Data and analytics have become crucial assets for modern organizations. The exponential growth of data, estimated to reach 175 zettabytes by 2025, poses significant challenges such as insufficient data storage, lesser computational power, and ineffective decision-making for businesses when processing and analyzing this immense amount of information.
Artificial Intelligence (AI) plays a pivotal role in accelerating analytics at a massive scale, transforming how companies harness data to make well-informed decisions. Let’s understand the indispensable contribution of AI in data analytics while highlighting the advantages of leveraging an AI/ML-powered analytics accelerator.
The Role of AI in Big Data and Analytics
AI revolutionizes data utilization for organizations, going beyond structured data from databases to tackle the challenges posed by unstructured data like images, videos, audio, and text documents. This unstructured data constitutes a significant portion, around 80% of enterprise information.
AI has unlocked the true potential of this unstructured data. Advanced analytics accelerator solutions with smart aggregation technologies can extract valuable info from various sources by transcribing audio, analyzing text sentiment, and recognizing objects and faces in visuals.
However, the role of AI in big data and analytics extends far beyond unlocking unstructured data. It can also uncover hidden patterns and trends within vast volumes of structured data. Using AI, large datasets can be analyzed to identify predictive patterns and correlations that are not immediately apparent when analyzing them manually. This empowers organizations to make data-driven decisions, improve operational efficiencies, and gain a competitive advantage in their respective markets.
Why Do Organizations Need Analytics Acceleration Powered By AI/ML?
Analytics acceleration involves optimizing data processing and analysis to deliver faster and more efficient results. It utilizes advanced technologies and methodologies, like pre-aggregated OLAP models, to speed up complex queries and enhance overall analytics performance. The main objective of analytics acceleration is to significantly reduce the time it takes to generate insights, enabling business users to make real-time data-driven decisions.
Handling Unprecedented Data Growth
According to a forecast by the International Data Corporation (IDC), 55.7 billion connected IoT devices will exist by 2025. This massive number of devices is projected to generate an astounding 80B zettabytes (ZB) of data. This exponential growth of data can be attributed to several factors, such as online transactions, social media interactions, digital communications, and the widespread adoption of IoT technologies.
To effectively manage this overwhelming influx of information, organizations require robust analytics accelerator solutions capable of swift data processing and analysis.
Unlocking Unstructured Data
Despite being valuable, an enterprise’s unstructured data consisting of images, videos, audio files, and text documents remains untapped. There are challenges associated with manual data entry and the limited availability of accessible AI tools.
However, advancements in AI engineering platforms have now made it possible to exploit the wealth of information within unstructured data. This significantly enhances the potential for extracting valuable business intelligence.
Overcoming Performance and Scale Limitations
By 2025, Gartner predicts that context-driven analytics and AI models will surpass 60% of the traditional data models currently in use. This forecast emphasizes the growing significance of AI-powered analytics in reshaping how organizations handle and analyze data to maintain competitiveness. These accelerators empower organizations to tap into the potential of AI when handling intricate queries on vast cloud-scale datasets, resulting in faster insights, enhanced decision-making capabilities, and optimized resource utilization.
Cost-Effective Solutions
Cloud data warehouse pricing is based on compute time, which can result in significant costs when processing large datasets. Cost-effective solutions like analytics acceleration using pre-aggregated OLAP datasets contain pre-computed aggregations and summaries of information, significantly reducing the need for complex calculations during query processing. The result is minimized compute requirements, leading to faster query response times and reduced costs.
Seamless Integration
AI-powered analytics acceleration solutions offer flexibility through seamless connections to various data science and analytics platforms. These connections utilize industry-standard connectors and APIs like SQL, MDX, and DAX. As a result, organizations can adopt these solutions smoothly and without disruptions or the need for major system overhauls. This eliminates the need for significant investments in new technologies while maximizing the value of existing data ecosystems.
Conclusion
By harnessing advanced customer experience analytics accelerator technologies, businesses can gain real-time insights, facilitate data exploration, enhance scalability, optimize costs, and promote accessibility to data for all. Uncover valuable insights to drive informed decision-making and achieve success in this era driven by data.
