Any company whose strategic and business processes are linked to some extent to metrics and information analysis is an ideal candidate to promote data initiatives. 

And there are usually two motivations for getting started with data solutions: 

How can I be more timely and accurate in delivering results?

How can I learn more about my information to create new strategies?… For example, to generate personalized offers one by one, from understanding in greater depth habits and consumption preferences. Imagine, instead of sending 20 text messages with offers, sending only one, but that is the most relevant for each user. Or that your newsletter adapts to the contents that each reader is looking for. In the twenty-first century, it does not matter the size of the company, whether it is public, private, medium, large or small. All will be able to innovate and grow from the intelligent use and search for value in data

Imagine that we have a Spotify list with all music genres, therefore, we are prepared to interact with a large repertoire of data sources, such as: MS SQL databases, Oracle databases, different ERP and CRM, Warehouse Mmgt Systems, NoSQL databases, among others.

Although there is a minimum required for the quality of the data, we initially do the evaluation of your data, show you those results and guide you so you can improve the quality

One thing is one thing and another thing is another thing.

Business intelligence is a discipline that, with the passage of time, has become fundamental in everyday life, because its objective is to offer knowledge to support business decisions. How is it done? with intelligent information based on the company’s data and relating them to, for example, data from the market in which it operates. It is a very broad term, yes, that we could connect with dashboards and visualizations of indicators that focus on the current functioning of an organization.

What is machine learning?

The literal translation of machine learning is machine learning. It is one of the branches or disciplines of artificial intelligence whose main function is to create systems that can learn autonomously. How do you do it? The basis is the identification of complex data in the middle of a lot of information. They use history to predict the future. 

Differences between machine learning and business intelligence

As a result of what we have seen, we might think that both things are the same, but, although they have similar bases, there are some differences: 

As a result of what we have seen, we might think that both things are the same, but, although they have similar bases, there are some differences:

Predictive algorithms automatically learn from data and their models can be integrated into different types of applications.

Models are periodically restrained so that they automatically learn from new data.

Yes, we can use structured or unstructured data. This is for example CSV files, data coming from IOT sensors, videos, photos, audio files, JSON, XML, spreadsheets and much more. Your imagination is the limit. 🙂

Between heaven and RKD there is nothing hidden, nah it is a lie, yes, you can maintain 100% data privacy. In all our projects, the issues of data security, privacy and ethics are considered when creating our algorithms. Sensitive data will be protected at all times.

Mmm, no, we use ERP or CRM as data sources for some projects, since with this information we can answer business questions that go beyond the systematized registration that these tools allow

It depends, it depends on the type of project, but you can consult more information here. What we can tell you is that in RKD we fervently believe that the data initiatives of your business do not need millionaire investments or complex solutions, typical of large manufacturers. We also help you create your own business case, so that data initiatives deliver ROI-based business results as quickly as possible.

They generally last less than listening to the full Pink Floyd discography. 

We always suggest finding things of impact and short duration, for example between 4 and 6 months, to show profitability results to the business and continue to increase the level of maturation of data use in each of our clients.

-When institutions are looking to make data-driven decisions, the entire company must develop the data skills needed to drive these initiatives forward

 Incorporating tools such as basic statistics, creating visual dashboards, telling stories with data and querying data using languages such as SQL and Python.

-You can learn more about our solutions to develop this knowledge in our Data Academy section.