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Big Data and The AI Stack

[Boston, Massachusetts - Forbes]



- Trends and Emerging Technologies in AI

The growth of artificial intelligence (AI) is accelerating. AI has left research and innovation labs, and nowadays plays a significant role in our everyday lives. The impact on society is graspable: autonomous driving cars produced by Tesla, voice assistants such as Siri, and AI systems that beat renowned champions in board games like Go. All these advancements are facilitated by powerful computing infrastructures based on HPC and advanced AI-specific hardware, as well as highly-optimized AI codes. AI has acted as the main driver of emerging technologies like big data, robotics and IoT, and it will continue to act as a technological innovator for the foreseeable future. 

AI and ML are now giving us new opportunities to use the big data that we already had, as well as unleash a whole lot of new use cases with new data types. We now have much more usable data in the form of pictures, video, and voice, for example.

For an AI system to decode huge loads of data, make connections, and churn out insights that you can work on, you need to feed it with the right data. Unfortunately, this is where most organizations fail. Failure to integrate raw data from different sources will make the AI system to give you results that aren’t helpful. For the algorithm to draw accurate conclusions, ensure you run all-rounded data. 

Basically, AI is the goal; AI is the planet we’re headed to. ML is the rocket that’s going to get us there. And Big Data is the fuel. AI is impacting the future of virtually every industry and every human being. 


Big Data And AI Work Together

When it comes to the fundamental workings of the two technologies, Big Data and AI can not be far apart. The first one deals with better handling of data, generating insights. And the second one uses that data to automate systems and make decisions without any external help. The more data we put through the ML models, the better they get. It’s a virtuous cycle.  

Data is 21st century gold. Data is the fuel that drives today’s digital economies. Large organizations, small businesses, and individuals are increasingly relying on data to perform their day to day tasks. Massive sets of data, which are referred to as big data, are analyzed by AI systems to give insights. These insights can be trends, patterns, or predictions. When combined, big data and AI become a formidable force. They are the powers behind the innovations we are witnessing today.

For decades, data was viewed as something that consumes space; it was stored away or hauled away. In this digital age, data has become a vital asset. It is the lifeblood of every successful organization. To keep pace with your competition, you need to review your strategies and adopt the latest data and AI trends. These two technologies can work together to help you get accurate insights regardless of the sector you work in. By making data-driven decisions, nothing can stop your business from reaching the heights it deserves. 


- AI Fuels Data Analytics

When we talk about AI in data analytics, often we are referring to its subset: machine learning (ML). ML algorithms enable the automation and optimization of analytics, giving businesses a better return on their insights. In particular, it can identify things the human eye simply couldn't. As a result, you widen your pool of exploration and drive your business further. AI in data analytics offers new potential to businesses in any arena, but customer-facing organisations may find it particularly advantageous. This is because AI enables businesses to pull more insights from interactions and tailor their services in turn. 

Data is a critical business asset. It’s what drives innovation today and enables firms to stay competitive in the global marketplace. And now with the convergence of big data and AI, companies can more easily leverage advanced analytics capabilities like predictive analytics and more efficiently surface actionable insights from their vast stores of data. With big data and AI-powered analytics, firms can empower their users with the intuitive tools and robust technologies they need to extract high-value insights from data, fostering data literacy across the organization while reaping the benefits of becoming a truly data-driven organization.


- The Convergence of Big Data and AI

Big data and AI have a synergistic relationship. Data is the fuel that powers AI. The massive, complex, and rapidly evolving datasets referred to as big data make it possible for machine learning applications to do what they were built to do: learn and acquire skills. Big data supplies AI algorithms with the information necessary for developing and improving features and pattern recognition capabilities. Without large quantities of high-quality data, it wouldn’t be possible to develop and train the intelligent algorithms, neural networks, and predictive models that make AI a game-changing technology. 

AI, in turn, helps users make sense of sprawling, diverse datasets and sort through unstructured data that can’t be organized into neat rows and columns. AI enables firms to use big data for analytics by making advanced analytics tools more powerful and accessible, helping users discover surprising insights in data that was once locked away in enterprise information silos. Leveraging big data, AI, and advanced analytics, companies can provide their decision-makers with greater clarity and understanding of the many factors influencing their business while encouraging creative, intuitive exploration of large-scale, multi-dimensional datasets.  

[Lauterbrunnen, Switzerland - Civil Engineering Discoveries]


Building a Data Architecture to Scale AI

Large-scale data modernization and rapidly evolving data technologies can tie up AI transformations. For today’s data and technology leaders, the pressure is mounting to create a modern data architecture that fully fuels their company’s digital and AI transformations. 

Well-managed Data Architecture and AI technologies are poised to drive future innovations in IT, which will bring in better opportunities for businesses through technological disruptions. However, these trends also indicate that the businesses will need highly capable Data Science field experts, groomed in AI, predictive modeling, ML, and DL, among other skills, to drive this transformative tech leadership. 

Big data platforms and big data analytics software focuses on providing efficient analytics for extremely large datasets. These analytics helps the organizations to gain insights, by turning data into high quality information, providing deeper insights about the business situation. This enables the business to take advantage of the digital universe.

Information Architecture plays a key role in establishing order in the continuous evolution of emerging data technologies. Organizations should take to embrace AI and streaming data technologies, and the long-range impact of General Data Protection Regulation (GDPR) on enterprise Data Management practices. While streaming data is the only way to deal with the high velocity of big data, strong Data Governance measures will ensure GDPR compliance. 


- The New Enterprise AI Technology Stack

To develop an effective enterprise AI or IoT application, it is necessary to aggregate data from across thousands of enterprise information systems, suppliers, distributors, markets, products in customer use, and sensor networks, in order to provide a near-real-time view of the extended enterprise. 

Today’s data velocities are dramatic, requiring the ability to ingest and aggregate data from hundreds of millions of endpoints at very high frequency, sometimes exceeding 1,000Hz cycles. The data need to be processed at the rate they arrive, in a highly secure and resilient system that addresses persistence, event processing, machine learning, and visualization. This requires massively horizontally scalable elastic distributed processing capability offered only by modern cloud platforms and supercomputer systems. 

The resultant data persistence requirements are staggering. These data sets rapidly aggregate into hundreds of petabytes, even exabytes. Each data type needs to be stored in an appropriate database capable of handling these volumes at high frequency. Relational databases, key-value stores, graph databases, distributed file systems, and blobs are all necessary, requiring the data to be organized and linked across these divergent technologies.



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