Written on 22nd November 2023 - 3 minutes

Best practices for managing and organising data

Best practices for managing and organising data

At Software Solved, we excel in the art of custom data solutions, bringing unparalleled expertise to the table. Developer Jack Parsons shares his insights on how smart data organisation is key to achieving business excellence. 

Data plays a pivotal role in the operation and decision-making of businesses. It not only drives business needs but also influences the way an organisation perceives and secures its data. 

Effective data organisation is a fundamental practice that enhances data utilisation and ensures its confidentiality and integrity. 

 

Database Design Principles 

 

Structured databases, such as SQL, offer advantages like efficient querying. However, selecting the right database management system is essential. For instance, if you’re building a transactional system, SQL databases are often the preferred choice due to their support for complex transactions. In contrast, NoSQL databases like MongoDB are suitable for scenarios requiring flexible and scalable data storage, such as content management systems. Document and graph databases are excellent choices for handling unstructured and interconnected data, respectively. 

When designing a database, data normalisation is a critical principle. For instance, consider a customer database. Instead of duplicating customer information in every order record, normalise the data by maintaining a separate customer table linked to orders through a unique customer ID. This reduces redundancy and enhances data integrity. Scalability is another key aspect. Design your database with future growth in mind. For example, sharding, which involves distributing data across multiple servers, can help accommodate large amounts of data efficiently. 

At Software Solved we mainly work with structured databases such as SQL server, this is due to our experience and the versatility of the software. It also works with Azure as Microsoft provides an ‘Azure SQL’ database that works well with other hosted Azure programs. 

 

Data Security and Privacy 

 

Protecting data from potential threats is a continuous process. One prevalent threat is SQL injection, where hackers exploit unprotected input fields on websites to run malicious SQL queries. To prevent this, use prepared statements and input validation in your code. 

Encryption is crucial for data security. When transmitting data, use technologies like SSL/TLS to encrypt data over the internet. In the database, employ encryption at rest to protect stored data. For example, SQL Server offers transparent data encryption (TDE) to encrypt entire databases. 

Access control is vital. Implement role-based access controls (RBAC) to restrict access based on user roles. For instance, in a healthcare application, only authorised medical staff should have access to patient records. Within Software Solved, we have used RBAC to ensure that vital information for our customers is not exposed to incorrect employees. As well as internal documents, we utilise permissions within OneDrive and Azure to make sure only the correct employees can have access. 

Regulations like GDPR require organisations to protect sensitive data and respect privacy rights. Compliance includes data anonymisation, consent management, and data subject access requests. 

 

Optimising Data Retrieval 

 

Efficient data retrieval is critical for application performance. Database indexing, akin to a book’s table of contents, helps databases locate relevant data quickly. For instance, in a product database, create an index on the product name to speed up searches. Caching involves storing frequently accessed data in a separate memory space. This improves query performance. For example, a web application can cache user profiles to reduce database queries, but it must address cache expiration and consistency issues. 

We have utilised both caches and indexes within many of our databases to improve query efficiency and response time. It is an excellent tool to improve the overall speed and feel of your program. 

 

Strategies for Data Archiving and Retention

 

Archiving historical data is essential for various reasons. Cold storage, like Amazon S3 Glacier, is suitable for rarely accessed data, such as long-term backups. Hierarchical storage management automatically moves data from expensive storage to cheaper media as it ages. Data warehousing, like Amazon Redshift, provides dedicated databases for analysing structured and semi-structured data, supporting advanced reporting and analytics. 

Analysing historical data can uncover valuable insights. For example, an e-commerce company can use historical sales data to predict future trends, ensuring they have the right products in stock. Data has a lifecycle, from creation to deletion. Understanding and enforcing data lifecycle policies, such as retaining customer records for three years, ensures compliance with legal requirements like GDPR. 

Effective data management is essential in a data-centric world. It fosters growth, innovation, and resilience. By delving into data management areas, you’re better equipped to manage and organise your data, ensuring its security and usefulness for your organisation’s success. 

The importance of historical data is highlighted most when an issue arises within your current live database. By having point-in-time backups, like Azure implements, it allows for the database to revert to a state before the issue has occurred, this can save the users time and recover valuable data. We have utilised this backup technique when working on policy databases, or to recover lost data. 

Ready to enhance your data management practices and secure your organisation’s success?

Explore our expertise in structured databases, data security, optimisation, and more. Contact us today. 

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