Home > Why Data Model? Part 4: We Data Model to Give Proper Focus to the Enterprise

Why Data Model? Part 4: We Data Model to Give Proper Focus to the Enterprise

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This blog post was authored by Todd Schraml.

Unify the Enterprise With Data Modeling

Acting locally can be very different than acting globally. Project development staff, left to their own devices, naturally lean towards their own myopic needs. Enterprise naming conventions often are not even read. If the solution has one attribute containing a name and no others, then the engineer may think the name “Name” is well and good. Everyone within the development team knows what it is. Done. Once one steps back, and starts applying more of a global perspective, hopefully one can appreciate the hubris about choosing the name “Name.” Could it really be true that across all possible names throughout the organization, this particular field is so very important, special, and obvious that in seeing “Name” everyone can unmistakably understand its detailed and specific meaning? Chances are this is not true. General good naming conventions have multi-part names with the last element being a class word that flags to users the kind of data contained within a field. “Name” is often one of these class words — as in “Customer Name”, “Employee Name”, or “Product Name.” Elevating an unqualified “Name” to fully qualified attribute name status only increases chaos.

Data Modeling Helps with Naming Standards

Naming standards can be tricky. Engineers may glance at naming standards and yawn because the standards are simple or too obvious; yet those same engineers may turn around and create names they believe follow those obvious standards when in fact the selected names really don’t follow acceptable naming standards. At the time such discrepancies in applying standards are pointed out, said engineers more likely than not get defensive, refuse to change, and dismiss any importance of the standards. Similarly, there may exist enterprise standards on data structure patterns to be followed. For example, supertype/subtype configurations within a logical data model may translate to one of three different physical implementations. If an organization has a standard that says supertypes should always be addressed a set way, how can that be enforced? The value of following a formalized data modeling process is seen in standards enforcement, both in naming and in reusable data structural patterns. Beyond a detailed standards review, having a team focused on data modeling within an organization can increase consistency and coherence across data structures used by differing solutions and platforms.

Enforce Standards Across the Entire Organization

The power of consistency is pretty sneaky. Once consistency is followed well enough then people magically know more than they think they know. As folks get used to seeing columns and attributes named “Country Identifier” or “Country Name” instead of simply using “Country” users then know with a level of certainty what the actual content is within the attribute. They know which one they need, which is right, and which is not. Once applied across all the data stores, it adds up to a lot of knowledge for everyone across the enterprise. Awareness is leveraged. Communication is enhanced. People can be functional in new areas/solutions more quickly. People will wonder how they lived before this consistency was there. Overall, as consistency increases, development and maintenance times decrease. Reuse of data is better facilitated because data is better known, data dictionaries and data glossaries are found to be much more useful to everyone in the entire organization. Across the business, teams start rowing in the same direction more explicitly. Tools like Idera’s ER/ Studio support a repository that serves as a data hub for the enterprise to learn about its data content. An enterprise perspective is important, and spending a little effort to enforce and follow standards is well worth the effort. Every organization should model data so that the enterprise can be given due respect. Make use of the sneaky power of consistency across your data structures.

Next Steps

Enterprise data architecture eliminates time-consuming manual tasks while improving governance and visibility to deliver maximum value from enterprise data. It enables organizations to:

Manage data across complex IT environments by properly understanding metadata.
Gain insights from enterprise-wide data assets.

Enable robust information governance and data stewardship.
Shape future information technology strategy.

ER/Studio has earned its reputation as the best solution for enterprise data architecture because of its comprehensive data modeling, metadata management, built-in data governance, enforcement of standards and best practices, scalability and flexibility, as well as collaboration and teamwork.

Experience for yourself how ER/Studio can help you use data models to be good business partners by scheduling a product demonstration with one of IDERA’s industry experts.

About the Author

Todd Schraml has over fifteen years of experience in application development and maintenance. This includes eleven years focused on data warehousing initiatives and five years of experience in database administration on massively parallel processing database management systems. Positions held include Project Manager, Data Warehouse Architect, Technical Lead, Database Administrator, Business Analyst, Developer, and Teacher.

Todd’s focus is on data analysis and design, implementing databases for operational applications and data warehouses, using new and emerging technologies, and seeking ways to integrate formalized quality practices into Information Technology arenas.

Aligning complex data environments with business goals for over 30 years.
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