According to Big Data Analytics News, the Big Data market volume is expected to reach $84 billion in 2024. Amazing, isn’t it? Besides, over 57% of the data worldwide is generated by internet users worldwide. 70% of the world’s data is user-generated. An interesting point concerns the labor market in Big Data: 96% of companies plan to hire job seekers with big data skills

As far as Data Science is concerned, the field is expected to grow at a 22% rate from 2020 to 2030, says the US Bureau of Labor Statistics

Many companies have been using Data Science and Big Data in business for a long time now, gaining various benefits. For example, these technologies allow you to make better decisions and justify them to management. And the synergy of these technologies combined with Artificial Intelligence (AI) allows for better results.

We interviewed experts working with these technologies and asked them several questions about the advantages and disadvantages of Big Data and Data Science, their use in business, and development forecasts.

How does the business use Data Science or Big Data and what services does it offer to customers?

As Iu Ayala, the Founder and CEO of Gradient Insight, notes: 

Our approach is deeply rooted in understanding the unique challenges each client faces, tailoring our solutions to meet their specific needs.

One compelling way we leverage these technologies is by assisting companies in optimizing their supply chain management. Consider a scenario where a retail client approached us with inventory management challenges. Through the implementation of advanced predictive analytics, we were able to analyze historical sales data, current market trends, and external factors like weather patterns.

Usetech experts also note that they offer Big Data and Data Science services to companies, such as:

— Data engineering;

— Data analysis and visualization;

— Data and pipeline migration;

— Customer behavior;

— Fraud detection and security.

We are using Big Data analytics to better understand our customers and their needs. By analyzing customer behavior data, we can tailor our products and marketing more precisely. Data Science techniques like machine learning allow us to build predictive models that forecast future trends and automate processes. This improves efficiency and drives innovation. — notes Dmitriy Bobriakov, Marketing Manager at RealEstateU.

Data Science is more popular than Big Data

According to experts, Data Science is more in demand among businesses, and this is not just for nothing. Data science helps to make effective data-driven decisions and predictions.

I would say Data Science is currently more popular than Big Data. Data Science is focused on gaining insights and making predictions from data using advanced statistical and machine learning techniques. It is a fast-growing, interdisciplinary field that allows organizations to get real value from their data.

Big Data refers more to the massive amounts of data that companies have access to, but collecting and storing huge datasets is not very useful on its own. Data science allows companies to analyze Big Data and extract meaningful insights from it. So while Big Data provides the raw materials, Data Science delivers the tools and expertise to turn that data into a strategic asset. Overall, Data Science seems to generate more interest and drive more significant business impacts, which is why I see it as the more popular discipline currently. — notes Dmitriy.

Iy Ayala notes the following: 

While both BD and DS are indispensable, I believe that Data Science, with its ability to extract meaningful insights from complex datasets, holds a slight edge. The fusion of statistical analysis, machine learning, and domain expertise allows us to unlock hidden patterns and correlations, providing actionable insights that drive informed decision-making.

Predictions on the use of Data Science and Big Data

Jun Qin, Head of Solutions Architecture at Ververica, predicts the following: 

While driving results from a large volume of data (from a variety of sources), data lineage, privacy, and governance will all follow suit to become hot topics for 2024 as well. 

We’re also seeing a shift in AI momentum. I predict Big Data will be more and more integrated with AI and Machine Learning for advanced Predictive Analytics, Natural Language Processing (NLP), Image and Speech Recognition and robotics, to name a few.

Needless to say, more industries will embrace real time data processing. Key adopter industries following suit include: finance, manufacturing, healthcare and retail. And more Big Data roles – such as Engineers and Architects will be needed.

My prediction is that with the rise of GenAI, leveraging the full breadth of your data will become increasingly easier. Over time, DS and BD will be driven through natural language questions and no-code query builders as opposed to in-depth queries needing specialized knowledge. The veracity of the underlying data then becomes paramount, so we’ll see a shift in BD and DS specialists in really ensuring that the data is reliable and performing quality assurance. — notes Parker Gilbert is the CEO and Co-founder of Numeric.

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