The Wrong Way And The Right Way To Build Data Models
Do you think there can be easier and faster ways to build data models from English?
Have the comfort and luxury of better and faster UML data models from simple sentences
Improve your productivity in analysis and design of better data models
From: Venkat Reddy
Friday 2:22 PM
I am Dr. Venkat Reddy. I am a PhD in Computer Science from University of London. After researching and teaching
logic applied to Computer science for about 13 years, I moved to software industry where for the last 10+ years,
I have worked as a senior software architect.
Having worked on more than a dozen projects in Network
Management, and Internet Applications in the software industry, I found building data models has been a
difficult job for more than 80% of software engineers. To the same extent, I witnessed projects suffering
because of poor data models.
Presently popular data modeling methods are ER, OMT, UML, Booch, etc. I consider them to be at
fault to conveniently add to the miseries of bad data models. This is because these methods depend
on nouns identification, and then deciding classes / entities and attributes from them. Also these
methods do not have any clear cut rules for this task. Moreover focusing on nouns leads to poor
communication of the information about data models. As a consequence we have the following 4 common problems
in building models from software requirement specifications.
The 4 common problems of wrong data modeling methods
I state the problems for UML class models in particular, wherever needed, to keep the discussion short. But these do apply to ER models too.
Even if you do not follow any methodology and have a habit of directly creating classes or database tables for your applications, these observations apply. Below wherever you see the term class, replace it by entity, or table; and the term pointer by foreign key.
The analysis methods for data models are ad hoc and require longer learning time.
This is because currently and widely practiced methods such as OMT, UML, ER modeling methods offer no clear
rules to decide classes and their attributes from nouns that occur in software requirement specifications.
Messy classes cause nightmares to maintain applications. Such models are possible
since there is freedom to arbitrarily introduce pointers in any class.
Poor communication across all stake holders of a software project.
This is because class model is an abstraction of information with a focus to represent data.
So it represents only partial information which can be misinterpreted. A worse example of a
fatal error is the explosion of ARIANE-5 due to misinterpretation of data as signed or
Validating and maintaining class models are slow against
software requirements and their changes.
Have you spotted these problems as a data modeler when you applied these methods which use nouns as units of
analysis? The answer to these problems is a new kind of analysis method.
How a new kind of analysis method makes the data models easier, faster, lovely
- For reasons that anybody will easily understand
For data modeling purposes, we will be interested in single clause facts. Mind you, facts are just meaningful
sentences. These carry information and are quite handy in analysis for data modeling. In fact the new kind of
analysis method uses facts as basic units of analysis instead of nouns as in popularly practiced methods.
The method has three main steps: 1. Write facts from an understanding of software requirement specifications,
2. Analyze facts, and 3. Generate a data model from analyzed facts.
EnglishToUML is the CASE tool to support the new method. The tool's main screen shot, given below, shows
its special text editor to edit and analyze facts in styled text. The main screen has a diagram editor to display
generated UML class model from its editor's analyzed facts. You write the facts and systematically analyze
them fast and easily, in a disciplined way, in the text editor, without any room for confusion. EnglishToUML
does the rest to extract and display a model from the analyzed facts. And that is the right way we are talking
about for data modeling.
(click on the screen image for larger view)
View the new method in action
View the demo to quickly learn how the new method and EnglishToUML work and how you can harness its
power for your own projects. In the demo you will see how the facts are analyzed, and then how a class model is extracted from them.
Explore how easily you can learn data modeling this new way
Every new skill requires some learning. This is also the case with the new kind of analysis method.
You need to know all the basic concepts, and the techniques of applying them using EnglishToUML.
No doubt EnglishToUML's demos cover the usage of the tool. But that is not good enough. So I have
designed an e-mail course made available from my newsletter. And it is free for my newsletter readers.
What more you can expect?
A word about the course. After you register for the newsletter, in the first 3 days,
you will receive the course in 7 e-mails. The course includes study material on basic concepts,
which includes tutorial slides(power point ones), and a technical paper. The course also
includes three exercises to test your familiarity with EnglishToUML, and to get you familiar
with the extraction of facts from requirements specifications. And nothing else.
The objective of the newsletter is to make your learning easier and a pleasure of which you
can be proud of. The newsletter is also a low traffic mailing service.
The new kind of analysis method is a fact oriented analysis method. And the literature of the existing fact oriented
analysis methods will not be much direct help for you to practice the new method. So for your faster
and easier learning of the new method, the e-mail course is the best place to start with. This is only
available free for the subscribers of the newsletter. So I strongly recommend you to register
for the newsletter to get your free e-mail course.
* Your name and email address
will not be sold, shared or disclosed to anyone. We promise to respect your
The 4 more benefits you can adopt for your team
I see the following 4 benefits from the new kind of analysis method and EnglishToUML for data modeling.
Enhanced UML data modeling productivity. Experienced data modelers
efficiency to go by 20% - 30%, and for beginners by 30% - 50%. We shall be able to convert requirement
specifications to data models with less experienced engineers. Also expect 70% to 80% improvement
in teams collaborative effort as team members can independently collect facts, and later collate
them using EnglishToUML.
Savings of time. Productivity implies savings of time too. This mean business
analysts can save 20% to 40% because of improved communication with customers and domain analysts,
and the rest of team members such as software architects, software engineers, and maintenance engineers
to a tune of 20% to 50%.
Requires less number of licenses. This is so in comparison to other tools
such as IBMs Rational Rose, etc, as the main task of modeling is writing facts.
Faster and cheaper to train new data modelers. This is mainly because
we have turned around analysis problem as natural language exercise of writing single clause sentences.
Edit facts - You can edit facts in EnglishToUML's styled test editor
Analyze facts - You can also analyze facts using the styled editor.
Extract and display UML data models - You can extract a UML data model for all those facts that are seen in the styled text editor.
Export UML Data model in pure Java - You can generate pure Java code for the generated UML data model. This code can be easily reverse engineered in your favorite UML tool
We are language animals. So the idea of using facts for generating UML data models from English
sentences is a natural one. Along with it comes better and natural communication, and focus on the
problem of analysis too.
EnglishToUML helps you to edit and analyze facts. From the analyzed facts it extracts and displays
UML class models.
It uses facts for analysis purposes. Thereby it improves the
communication, and allows analysis to
be systematic. Also allows you and others in the team to easily verify the models by
You become faster, and accurate with less effort to produce quality data models. Also there will be better
communication within the team to validate the models faster too. Even your customer can also understand what
these models are doing.
We guaranty "90 Days Full Refund"
Buy the product now. If you are not satisfied, we guaranty you to refund the full money in 90 days.
Be an early bird to explore EnglishToUML
The productivity gains from using both the new method and EnglishToUML are
estimated to be higher than 20%.
Both the analysis method and EnglishToUML help you to take the advantage of your English skills
which no other tool in the market does.
Look at it this way -- $249.00 is
really a worthy investment to get your hands on
EnglishToUML Basic and start using it right away to change yourself to be a proud data modeler.
You Really Can’t Afford Not To Invest In
It's easy to get started right away. Just click the order link below.
Click here to order right now for only
$249.00 (even if it’s 3:00 am in the morning).
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You need to act now to benefit from the real power of the new method
Imagine your team members' energy levels and synergy going up, as they can easily
communicate their data models.
Imagine your team members can working independently to extract the facts from requirements,
verifying them and then finally collating them to get your better quality data model in a ziff.
Imagine your customers, the end users and domain experts all understanding well what is going well into
the data models because it is all about understanding facts when it comes to verifying data models.
Do you want to miss these benefits?
Don't you owe it to yourself to try EnglishToUML Basic today?
Sincerely, Venkat Reddy
P.S. Commit yourself to learn the new method.
P.P.S Right away start exploring the new method and EnglishToUML. Why any delay?
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