Analytics and business intelligence platforms — enabled by IT and augmented by AI — empower users to model, analyze and share data. Analytics and business intelligence (ABI) platforms enable organizations to understand their data. For example, what are the dimensions of their data — such as product, customer, time, and geography? People need to be able to ask questions about their data (e.g., which customers are likely to churn? Which salespeople are not reaching their quotas?). They need to be able to create measures from their data, such as on-time delivery, accidents in the workplace and customer or employee satisfaction. Organizations need to blend modeled and nonmodeled data to create new data pipelines that can be explored to find anomalies and other insights. ABI platforms make all of this possible.
Gartner defines augmented data quality (ADQ) solutions as a set of capabilities for enhanced data quality experience aimed at improving insight discovery, next-best-action suggestions and process automation by leveraging AI/machine learning (ML) features, graph analysis and metadata analytics. Each of these technologies can work independently, or cooperatively, to create network effects that can be used to increase automation and effectiveness across a broad range of data quality use cases. These purpose-built solutions include a range of functions such as profiling and monitoring; data transformation; rule discovery and creation; matching, linking and merging; active metadata support; data remediation and role-based usability. These packaged solutions help implement and support the practice of data quality assurance, mostly embedded as part of a broader data and analytics (D&A) strategy. Various existing and upcoming use cases include: 1. Analytics, artificial intelligence and machine learning development 2. Data engineering 3. D&A governance 4. Master data management 5. Operational/transactional data quality
Gartner defines data integration as the discipline comprising the architectural patterns, methodologies and tools that allow organizations to achieve consistent access and delivery of data across a wide spectrum of data sources and data types to meet the data consumption requirements of business applications and end users. Data integration tools enable organizations to access, integrate, transform, process and move data that spans various endpoints and across any infrastructure to support their data integration use cases. The market for data integration tools includes vendors that offer a stand-alone software product (or products) to enable the construction and implementation of data access and data delivery infrastructure for a variety of data integration use cases.
Reviews for 'Data and Analytics - Others'
Master data management (MDM) of product data solutions are software products that: Support the global identification, linking and synchronization of product data across heterogeneous data sources through semantic reconciliation of master data. Create and manage a central, persisted system of record or index of record for product master data. Enable the delivery of a single, trusted product view to all stakeholders, to support various business initiatives. Support ongoing master data stewardship and governance requirements through workflow-based monitoring and corrective-action techniques. Are agnostic to the business application landscape in which they reside; that is, they do not assume or depend on the presence of any particular business application(s) to function.