Database Management Best Practices

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database management best practices

Database management best practices promote the efficient use of data throughout the organization. These practices support the collection and storage of quality data, as well as provide easy access to the data by the appropriate people. With today’s data growing in volume and becoming increasingly complex, the intelligent management of data has become a necessity. 

Database management describes how businesses control and alter their data to promote efficiency and effectiveness.

A database is a collection of data, normally stored and organized electronically within a computer system by software. The most common form of data storage uses SQL (structured query language) and stores data using rows and columns. NoSQL (“not only” structured query language) does not use rows and columns but employs other ways to organize and find the data.

Database management does not have a single objective but includes a variety of goals and responsibilities, such as data storage optimization, efficiency, performance, security, and privacy. By truly managing the data from its creation to its erasure, businesses can prevent activities that reduce efficiency and revenue loss. These same prevention measures should improve data integration and make business intelligence more accurate. 

A database management system (DBMS) is normally used to automate data processes.

Database management systems are packages of software designed to manage, store, retrieve, and locate data. DBMSs are important because they provide administrators, end users, and programmers access to the data (preferably in a user-friendly way). 

Data Management vs. Data Governance

Data Governance and Data Management are two separate, distinct systems that are used for handling data. They are normally used simultaneously, and each emphasizes different priorities. While distinct, they should support one another and work in tandem. Both Data Management and Data Governance place an emphasis on Data Quality.

Data Management is focused on the software used by the database. Database management software creates, maintains, updates, and edits data files and records. The software, which is controlled by a data administrator, also deals with data storage, backups, Data Quality, and security. The selection of software must ensure that the data is accessible to the right users when they need it. 

On the other hand, Data Governance is more concerned with Data Quality and ensuring laws and regulations are complied with. The software helps in creating standardized policies, backup schedules, and naming conventions. Data Governance also involves modifying the workplace culture to support high Data Quality and traditionally involves a data steward to answer questions and enforce appropriate data processing. 

Data Governance promotes the quality of data and supports Data Management. If an organization has not yet developed a Data Governance program (or framework), developing it alongside the Data Management program would be ideal, promoting their mutual support of one another. 

10 Database Management Best Practices                   

Developing certain behaviors that support the proper use of data, and using the most advantageous software, can result in greater efficiency and (theoretically) increase profits. As the digital workplace continues to evolve, using database management best practices while managing data has been shown to optimize profits and labor. The use of best practices in managing data can also improve the accuracy of data analytics and business intelligence. 

A well-designed Data Management program will include automation (with the effect of reducing human error, while speeding up processes, dramatically) and machine learning (which can analyze and make decisions using input data). Common database management best practices are listed below:

An awareness of business goals: Listing business goals should be done to ensure everyone is on the same page. Having listed the business’s goals, determine what the goals for the data are. (How will the data be collected and stored? What research projects are coming up?) Understanding what data is relevant to the business’s goals (and research) will help in deciding the types of software that is needed and help to ensure the Data Management storage doesn’t become overcrowded and disorganized. Examples of business’s goals might include:

  • Automating and improving data processes
  • Making effective business decisions 
  • Identifying trends and patterns 
  • Discovering customer buying habits/patterns

Developing useful policies and procedures: Policies and procedures can provide uniform, understandable behavior in routine situations and intelligent responses in emergencies. Creating intelligent data handling processes prevents mistakes and makes it easier to identify the source of a mistake after it has been made. 

Interoperability and data integration: When data is acquired from multiple sources (IoT devices, payment processors, other databases, e-commerce platforms, social media) the database must be able to connect with other systems (interoperability). Data taken from a variety of sources is used for analysis. Without interoperability and integration, this is not possible. There are two main data integration approaches:

  • ETL (extract, transform, load) is used to process batches of information and transfer it from the source to a data warehouse 
  • Transporting data from local repositories into a warehouse

Data analytics: This is a necessary part of developing modern business intelligence. Data analytics is used to develop algorithms designed to discover hidden business insights taken from massive amounts of data. Business intelligence is used to make better decisions. How data analytics is used should be determined by the type of desired business information. It’s important to choose the appropriate analytics software.

Implementing automated services: Computer-automated services minimize human error and accomplish tasks much more quickly. Automation can be used to sift through massive amounts of data, providing useful insights about products and customers. Automated tools can be used to upload, handle, and process data. 

Combining automation with data analytics allows researchers to focus on data analysis, rather than preparing the data for analysis. Automation can also be used to improve data integration. Examples of useful automation include:

  • Desk support
  • Customer support
  • Scheduling meetings
  • Employee analytics
  • Purchase order automation

Data security: Good data security prevents data breaches. Good data security policies include processes and technologies designed to prevent unauthorized access to the organization’s data and deter inappropriate use. There must be controls in place that can restrict who can access the company data.

Ensuring data integrity: Database errors can damage a business in so many ways. A process should be developed to keep database errors to a minimum. You can use software that promotes data integrity, though it can also be done manually. Data should be monitored on a regular basis. 

Developing a backup and recovery process: A plan that backs up data and allows for recovery should be established, as disasters do happen. Backed-up data should be stored in a separate, independent, secure location. The backup system should be extremely secure, and accessible to only a few trusted managers. 

Reducing duplicate data: Duplicated data (different from deliberately backed-up data) generally reduces the database’s performance simply because it takes up large amounts of storage space, unnecessarily. Additionally, duplicated data can lead to wasted resources and labor. (Is someone repeating the work a second time? Are duplicate copies being sent unnecessarily to different departments?)

Eliminating silos: Departments that manage their own area of a database (or have their own database) will sometimes duplicate data or block access to the rest of the organization. While data silos do serve a purpose for the controlling department, they are generally considered a hindrance to the overall flow of data through the organization. 

Database Management in the Cloud 

The future of database management would seem to be in the cloud. According to Gartner, 50% of database management systems will be based in the cloud in 2023.

Organizations are increasingly using the cloud and its services, sometimes combining them with their own on-premises systems to create a hybrid system. Other organizations rely solely on the cloud, with no on-premises computer system. (It’s a reasonable alternative for a startup that doesn’t want to pay the upfront expenses needed for an on-premises computer system.)

Use of the cloud is primarily based on storage and/or a desire to access software-as-a-service applications. Consequently, cloud-based database management services are being used regularly as well. These cloud-based systems often improve data sharing and data integration and provide excellent backup services.

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