As the world continues to urbanize and the amount of data generated by cities grows, the importance of big data analytics in shaping the future of urban life will only increase.
Big Data
Data Lake is a term that has emerged in the past decade to describe an important part of the data analysis pipeline in the big data world.
What is the significance of digital transformation? This is the answer to the question that every enterprise is looking for, and the most common answer is the reinvention of business processes.
This year's Gartner release on the key data and analytics trends for data and analytics leaders to leverage in the enterprise in 2022 breaks down into three main themes: energizing and diversifying the enterprise, empowering people and decision making, and institutionalizing trust.
What we can predict is that the future of big data technology will continue to evolve along the direction of heterogeneous computing, cloudization, AI convergence, and in-memory computing.
By 2025, healthcare data is expected to grow at an average annual rate of 36%, far outpacing other sectors such as manufacturing, financial services, the media industry, and entertainment.
Data silos and unlinked systems caused employees to waste a lot of time moving information around. In addition, the sheer volume of paper and electronic forms forced employees to manually process documents and verify their contents.
Low-latency analytics is a technology that enables processing and analyzing big data in real time or near real time. It is critical in big data processing because it allows organizations to extract insights from data faster.
In order to unlock the potential of advanced visualizations that enable organizations to analyze multiple sources of information and uncover hidden patterns and trends, certain challenges of leveraging big data should be addressed.
The application of big data is just like the use of credit cards. The better you use it, the greater the income. On the contrary, can enterprises bear the cost of mistakes in big data? This article describes 6 major mistakes and solutions.