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For years, information systems developers have been applications centric. They copy data over and over for each of the thousands of applications across their enterprise. Each application has its own data model and consists of hundreds or often thousands of tables. Each table has dozens of columns – many with conflicting column names. One application is complex; thousands are the product of all that complexity.
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For years, information systems developers have been applications centric. They copy data over and over for each of the thousands of applications across their enterprise. Each application has its own data model and consists of hundreds or often thousands of tables. Each table has dozens of columns – many with conflicting column names. One application is complex; thousands are the product of all that complexity.
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For years, information systems developers have been applications centric. They copy data over and over for each of the thousands of applications across their enterprise. Each application has its own data model and consists of hundreds or often thousands of tables. Each table has dozens of columns – many with conflicting column names. One application is complex; thousands are the product of all that complexity.
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Paragraph Text: 14px [13,70%]. Line Space 1.4 Em
For years, information systems developers have been applications centric. They copy data over and over for each of the thousands of applications across their enterprise. Each application has its own data model and consists of hundreds or often thousands of tables. Each table has dozens of columns – many with conflicting column names. One application is complex; thousands are the product of all that complexity.
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Data-centric uses the power of semantic technology to provide foundational capabilities that work together to create business value. We assign a unique identifier to every data concept. This enables firms to link data wherever it resides to one master ID – eliminating the need to continually move and map data across the enterprise. We are specialists at building ontologies to ensure a shared understanding of requirements between business stakeholders and application developers. These two standards expressed in the language of the Web provide a cost-efficient, non-intrusive breakthrough for data management.
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Designed a structured approach to keep track of the complex sharing arrangements among upstream and downstream sellers
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This is about cost containment and process automation. Firms can take back the 40% of money that is wasted on data integration by standardizing meaning, reducing the need to move and reconcile data and eliminating redundant systems.
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Take advantage of flexible query for better customer profiling and targeted selling. By eliminating the rigid schemas of relational technology, analysts have the tools they need to ask questions of the data instead of spending time as ‘data janitors’ restructuring it and reconciling its meaning.
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Combine data across lines of business to mitigate operational risk and support compliance with regulatory requirements. With a data-centric approach, you can control access at a data level to trace the flow of data, protect intellectual property and secure sensitive data from falling into the wrong hands.
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Semantic Arts designs systems around data requirements, rather than trying to make data fit the architecture. Our approach does not require lengthy integration activities and many layers of data replication. Instead of standing up new databases and building additional silos, data-centric architecture enables data to be shared and reused across all your applications without making copies. By eliminating data copies and integration efforts, new solutions can be built in days instead of weeks.
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Semantic Arts team of developers has extensive expertise in knowledge representation and ontology engineering. Whether it is the complexities of life science, financial services, publishing, manufacturing or any other information-intensive industry, our experts have the knowledge and capability to design ontologies tailored to your specific domain. We work closely with your subject experts to capture requirements and develop custom ontologies that align with your business objectives.
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genomic sequencing, biomedical research, decision support systems and clinical trials. We help clients integrate these diverse data sources by providing a common framework for retrieving, representing and linking data.
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genomic sequencing, biomedical research, decision support systems and clinical trials. We help clients integrate these diverse data sources by providing a common framework for retrieving, representing and linking data.
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genomic sequencing, biomedical research, decision support systems and clinical trials. We help clients integrate these diverse data sources by providing a common framework for retrieving, representing and linking data.
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genomic sequencing, biomedical research, decision support systems and clinical trials. We help clients integrate these diverse data sources by providing a common framework for retrieving, representing and linking data.
Steve Case
Data-Centric Ambassador
BA in Marketing/Management from Minnesota State University
Certified Digital Marketing Professional
email: [email protected]
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Steve Case has over 25 years in technology consulting business development solutioning for a wide cross-section of industries including healthcare, financial, manufacturing, life sciences, retail and education. At Semantic Arts, Steve’s diverse role includes account management, consultant utilization, corporate branding, marketing, event planning, lead generation, and innovative semantic solutioning.
He brings an insatiable curiosity and thirst for learning new things. Steve is passionate about high integrity relationship building when offering enterprise solutions; seeking to create win/win working scenarios. Outside of work Steve is an avid sports fan, participating in the MN Senior Golf League. He also enjoys grilling/smoking, bicycling with his wife, walking the dogs (Milo-Poodle/Bichon and Reggie-West Highland Terrier), and testing his taste pallet in new dining experiences.
Operational Efficiency
This is about cost containment and process automation. Firms can take back the 40% of money that is wasted on data integration by standardizing meaning, reducing the need to move and reconcile data and eliminating redundant systems.
Enhancing Capability
Take advantage of flexible query for better customer profiling and targeted selling. By eliminating the rigid schemas of relational technology, analysts have the tools they need to ask questions of the data instead of spending time as ‘data janitors’ restructuring it and reconciling its meaning.
Aggregate Data
Combine data across lines of business to mitigate operational risk and support compliance with regulatory requirements. With a data-centric approach, you can control access at a data level to trace the flow of data, protect intellectual property and secure sensitive data from falling into the wrong hands.
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Most enterprises are ‘applications-centric’ where data is thought of as secondary. We advocate the opposite approach where data is at the center of your architecture (i.e., stored only once and then retrieved when needed). Instead of having to transform the data for each new viewpoint, keep the data in its pure form so that it can be reused for other purposes. What follows are enterprise systems that you might think will make you data-centric, but fall short of the core goal.
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A data warehouse is a centralized repository for storing data from various systems and conforming it to a common model for reporting. This is similar to data-centric but with several limitations. The extract, transform and load (ETL) process of getting data from source systems to the warehouse is complex and time consuming. It requires the firm to harmonize the format of the data and define the schema before analysis. The biggest challenge is this transformation part because it involves understanding what the data means as well as conforming it to the one central model. Adopting the data-centric approach achieves the benefits of a data warehouse but with shared meaning.
