Advanced analytics: the pillar of business

In addition to discussing the most effective areas of advanced analytics, we also address a crucial question related to the ethics that should apply to the solutions derived. Stay on to the end of the article and discover everything you need to know about it!

What is advanced analytics and why is it so important for businesses?

Advanced analytics can be defined as a set of techniques that use statistics and mathematical models to discover patterns in the activity of a business in order to gain more knowledge of its operations and make better decisions. In the process, large volumes of information are analysed and processed in such a way that it is possible to extract practical and valuable conclusions.

Advanced analytics represents a great opportunity for any businesses. It provides departmental managers with a global, broad, precise and in-depth view of their operations, customers, markets and competitors. As a result, more effective decisions are made that anticipate and correct failures, improve processes and achieve greater profitability.

There are three areas that are currently key in the field of advanced analytics. They are the following:

• Predictive analysis.
• Machine learning.
• Conversational language models.

Let's take a closer look at the implications of each of these more specific areas related to advanced analytics.

Three fields to master advanced analytics

Let's take a look at the details of each of the areas mentioned in the previous section and examine what impact they may have on your business.

Predictive analytics

Predictive analytics positively influences competitiveness. This technique can optimise an organisation's activity to the maximum. It allows quick, but well-informed decisions to be made, better managing risks and uncertainties and detecting in advance possible obstacles to the performance of business operations.

This methodology also encourages customer retention and loyalty. How is this achieved? Most in three different ways:

• Pattern identification. By analysing data in a predictive way, it is possible to anticipate the customer's needs by revealing their behavioural patterns. This makes it easier to implement marketing campaigns tailored to their preferences and which increase their satisfaction and commitment to the brand, for example.
• Deep knowledge of your customer. Advanced analytics in its predictive form also provides data on each of the brand's customers. As a result, it is easier to provide more personalised customer service.
• Discovery of dissatisfaction. A customer may not be fully satisfied but not yet considered changing supplier or company. Predictive analytics allows us to detect signs that something is not going well, such as a drop in orders or an increase in complaints filed with the customer service area.

When predictive analytics is cloud-based, these and other benefits are achieved without investing and maintaining large infrastructure, while gaining access to data from anywhere in the world.

Machine learning.

Another noteworthy field is machine learning. Why is this technique so necessary in the world of advanced analytics? Thanks to artificial intelligence, processes can be automated and optimised, establishing predictive models that are impossible to obtain with human processing. Most importantly, continuous improvement of processes can be achieved.

To achieve a truly effective machine learning system, it is essential to obtain quality data, understand models and efficiently implement them in a company. Similarly, this technique requires significant computing resources, so most companies rely on cloud infrastructure, which enables power and scalability according to their needs.

The main applications of machine learning include: The recognition of images and videos, the automation of processes (from logistics and production to quality controls) and autonomous vehicles. Overall, machine learning drives innovation and efficiency in organisations, facilitating greater personalisation and anticipation of market needs.

Conversational language models

This is another revolutionary technology. The emergence of conversational language models is improving communications and reducing the workload of some key departments.

This technology, usually in the form of chatbots and conversational interfaces, is successfully applied in areas such as customer service and after-sales service, allowing for a more fluid and natural interaction. Similarly, conversational language models have the ability to customise the customer's experience, providing precise and relevant answers to their queries and needs.

Finally, natural language models are able to interpret the context and maintain coherent conversations. Companies that implement such solutions are better able to adapt to their customers and, at the same time, streamline the processes involved.

We have recently seen how conversational language models such as ChatGPT have revolutionised the landscape of such systems, even surpassing the learning capabilities originally envisaged. 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 advanced analytics. As organisations 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 while mitigating the effects of biases, often inherent in a wide variety of data sets.

Let's look at three ways of making advanced analytics more ethical:

• Supervision. Automation is very beneficial, but systems must be inspected rigorously. This results in models adopting valued qualities, such as fairness or impartiality, and avoiding inappropriate behaviour such as discrimination.
• Data quality. Another key point for achieving advanced ethical analytics is to ensure that data is accurate, complete and reliable, avoiding the inclusion of biased information. Transparency in the data collection and storage process is essential to maintaining trust and integrity during all subsequent phases of analysis and operation.
• Training and capacity-building. Many of the aforementioned technologies have just emerged. Despite this, their potential impact is extremely high. They must therefore be implemented with a high level of responsibly. The way to achieve this is to raise awareness and train internal staff on the ethical aspects of advanced analytics. Teams must understand the possible impacts and risks, adopting good practices in the use of data and decision-making.

Advanced analytics with SEIDOR is a winning proposal

Advanced analytics is becoming a real revolution, especially after the arrival of incredible technologies like natural language models. At present, any company can make the most of data and make informed decisions. However, the incorpation of a series of best practices and reference methodologies is indispensable to ensure success during and after the implementation of this type of innovative solutions.

The SEIDOR Data & Analytics team has a team of experts ready to harness these new technologies and cutting-edge solutions and put them at the service of your business. In over 25 years of experience, we have helped more than 2,500 customers. If you want to benefit from the advantages of advanced analytics and have the backing of experts in the field, get in touch today. We will be happy to assist you in making the most of your data and to help your company achieve success in today's competitive business landscape.