Big Data and AI, the highlights of 2023, and the topic of the future – everyone is concerned and at the same time curious about what could happen next. Here’s what you need to know!
What is Big Data?
In layman’s language, it refers to the massive volume of data that’s been generated at an unprecedented pace. This data can be both structured as well as unstructured. The key characteristics of data churned are – volume, velocity, and variety.
Back in 2021, approximately 2.5 quintillion bytes of data was created every day. And during that time itself, around 90% of the world’s data was generated. The recent stats are not available, but it’s quite evident that it is going big every day.
What are the sources of it?
Now that you have explored what it is all about, next comes the very concerning question – what are the sources? it originates from diverse sources such as social media, sensors, IoT devices, and more.
If talked about Social media alone, this space generates approximately 500 terabytes of data daily. It’s quite a treasure trove. Considering AI applications these days actually need mass data to work on, and social media alone extracts valuable insights and patterns.
How is it related to AI?
For machine learning algorithms, it acts like a fuel. It’s actually the backbone of AI. To give you a quick sneak peek, let’s take an example of an industry: healthcare. Over here, the use of Big Data has resulted in a 30% reduction in hospital admissions. And as a result, around $300 billion was recorded as annual savings.
Benefits of Using Data-Driven Networks in AI
Above was example of just an industry, but the benefits down the line of using data driven networks in AI is a lot, some remarkable ones are:
Seamless Integration
Data-driven networks seamlessly integrate Big Data into AI applications. And this itself ensures a smooth flow of information. Talking about why this integration is crucial, with just this single step: Business thinking of leveraging data-driven insights might have a 23 times higher customer acquisition. (Just an estimation)
Enhancing Efficiency
After integration comes efficient. Data driven networks bring this efficiency on the table. Infact, according to A McKinsey report, data-driven organizations are 23 times more likely to acquire customers. Additionally these organisations are six times as likely to retain customers, and 19 times more likely to be profitable. And all these numbers definitely indicate a positive future ahead.
Challenges and Considerations in Using it for AI
Benefits are there, but with the advantages comes a few challenges that must not be overlooked, some of them are:
Data Security and Privacy Concerns
Data breaches happen. Hence robust security measures has to be there. Talking about numbers, according to one report by Cybersecurity Ventures, cybercrime costs will reach $6 trillion annually by 2021. The number itself underlines the urgency of addressing security concerns.
Scalability Issues
Last comes the issue of scalability. Handling immense volume of Big Data requires scalable solutions that one organisation must have. According to IDC, the global datasphere is expected to grow to 175 zettabytes by 2025. We have just an year ahead, and the number itself highlights the need for scalable infrastructure to accommodate this exponential growth.
Key Components of Data-Driven Networks
Network Architecture
Data driven networks comprise of storage, servers, and processing units. Infact, the global data center server market size is projected to reach $63.5 billion by 2025. This states that there will be a rapid expansion of infrastructure to support data-driven applications in the coming years.
Servers and Storage
There must be innovation in server and storage solutions in the years ahead. There is a massive adoption of hyper-converged infrastructure (HCI). The HCI market itself is expected to reach $27.1 billion by 2025. The number highlights the importance of integrated and scalable solutions.
Data Collection and Storage in Data-Driven Networks
Diverse Data Sources
Data from just a source would not do justice. There must be diversity in data sources for better AI applications. Coming to the Internet of Things (IoT) market share, it is projected to grow to $1.1 trillion by 2026.
Storage Strategies
Next comes the storage part. Businesses must continuously invest in storage solutions. Infact, talking about the global data storage market it is forecasted to reach $114.9 billion by 2025.
Data Processing and Analysis in Data-Driven Networks
Real-Time Processing
Since the sources and storage part is done as of now, next comes the processing part. For immediate insights, real-time data processing is a must. Infact, the global real-time data analytics market is expected to reach $23.2 billion by 2026, showing how the demand for instant data analysis is increasing.
Advanced Analytics
Machine learning algorithms play an important role in data analysis. Even the machine learning market is predicted to reach $96.7 billion by 2025. This growth showcases how the adoption of advanced analytics in various industries is increasing.
Using Big Data for AI Applications
Personalization in AI
With Big Data, comes personalized experiences. 77% of consumers are more likely to choose a brand that offers personalized experiences. Infact us too. This scenario itself showcases the importance of using data for tailored interactions.
Predictive Analytics
Big Data helps with predictive analytics. And in fact according to Statista, the predictive analytics market is expected to grow to $22.5 billion by 2026. This showcases the rising adoption of data-driven predictions in business strategies.
To wrap up!
The future is brighter with the integration of emerging technologies like edge computing, 5G, and blockchain. But ethical considerations should not be overlooked too. The above was quite a sneak peek into how data driven networks use big data for AI applications, however, in future one can experience more advancements, and together we are ready to witness the changes!
