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How to Avoid Critical Mistakes Architects Make in Data Modeling? This one and something else you will learn here. The goal of maintaining any enterprise database nowadays is to gain valuable information and actionable insights from it. This is the primary reason why you are collecting all the data over time. So, how can you make sure that you model your data so that you can gain these insights accurately and get strategies for your business success?
For this, you need to plan it well. If you the planning stage or mess it up, then the result may be horrible.
You may be compromising on your analysis and performance by doing so. Ensuring data integrity and database security will ensure that you cut off a lot of overhead in terms of database administration and cut short the development balloon to a large extent.
Common data modeling mistakes to Avoid
Here in this article, we will try to close look at some of the most common mistakes the database architects tend to make while modeling the data to prepare the same for analysis. If not done well, then these mistakes will undercut your analysis’s efficiency and prevent you from gaining any actionable insights you wish to receive.
We will start the primary three prevalent data modeling errors, which should be avoided. Irrespective of which tool or database technology you use, you need to focus on these most problematic aspects and try to eradicate it.
Starting the database architecting without a clear action plan
When you are modeling the data resources and setting them up for analytics, it is essential to plan your actual goals. There are many reasons for this, but the central theme here is that you cannot use if efficiently for your analytics resources if there are no specific goals set. Design a perfect data model that allows business users to investigate key performance indicators. Those looking only for web traffic, conversions, or opt-in rates are by far different from the models that can insightfully analyze the product sales.
The best practice in modeling is to plan, design, set up, and allocate the resources for each area you intend to run the analysis. This should also be done in a BI project at the planning phase and during the comprehensive requirements’ elicitation. You may notice an improvement in the performance, feasibility, and security when it comes to implementing the changes in your analytical goals.
It is also possible for those who plan it to pump too much data to a single resource. With the legacy tools working on SQL-based databases, for example, this approach may significantly slow down the entire system and thereby increase the query time and analytical ability. However, with the database platforms with optimum performance, it is easier to handle big data and disparate datasets. Cloud storage also now cut short the challenges in terms of storage space.
If you are planning to take external support of a reliable consultant for remote database administration, it is worth exploring the remote database administration services of RemoteDBA.
Inadequate usage of the Surrogate Keys
While you are bringing in the data from various sources to a common pool, it is essential to ensure the data integrity. A popular measuring strategy should be there. However, it is not mandatory to do this, but a fair practice to follow along with the surrogate keys’ usage. Many times, there are natural keys available in out there in the data which work well. The natural key values are like social security numbers, customer IDs, or the composite keys, which are already used by transactional data as the primary keys. These are, in fact, stable enough to retain the characteristics which primary keys need.
The surrogate keys may not have any relationship to the data. That means it should not subject to the actual business rules. However, these rules may keep changing from time to time and may render the previous values non-unique. The primary keys should be compact. It can be cumbersome to have complicated, large, and composite keys of three or more fields. If you find the natural key stale and compact, there is no need to add a separate surrogate key.
Another major underpinning to the data is the need for consistency. It should extend to the names we give to the tables, constraints, columns, measures, etc. There are many benefits to follow a standard naming convention, which will become evident in overtime. If you try to create the queries for analytical purposes, but your tables and measures do not follow any specific logical way based on their names, it won’t be easy to digest.
There are various standards used in naming conventions. It is comparatively easy to pick one that works the best for your organizational needs and implement it. There is no such need to come up with unique naming conventions of your own. If you are the one in charge of the data architecting to create an analytical framework, you must implement the standards that should be followed for future analysis. Failing to do this can be a grave oversight.
Mistakes on working with the legacy tools
- A wrong granularity level could be a terrible mistake. Which refers to the degree of detail that your data could see.
- The calculated fields – not all the measures out there can be found in hard data. There are many such instances where the values which are derived or processes may be needed for analysis. However, not being planned for such derived values in the data model can be a mistake. While running analytical queries on the legacy systems. Making many calculations may be cumbersome, and it may slow down the process. It may also cause some major inconsistencies.
Not you know some of the major mistakes in data modeling. Which may cause inconsistencies in the system. And also may end up in considerable overheads in terms of time and money. So, while designing the database. One should be very careful about these aspects to be set right at the first point to avoid future chaos.
P.S. Please do not forget about security while working with legacy tools on SQL-based databases, because it may lead to a data breach with very expensive consequences.