The data-driven market is continually evolving, and with the advent of Big Data, professionals are faced with the challenge of identifying, gathering, and analyzing massive datasets in ways that provide quantifiable value for their organization. This is where cloud-native, high-speed data analytics querying comes into play, offering the ability to handle vast amounts of data quickly and providing relevant insights that drive decision-making in real-time.
This capability is exemplified in ways such as spatial data analytics, where connected autonomous freight vehicles can optimize routes and monitor vehicle health for a more efficient fleet.
I’m Ronald van Loon, a Kyvos Insights partner. High-speed data analytics querying is an essential capability more businesses are discovering, understanding that leveraging real-time analytics provides a host of benefits that improve the overall health of the organization.
High-speed analytics on various types of data, including both aggregated and non-aggregated data, is crucial for timely decision-making and remaining competitive in dynamic market conditions. The ability to instantly derive insights from larger datasets enhances the accuracy and comprehensiveness of the analysis, providing a solid foundation for informed strategic decisions and fostering agility and a data-driven culture within organizations.
In such an environment, an analytical data warehouse – or a specialized database tailored for the analysis and reporting of data – becomes central to business intelligence (BI) and decision-making processes within organizations. Housing both aggregated and non-aggregated data, the structured storage and management of both types of data within the data warehouse allow for a broad spectrum of analytics. This can include high-level trend analysis to intricate, detailed analysis, thereby supporting informed decision-making and strategic insights. Kyvos, for example, streamlines enterprise data management by consolidating aggregated and raw data storage, catering to the analytical needs of modern data-driven businesses.
The reduction in total cost of ownership is achieved by utilizing a single storage layer that leverages the best of computing and hardware technology. This approach minimizes data movement, additional infrastructure costs, and optimizes resource usage, thus bringing down operational costs.
Considering the described environment, a universal semantic layer is a game-changer, as it democratizes data access across the enterprise, allowing live access and instant insights from any BI or analytics tools. This layer serves as a single source of truth, eliminating data disparity and promoting a consistent understanding across various business units.
Cloud-based universal semantic layers, such as the one offered by Kyvos Insights, facilitates the definition of centralized rules for data access, ensuring controlled access, and compliance with organizational policies. In other words, it simplifies and speeds the consumption of unified data to reduce or eliminate costly and redundant work across a variety of BI tools.
By embracing a decentralized data architecture, collaboration among departments and teams is enhanced, making department-owned data models a reality. Data mesh decentralizes data architecture by organizing data by business domain and hence , empowering data producers within these domains.
Data mesh also enhances scalability and data team autonomy in large organizations, aiding in more flexible data integration for business analytics. When paired with business intelligence tools and a universal semantic layer, Data Mesh facilitates complex data queries and analysis, bridging various data domains with BI tools to provide richer insights, while promoting interoperability and informed decision-making across the organization.
In practice, companies like Kyvos Insights offer high-performance analytics with versatile querying, the creation of optimized data products, a business-friendly universal semantic layer for end-users, and inter-domain analysis for integrated data insights.
With the discussed tools and capabilities at our fingertips, organizations can develop customizable, real-time dashboards invaluable for tracking business metrics. Additionally, the potential of automated machine learning is significant in uncovering hidden patterns in data.
Also, breaking down data silos enhances data accessibility and promotes a unified view of business information, essential for informed decision-making. Furthermore, self-service analytics empower business users to independently access and analyze data, fostering a culture of data-driven insight without over-reliance on IT teams.
For example, utilizing predictive analytics enabled Macy’s to achieve a 10% uplift in sales within three months, courtesy of tailored email campaigns driven by insights from customer data. In addition, according to McKinsey & Company, using predictive analytics to configure a personalized shopping experience to drive growth is key, as 75% of consumers change shopping habits when given additional choices. Leaders in this area generate 40% more revenue and potentially add over $1 trillion in US industry value.
To leverage the capability of these technological advances, a comprehensive tool for data engineering and analytics is necessary to facilitate the efficient preparation, integration, and analysis of data. With a singular tool, businesses can significantly improve operational efficiency, reduce time to insight, and foster a collaborative environment for cross-functional data-driven decision-making.
The Kyvos platform on Azure cloud encapsulates key features like high-speed data analytics, a universal semantic layer, and supports a data mesh architecture. It aligns with the modern needs of data analytics, offering a robust solution for businesses. It’s also worth noting that Kyvos Free offers a cost-free data analytics platform where you only pay for Azure infrastructure, including a Virtual Machine and Databricks cluster. For more information, visit the Azure marketplace to see how Kyvos Insights integrates with Azure cloud solutions.
By Ronald van Loon
