Do you know how advanced analytics can help your business? In this article, we explain how this set of techniques allows you to make better data-driven decisions, improve the efficiency of your business, and surpass the competition.
In addition to discussing the most effective areas of advanced analytics, we also address a crucial ethical issue that must be applied to any solutions put in place. Read the article and discover everything you need to know about this subject.
What is the significance of advanced analytics for companies?
We define advanced analytics as a set of techniques that use statistics and mathematical models to discover patterns, gain more insights about business aspects, and make better decisions. In the process, large volumes of information are analyzed and processed in such a way that practical and valuable conclusions can be drawn.
For businesses, advanced analytics is an opportunity. It provides each department's decision-makers with a global, comprehensive, accurate, and in-depth view of their operations, customers, markets, and competitors. As a result, more effective decisions are made that correct and anticipate failures, improve processes, and achieve greater profitability.
There are three areas that are currently key in the field of advanced analytics. They are as follows:
Predictive analysis
Machine learning
Conversational language models
Let's delve a little deeper into the implications of each of these more specific areas related to advanced analytics.
THREE FIELDS TO MASTER ADVANCED ANALYTICS
Let's explore the details of each of the mentioned areas in the previous section and the impact they can have on your business.
Predictive Analysis
The predictive analysis positively influences competitiveness. This is a technique that can optimize an organization's activity to the maximum. For example, it enables quick but well-informed decision-making, better risk and uncertainty management, and early detection of potential obstacles in operational performance.
Additionally, it is a methodology that promotes customer retention and loyalty. How does it achieve this? Mainly in three different ways:
Pattern identification: By analyzing data predictively, it is possible to anticipate customer needs by revealing their behavioral patterns. This makes it easier to launch marketing campaigns tailored to their preferences, thereby increasing their satisfaction and engagement with the brand, for example.
Deep customer understanding: Advanced analytics in its predictive form also reveals more data about each of the brand's customers. As a result, it becomes easier to provide more personalized customer service.
Uncovering dissatisfaction: A customer may not be entirely satisfied and may not have considered switching to another provider or company. Predictive analysis can detect signals that something is not going well, such as a decrease in orders or an increase in complaints filed with the customer service department.
When predictive analysis is performed in the cloud, these and other benefits are achieved without the need to invest in and maintain a large infrastructure, with data access from anywhere in the world.
Machine Learning
Another notable field is machine learning. Why is this technique so necessary in the world of advanced analytics? Thanks to artificial intelligence, it becomes possible to automate and optimize processes, as well as establish predictive models that are impossible to obtain with human processing. Most importantly, we achieve continuous process improvement.
To achieve a truly effective machine learning system, it is essential to work on obtaining quality data, understanding the models, and efficiently implementing them in a company. Similarly, it is a technique that requires significant computational resources, so most companies rely on cloud infrastructure, which provides power and scalability according to their needs.
The main applications of machine learning include image and video recognition, process automation (from logistics to production, including quality control), and autonomous vehicles. Together, machine learning drives innovation and efficiency in organizations, allowing for greater personalization and anticipation of market needs.
Conversational Language Models
Now let's address another revolutionary technology. The emergence of conversational language models is improving communication and reducing workload in key departments.
This technology, usually manifested in the form of chatbots, is successfully applied in areas such as customer service and after-sales support, enabling smoother and more natural interaction. Similarly, conversational language models have the ability to personalize the customer experience by providing accurate and relevant responses to their inquiries and needs.
Furthermore, natural language models are capable of interpreting context and maintaining coherent conversations. Companies that implement such solutions adapt better to their customers while streamlining corresponding processes.
We have recently witnessed how conversational language models like ChatGPT have revolutionized the landscape of these systems, surpassing even the expected learning capabilities initially. We are witnessing a revolution that is already influencing the improvement and agility of any area of the company, not just customer service.
Advanced Analytics and Ethics: Two Sides of the Same Coin
Ethics plays a fundamental role in the context of advanced analytics. As organizations use advanced data analysis techniques to obtain valuable information, it becomes necessary to establish policies and practices that promote professional ethics in the use of these technologies and simultaneously mitigate the effects of biases, often inherent in a variety of datasets.
Let's discuss three ways to make advanced analytics more ethical:
Supervision: Automation is highly beneficial, but systems must be rigorously inspected. Consequently, this ensures that models adopt well-regarded qualities such as fairness or impartiality and avoid improper behaviors like discrimination.
Data quality: Another key point in achieving ethical advanced analytics is ensuring that data is accurate, complete, and reliable, avoiding the inclusion of biased information. Transparency in the data collection and storage process is essential to maintain trust and integrity throughout all subsequent phases of analysis and exploitation.
Training and education: Many of the mentioned technologies have only just begun to emerge. Despite that, their potential impact is extremely high. Therefore, they must be implemented responsibly. The way to achieve this is by raising awareness and training internal staff on the ethical aspects of advanced analytics. Teams need to understand the potential impacts and risks, as well as adopting good practices in data usage and decision-making.
ADVANCED ANALYTICS WITH CLARIBA SEIDOR IS THE WINNING PROPOSAL
Advanced analytics is becoming a true revolution, especially with the arrival of advanced technologies like natural language models. Currently, any company will be able to make the most of data and make informed decisions. However, it is essential to incorporate a series of best practices and reference methodologies to ensure success during and after the implementation of these innovative solutions.
The Data & Analytics team at Clariba SEIDOR has professionals prepared to tackle these new technologies and cutting-edge solutions and put them at the service of the business. Clariba SEIDOR, as part of SEIDOR, have helped more than 2,500 clients worldwide achieve their goals in analytics. If you want to benefit from the advantages of advanced analytics and have the support of experts in the field, contact us. We will be delighted to help you maximize the potential of your data and lead your company to success in the competitive business landscape today.