challenge, but the guarantee must be that data is collected, used and stored in accordance with regulations. Not to mention security and transparency in the process.
Therefore, governance is a main topic for those who want to get involved in this new technological concept.
Implementing projects and the secret to success
The biggest secret to introducing AI and implementing your projects is nothing more or less than understanding that it can be an improvement to a solution that may already exist within your business model.
But how do you find out? We tell you in much more depth in the webinar “ DATA + AI: Crucial questions for the new data journey ”, in the presence of experts in this market, such as Marcel Ghiraldini , Founder and Chief Growth at MATH Group, Lourenço de Paula , Growth Executive Director at MATH TECH and Thiago Dutra , Delivery Executive Director at MATH TECH.
Watch in full and gain even more insights into the data journey through AI, and how you can take even more control of technology in your business.