operational data store

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Published By: Workday     Published Date: Sep 19, 2018
The data deluge problem isn’t just about the amount of internal, operational data being stored, but also the level of granularity available. The finance and HR teams of many institutions still operate on outdated systems that are only able to store aggregate data with complex details summarized. While these systems may be sufficient for the purpose of financial reporting, they’re unable to keep up with the level of complexity needed to drive business decisions.
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Workday
Published By: Oracle CX     Published Date: Oct 20, 2017
With the growing size and importance of information stored in today’s databases, accessing and using the right information at the right time has become increasingly critical. Real-time access and analysis of operational data is key to making faster and better business decisions, providing enterprises with unique competitive advantages. Running analytics on operational data has been difficult because operational data is stored in row format, which is best for online transaction processing (OLTP) databases, while storing data in column format is much better for analytics processing. Therefore, companies normally have both an operational database with data in row format and a separate data warehouse with data in column format, which leads to reliance on “stale data” for business decisions. With Oracle’s Database In-Memory and Oracle servers based on the SPARC S7 and SPARC M7 processors companies can now store data in memory in both row and data formats, and run analytics on their operatio
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Oracle CX
Published By: SAP     Published Date: Sep 19, 2017
SAP S/4HANA Retail for merchandise management is at the core of a comprehensive suite of retail offerings designed to help retailers meet the demands of a digital economy. It supports retail core processes end to end, starting with master data down to point-of-sales (POS) connectivity. It allows insights into operational retail data, empowering users with contextual, real time information for faster and better decision making. Processes can easily be extended to connect to business networks to form a digital ecosystem and collaboratively drive business model improvements. Equipped with a simple and intuitive user experience, the solution offers better support for headquarters users as well as store associates to drive compelling customer experiences.
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master data, management, retail price, merchandise buying, collaboration, vendor agreement, forecasting, sap
    
SAP
Published By: IBM     Published Date: Jul 26, 2017
To compete in today’s fast-paced business climate, enterprises need accurate and frequent sales and customer reports to make real-time operational decisions about pricing, merchandising and inventory management. They also require greater agility to respond to business events as they happen, and more visibility into business activities so information and systems are optimized for peak efficiency and performance. By making use of data capture and business intelligence to integrate and apply data across the enterprise, organizations can capitalize on emerging opportunities and build a competitive advantage. The IBM® data replication portfolio is designed to address these issues through a highly flexible one-stop shop for high-volume, robust, secure information replication across heterogeneous data stores. The portfolio leverages real-time data replication to support high availability, database migration, application consolidation, dynamic warehousing, master data management (MDM), service
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ibm, infosphere, data replication, security, data storage
    
IBM
Published By: IBM     Published Date: Jul 02, 2018
Cloud has evolved from a technological innovation to an integral part of business. Companies in every industry are investing in Digital Transformation initiatives to evolve and grow; often, cloudbased platforms are foundational elements of these transformations, as businesses increasingly seek the flexibility and agility to roll out new software services in days or weeks, versus months or years. As Digital Transformation initiatives unfold, one key challenge is to modernize the data center to facilitate rapid delivery of new applications and services—while still ensuring that existing missioncritical applications remain high performing, available, and secure. Another challenge relates to new requirements for accelerating the analysis of organizational data to near real time, much faster than previously possible with earlier incarnations of Business Intelligence (BI). Agile businesses are demanding faster access to the information contained within operational and business data stores to
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IBM
Published By: Oracle     Published Date: Oct 20, 2017
With the growing size and importance of information stored in today’s databases, accessing and using the right information at the right time has become increasingly critical. Real-time access and analysis of operational data is key to making faster and better business decisions, providing enterprises with unique competitive advantages. Running analytics on operational data has been difficult because operational data is stored in row format, which is best for online transaction processing (OLTP) databases, while storing data in column format is much better for analytics processing. Therefore, companies normally have both an operational database with data in row format and a separate data warehouse with data in column format, which leads to reliance on “stale data” for business decisions. With Oracle’s Database In-Memory and Oracle servers based on the SPARC S7 and SPARC M7 processors companies can now store data in memory in both row and data formats, and run analytics on their operatio
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Oracle
Published By: SRC,LLC     Published Date: Jun 01, 2009
Today, organizations are collecting data at every level of their business and in volumes that in the past were unimaginable. Data sets are stored in different database systems or in files with distinctive formats, all reflecting business process, application, program software, or information type dependencies. Adding to this complexity is the distribution of these data sets across the enterprise in silos requiring a varied set of tools and/or specialized business rules for data transformation, classification, matching, and integration. Because of the massive amounts of data stored in a variety of representation formats, decision makers strain to derive insights and create business solutions that adequately span and integrate information from these disparate technology islands. Learn more today!
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src, data transformation, classification, business value, geographic business intelligence, geo-bi, etl, extract, transform, load, spatial analysis, operational data stores, ods, spatial data, etl, cdi, point of sale, retail floor space management, rfsm, alteryx
    
SRC,LLC
Published By: IBM     Published Date: Feb 02, 2009
Learn how IBM’s change data capture technology can be used in conjunction with IBM’s performance management solutions from Cognos to provide access to the trusted information that systems and employees need to make informed decisions at the speed of business.
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ibm, information management software, performance management, business intelligence, ibm cognos, bi-ready data, departmental reporting with ibm cognos, in-memory data store, operational data stores, enterprise data warehouse, ibm® infosphere change data capture, cdc, kpi, cognos now!, operational data store, ods, ibm infosphere datastage, enterprise applications
    
IBM
Published By: IBM     Published Date: Oct 14, 2009
Discover the unique support for data warehousing and business intelligence offered by the IBM DB2 portfolio. This white paper shows how an IBM System z server, integrated with the IBM DB2 family of solutions, can help you meet requirements for data warehousing and business intelligence.
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data warehousing, ibm, business intelligence, total cost of ownership, tco, integrated facility for linux, eal5, workload consolidation, scalability, disaster recovery, workload management, operational data stores
    
IBM
Published By: SAP     Published Date: Jun 23, 2009
Discover the importance of data integration in your environment, and learn about the key challenges, benefits, and approaches to integrating data.
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business intelligence, data integration, sap, businessobjects, data warehousing, sarbanes-oxley, data consolidation, data marts, enterprise information integration, eii, enterprise-class, operational data stores, operational systems, master data management, mdm, data transformations, data management
    
SAP
Published By: IBM Software     Published Date: Oct 04, 2011
With a network of more than 200 stores across Canada, Giant Tiger wanted to find a better way to align local financial and operational planning with central corporate objectives. Learn how they automated budgeting and forecasting processes and enabled store-based users to enter actual data directly into the system via a simple web interface to reduce the budgeting cycle by up to 85 percent, save approximately 220 hours per month for store-based staff, and receive a full return on investment within 24 months.
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customer, perfomance management, technology, ibm, planning objectives, budget, forecasting, investment
    
IBM Software
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