Planning a Proper Database Design Structure for Startup Businesses

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Posted on: 21 January 2019 by Tom Clark

Excellent database management practices are essential for any business to survive and become successful in the long run. A startup business owner nowadays should give a keen consideration to a proper database structuring even before the first piece of data pops in. A well-designed database will not only give the users any time access to the needed information but also will aide in crucial areas like business intelligence by providing insightful inputs for perfect business decision making.

Objectives of a perfect database

  • To consume only minimal disk space by eliminating redundancy.
  • Ensure accuracy and integrity of data.
  • Provide restricted by easy access to data.
  • Presenting data in various useful ways.

Building of an efficient database is a multi-phased process including:

  • Identifying your purpose of DB, i.e., requirements analysis
  • Structuring data – organizing it into tables
  • Specifying the primary keys.
  • Analyzing data relationships
  • Standardizing tables through normalization.

Requirements analysis

Exactly understanding the basic objectives of your database may give you various choices in designing the DB. You have to consider the database from all perspective of business management. Say for example, if you are designing a database for a library, then considerations needed to be given to how the member and the librarians may interact with the data. You may interview the users of the database, analyze various input forms, business documents like timesheets, invoices, surveys, and also evaluate the existing data system to plan the DB design.

Structuring database

One if you have clarity on requirements, the next step is to draw out your database's visual representation. For this, it is important to have a clear idea about the relational DB structure. In this, data is grouped into various tables, which have further rows and columns similar to spreadsheets. A column contains information about a single category and each row in a table, called records, consists of specific data about someone or something. Common types of data in relations databases are:

  • CHAR – text of a specific length
  • VARCHAR – variable length texts
  • TEXT – a huge amount of text
  • INT – number, either positive or negative
  • BLOB - binary data
  • FLOAT, DOUBLE – storing floating-point number

Primary keys

Prepare a diagram to specify the entity-relationship by marketing each table as a box in it. In the database table, you should define which attribute will be the primary as a unique identifier for each entity.

Data relationships

Once the database tables are created, you have to analyze the inter-table relationships next. Identifying cardinality (elements which interact between relational tables) will further help define the data into the tables. As RemoteDBA.com specifies, every single entity may have a relationship with a significant other, which are basically of three types as:

  • One-to-one

If there is on a single instance of one entity corresponds to an instance of another entity, it is called a one-to-one relationship.

  • One-to-many

This relationship is established when an instance in one table associated with more than one entity in another. In an e-com database, a single customer may have made many orders.

  • Many-to-many

Now, we can easily understand the many-to-many relationship as multiple entities of a table corresponding to multiple entities of another table. If you prepare a college database, the students and classes may have many-to-many relationships as one student may attend different classes.

Normalization of database

Once on having a primary database design to start with, you can start applying the normalization rules to ensure proper table structuring. OLTP (online transaction processing) databases with the need for creation, updating, reading, and deleting records needed to be normalized.

Handling multi-dimensional data

There are many instances now where the need may access multiple dimensions over a single data type. For instance, an administrator may need to make sales by a particular customer, location, or season. In such situations, it's ideal to create central fact tables, which other corresponding tables can refer to.

When it comes to database management to incorporate all the varying requirements as above, the major choices for businesses to make in relational DBMS are MySQL, Oracle DB, IBM DB2 PostgreSQL, Microsoft SQL Server, etc. Given the choices, you may pick the most appropriate system in terms of features and cost.

A few database mistakes to avoid

Things will flow smoothly if the database design is done rightly, and if not, the DB development, deployment, and performance will be facing troubles. Here we will discuss a few common database mistakes made, which can be avoided if taken a little extra care.

  • Poor design and planning

A database is the cornerstone of any business project. However, startup business administrators tend to downplay the planning phase. However, as the project takes off and certain problems arise, you may find it difficult to go back and fix the database structure issues, and on a long run, you get stuck to live with it.

  • Avoiding normalization

We have to see normalization as one of the baseline tasks in database planning as above, which breaks down the tables to further constituent parts to define the exact thing a table represents. Normalization is the base on which a relational database implemented. SQL is also created to work only for normalized databases.

  • Poor naming conventions

A name you choose is not just for you to identify an object's purpose, but you should also consider the future users, programmers, and stakeholders who need to understand what they deal with instantly. The best practice to follow in object naming is the use of space and quoted identifiers. Try to avoid a column name as Part Number, where the alternatives in the standard naming convention are part_number or PartNumber. Remember, consistency is the key when it comes to naming.

  • No proper documentation

A database you design is for many years to come, so you should make it clear to anyone who refers to it in future as for how your database is modeled. The bitter reality is that many tend to skip proper documentation or do it incompetently, which creates a lot of confusions in the future. A good data will not only have a solid naming convention but also will have definitions for each table and definition of relationships.

 

These tips are picked out from the expert discussions about proper database management for startups, but it is not an exhaustive list.

 

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