Data Driven Company

The AI ​​Revolution: First Steps to become a Data Driven Company


Big data, analytics, machine learning and AI have become the most powerful levers of the current digital transformation. In fact, the most successful global companies in recent years show us the path: Provide value to the user while making an extensive use of their data. In this sense, the best-known cases would be Google, Facebook, Amazon or Netflix, which analyze their users’ information to offer personalized advertising in some cases, or automatically recommend content in others.

The good news is that in 2021 it is not necessary to have the investment capacity of Google or Amazon to make use of this kind of advanced analytics and AI techniques. In fact, these same companies offer cloud-based AI services within everyone's reach and in “pay-per-use” mode. There are no more excuses for not boosting our business with AI, whatever its size! Even an entrepreneur who sets up their own startup can start using AI from the first moment (and, indeed, that’s what they do: startups are usually cloud-native and make an extensive use of all possible technologies, in all of their corporate areas, be it marketing, logistics, HR, etc.).

la revolución de la ia

AI cloud services are easily integrated with our applications to address common use cases, such as creating personalized recommendations, performing text analysis with NLP techniques, creating chatbots, foreseeing demand, avoiding fraud, etc. In addition, they use the same deep learning technology that boosts their core businesses (Google search engine or Amazon store), which continually learn and improve over time. All this capacity to create business value, a capacity which learns and improves day by day, at your fingertips!

Sectorial use cases of AI

Business from all sectors can take advantage of the potential of data exploitation. Furthermore, to compete successfully and thrive in this decade, there is no doubt that both emerging companies and incumbents will have to become “IA First” institutions, adopting this type of technology as the basis to generate new revenue streams and create customized, distinguishing experiences for their clients. Some sectorial examples are:


The Financial Services industry is one of the sectors embracing the digital transformation more enthusiastically. Not in vain it is also one of the sectors most threatened by the appearance of new fintech and insurtech services. In terms of channels, the bank of the future will incorporate the  digital experience platform paradigm to offer intelligent proposals and experiences (that is, recommend actions such as cross-selling of products, or make decisions, such as to invest, automatic). These experiences will also be personalized (based on a detailed understanding of the client’s past behavior), and truly omnichannel (that break the barrier between physical and online contexts, making the experience consistent through multiple devices).

In the operations section, the automation of processes will be commonplace, reducing costs and allowing employees to focus on improving quality. On this matter, AI is already helping, for example, in the field of payments and fraud prevention, by evaluating the probabilities that a given transaction is fraudulent, alerting analysts of fraud if necessary, and taking actions through predefined workflows.


Big data can help people to learn faster and better. It is called Learning Analytics, a new trend that has just begun and sped up because of the popularization of eLearning as a result of the 2020 health crisis. Applications and services that process big data through new protocols such as xAPI will be able to help students with feedback on their progress and personalized recommendations on what can they do to improve. Consequently, and from now on, the academic path will be more and more personalized.


caso de uso elearning

In addition, the use of data can help teachers to track student commitment and performance, and allow school and university directors to review and evaluate staff and institutional performance by obtaining KPIs in real time, without forgetting the use of automated remote monitoring techniques (eProctoring) for online assessment processes.


Artificial intelligence solutions are helping businesses to align their products with their customers' expectations with personalized recommendations in the virtual store based on previous purchases. At after-sale level, chatbots can be a more efficient way to communicate with customers, not only by answering frequently asked questions, but also by conducting surveys, interpreting emotions, or managing and automating complaints and returns.

The contribution of AI in the retail sector is not limited to the virtual world: It can also offer recommendations to customers in the physical store, or reduce queues with paying methods that don’t need a cashier. Lastly, an AI-enabled logistics and inventory management can predict product demand by analyzing historical sales, buying trends, etc. as well as restocking supplies by monitoring stock in real time.


First steps

¿Cómo empezar? ¿De qué datos dispone mi empresa? ¿Están dispersos en múltiples silos? ¿Cuáles me faltan y cómo obtenerlos? ¿Qué valor de negocio me pueden aportar? ¿Cuál sería un primer objetivo o quick win? Serán preguntas que nos pasarán por la cabeza en estos momentos.

Para empezar, es recomendable realizar un taller divulgativo para la dirección de la empresa y que implique a todas las áreas, que ayude a entender el potencial de negocio de estas tecnologías. Y seguir luego con un workshop con técnicas de Design Thinking que impulsen la creatividad del equipo directivo, con la participación de un equipo especializado de expertos en datos, arquitectos de software y data scientists. Finalmente, podemos aterrizar un primer prototipo en tiempo récord mediante la técnica del Design Sprint.

Sin duda, estamos ante un reto ingente, y a la vez una oportunidad histórica. Es un reto porque en un futuro muy próximo, el mercado estará dominado por negocios data-driven o "IA First", esto es, aquéllas empresas que sepan aprovechar al máximo el potencial de los datos. Esto nos obliga a todos a repensar nuestras empresas, la forma que generamos valor para nuestros clientes, a imaginar nuevos modelos de negocio. Es una revolución transversal a todas las áreas de la empresa (no solamente IT) y que nos afecta sea cuál sea nuestro tamaño (Disney es un claro ejemplo de digitalización de una gran empresa histórica). A la vez es una oportunidad porque tenemos la capacidad IA de los gigantes del cloud a nuestra disposición, la cual va mejorando día a día. Que la sepamos aprovechar, solo depende de nosotros.



Autor: Martí Fàbrega