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Lesson 7Database and data management considerations
Objective Describe considerations for database and data management.

Database and Data Management System Considerations

In today's data-driven age, data management isn't just a technical term reserved for IT professionals. Instead, it's a sprawling concept that intersects with multiple domains, embodying the processes used to collect, store, use, and safeguard information. Data management's reverberations are felt across numerous fields, each adapting to its principles, challenges, and benefits. Let's embark on a journey exploring the various sectors intertwined with and impacted by data management.
  1. Business and Commerce: In the corporate arena, data management is synonymous with competitive advantage. From customer analytics to supply chain optimizations, businesses rely on data-driven insights. Effective data management ensures accuracy, leading to better decision-making, risk assessment, and financial forecasting.
  2. Healthcare: With the integration of digital tools, healthcare is transformed. Patient records, diagnostic results, and treatment plans are digitized. Effective data management ensures these records are accurate, readily available, and confidential, culminating in improved patient outcomes and streamlined healthcare processes.
  3. Research and Academia: Scholars, scientists, and researchers are constantly inundated with data. Be it genomic sequences or sociological surveys, data management ensures that research data is stored systematically, remains accessible for future studies, and is shared responsibly among the academic community
  4. Government and Public Administration: Government agencies handle a vast spectrum of data, from census statistics to public welfare schemes. Data management here implies efficient service delivery, policy formulation based on empirical evidence, and maintaining public trust through transparency and data security.
  5. Banking and Finance: In an industry where precision is paramount, effective data management minimizes errors, combats fraud, and supports algorithmic trading strategies. The seamless transfer of funds, credit risk assessments, and investment strategies all hinge on immaculate data management practices.
  6. Environment and Conservation: Tracking changes in ecosystems, analyzing climatic shifts, or monitoring endangered species all necessitate data. With robust data management, conservationists can make informed interventions, predict environmental trends, and mobilize resources more effectively.
  7. Manufacturing and Production: From just-in-time inventory to quality control metrics, manufacturing units are deeply entrenched in data. Efficient data management optimizes production processes, reduces wastage, and ensures consistent product quality.
  8. Media and Entertainment: In an era of streaming and digital media, understanding viewer preferences, optimizing content delivery, and safeguarding intellectual property are all underpinned by effective data management.
  9. Real Estate and Urban Planning: Data about property valuations, municipal records, or urban demographics guides urban planners and real estate developers. Properly managed data supports sustainable urban growth, infrastructure development, and real estate market predictions.
  10. Transportation and Logistics: Routes, schedules, cargo details, and more are managed daily in this sector. Efficient data management results in optimized routes, timely deliveries, and reduced operational costs.

Data management's tendrils reach far and wide, influencing numerous fields. Its meticulous application ensures that the vast expanse of data the modern world produces is not just a chaotic cacophony but a harmonious symphony that drives progress, innovation, and growth. As data continues its relentless expansion, so too will its influence, making proficient data management an indispensable tool for the future.

Data management
Data management

Architectural Commitment

Here are a few considerations the e-commerce architect should keep in mind when dealing with database/data management systems. Each organization carries different levels of commitment and preference for databases. For example, some companies are open to any relational database as long as it support SQL[1]. Other organizations are vendor-committed (for example, you might hear a customer say, "We are an Oracle shop!"). The feature/function and performance differences between leading databases are slight. Therefore, it is usually wise to go with the organization's preferences, rather than to force-feed them a new database. Even though object databases may offer truly optimized designs for sophisticated e-Commerce solutions, buyer organizations may not be prepared for the paradigm shift required to support object-oriented databases. As with programming tools, the potential efficiency gain of switching to new data management products should be weighed against the needs and desires of the buyer.
  • Availability of Expertise: A large community of experts exists for all of the major databases. This expertise is highly portable between each database subgroup. However, expertise is more rare with analytical databases, data warehousing, and business intelligence. Care should be taken to ensure that a resource pool of trained personnel is available to support the product.
  • Scalability: Certain data management solutions only run on certain operating systems. For example, Microsoft's SQL server only runs on Windows. Care should be taken to choose a database that can be easily ported[2] to another operating system, if and when scalability requirements increase.
  • Third-party Extension Products: A large market of products has been built around core database and data management products. These third-party products provide: Avoid database/data management choices that lack these third-party support products.
  • Development Tools: Most major development tools already work effectively with major database products. This interoperability occurs through ODBC[3], JDBC[4]or native interfaces [5]. Make sure that any peripheral database/data management choice is supported by the development tool(s) of choice. Also be aware that many OLAP[6] and business intelligence products include their own proprietary development tools. These tools add to the overall learning curve and integration challenges.

Architecture Engagement - Quiz

Verify your understanding of the concepts we have covered with a self-check quiz.
Architecture Engagement - Quiz

[1]Structured Query Language (SQL): The standard access language for relational databases.
[2]Ported: Move from one computer system to another.
[3]Open Data Base Connectivity (ODBC): Inserts a middle layer, better known as a database driver, making it possible to access any data from any application, regardless of which database management system handles the data. For this to occur, the application must be capable of issuing ODBC commands and the database management system must be capable of responding to them.
[4] Java Database Connectivity (JDBC): Enables Java programs to interact with any SQL-compliant database and execute SQL statements. It is just like ODBC but is designed to work with Java.
[5] Native Interface: Typically possess higher performance than ODBC/JDBC. However, this type of interface is not portable between databases like ODBC/JDBC.
[6] OLAP: Another term for an Analytical Database. This type of database stores and maintains information objects and their relationships in a fashion that facilitates numerical analysis.
[7] Interoperability: Ability of a system to use the parts or equipment of another system.

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