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Several prominent business veterans and a number of leading publications have described data as the new oil, capable of generating significant value if used appropriately
Fremont, CA: The digital economy today is powered by big data. Generated in abundance by both individuals and enterprises, these data is stored in large data centers and some of which cover hundreds of thousands of square feet.
Here are six leading trends in big data:
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AI-ready data
Technology vendors are implementing pre-enriched machine-readable data, specific to given industries to speed time-to-market for custom-built AI tools. These kits are intended to help data scientists and AI engineers and include the data necessary to speed up the creation of AI models.
Central Governance
Big[vendor_logo_first] data vendors had to take up the issue of data governance, security, and management, taking a back seat to accessibility and speed. As a result, most companies now openly prioritize data governance, leading to multiple solutions being replaced by single data management platforms to improve scalability, collection, and distribution of data.
Data Democratization
Data-driven business understandings have led to market demands that data be made available to the widest applicable base of users, enabling them to draw insights through the self-service analytics model. This push began with the emphasis on data consumers and has now expanded to target producers with new tools supporting data analysis and the creation of visualizations, and this trend is already transforming the publishing industry.
See Also: Top Big Data Analytics Companies in Europe
Data as a Service (DaaS)
DaaS, a cloud service that provides users with on-demand data access, helps enterprises address challenges such as increased infrastructure costs, low architecture flexibility, increased data complexity, complex data governance, and increased time to move data between systems. This service is deployed with data lakes, which are vast storage of unstructured, semi-structured, and structured data. It stores and manages enterprise data by compiling it into relevant streams, helping enterprises reduce storage and management costs, as well as enhancing quality.
Data Integration
Enterprise buyers need data integration and preparation tools capable of retaining access to various data sources without giving up data quality and security. Machine learning and artificial intelligence (AI)-enabled smart data integration tools can replace extract, transform, load (ETL) processes, and recommend the best solutions to help data scientists in organizations.
AI for Data Quality
The use of AI improves data quality, which is needed within any analytics-driven organization. With the growth of personal, public, cloud, and on-premise data, it has made it difficult for IT to keep up with user demand.
Companies want to enhance quality by taking advanced design and visualization concepts typically reserved for the final product of a particular business intelligence solution and putting them to work at the very beginning of the analytics lifecycle. AI-based data visualization tools are enabling enterprises to identify critical data sets that need attention for business decision-making, reducing human workloads.
Check Out: Top Big Data Companies in APAC
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