Data is Everywhere
You are surrounded by it… yet so many people that work with data on a daily basis really don’t know or understand the principles of how data is processed, stored, and utilized in a given system. Data architecture provides criteria for data processing operations that make it possible to design data flows and also control the flow of data in the system. (source: NationMaster)
There is no way to cover the entirety of Data Architecture and the topics it covers in one post however it is possible to understand the birds-eye-view and start to see what the topic covers. This article will introduce you to the main ideas and even give you a couple references to where you can go to continue learning more.
Data architecture defines how data is stored, managed, and used in a system. It establishes common guidelines for data operations that make it possible to predict, model, gauge, and control the flow of data in the system. This is even more important when system components are developed by or acquired from different contractors or vendors. (source: SEI)
In particular, a data architecture describes
• how data is persistently stored
• how components and processes reference and manipulate this data
• how external/legacy systems access the data
• interfaces to data managed by external/legacy systems
• implementation of common data operations
Elements of data architecture
There are certain elements that must be defined as the data architecture schema of an organization is designed. For example, the administrative structure that will be established in order to manage the data resources must be described. Also, the methodologies that will be employed to store the data must be defined. In addition, a description of the database technology to be employed must be generated, as well as a description of the processes that will manipulate the data. It is also important to design interfaces to the data by other systems, as well as a design for the infrastructure that will support common data operations (i.e. emergency procedures, data imports, data backups, external transfers of data).
Without the guidance of a properly implemented data architecture design, common data operations might be implemented in different ways, rendering it difficult to understand and control the flow of data within such systems. This sort of fragmentation is highly undesirable due to the potential increased cost, and the data disconnects involved. These sorts of difficulties may be encountered with rapidly growing enterprises and also enterprises that service different lines of business (e.g. insurance products).
Properly executed, the data architecture phase of information system planning forces an organization to specify and delineate both internal and external information flows. These are patterns that the organization may not have previously taken the time to conceptualize. It is therefore possible at this stage to identify costly information shortfalls, disconnects between departments, and disconnects between organizational systems that may not have been evident before the data architecture analysis. (source: Programme Management)
The Importance of Data Architecture
Can your employees get the data they need to do their jobs well? Is that data presented consistently and clearly? Time and time again, the 665 respondents to our survey link good data architecture practices not only to employees’ ability to access important data, but to whether the company is achieving its strategic goals. And both easy access to data and strategic success are strongly correlated to the involvement of top executives from both IT and business in making decisions about data architecture.
If you don’t have your data architecture house in order, our survey suggests, you’re probably hurting. Executives at successful companies – those that were successful or extremely successful at reaching strategic organizational goals last year – are more likely than those at less successful companies to say their current data architecture helps them respond quickly to changing business conditions and customer demands.
A number of good practices are being used by companies whose executives say their employees are extremely satisfied or satisfied they can access the data they need to support the strategic goals of the organization. These companies are much more likely to have a formal data architecture plan and data architects on board. And their business units are far more involved in helping to develop requirements for the organization’s data architecture. (source: CIO Insight)
The Role of A Data Architect
A data architect is a person responsible for ensuring that the data assets of an organization are supported by an architecture supporting the organization in achieving its strategic goals. The architecture should cover databases, data integration and the means to get to the data. Usually the data architect achieves his/her goals via setting enterprise data standards. A Data Architect can also be referred to as a Data Modeler, although the role involves much more than just creating data models.
Data architects usually have experience in one or more of the following technologies:
- Data dictionaries
- Data warehousing
- Enterprise application integration
- Metadata registry
- Relational Databases
- Data retention
- Structured Query Language (SQL)
- Procedural SQL
- XML, including schema definitions and transformations.
Data architects also have the following skills and/or experience:
- Can design Data Architectures.
- Can design and build relational databases.
- Can Develop strategies for data acquisitions, archive recovery, and implementation of a database.
- Cleans and maintains the database by removing and deleting old data.
- Be able to design and develop Databases, Data Warehouses and Multidimensional Databases.
- Typically reports to a project leader or manager.
- A wide degree of creativity and lateral thinking is expected. (source: wikipedia; indeed)
The topic of Data Architecture is a central focus for many working with Databases, Data Mining, Business Intelligence, Data Scientists, and many more – stay tuned for more in depth information on these areas.
Joshua is working to become a Data Scientist with focus on Analytics, Big Data, Machine Learning, and Statistics. His passion for Data and Information are second to none. He is a certified IBM Cognos Expert with more than 10 years experience in Business Intelligence & Data Warehousing, Analtyics, IT Management, Software Engineering and Supply Chain Performance Management with Fortune 500 companies. He has specializations in Analytics, Mobile Reporting, Performance Management, and Business Analysis.
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