Understanding the Importance of Data Analytics
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Understanding the Importance of Data Analytics

Santiago Castro, CDO, FBN Bank

How has the data analytics landscape evolved over the years?

And what are some of the advantages of the current technological evolution that you have witnessed?

One of the biggest changes or evolution is that there are more user-friendly or code-free technologies that allow business analysts or data analysts to create reports. In the past, if you wanted to analyze data or to create a business process diagram, you needed to go to the IT team. This usually increases the gap between the business owner and the person who generated the analysis and reports. You need to define reporting requirements and then wait for data engineers or IT staff to find the information you want, while the process was very slow. However, at present, even if you don't know SQL or programming, tools like Power Bi or Tableau can drag and drop and ensure query assistance. Business analysts can connect to the data themselves, or they can create dashboards with Tableau or Power Bi. All of these technologies are evolving now because anyone can easily use them without being a scientist or developer. This will start a revolution as it can deliver data projects very easily meaning you don't have to wait weeks or months for projects to be completed, you can connect on-site to change your mind, find insights, ask yourself the next question, and repeat quickly. This is the beginning of work delivery, and these technologies are driving this goal.

What challenges have you seen plaguing the data analytics landscape and how do you feel they can be effectively mitigated?

There are some challenges and those would be:

1. The gap between technologies widens rapidly.

Data technology is developing rapidly. We discussed Power Bi, advanced analytics, big data, machine learning, robotics, and process automation. So there will always be new things that will appear soon. On the other hand, the scope for the people to adapt and learn is much lower. Therefore, technological development is very rapid, and cultural development in the organization is very slow. This brings challenges. Because fundamentally, it creates a little resistance to adoption, and people ask themselves why should I learn a new technology? What if I am dealing with the old technology? When I am used to learning in other ways, why do I need to learn a new technology? Therefore, we need to get out of our comfort zone and remain confident to fill the gap between rapid technological change and very slow cultural development.

2 . Two possibilities in an organization.

On the one hand, you will have people who don’t understand the purpose and value of the data. You need to create a business report or analyze the data and propose and gather your advocates to create the momentum and show them the report. This is the category of so-called data literacy. You need to educate and show the range of possibilities of analytics and evangelize. This way people can understand the value of data.

On the other hand, you will also have people who specialize in data work. They are really good as they have experience in dealing with the data. They create their data, a territorial project, which is a great model for data scientists to create very interesting risk situations, calculations, or algorithms, or attachments. 

3 . New technologies are evolving.

Modern technologies such as robotics, process automation, machine learning, artificial intelligence, etc. allow you to automate processes. For example, a person in a bank's logistics department is looking for documents and has to spend some time checking whether the documents are related to contractors or loans. However, if he had a system, all this could be automated. People need to understand that this automation process cannot replace them, but rather it will improve their capabilities. This will make them more efficient.

What are some of the best practices businesses should adopt to stay ahead of the competition?

All of these new technology trends are constantly emerging, such as big data, machine learning algorithms, and there are different types of machine [V4] learning - supervised machine learning, unsupervised machine learning, neural network processing, and computer democratization that allow us to process huge data. So all of this allows you to automate things and increase employee capabilities. In a way, computers have the brainpower to perform faster, larger, and more complex calculations. Now, these technologies enable organizations to transform their business models using AI or machine learning.

Now, the last thing I want to say about these trends is that the pandemic has further exacerbated this situation, because now we can’t go to the bank, so we have to stay at home, and naturally, we have to work remotely. Therefore, more and more consumers are using applications, Internet banking, and remote access to banking services, and even hope banks provide the services in this way. Now those banks that have achieved digitization and automation are operating and executing the process from the back office through automation and machines. Those banks that survive the pandemic are more resilient because now everyone has to work at home, and if you know process automation capabilities, these banks will be more dynamic and can continue to provide services while working remotely. This pandemic will be an opportunity for the organizations to adopt technologies such as open banking, or explore new technologies that are emerging quickly, and some people even talk about blockchain and its help to China. Therefore, all these digitizations and technologies that were once challenging in the past have now become necessary and put us in the right place to continue after the crisis.

How the future could be better and can be improved using the data analytic space?

We might return to the idea of making these technologies more user-friendly, iterating faster, and creating data projects with anyone in the organization. It does not require specialization, nor does it need to be a mathematician, programmer, or data scientist on certain projects. Moreover, you will also have a culture that becomes more technologically inclined, more digital, and therefore more competitive to adopt all these very interesting breakthroughs, and technology allows us to be faster in an intermediary way - Iterate and innovate which is more flexible to some extent. In the past, data and data warehouses were a bit rigid for us. You need to create requirements that used to take a lot of time to create projects. Today, you can be agile, you can iterate, and you can fail. However, if you fail fast, learn and keep learning, you will have an idea, then everyone can start to access the data. Therefore, I think my advice is actually to move and embrace agility, flexibility, innovation, and data democratization for everyone in the organization.

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