El poder de los datos por Bruno González

Pyxis Comunicación

15 de junio de 2020


Bruno González, lead data engineer en IDATHA, nos trae una nueva entrada en su blog acerca del poder de los datos (“data”) y cómo deberíamos usarlos, ya que se han convertido en un activo extremadamente valioso para el desarrollo de todas las industrias.

“Con un gran poder viene una gran responsabilidad”, explica Bruno. Y nos invita a reflexionar al respecto. Para ello, se basa en 3 premisas esenciales: ser claro acerca de cómo usan los datos, obtener y respetar el consentimiento del cliente y hacer todos los esfuerzos posibles para mejorar su experiencia al usar los datos.

Para saber más, los invitamos a seguir a Bruno en su blog y conocer sus valiosos aportes haciendo clic aquí.


Since data is the new electricity, you must know its power and how to start using it

There are many comparisons between data and things like electricity, oil, gold, among others. In these days we know that data is a valuable asset and it has become important for the development of all industries as electricity was in the 20th century.

In the most memorable scene of Spider-Man movie (2002), Uncle Ben had an important conversation with Peter Parker. Uncle Ben talked with him about his changes and behavior, and finally told him “With great power comes great responsibility”.

We as people and companies have a great power in our hands and we must be responsible about how we use it

This story could continue in many ways, but I choose to continue from the perspective of a company and leave all the other philosophical options for another opportunity. In the next lines I will direct my writing to the different companies that “owns” data and try to give them an idea of how to start.


You don’t really own “your own” data as a company. Why? It is just because you collected that data and stored it on your servers? I’m not trying to redefine the laws around data. I’m just pointing out the fact that you have to be responsible about how you use it, because the real owners are the people that used your services, bought your products or interacted with you in some other way (that also includes your employees). If you are going to use data to make decisions, improve products or services, or drive data-driven marketing campaigns, you always need to remember that you owe something to the real owners of the data: accountability.

What does it mean to be accountable? From my perspective, it means three things: be clear about how you use the data (Art.12 of GDPR is an example), get and respect the consent of the customer (Art. 7 of GDPR is an example), and make all the efforts you can to improve their experience using data.

I know that my perspective of accountability is not the most common. Maybe you are wondering why I mixed the GDPR-like statements with the third point, but I think that as a company you are responsible of improving your customers’ and employees’ experience with the data you have.


The experience economy is not new. If you check on Wikipedia it is an “old” concept from 1998. But in the last years it exploded in a very interesting way, and now almost everything is about experiences. From UX in mobile development to shopping experience in a store, we know that experiences matter and make a huge difference between many options we have today from using applications or buying groceries.

How can I start using data to drive better experiences?

The first thing you need to focus on and understand is from where and how the data is gathered. If you have dirty data or not much data at all, you are losing the opportunity of get value from it. Data quality is an issue that all the companies face, but few are honest with themselves about it.

Some questions around data gathering could be: Are you getting the data from a form? Social media? Behavioral data from your website? Customer review of a product? Do you have a validation process? Do you need to login for that action? There are many options out there, but you really need to focus on the quality of the data gathering process. Let me explain the problem with three simple examples you know for sure.

Random “enjoy free Wi-Fi” image I found on the internet

This is a basic example. Maybe you were at the airport or in a hotel, and you were asked to drop your email address for free Wi-Fi. I don’t know how much people put a real email address there, but it is not my case. I always try to put some random letters with an @ ending with .com. It always worked. The best control I have ever seen is a domain validation. To be honest, I don’t know which is the value behind getting your email address in that context, but the example is about basic data input validation that is not always there. You leave in users hands the data quality and they will always try the easiest path. Are you trying to get an email address or mobile phone for communication? You must have an online validation. Do you want to get dates or numbers? Use date pickers, validate the format and then validate the input in your internal logic (JavaScript can be avoided easily). Try to do as many validations as you can taking into account the user experience.

Frictionless user feedback

The second example is this “frictionless user feedback” device. Ok, you are getting “customer experience” data, but I have many doubts about this option. How does it impact your service? How you can change the customer experience with this type of devices? Are you going to fire all the employees that worked in the “worst experience” time in the day? I think that it has many flaws and doesn’t give you any input about what you do well and what you need to improve.

Long registration form

The third and last example is the long registration forms. Why you think that getting all the data at once is the best option? The customer could normally fill around 10 fields, all required, and half of them seem to be useless for the product or service they are looking for. The other option is to use progressive profiling, let the customer start with just the fields you need to survive and make a journey to gather more information showing why you are asking for that and the value you will deliver with that type of information.

Data gathering must focus on data quality and have context, get relevant information, be easy to understand and progressive

Pyxis Comunicación

15 de junio de 2020