Database Management Systems (DBMS) refers to the technology solution for optimizing and managing the storage and retrieval of data from databases. The management responsibilities for DBMS include information within the databases, the processes applied to databases, such as access and modification, and the database’s logic structure DBMS also facilitates additional administrative operations such as change management, disaster recovery, compliance, and performance monitoring, among others.

Introducing an DBMS software can benefit your company of data security, fast data sharing, data integration, upgrade storage and scale the infrastructure without impacting database operation and easier management and administration.

Figure 1: DBMS Schema (Accessible in PDF Version) 

Relational Database Management System

RDBMS (Relational Database Management System) is a DBMS that incorporates the relational-data model, normally including a Structured Query Language (SQL) application programming interface. It is a DBMS in which the database is organized and accessed according to the relationships between data items. In a relational database, relationships between data items are expressed by means of tables. Interdependencies among these tables are expressed by data values rather than by pointers. This allows a high degree of data independence.

There are several features distinguishing RDBMS and DBMS. DBMS maintains databases within the computer network and system hard disks. A RDBMS manages the relationships between its incorporated tables of data. DBMS is hierarchical and RDBMS is tabular. RDBMS can operate with multiple users while DBMS can only manage one user at a time. RDBMS has greater software and hardware requirements, and it can manage an unlimited amount of data.A DBMS does not provide support for distributed databases while a RDBMS does.

Database Management Strategy

To reach the goal for an organization, developing a database management strategy is necessary, which ensures that all the activities surrounding data management, from collection to collaboration, work effectively and efficiently.

With a data management strategy in place, a company can avoid some of these common data challenges:

• Incompatible, duplicate, or missing data from undocumented or inconsistently documented sources
• Siloed projects that use the same data, yet duplicate the efforts and costs associated with that data
• Data activities that consume time and resources but do not contribute to overall business objectives

A data management strategy will be the strong foundation needed for consistent project approaches, successful integration, and business growth.

Steps of Creating a Database Management Strategy

1. Identify business objectives

To avoid wasting time and resources on collecting, storing, and analyzing the wrong types of data, you must define your business objectives from the beginning. With business objectives at top of mind, focus on a small number of critical data applications. Think about what data is needed to meet the objectives, and what types of insights and information are required to make progress against these initiatives.

2. Create strong data processes

The second step is to think through the processes for collecting, preparing, storing, and distributing the data.

Collect: 

• What will be your data sources?
• Will you need access to both external and internal assets?
• Do you need structured data, unstructured data, or a combination of both?
• How will the data be collected?
• Is this a task that will be done manually as needed or will you set up extract scheduling?

Prepare: 

• How will you clean and transform raw data to prepare it for analysis?
• How will you identify incomplete or disparate data?
• What will be the guidelines for naming data, documenting lineage, and adding metadata to increase discoverability

Store: 

• Where will you store your data?
• Will you use XML, CSV, or relational databases for structured data?
• Do you need a data lake for unstructured data?
• How will you keep your data secure?

Analyze and Distribute: 

• Which teams or departments need the ability to collaborate?
• How can you make access to data and analysis easier for the end-user?
• How will you communicate any data insights?

Identify the right tools and platforms to build your data management strategy in the process of answering these questions. Prepare the required software and hardware.

3. Establish data governance 

As the data grows, it becomes more and more important to establish data governance. Procedures and policies need to be put in place to ensure the proper use of data. Data governance ensures that data is used correctly and consistently across the organization, so policies and procedures should not only be communicated and understood by owners and stakeholders but by everyone in the company. This is a great step in fostering an organization-wide data culture.

Some themes to explore during this step:

Data quality: How are you ensuring that data is accurate, complete, and current? Data security: What steps are you taking to securely store data? Data privacy: Do you have permission to collect and use data? Data transparency: How do you foster an ethical data environment?

4. Train and execute 

A critical part of your data management strategy will be to provide the knowledge and skills your team needs to analyze and understand the data. This could mean putting data analysis tools in the hands of departments outside of IT or getting buy-in from your organization’s leadership so they’re ready to support your data initiatives.

Selecting Database Management Tools

Finding a well-organized, functional system that allows your team to perform data-related tasks with ease is crucial to the success of your business.

Tools that provide sufficient access

The practical benefits of data depend on one thing: access. Do employees have easy access to the data they need? Do working groups properly share data? Is your data able to be leveraged to drive change? Try to find a balance between company-wide, open data-sharing and a restrictive, siloed data security approach.

Frameworks that let you Consume, Query, and Analyze Data Sources

The most useful database management framework provides the ability to consume a variety of data sources with the ability to easily query and analyze the data across a variety of different dimensions. Databases are used for different purposes, such as storing streamed data, time-series data, and configuration data. All this data must be easily imported and exported to add real value.

Appropriate Systems For Your Dataset Size

Small transactional datasets often call for relational databases (e.g., MySQL, Oracle, SQL Server). Larger datasets are best often handled with NoSQL databases (e.g., MongoDB, Cassandra). Even larger datasets like electron microscopy data or semiconductor designs are best left as files indexed by metadata (e.g., Igneous).

Recommended Database Management Tools:

• MySQL
• Microsoft SQL Server
• Management Studio
• DevOps
• Salesforce
• Visual Studio Code
• ESM Tools
• PhpMyAdmin

Steps of Creating a Database on MySQL

MySQL is an open-source relational database management system based on the language SQL.

Planning & Conceptualization

The first step in creating this database was to instantiate entities and their attributes. Data entities are objects used in a data model meant to break down data and organize it into different “categories”. Attributes, as the name suggests, are characteristics of data objects. Basically, attributes are used to sort data within an entity. The database need to have several entities with relation to at least one other entity so as to stay interconnected. Relationships come in four forms: one-to-one, one-to-many, and many-to-many. These relationships are determined by the quantity of an entity associated with another in said relationship.

Construction

In order to build the entities, a set of empty tables will be created within SQL that can be filled with sample information. These tables use entity attributes as columns for organizing data instances.

An ER Diagram is a visual representation of data entities. The relationships between entities, as well as the types of relationships, can be clarified by creating an ER Diagram. ER Diagrams can be created using a program called Visual Paradigm, a tool used for standard modeling in Computer-Aided Software Engineering (CASE). After the planning and conceptualization stage of the database was finished, it is time to start actually coding and constructing the database. Use commands in SQL to insert data into tables. Then print out and check the entire table after filling the data.

Testing Functionality

Query is introduced to test the database to make sure it could function well. A query refers to a request for data or information from a database table or combination of tables. This is how many large companies typically interact with large databases. The queries assigned are meant to challenge our abilities to query very specific combinations of data from our databases and create functions that can form new data from the existing data.

Conclusion

Businesses in many industries rely on data to achieve their goals, and as businesses grow, questions about data will emerge, often resulting in loss or waste. Creating an effective data management strategy for a company requires careful preparation and extensive research, and ultimately developing a governance system throughout the organization. This article provided an introduction to DBMS and RDBMS, the steps of creating a database management strategy, considerations for selecting software, and an example of establishing a database.