Data modeling

ERD vs UML? what do employers want? {Comments Off on ERD vs UML? what do employers want?}

by Willen L
In this article the author talks about the employment demands of ERD vs. UML. Whether employers prefer one over the other and with the ever fast changing IT field it’s sometime difficult to gauge what skills are preferred in the profession but with all these job search tools online it’s possible to gauge where the demand is. The author analyzed data that he obtained from SkillPROOF since the beginning of 2004 and wrote this article 2 years later in 2006. The data was collected from 137 IT focused companies and the data was collected daily and there were a total of 35,932 jobs recorded. They did a keyword categorization according to history and a sampled content analysis for a week to dig deeper into the matter. They found that data modeling when searched without a specific methology is one of the required knowledge bases. That means a lot of jobs want data modeling but did not specify what type of modeling (ERD vs UML).  UML appears to be more on the application development side and are often listed as a critical skill. ERD tends to focus on database design and maintenance and is also often accompanied by skills in software such as ErWin, Visio and TOAD. read more...

The differences of UML and ER model {3}

by Polun L
The article, “How to Draw an Architectural Data Model in UML”, by David C. Hay, talks about the differences of UML model and ER model. At the beginning of the article, the author described the development of entity/relationship data model which was formalized in 1970’s. Two decades later, the Unified Modeling Language (UML) was officially released. However, programmers found out that UML model is very different from ER model because they have significantly different structure in relational database. Even though object oriented programmers attempted to save persistent object data in relational database, it still could not make UML and ER model to be consistent. Finally, programmers realized that UML was developed to support object oriented design while entity relationship modeling was designed to support the analysis of business structures. In the end of the article, the author listed a guideline for those who would like to draw a ER model using  a  UML diagramming tool. read more...

Data Modeling a Necessity for Information Quality {5}

by Willen L
In this article, the author talks about how conceptual data modeling is a necessity. That it is impossible to have good data quality without first understanding what the data is supposed to represent. He goes on saying that if you don’t understand what the relationship thoroughly. He even uses the analogy that it’s kind of like when children attempt to solve a puzzle game and try to force the piece by pushing into it to complete the puzzle but in the end you don’t get the right picture from the puzzle. It’s the same way in databases, it seems to fit but not exactly and that could compromise information quality. He says that the conceptual data model is the picture on the puzzle box that provides the vision of what the information puzzle should look like at the end of the day. We can definitely develop a database without a conceptual data model but it’s likely to have errors that you have missed it is best to plan with a data model and use cardinalities to describe the relationships as well as understand / analyze all the relationship for improved information quality. read more...

Developing a Successful Conceptual Model {3}

by Antonio M
This article talks about how databases are a backbone to any information system. Since that is the
case it is very important that the conceptual modeling of a database is done correctly. The author
also says how much effort when dealing with the the conceptual design will be spent in communicating
to the stakeholders. However it can be difficult since not all stakeholders have any knowledge in
database design. Therefore it is very important to gather good and relevant information from the
end user as well as making an understandable presentation of the information that has been collected
so they can understand the database design.Typical end users do not understand visual graphics such as
UML, it is best to do “Verbalization” which pretty much means explaining the notation on graphical
presentation to the end users. The author suggests that 3 representation types can help the
stakeholders understand the conceptual design of a database. The first being the graphical representation
such as ERD diagrams, the second being verbalization and the next being a glossary type of section
,which would consist of detailed descriptions of the concepts. The author concludes his article
by re-emphasizing how the conceptual model is very important when talking with the stakeholders. All
stakeholders have different skills and expertise which is why different techniques should be used
for communicating to them.

Seven common Data Modeling mistakes to watch out for {3}

by Kevin Q
The article briefly describes the benefits of data modeling and how it helps a company and its projects. It then immediately jumps into the seven common mistakes that occur. The First mistake is thinking that the data model is a final structure. This kind of thinking is incorrect, since the data model should be thought of more as a version that can be updated due to new changes. The second mistake is have data models invisible. Data models should be easy to access, be clear and understandable and organized so that it can be used. Having an invisible model defeats the whole purpose of data modeling. The third mistake is assuming business users can read data models. Whether it is assumed by the data model creators or the employees, all that is needed is training to be able to understand the models. It’s important that everyone be able to read and understand the models so that they can make business decisions with helpful information. The fourth mistake is thinking that data models are only used for databases. Data models can go beyond databases to explain and show physical procedures that take place in a company. This can be really helpful in showing the nature of a certain area within the company, so that a well-informed decision can be made. The fith mistake thinking of the data model only as an early deliverable. The data model should be thorough and used as a guide when entering the implementation phase and not just a early deliverable. The sixth mistake creating and overall bland data model. By creating a more colorful and somewhat exciting data model with plenty of comments and guides to help the users, you can keep people engaged and impressed. The seventh mistake think of the data model as your own and not the company’s. They should be available and shared to everyone within the company for viewing, otherwise they will not help the company if only you or a small group of people are the only ones allowed to view it. read more...

A Model for Models: Data Modeling Basics {Comments Off on A Model for Models: Data Modeling Basics}

by Brian T
Data modeling is a vital aspect of database creation and management. The ER diagrams lay the logical framework for an entire system, upon which so many people and other systems will be relying. Fundamentals, as with every other academia subject, are a complete necessity. Luckily, articles such as this one exist which aid in understanding. It outlines the basics of modeling which we have also touched on in class. It covers a variety of styles, patterns, and classifications of ER diagrams which may be used in the modeling process. read more...

Data Modeling in the Medical Industry {Comments Off on Data Modeling in the Medical Industry}

by Jorge R
The main topic of my article included how Teradata a, “world leader in enterprise intelligence and analytics” is committed to the health care industry,with hopes of having one of the worlds most comprehensive health care industry logical data model (LDM). By having an LDM to establish a data structure, made it a common practice for health care organizations to implement it into their system. Some of the main benefits include documenting, “…business rules at the corporate level, supports consistent reporting and analytical results, and helps eliminate data redundancy”. Along with all of these benefits, health care providers will be able to pull your medical records with ease and speed. Having family members in the medical field, has made them switch from having hundreds of file cabinets full of medical records to a computer database filled with endless information. read more...

Is Data Modeling a waste of time? {5}

by Polun L
The article, “Why does Data Modeling Take so Long”, by Jonathan G. Geiger, is an answer to the question asked by one of the attendees at a conference. The author explains that data modeling is used for designing data structures for businesses. Also, he mentions that when we begin building data modeling work, we should consider the four major steps  involved in developing the data model which are Requirements and business rules gathering, Source analysis, Logical model development, and Physical model development. These steps would guide data modelers to construct a better data structure as well as a more extensible model for future needs. The first step of requirements of business rules gathering is that we first need to understand the requirements of business questions and respond to questions for various business subjects. Once we understand the business rules, we should be able to use those information to create a desired level of data model. The second step of source analysis is that we use the collected data to analysis the possibilities of use and condition because we need to make sure the collected data fulfills the business needs. Finally, logical model development and physical model development basically use all the previous analyzed data in order to develop and form  a data model quickly. Therefore, data modelers spend a lot of time on building data modeling because business rules need to be understood, and collected data needs to be analyzed so that a data model is good enough for businesses. read more...

Data Modeling for Big Businesses {2}

by Jongwoo Y
How has Mu Sigma, a data analytics company, become such a major player in today’s economy? With the vast amount of “big data” that fortune 500 companies are forced to deal with, companies are forced to either try to untangle their vast amounts of data by themselves or hire third party companies, such as Mu Sigma, to set up an effective data modeling scheme that will be able to make sense out of the pedabytes of data that companies store through their daily transactions and their everyday company needs. Mu Sigma has recently raised over $100 million to expand their business, which include clients from fortune 500 companies such as Sequoia Capital, General Atlantic, and Microsoft (Gage, 2011). Through Mu Sigma, these companies are able to unravel their vast amounts of data and set up an effective data model that helps run the company more efficiently and save them millions of dollars. Investors are very intrigued by the business model that Mu Sigma has carried themselves by since 2008 because of their 886% growth in revenues since that time (Gage, 2011). Based in Chicago, Mu Sigma is currently employed by many huge companies around the world. Not only do they help organize big data for businesses, Mu Sigma analyzes a company’s data and gives further insight on more effective marketing decisions, forecasts, and ways for companies to run more efficiently (Gage, 2011). read more...

Effective data modeling {Comments Off on Effective data modeling}

by Andrew J
It is important for companies to plan out their plan before they design and deploy their data model. Three models are used for the design phase of the project. The first is conceptual data modeling, second is logical, and third is physical data modeling.
Conceptual data modeling usually includes the entity relationship diagram. What creates relationships in this type of data modeling is not informational similarities but the business practice itself.
The logical data model involves taking the business information gathered from the conceptual data model and transferring it into an observational layout of the data in a relational style.
The physical data model is the final stage of planning. It describes the absolute design for the database implementation. read more...