But, designing and implementing a robust architecture that will serve the immediate needs to the enterprise and in consistent sync with the long-term strategic vision of the organization is little bit difficult.
Such a strategy should have the following facets: The data quality issues to which reference data is prone means that there should be central maintenance of it.
This may not apply to some personal and Enterprise architecture as strategy classification schemes, but it will apply to most reference data.
If there is to be central maintenance of reference data, some persons will have to be responsible for it. These persons do not own the data, but they maintain it for the organization, and are known as data stewards.
The must have the authority to match their mandate and the resources to carry it out.
Central maintenance also implies one location where the reference data is stored. Not only must the reference data be stored there, but also its associated metadata, e. A means of accessing the central repository of reference data needs to be put in place. At a minimum this should allow users to review what is in the repository, and to understand the reference data kept there.
Obviously, the Internet is an ideal way of doing this. It may also be possible to have remote databases access the repository to obtain reference data values. Even if remote systems can access the central repository to obtain reference data, performance issues may require these systems to have their own copies of reference data.
There then needs to be an efficient and secure mechanism for distributing reference data from the central repository to the remote system.
It is possible to use the Internet to perform this distribution task. One of the objectives of central maintenance of reference data should be to rationalize the design of all reference data tables in all systems and databases across the enterprise.
Yet after decades of systems being built as islands this is really not practical. If the access mechanism and distribution mechanism described above are to work, legacy systems will probably need special interfaces.
Everything should be done to minimize the amount of work needed to build, maintain and operate these special interfaces. The data stewards responsible for reference data should have a role on every project that creates or implements a new system or database.
After all, reference data is found in all business databases. They should attempt to utilize the functionality and data content of the repository which they administer to satisfy the reference data requirement of the new system or database. They should not permit new standalone reference data maintenance functionality to be built.
The management of reference data as part of an enterprise's information architecture can yield great savings, reduce exposure to risk, and improve data quality. If an enterprise implements such a strategy it will need senior management commitment because reference data has no natural community of users.
Even after the strategy is implemented, senior management will need to retain a commitment to it. There can be a fast payback, but there are always pressures to fall back to poor practices when it comes to reference data. Senior management should insist on quantifying the resources spent on reference data management, and understanding what this is buying them.
The data stewards have the obligation to provide this information if they are to retain the support of senior management.Enterprise Architecture processes, including governance, roles and responsibilities Optionally offer an innovation lab to help in keeping up with technological advancements Support the path to the desired maturity by providing our own consultants to fill skills, resource gaps and cross-training.
Still, for enterprise architecture to be all that it can be, and for enterprise architects to make the contribution we hope to make, we need to be capable of making a valuable contribution to the strategy .
Enterprise Architecture As Strategy is perhaps the most quoted book on the topics.
Enterprise Architecture helps business management achieve its strategic goals. It supports the company in creating competitive advantage, reduces risks and enhances cost-efficiency and scalability. Mergers and acquisitions, outsourcing and major organizational changes all set additional demands on the flexibility of Enterprise Architecture. Using the term "strategy", especially when discussing the development of an Enterprise Architecture, can be confusing. A strategy and an architecture are relatively analogous terms. However, an architecture is often thought more of as a static picture that you draw on the wall. In Enterprise Architecture as Strategy: Creating a Foundation for Business Execution, authors Jeanne W. Ross, Peter Weill, an If so, construct a solid foundation for business execution—an IT infrastructure and digitized business processes to automate your company’s core capabilities/5.
Online forums on the topic quote this as one of the few really good references on the topic; and I agree. The authors do a great job of introducing and packaging concepts in Enterprise Architecture – operating model, maturity model, core diagrams, IT engagement /5().
Enterprise Architecture As Strategy is perhaps the most quoted book on the topics. Online forums on the topic quote this as one of the few really good references on the topic; and I agree.
The authors do a great job of introducing and packaging concepts in Enterprise Architecture – operating model, maturity model, core diagrams, IT engagement /5().
Mar 16, · At its most basic, EA is only business and technology architecture; at its most comprehensive, EA consists of business and technology architectures, enterprise culture, and business strategy.
EA enables one to look at the entire enterprise (business & . Enterprise Data Strategy is the comprehensive vision and actionable foundation for an organization’s ability to harness data-related or data-dependent capability. It also represents the umbrella for all derived domain-specific strategies, such as Master Data Management, Business Intelligence, Big .