The Evolution Of Data Science In The Cloud

Data science in its current form has only been around since the beginning of the 21st century when statisticians who felt they had an exceptional set of skills decided to separate themselves from traditional computer scientists and mathematicians. Data science has been evolving ever since.

Today, the amount of information data scientists have to process is becoming increasingly overwhelming, especially given the growing volume of data sets produced by sensors, devices, and users. While data-driven entities continue to evolve, the cloud is becoming the common factor that can equip data scientist with the appropriate tools to effectively manage and share information across organizations.

That said, let’s look at how embracing cloud technology would impact data science.

Cloud as a Security Solution

Less than a decade ago, data scientists were largely concerned about data security when it comes to adopting cloud solutions. Today, however, the cloud has become a security solution, rather than a security threat. Namely, as hackers become more advanced in their craft, keeping your data in-house can make you more vulnerable compared to when you rely on cloud services.

Before choosing a data-storage solution, you should ask yourself this question:
Can your business afford a data security expert who would be better able to secure machines in your premises rather than a cloud service provider who can secure it at a data center that employs several security experts?

Cloud as The Foundation for Data Potential

Today, one of the major challenges with the cloud is getting data into the cloud. However, you should expect to witness a fast evolution of the capacity getting data into the cloud, and that should be regarded as a first-class citizen. You know what that means?

When you put data in the cloud, customers want not only to target one consumer for analytics, but they also want to have the ability to access data from their entire consumer base. In the past, the application served as the anchor point in various environments. Conversely, the data becomes the anchor in data-intensive analytics.

Once data is put into the cloud, pre-processed and cleaned, the data set stays constant. Then, opportunities arise for what more you can do with it. By treating data as a first-class citizen, services will be in the background instead of having to look them as an anchor point.

As a consumer, you should be able to look at your date right away and figure out what analytics are available to augment or make inferences from the data.

Bringing Unstructured Data To Life

Besides putting into the right hands, the cloud can also be designed so that you can integrate your data with services that customize and build solution unique to your specific industry and challenges associated with it.

Take banking, for instance; the information that can provide the most insight is normally scattered around a bank’s unstructured data sources such as information on financial products owned by the consumer. That information can be virtually impossible to extract and make good use of.

By equipping data scientists with cognitive analytics, as well as data cataloging capabilities, on the cloud, you can be able to draw insights from this pool of unstructured data, promoting your efforts like analyzing customer transaction trends in a bid to develop and test various marketing offers.

Final Thoughts

Data science is a growing area which businesses are investing to promote better decision making, improve their productivity, and handle customer data more efficiently. It’s set to get even better with its evolution in the cloud, as outlined in this article.