Data science is one of the most sought after fields in today’s generation. After all, with the abundance of data in the world, it is something that helps derive sense out of it. With digitization sweeping off the world, data has not just become more accessible but also an urgent need of the hour. No matter where you look, it is easy to find data governing processes, businesses and even industries.
It wouldn’t be unfair to say that data has become the new norm of any industry and the only ones to excel are those who capitalize on its potential. The point is that we live in a world of data and long gone are those days when traditional business models revolving around beliefs and intuitions used to function.
Data Technologies Transforming the World
As much as we love data for helping accomplish a plethora of tasks, we need data technologies to make some sense out of it. Raw data might have an equal amount of garbage as it has valuable insights. But, unless organizations and enterprises use data technologies over it, it is hard to make out the difference and utilize the data for something valuable. Today, there are a handful of technologies out there in the market that is helping organizations achieve their business goals using data.
One hand we have Kubernetes leading the way in the automation of applications. While on the other hand, more organizations are seen betting on cloud platforms and enterprises relying on data science. Moreover, these technologies are nowhere stranded alone. Most organizations and researchers are coupling data technologies with artificial intelligence, machine learning, and advanced analytics opening doors to even more opportunities for the world.
But, at this point, one might think about the relevance of traditional business methods performed over data, such as business intelligence. Even though BI has been able to create a lot of buzz in the world, the fact is that it would mostly work on a small-sized and structured chunk of data. This worked well with traditional business systems because they had their data largely structured. But, that’s not the situation with businesses anymore.
Today, the data flows into a business from more than a few sources. There are different devices, different platforms, channels, retail as well as digital sources that generate valuable data. This makes the data at the end of the day largely semi-structured or unstructured. Even the data trends in 2020 suggest that more than 80 percent of the data will be unstructured. And that’s why we need more advanced data technologies and analytics like data science platforms that help analyze all this data and provide useful insights at once.
Similarly, as organizations continue to utilize artificial intelligence and machine learning in their core processes, they need something to measure the impact generated by these. There are multiple data points that AI studies and for it to fruitfully take a decision, data science platforms are required. However, as useful as it is, data science can be a complicated affair, which is why organizations shy away from fully utilizing it.
Data Science in 2020
As we progress into the year, we will witness a simplified version of data science advance into the world. All thanks to the low code or no code technologies. The need for such a simplification came from the fact that organizations are utilizing advanced analytics and AI solutions more rapidly than ever. They are in fact, betting their business based on nothing but data insights from AI and ML platforms. Therefore, to get an in-depth analysis of such a vast amount of data, organizations require a skilled resource who understands programming as well as deep mathematics.
Since finding such a resource is challenging, there had to be another way around. Organizations wanted to implement AI and work on their insights in a matter of days, or more adequately, as soon as possible. This was hitherto not possible with the limited supply of skilled professionals. As a result, organizations began becoming independent and deriving valuable insights from the data on their own.
It paved a way for no code or low code technology that brings machine learning to the forefront and makes services smart so that businesses no longer rely on individuals to carry out the data science tasks. Therefore the trend will shift from building and deploying models to bringing models that will be trained by autonomous technologies.
A piece of concrete evidence in this direction is projects such as Google’s Cloud Auto ML, basically a no coding AI trainer, and Teachable Machine 2.0 which is a ramp for new ML practitioners. Technologies like these will empower even the non-technical user or the end-user to run models while avoiding any kind of mistakes. This isn’t possible with conventional methods as there are a lot of errors in building and training a model.
As time passes, several other organizations are seen jumping into this no-code/ low code data science phenomenon. Google’s project has just flared up something that was holding back organizations from unleashing their true potential. Today, several python companies from the AI space bringing up such dynamics 365 solutions are C3.ai, Mendix, and Appian, all of which are no coding platforms, enabling all kinds of businesses to jump into the sphere and start reaping the benefits.
Conclusion
As a small and medium organization, the low code/ no-code approach might seem like a great solution for you, considering the trouble of finding the right resource. But, make sure the technology you pick has a built-in application logic, the right managed or declarative layer along with an underlying framework that works miraculously with data sets and modules.
