Data increasingly proves value to organizations. Data allows them to improve products, services and processes, reduce risks and gain competitive advantage by being able to predict demand more accurately.
Data Management is the key to optimally profit from the benefits data has to offer. Successful Data Management (DM) delivers the right data at the right stakeholder or process at the right time.
DAMA provides a standardized approach to organize Data Management. DAMA addresses several topics (‘knowledge area’s’) that are separately elaborated in the DAMA/DMBOK (Data Management Body of Knowledge). Think of domains like Data Storage, Data Interoperability, Data-warehousing and Data Security. Each domain describes its own objectives, activities, processes, deliverables and principles – the rules to conduct data management effectively. In total DAMA lists about 150 of those principles.
As this number of rules is hard to live by, we have analyzed all and condensed them into a manageable set of 10 golden rules that together define fundamental critical success factors of DAMA.
Harald van der Weel10 Golden rules for Data Management
“Data is het nieuwe goud”, een veel gehoord credo. In tegenstelling tot bij goud hebben wijzelf echter grote invloed op de waarde van onze data. De SynTouch Data Value Chain toont hoe deze waarde stap voor stap gecreëerd kan worden van ruwe “grondstof” tot een kostbaar “bedrijfsjuweel”.#datavaluechain #data
“Data and Analytics Programs Will Become Mission-Critical for all Businesses”, gaf Gartner al aan, terwijl Forrester stelt dat “Your Business Is Only As Fast As Your Data”. Data wordt door succesvolle bedrijven meer dan ooit beschouwd als een strategische asset, een noodzaak om het tijdperk van digitale transformatie te overleven.
Harald van der WeelVergroot de waarde van uw data (Data Value Chain)
Data is Increasingly acknowledged as a strategic asset. It is required by organization to survive in a world that cries for agility, transparency and the power to act. Data Virtualization (DV) and API Management (AM) are two technologies that respond to this need. Therefore, they are in the spotlights today. Both DV and AM aim to provide you an effective, efficient, and controlled access to your data.
Harald van der WeelData Virtualization + API management: A powerful couple in the data era!
This is the sixth blog in a series of 6 blogs about integration 2.0. Did you miss one blog, read the fifth blog Services!
The API- and Event hub may support most of the integration needs. Certain data however may require a specific approach, for instance if the size, speed or format of data requires this, or an external party demands it.
Though Integration 2.0 does not explicitly mention Services or Service Oriented Architecture (SOA) it makes a clear distinction between stateful services -services that maintain specific data – and stateless services that don’t.
The Event and API hub are the central components in the integration landscape: In principle all communication will go through them. Besides loosely coupling EAC-s and processes it connects with other generic IT capabilities (functions) as well, like Master Data Management (MDM), Reference Data Management (RDM), Business Rule Management (BRM), Identity and Access Management (IAM) and Audit and Compliance Management. These all may provide functionality or data required by processes, EAC-s or each other. However, these services and EAC-s themselves are passive components other than publishing events that might be listened to by other components.
This is the second blog in a series of 6 blogs about integration 2.0. Read the first blog Integration 2.0!
These recent developments have urged us to define a new Integration approach, that we call “Integration 2.0”. This approach aims to address the challenges posed by the technical and business developments for the next decade.
Digital disruption asks for new priorities in ICT-strategy. From killer app to killer data – this is how to start:
This blog is a new view to my earlier presented Data Value Chain-model. Data is an enterprise asset which represents value. The Data Value Chain visualizes and puts all data assets in sequence of increasing value. It is intended as a communication-tool to position our services and explain the opportunities and challenges of a data-related landscape.
Harald van der WeelSynTouch Data Value Chain – Reference Model