There is an importance for Enterprise Data Architecture (EDA) within an organization. The Data Strategy is for outlining road maps, setting scope and development of list on data technologies used. EDA is able to do this by building a blueprint which includes hardware, applications, business processes, technology choices, networks and data. EDA should also support IT programs and information assets that lead to the success of business strategy.
Why is data strategy so important? Without a centralized vision and foundation, different parts of the organization will see data competences differently. This leads to out of sync data across the organization, which makes poor quality of data, makes it difficult to determine what is correct about the data, and in turn drive up costs. The Data Strategy provides a foundation for all enterprise development efforts connected to data proficiency. The Data Strategy is the instrument that allows for merger of Business and IT expectations for all enterprise data capabilities. It is important for the Data Strategy to be detailed, because it will allow for defined metrics or service level expectations that should apply across the organization. Enterprise data can also be leveraged for the support of organizational objectives or processes. Enterprise Data Architecture is clarified by models at four stages
High-Level Data Model (HLDM): Constitutes a collection of HLDMs that describe business data through a conceptual viewpoint independent of any present realization by real systems. The HLDM consists of a standard UML class model of the primary data items and their relationships; a super-set of business features, such as semantics, universal constraints and syntax.
Realization overviews: Describes the relationships between the real vital data objects of the present or planned systems and the conceptual units of the HLDM. This shows the way in which conceptual units are realized by actual units.
Source and consumer models: Demonstrates the correlations between various realizations of the same data items, diverse organizational custodians of data elements and the way in which modifications are circulated around different systems.
Transportation and transformation models: Explains the way in which data in the implementation systems changes when moved among systems. They include attribute structure and physical class of system interfaces. This model also depicts the realization of the HLDM within the interface mechanisms, including a backbone or an enterprise application integration (EAI) hub.
Enterprise Data Architecture help build Enterprise Data Strategy because they are typically defined around all existing and future data competency. EDA supports the business stakeholders and thus helps to ensure both business and IT perspectives are taken into consideration while constructing the Data Strategy. Along with build the governance and an understanding to support the data which
Data silos exist in organization due to a lack of trust with in business units as well as each business unit having different goals, priorities and accountability. They will also occur when there is competition, such that can occur with lines of business (LOB), instead of the everyone working for the success of the organization. Data Architecture silos are a disruption to the organizational flow, with the starvation for collaboration, accessibility and clean data, these silos are being criticized for a lack of production and data quality.
Gartner describes silos can be created from a term they coined “inside-out”. These types are compress the data so that it can house as much as it can to increase the performance and keep the business and stakeholders happy. In doing data architecture this way it brings risk with disaster recover, legacy systems go beyond their usefulness, and information sharing becomes expensive difficult to accomplish. On the opposite side of the spectrum is Outside-In which will allow for business to connect with customers and partners. Outside-In allows for the understanding of what is need by the business and then build the data infrastructure. A novel concept I know.
Other way to conquer data silo is to first create an integrated vision. This means to have a buy-in from executives as well as employees and a full understanding of the data so that long term goals, department objectives, and key initiatives can be achieved. Next work towards achieving a common goal. Having a vision, it is now time to find the underlining issues that initially caused the silo in the first place. Once the issues are identified, leadership now has the task to define risk and priority on cleaning up inadequate data. Finally motivate and build incentives. Once the data is cleaned, procedures put in place, and data strategies built, half the battle is won. The next step is totally eliminating the data silos by motivating teams and building incentive plans that help employees to improve quality of the data, build trust and increase business and stakeholder satisfaction. The final step is collaborating and create. Data has a very pivotal role in modern organizations. There are a few key factors when creating a thriving and productive team. That is knowledge, collaboration, creativity, and confidence. That cannot be obtained without these four basic factors any Data architecture team is destined to fail.
Best Practices for Data Stewardship
Is there a best way to deal with the governance over data within an organization? There is a lot of organization that are decentralized when it comes to data architecture and have a lack of focus on what are the important concepts when it comes to the governance of data. To them data is a second thought when it should be first on an organizations agenda. Organization have governance for every other aspect of the business why not Data. Organization’s business units are finger pointing as to what the issues are regarding data, but not giving ideas on how to solve the actual problems. I believe the reason for poor data quality and silos can range from individual business units within the organization do not know how to use the data, lack of availability, poor accuracy in the data and lack of cross-function governance across the organization.
This is where a data steward is needed to organize and build a data governance for data architecture. It is important for the data steward to not only data and data management concepts, but also understands the business holistically. Capabilities of a Data Steward include business knowledge, data management knowledge, analysis, and communication. The data stewardship purpose may be recognized either formally or informally. The formal approach is for mature organization and involves building Data governance body and nomination of a steward to address the issues regarding issues with the organizations data. Informal approach and for less mature organizations. A Data Stewardship is to be the protector of the data for the organizations and the data steward will be responsible for data warehouse project, security and business input on the enterprise definitions and business rules.
For Data Steward programs to be successful we finally must review what is important for organization must haves list. Data is a vital asset is a number on the list, the data that exist within the data architecture can make or break a company. As a final consideration on this concept, if organization don’t consider data as an asset. Data Steward programs will never get off the ground. Next on the list, a cross functional governance team must be set into motion. Without it, Data Steward roles and responsibility will not realize. Procedures and policies will not be set into play. Conflicts resolution plans will not be recognized, as a result poor quality data. Another concept for this list is Data Stewards should work together to make sure security, risk, compliance, and consistency across the organization. This is important, so that business decision can made, with limited risk to business strategy and success. Next create and maintain data business model. The model will consist of what will be and not be regarding the data. For example: who will access the data, how it will be accessed and what type of data will be maintained. Finally, the capability and willingness of the Data steward is the corner stone for the success of the program. The data steward must be a strong business analysis and be able to resolve concerns between the business and data professionals. These solutions must be based on the business rules in regards to the data. Increasing the odds of success