DataBuck
Big Data Quality must always be verified to ensure that data is safe, accurate, and complete. Data is moved through multiple IT platforms or stored in Data Lakes. The Big Data Challenge: Data often loses its trustworthiness because of (i) Undiscovered errors in incoming data (iii). Multiple data sources that get out-of-synchrony over time (iii). Structural changes to data in downstream processes not expected downstream and (iv) multiple IT platforms (Hadoop DW, Cloud). Unexpected errors can occur when data moves between systems, such as from a Data Warehouse to a Hadoop environment, NoSQL database, or the Cloud. Data can change unexpectedly due to poor processes, ad-hoc data policies, poor data storage and control, and lack of control over certain data sources (e.g., external providers). DataBuck is an autonomous, self-learning, Big Data Quality validation tool and Data Matching tool.
Learn more
Google Cloud BigQuery
BigQuery is a serverless, multicloud data warehouse that makes working with all types of data effortless, allowing you to focus on extracting valuable business insights quickly. As a central component of Google’s data cloud, it streamlines data integration, enables cost-effective and secure scaling of analytics, and offers built-in business intelligence for sharing detailed data insights. With a simple SQL interface, it also supports training and deploying machine learning models, helping to foster data-driven decision-making across your organization. Its robust performance ensures that businesses can handle increasing data volumes with minimal effort, scaling to meet the needs of growing enterprises.
Gemini within BigQuery brings AI-powered tools that enhance collaboration and productivity, such as code recommendations, visual data preparation, and intelligent suggestions aimed at improving efficiency and lowering costs. The platform offers an all-in-one environment with SQL, a notebook, and a natural language-based canvas interface, catering to data professionals of all skill levels. This cohesive workspace simplifies the entire analytics journey, enabling teams to work faster and more efficiently.
Learn more
Microsoft 365 Copilot Analyst
Microsoft 365 Copilot Analyst is a sophisticated AI tool that converts unrefined data into meaningful insights. With its robust data analysis features, including the ability to utilize Python coding, Analyst assists users in making smart, data-oriented decisions. The tool can handle intricate datasets, produce comprehensive reports, and identify patterns, all while smoothly connecting with the Microsoft 365 ecosystem. By enabling users to automate their data analysis processes, Analyst not only saves time but also allows businesses to enhance their strategic decision-making based on timely and precise insights. This innovation represents a significant leap forward in how organizations can leverage technology for improved operational effectiveness.
Learn more
Athenic AI
Uncover the intricacies behind emerging trends by embarking on a guided exploration of data analytics inquiries that reveal the underlying dynamics at play. Enable your stakeholders to harness the power of self-service data analytics, granting them the capability to retrieve and examine the data they require, precisely when they need it. This approach enhances efficiency, diminishes reliance on IT support, and accelerates the process of making informed, data-driven decisions through a self-service analytics platform. Athenic AI seamlessly integrates with your data, whether it is housed in a database, data warehouse, or applications like CRM or ERP systems, providing answers to your queries without necessitating expertise in SQL or the involvement of a business analyst. Designed to comprehend natural language, Athenic translates your inquiries into SQL queries effortlessly. Moreover, we have incorporated a feature that allows users to provide additional context in natural language, further enriching the interaction and insights derived from the data. This empowers users to gain a deeper understanding of their data landscape, fostering a culture of analytical thinking across the organization.
Learn more