Tech

Has Data Science Already Exceeded our Expectations?

It’s true that Artificial Intelligence has expanded the vision of data science professionals beyond their own expectations. Compared to AI applications that...

· 2 min read >

It’s true that Artificial Intelligence has expanded the vision of data science professionals beyond their own expectations. Compared to AI applications that were built in the time period between 1991 and 2011 are by far the most critical in terms of how data scientists leverage these to create the future of AI in 2022.

We have discussed many opportunities associated with the way we define what is data science, but hardly anyone discusses the pertinent difficulties linked with these definitions and invariably the community falls short of expectations in delivering accurate results.

These challenges arise due to 5 major reasons. I have mentioned these below:

  1. Inability among the data analysts to correctly define the scope of “what is data science?” as applied to different industries and applications.
  2. Not experimenting with enough data
  3. Faulty data management and analytics
  4. Lack of quality techniques to verify data authenticity
  5. A blurred distinction between what machine learning algorithms can do, and what they can’t do for data science.

Data scientists often take a sophisticated approach to solve at least one of these problems at a time. However, due to lack of evidence and delay in analytical experiments derail the data science domain from fully solving any of these challenges satisfactorily. If you are willing to diver deep into the expansive universe of data science, this is the best time to invest quality time, effort, and resources.

Let’s understand how this would help evaluate the scope of data science and if we can really achieve something extraordinarily great with all the new age technologies built around data science.

Turning Data Science into a “-as-a-Service Industry”

Data science involves an active learning approach to solving complex problems. Leading industry groups such as Healthcare and Pharma, education, manufacturing, online marketing and sales, e-commerce, and graphical programming – these are the biggest marketplaces for anything that has data science in its core deliverables. Therefore, companies are increasingly trying to become data-backed organizations and design their products, services, and solutions as a data-as-a-service package. While some companies such as Google, Amazon, and Facebook tilt their current services based on whatever first party data and subscriber analytics deliver, others such as Uber, Twitter, Snapchat, Microsoft, and so on, use a mix of a third party and first party database to improve their overall user experience.

Data science professionals are therefore aligning their skills around the fence of companies and industry groups that either deliver or seek data as a service.

Solving Big Data Problem of Storage            

The more data you work with, the bigger are your problems. The crux of the issue lies with data storage – the space that is utilized when your data rests. And, in most cases, 90% of the data rests in storage forever—never getting mined or analyzed for the purpose these were collected in the first place. This puts immense pressure on the existing infrastructure. And, we shouldn’t forget the concepts associated with “data decay” and dark data that further pose grave risks to data storage management.

Big Data companies identify with data storage challenges easily- but answers to these are far from being conclusive or convenient to solve!

 Despite the rise of Cloud automation and virtualization, companies are faltering with data science management operations. Lack of infrastructure, poor skill inventories, and perennial risk of major cyberattacks always keep the IT teams on their toes. These result in data science becoming a high cost operation even when organizations have invested in the best technologies, infrastructure, security frameworks, and people to manage all these.

But, the industry doesn’t seem to find a way around the data storage issues as yet.

Machine learning is yet to provide a solid answer to the data science industry as far as working with Big Data storage facilities is concerned! The only way to solve Big Data storage problems seems to have inspiration from conventional IT philosophies of Agility and Scale.

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