Top 10 Benefits of Using Data Modeling Tools

Download ebook

Benefits of Data modeling… the very essential key tool for many organizations to accelerate their application development and unlock the value of their data. However, perhaps the exact definition of data modeling has eluded you.

If you are a professional who is reading this, you might get the scientific terminology which could solve all the issues. And if you are a fresher or someone who is about to graduate, you might have some idea on how do we solve issues and if you are a layman you might be thinking why not spend some time in blueprinting the database and then create them from scratch with a better design and better understanding of the same. All of your queries are answered by a simple term known as Data Modelling.

I’ll help you in making sure you won’t again turn back to be in the jargon deciding in usage of data modeling tools. In this article, I’ll detail everything about how are data modeling tools going to be useful to your databases and your organizations/companies.

Let’s just start…

What is data modeling?

Data modeling is a technique used to define and organize your business processes. Data modeling allows you to create a visual description of your business by analyzing, understanding and, clarifying your data requirements and how they underpin your business processes.

Data models are technical in nature but they are also designed to be simple in way and, (for the most part) visual in nature.

This means the data modeling tools hit the sweet spot between hard to digest tech-speak jargon and easy to understand – everyday terminology.

Thanks to data models, these made everyone in the organizations understand and collaborate with the data more effectively.

To really understand what data modeling is, you have to, root out and look at these specific benefits it delivers. Naturally, these benefits are only achieved when you set out the data models effectively, and when business and IT teams work in harmony.

“In many a way, up-front data design with databases can actually be more important than it is with traditional relational databases. Beyond the performance topic, databases with flexible schema capabilities require more discipline in aligning to a common information model.”

  – Ryan Smith, Information Architect at Nike.

• Information drives businesses that make decisions based on the data. Data is a corporate asset and data modeling is critical to understanding data, its interrelationships, and its rules.
• Yet, some people don ‘t understands the value that data modeling provides. Some assess it as just documentation, as a bottleneck to quick-wit the development, or even as too expensive to be worth it.

 A data model is not just documentation, because it can be forward-engineered into a physical database. Not only is data modeling not a bottleneck to application development, it has also demonstrated time and again that it accelerates development, significantly reduces maintenance, increases application quality, and lowers execution risks across the enterprise.

Experience has shown that relying on the second sight of the software developers is not a repeatable process or one insuring the first-time-right success.

the old chest nut goes that the one difference between the data modeler and a terrorist is that you can’t negotiate with a terrorist in spite of the fact that there are many notions available and many ways to approach the effort using each of these notations, data modeling is such an intense, personal experience that it is very easy to become emotionally attached to a particular way to do it. On top of that, the difficulty that goes into making a modeling tool work just the way we want it to, also tends to make us uncomfortable with the prospect to change.

It’s a wonder that any of us can talk to each other at all.

Still data models are products to delivered to one’s consumers, and it is clear, on the surface at least that there is a difference between a bad model and a good one.

Unfortunately, however, that difference between good and bad is rarely articulated.

 …

Looking forward to becoming a Data Scientist? Check out the Data Science Bootcamp Program and get certified today.

So, why is data modeling being a vital part of any data strategy?

So we’ve had an over view about data modeling, but defining only tells a bit of the story… Anyway, moving on-

Data modeling is a process of creating a data model for the data to be stored in a Database. This data model is a conceptual representation of data objects, the associations between different data objects and the rules. Data modeling helps in the visual representation of data and enforces business rules, regulatory compliances, and government policies on the data.

Also, data models ensure consistency in naming conventions, default values, semantics, security while ensuring the quality of data.

The primary goal behind using the data models are:

Ensuring that all the data objects required by the database are accurately represented. Omission of data will lead to creation of faulty reports and produce incorrect results. A data model helps design the database at the conceptual, physical and logical levels. The structure of the data model helps to define the relational tables, primary and foreign keys and stored procedures. It provides a clear picture of the database and can be used by database developers to create a physical database making it easy to identify missing and redundant data.

Though the initial creation of data model is labor and time consuming, in the long run, it makes your IT infrastructure upgrade and maintenance cheaper and faster.

To dig in deeper how data modeling is functional and practical, let us check the Benefits of data modeling:

Higher application quality-

A data model is the equivalent of an architect’s blueprint before a building construction starts. Data modeling is the visual expression of a development team’s understanding of the business and its rules. The data modeling process is the most effective way to gather correct and complete business data requirements and business rules, so as to ensure that the system will operate in the intended manner. The process generates more questions than any other modeling approach, leading to higher integrity and discovery of the relevant business rules. And its visual nature facilitates communication and collaboration between business users and subject matter experts.

Reduced cost:

You can build applications at lower cost via data models. Data modeling typically consumes less than 10 percent of a project budget, and can reduce the 70 percent of budget that is typically devoted to programming. Data modeling catches errors and oversights early, when they are easy to fix. This is better than fixing errors once the software has been written or – worse yet – is in customer hands. Given the exponential evolution of bug fixing costs as a project progresses, it’s always better to evaluate and think through options early, rather than after the software has been written. Even more so in an Acute developing environment, development costs can be reduced significantly because a good data model will reveal the upfront otherwise, unknown or unanticipated requirements.

Routinely using data models as the nucleus for building applications can build its applications faster and with fewer errors. The models promote clarity of thought and provide the basis for generating much of the needed database and programming code.

And with the data models’ flexibility, the data model can rapidly evolve in an organized manner.

Quicker time to market:

Thanks to proper data modeling, it has made this possible to build the software faster by catching errors early. Also, application developers don’t have to discover unknown requirements themselves, and can focus on developing with fewer errors and reach their sprint commitments. This will in turn lead to the quicker delivery of high-quality, value-adding functionality, easier acceptance testing, and a quicker payback on development.

In addition, a data model can automate some tasks – design tools can take a model as an input and generate the initial database structure, as well as some data access code.

We will be able to accelerate the development by using data models as a guide for writing codes.  With this method we can develop the database 10 times faster than by preparing conventional ETL (extract, transform, load) programming code.

Clearer scope:

A data model provides a focus for determining scope. It provides something tangible to help business sponsors and developers agree over precisely what is included with the software and what is omitted. Business staff can see what the developers are building and compare it with their understanding. A data model also promotes agreement on vocabulary and jargon. The model highlights the chosen terms so that they can be driven forward into software artifacts. The resulting software becomes easier to maintain and extend.

Models promote consensus among developers, customers and other stakeholders.

Often, there are different schools of thought among departments, and the model must triangulate their respective understandings. The model provides a nucleus for reaching an agreement.

Improved data quality:

Data corruption and inaccurate data are even worse than application errors. A good data model defines the metadata so the data itself can be properly understood, queried, and reported on. To truly leverage the power and flexibility of databases, it is still important to ensure the enforcement of domain definitions, field constraints, editing rules, and integrity of relationships. It actually turns out to be more important given that such enforcement is seldom possible at the database level, and needs to be maintained in the application code. And a data model will provide the developers with a roadmap and checklist for such enforcement.

Better performance:

A sound model simplifies database tuning. A well-constructed database typically runs fast, often quicker than expected. Data modeling provides DBAs with the means to understand the database and tune it for fast performance, without having to search through the code to discover the schema. Given the nature of few databases, the data modeling process outlines a method to start thinking in terms of queries and data representation, rather than in terms of storage.

To achieve optimal performance, the concepts in a data model must be crisp and coherent. Then the proper rules must be used for translating the model into a database design.

Modeling provides a means to understand a database so that you are able to tune it for fast and better performance.

Business intelligence:

What’s the use of possessing a great deal of data, only to have no efficient way – or no way at all – to use it?

Data modeling would guide you to be successful in Business Intelligence (BI). It is predominant that the process is business-centered. It begins with the clear understanding of the business, its purposes, and how the data will be used to support the business.

A data model for one line of business is hardly appropriate for another line of business. Only after a thorough analysis of an organization can a data model for the business lines be established to support the Business Intelligence process for the organization.

How can one productively query his/her Big Data if they do not know what is in it, or how it is structured? A good data model, built on query and reporting requirements, is a starting point for data mining. It will spot trends and patterns, and make predictions to help a business navigate challenges and opportunities.

Fewer errors:

A data model causes participants to well define concepts and resolve confusion. As a result, application development starts with a clear vision. Developers could still make detailed errors as they write application code, but they are less likely to make deep errors that are difficult to resolve.

Data errors are worse than application errors. It is one thing to have an application crash, entailing a restart. It is another thing to corrupt the data in a large database.

A data model in addition improves the conceptual quality of an application and also lets you grasp the database features that improve data quality. Developers can weave constraints into the fabric of a model and the resulting database. For example, every table should normally have a primary key. The database can enforce other unique combinations of fields. Referential integrity can ensure that foreign keys are bona fide and not dangling.

Consider the recent troubled rollout of Web software for the Affordable Care Act. Insurers are having difficulty providing coverage because the data they receive is too often corrupted by application errors. Data errors can have severe consequences that are often difficult to understand and correct.

Improved collaboration and enhanced integration:

Now, your IT team can collaborate more easily with non-technical staff. Using data models, they can communicate in a technology-neutral way, but still with enough detail to create physical data structures when needed.

Data modeling makes it easier to integrate high-level business processes with data rules, data structures, and the technical implementation of your physical data. Data models provide synergy to how your business operates and how it uses data in a way that everyone can understand.

With data modeling of all corporate applications, the creation of a meta repository provides a common vocabulary, identifies relationships and redundancies, and resolves discrepancies so disparate systems are well integrated together.

Here’s an example for a database:

A good start for data mining:

The documentation innate in a model serves as a starting point for analytical data mining. You can take day-to-day business data and load it into a dedicated database, known as a “data warehouse.” Data warehouses are constructed specifically for the purpose of data analysis, leveraging that data from routine operations.

In addition, it provides data so that sponsors could see the progress of disbursement and repairs, as well as bottlenecks.

Let’s just get a quick recap of what all can data modeling tools can help us in:

Anything that makes the monotonous activity of building information models, simplify that for the experts which can be viewed as an information demonstrating instrument. There are a lot of information demonstrating instruments accessible for the equivalent; a significant number of which run on practically all the working frameworks while some of them may be limited to some working frameworks, for example, Linux, Unix, and so forth. A portion of these apparatuses are accessible on the web and can be utilized straightforwardly without the need of any establishment and in this manner diminishes the expense of support. A large number of these devices come outfitted with a great deal of extra highlights, for example, making information objects from the relationship charts, information import-trade offices for cross stage use, upgraded documentation to help with the documentation of the information model, network to different databases and furthermore with the capacity to be incorporated with Hadoop and other huge information stages.

This carries me to the core of the article, the best 10 information displaying devices that are right now being utilized in the product business. If it’s not too much trouble note anyway that the rundown is simply founded on data accessible from different sources on the web and the contributions from different experts in the business. There may be other information demonstrating apparatuses which better suits your necessities and probably won’t be referenced here. Likewise note this is certifiably not a positioning based rundown of information displaying devices.

Download ebook