A project can be defined as “any outcome the company or one of its team is committed to achieve that will take more than one action step to complete”. Alternatively, projects can also be explained as a management environment that is created for the purpose of delivering one or more business products according to a specified business case.
However, project management, on its own; do not constitute the sole important architecture practice of a successful corporate business house. Apart from management of projects, the most important analytical tasks of a business firm are:
- Databases, Appliances & Emerging Technology,
- Architecture & Implementation Consulting,
- Training and Mentoring personnel to help them experts in modeling the available data.
The most important component based on which all business houses function is “data”. Data typically refers to the set of information (real time and/or past) that is available to the company. The company, based on the available data, performs requisite functions, be it data modeling, sampling or simply analyzing it.
In the world of information technology (IT), data modeling is the process of creating a data model by applying formal data model descriptions using data modeling techniques. As companies have grown, so has the magnitude and complexity of the data available to it. Hence, in order to properly analyze and examine the data, avoid sheer confusion, the need for proper data modeling has also gone up enormously.
Over the years, different techniques have been designed by IT designers and coders for creating more and more effective data models. The focus has always been to handle the data that comes to a business firm are always prepared for detailed analysis. Several techniques have been developed for the design of data models. However, while certain pre-determined methodologies guide data modelers in their work, different companies, using the same methodology might come up with very different results.
The most popular data models that are currently in use are:
- Bachman diagrams,
- Barker’s Notation,
- Business rules
- Data Vault Modeling ,
- Entity-relationship model
- Extended Backus–Naur form,
- IDEF1X,
- Object-relational mapping,
- Object Role Modeling ,
- Relational Model, and
- Semantic data modeling
Some of the major data modeling tools that are currently in use are CA Erwin, Sybase PowerDesigner, Oracle Designer and IBM Rational Rose.
Architecture practices in a business firm often refer to the manner and technique in which data is stored in databases, i.e., data warehousing. A Data Warehouse is usually one component of an overall business intelligence solution. Data warehousing and architecture practices are often thought in terms of products and services, but they are certainly not as simplistic as they sound when they are defined in these terms.
As data flows in an industry, a pyramid-like structure is generally used for modeling and analyzing the data. The structure comprises of:
- Data: This lies at the base level of the pyramid and consists of raw facts and figures coming into the possession of companies,
- Information: The relevant part of the received data is then filtered out. This part is called information,
- Knowledge: After analyzing the information, certain inferences and conclusions can be drawn. This is called the knowledge gleaned from the information, and
- Wisdom: At the topmost level lies wisdom – the strategic plans of action that can be drawn up on the basis of the obtained inferences.
- Improving profitability,
- Reducing cost, and
- Improving customer satisfaction
Architecture practices help to answer these important questions that, as a result, help a business to achieve its strategic focus. Architecture is about delivering an elegant solution that meets the solution requirements of the above-mentioned challenges that any company faces. It indeed is a mixture of science and technology and the art of analyzing business problems.
There are certain requirements that a good architecture practice must satisfy. Some of these necessary requirements are:
- it must recognize change as a constant,
- it should ideally take incremental development approach,
- it should allow existing applications to continue working, and
- It needs to allow more data and new types of data to be added.