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- Cloud computing is here today and even if you aren’t using it, your competitors are.
- Mobile computing is allowing for access to corporate data from anywhere at any time.
- Trusting your data goes beyond the quality of what it is, to knowing where it came from, who has had access to it, and how to use it.
- Data virtualization is changing data integration, warehousing, and reporting, but it also illuminates data quality issues.
- Big data is a big deal. It is changing everything data related from the infrastructure through to the analysis.
- Analytics are creating business value by finding hidden and unknown information buried in big structured and unstructured data.
- An enterprise level data architecture will help you deal with and plan for data disruptions.
- A holistic view of data repositories, governed by set principles, policies, and guidelines relevant to the organization and the information it manages will prepare you for dealing with changes in data, technology, and resource requirements.
- At the root of all data is a person, place, or thing: use your master data as an index to connect your data repositories. Master data management (MDM) maintains a single source of truth for people, places, and things relevant to an organization. Use a MDM repository as an enterprise data integration index.
Impact and Result
- Data architecture provides a holistic view of enterprise data repositories, their relationships with each other, and ownership.
- Data architecture sets the principles, policies, and guidelines relevant to an organization and the information it manages.
- Master data repositories provide a location-independent view of the truth.
- Master data needs to be the most trusted data in the organization.
- Master data management provides the relationships to derivatives of the people, places, and things.
- Master data provides the keys to linking big structured and unstructured data, and is the basis on which analytics are performed.
1. Assess current data architecture
Determine an action plan to close gaps and align with business and IT drivers.
2. Determine immediate and critical policy issues
Assess how cloud, mobile, and data trust issues affect the organization’s data architecture.
3. Manage the organization’s data in terms of quality, integration, governance, master data, and security
Discover how to improve data management in your organization.
4. Assess and plan for future data architecture issues
Position for initiatives like big data in your data architecture plan.
5. Use the data architecture plan
Follow the details of the plan, including resourcing, communication, and success factors.
This guided implementation is a five call advisory process.
Call #1 - Validate architecture documentation and focus
Review your organization’s current architecture documentation and identify areas of focus for data architecture improvements.
Call #2 - Identify critical areas of focus
Using your organization’s business and IT drivers for cloud and mobile computing, develop architecture building blocks to help mitigate the disruption while weaving data trust foresight into the mix.
Call #3 - Determine most effective use of data management technologies and approaches
Understand where and how data integration, virtualization, quality, replication, security, and master data management fit in your environment.
Call #4 - Take a position on big data and analytics
Plan now for the future.
Call #5 - Review your plan
Identify any gaps in the plan based on reference architectures and best practices.