The Expanding World of Data Science and Challenges to Address/data-science-insights/the-expanding-world-of-data-science-and-challenges-to-address

The Expanding World of Data Science and Challenges to Address

The Expanding World of Data Science and Challenges to Address

The digital world is like a living organism that is continuously growing at an unprecedented rate. Though this growth can be attributed to huge transformations for businesses, it also poses bigger challenges for them particularly, managing the ever-increasing volume, variety, and complexity of the data generated.

According to IDC, the data science industry is expected to handle a whopping 181 zettabytes of data by 2025. This amounts to 10 times more data than what was there in 2018. So, organizations need to effectively handle such a humongous amount of data.

Now, let us explore the threat growing along with the growth of the data science industry.

As the size of an organizations grows, the complexity of data management grows with it. For example, a new application launched can create a ripple effect that can strain the existing infrastructure even more.

Here are the growing threats.

1. Data velocity

The rate at which data is being generated today is larger than ever. Last year, a study by Think with Google found that users generate around 2.5 quintillion bytes of data every day. This continuous data generation poses a significant challenge to real-time analysis.

2. Data Variety

Now, data has become highly diverse. Data collected by organizations are just limited to the user likes and pictures they post, and comment on. Companies today need to handle structured data from traditional sources such as databases along with unstructured data from social media, IoT sensors, video recordings, among others

3. Data Sprawl

This is a major concern for businesses of all sizes. They often fall into the trap of collecting “just in case” data, leading to overflowing of information from data lakes.

4. Data growth

When an organization grows, their digital footprint grows automatically. Therefore, organizations face the growing challenge of converting those massive data volumes into meaningful assets.

Nowadays, traditional data lakes are no more than data swamps. Initially, they served as a solution to data growth, but now they have become limited to vast repositories of information that remain unutilized because of great difficulty in their analysis via data science tools. It is similar to old stuff getting dust in the basement, and it pushes the real-time analytics behind it.

Modern solutions to modern challenges in data growth

The growth of data is inevitable. Everything we do as an individual or an organization, generates data. So, how organizations utilize and handle this data becomes an important factor in deciding their success.

One cost-effective and efficient solutions is using object storage facilities. These storage solutions can help to easily retrieve data. Organizations must understand that focusing only on storing data isn’t enough, and proper attention should be given to intelligent searchability. By transforming data lakes into indexed, searchable resources just like the Google search engine or AI tools that run entirely on local machines and servers, organizations can properly utilize their data resources.

Use of Artificial Intelligence for Data Management Solutions

Artificial intelligence is one highly powerful technology that has been transforming every industry. Using AI in the data science industry has also proven very effective. A recent report by Gartner stated that 75% of organizations will implement a combination of AI and business intelligence (BI) to improve data-driven decision-making.

AI and machine learning systems can be used to understand the underlying structure of data and by using it, businesses can gain valuable insights without getting caught with complex data semantics.

Building a Unified Data Ecosystem: Data Fabric Architecture

Data collection, storage, processing, and analysis are some of the various stages in a data lifecycle. Data fabric architecture provides a distinct approach to managing data at each of these stages. This architecture helps organizations to leverage a wide range of technologies and solutions.

On top of that, it helps break down the data silos as it can seamlessly integrate with different enterprise systems and technologies, no matter what the size or origin is.

Incorporating a consumer-centric approach for complex enterprise solutions

It is very important to keep the end user in mind while designing or implementing complex enterprise data solutions. The need for advanced technical solutions is of course essential, but it shouldn’t come with the cost of user experience. It must be intuitive and accessible.

For example, unlike the traditional bulky mobile phones, today's sleek mobile phones are capable of browsing the internet, booking vacations, buying online, or sharing thoughts worldwide, all from the convenience of a device being in their palm. Similar simplicity is also needed when designing and implementing enterprise data solutions by keeping in mind the end users.


The data universe is growing and it’s not going to stop. Presenting both opportunities and challenges, this data growth needs to be effectively handled by organizations to make sense of the huge data the world is generating. They must acknowledge the interconnected nature of the four-dimensional data equation i.e., velocity, variety, sprawl, and growth, to leverage cutting-edge technologies and unlock the full potential of their data.

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