In the business world, either you move … or you expire. It is clear that keeping up with new trends is not a matter of choice, but of survival. It is often said that sharks, due to their particular anatomy, cannot stop swimming; The same happens with organizations and technology: you cannot stop learning and innovating, if you stop, you are out. And today, more than ever, Data Science as a Service is imperative for business.
The revolution was brought about by Data Science as a Service which is a current trend for modern companies. With DSAAS, business analysis can be done faster and of course at a more affordable cost.
Although terms like Data Science as a Service and big data are relatively recent, we have always been surrounded by raw information that we have tried to measure, analyze and use to our advantage. What happens when, with the development of new technologies, the volume of data within organizations is such that we need to learn new ways to process it?
It is clear that companies and institutions cannot turn their backs on new trends such as Data Science as a Service, they need to grow alongside them. Not taking advantage of its potential would mean being at a disadvantage against the competition and causing a drastic reduction in your level of productivity.
This is undoubtedly the age of data.
“Data is the new oil,” said Clive Humby, one of the leading figures in data science in 2006. And he added, “Valuable, but of little value if not refined.”
We live in a world that does not stop generating them, due in part to the incorporation of new technologies into different day-to-day production processes, in a process that has been called digital transformation.
At Domo, a veteran software company specializing in business intelligence and data, they calculated that currently 1.7 MB of data is generated per second worldwide. And this makes Data Science as a Service essential for all types of organizations.
We can define Data Science as a Service as a specialty that, through scientific methods, algorithms, and systems, orders and structures data so that it has an intelligible meaning. It is closely related to concepts such as machine learning and Big Data, which is why they are often mixed.
Almost all companies today have access to Data Science as a Service capabilities. Instead of implementing these capabilities to understand only customer retention and price optimization opportunities, companies can direct these capabilities to discover where human needs are not being met. This may involve using ideas to empathize with people and serve them in a deeper and richer way that furthers your end goals.
The work of a Data Scientist is therefore fundamental today within organizations. A Data Scientist knows how to convert a large volume of data into accurate, useful and easy-to-understand information to answer various questions, often related to decision making within an organization, but also to customer satisfaction.