We live in a world where the data is abundant. The technological advancements and digitization around us have led to these consequences. No matter what happens, there’s one thing for sure.
We will not be running out of data anytime soon. Taking a mere look around further confirms this fact. Smartphones, tablets, wearable devices, voice assistants, smartphone-controlled lights, and everything else in between. We are surrounded by devices that have become an inseparable part of our lives. And as useful as these devices prove to be with us, they are at all times collecting data with every move they make.
The point is that data is becoming the core of the world today. It is what is driving innovations, helping technological advancements and solving challenges in the real world out there. As companies capture data with multiple devices, they require to store it and further process it to get conclusive results. These results are behind every progress and a successful decision made around the world.
Challenges with data
If we take a look back, we can understand how difficult it was to generate data a few decades back. Organizations and enterprises used to run with traditional methods, that mostly revolved around their gut feeling, instead of something concrete. They seldom thought about obtaining the data from the world and utilizing it to drive their product. And it wasn’t after all completely their fault. There wasn’t a system that could store all the massive amounts of data generated adequately.
Thanks to services like Hadoop, cloud among others. At least data storage became simplified. Organizations could now store their data with ease and this led them to capture data at every step of the way. The next challenge, however, was to derive meaning out of this data. The vast amount of data that organizations could finally gather meant nothing unless they could be acted upon.
The underlying question was how does one make sense from something so haphazard in nature. The point is that data is just raw. It consists of as meaningful entities as it consists of garbage. If organizations randomly started acting upon it, it would take them years just figuring out which part of the data makes sense and which doesn’t.
eCommerce and Data Science
However, this isn’t the case anymore. The problems with the interpretation of data were solved with the advent of data science and analytics. Thanks to these technologies, the world is far more advanced than it was a decade ago and organizations are making decisions for their business and products based on data that finally makes sense.
Data science and analytics have become the go-to for any business, trying to make progress in their fields. It is the key to understanding the customer and carving a niche for themselves in the market by adequately catering to the demands of the customer. Needless to say, customers form the backbone of any business. They are far more rational and educated than they were a few decades back. While it is a good thing for the customer, considering the hardships of frauds and malpractices in eCommerce, it is also putting pressure on the businesses.
eCommerce businesses are under the burden of several factors – they have the customers to retain, compete in the market with big players and new entrants along with serving the customer adequately. They can no longer afford to be guided blindly by their intuition. As a result, they head to analytics and data science to the rescue.
The problem is that every other eCommerce platform out there is trying to harness cutting edge technologies to target the customer and make the most out of it. Small and medium-sized enterprises face competition from giants like Amazon who are the first ones in the industry to being the trends the customer. Be it using data analytics to study customer purchase patterns to recommending products based on it, Amazon is doing a great job with interpreting the massive data eCommerce businesses to possess.
Similarly, there are new entrants, who have their unique business models and knowledge of the changing market trends that give them an edge to entice and win over the customers. The only way out for small and medium businesses remains to capitalize on the data and look for answers to the most pressing challenges there. With this, the waves of change have started to sweep organizations off their feet.
eCommerce businesses are now working on data engineering insights and data science platforms that are evolving into one of a kind in the industry. This is not just helping them understand the changing demands of the customer but also keep up with them with highly personalized products. When data resides as the DNA of any eCommerce business, the decision making for any product definition or business as a whole depends on the story that the data has to tell.
Based on the insights received from data science platforms, eCommerce businesses have the potential to test their emerging ideas on a segment of customers and measure their impact.
Moreover, the further they bring their data analytics platform closer to data science and ML platforms, the more will they be able to derive deeper insights from customer trends.
As the year progresses, for eCommerce businesses the focus will be to build an ML platform along with a real-time data science pipeline. And that’s because data science withing ML models becomes valuable when they run in real-time. Utilizing this potential, eCommerce businesses can not just solve the pressing challenges of their niche but also innovate and win over the customers with personalized features.
Conclusion
Data science and analytics go a long way if an organization has the vision to innovate and stand out in the market. But, they must begin with setting up an infrastructure for their data that performs a series of checks and balances at every step before the data is ingested into the ML and data science platform. In 2020, the prime focus of eCommerce development businesses will be to make the most out of real-time ML and data science solutions to driver personalization and other customer winning innovations.
