How Do the Latest Database Management Trends Impact Industries?

To make the best use of converged data, companies nowadays have started focusing heavily on data analytics, data optimization, and lakehouse development.

However, data management is no longer just a collaborative process of storing, collecting, and managing data. In fact, it has gone beyond it, as businesses now look for improved ways to derive critical information from big data sets that can aid in making effective decisions.

But that does not change the fact that there is still a massive amount of unstructured data out there, and in order to gain valuable insights from it, efficient database management is the key.

95% of businesses have mentioned that managing unstructured data is one of the most complex challenges they have to face. The right form of data can successfully help businesses get a competitive edge, and moving with the prevalent database management trends is one way to do it.

What is Database Management?

The contemporary digital world is all about data. In order to make the best business decisions possible, companies are looking into every nook and cranny.

Database management has become the most crucial activity in this domain. It is basically a practice of keeping and maintaining enormous data in an efficient manner, primarily using database management systems (or DBMS).

But just having a massive amount of data can not help you get valuable insights. Besides that, managing it is also challenging, especially when the data generated by digital users every day is 2.5 quintillion bytes, and is increasing rapidly on a regular basis.

Since data is produced and consumed at an exceptional rate, businesses look for a popular database management system solution to store, organize, protect, and analyze it for effective decision-making.

These database management solutions ensure that business analysts get reliable and latest data which can help formulate strategies that enhance workforce efficiency and productivity. However, different databases have specific features. Therefore, it is crucial to assess the list of popular databases for mobile apps out there and choose the one that suits your business needs. 

What is the Benefit or Importance of Database Management System?

Data analytics has taken the business landscape by leaps and bounds offering actionable insights that yield productive results. It is often reported that 63% of businesses have observed improvement in work efficiency and productivity after incorporating data analytics. It results in the rise in the adoption of the latest database management system and analytics software.

Since data management is the first and foremost step in integrating effective data analytics at scale, it becomes crucial to keep up with the latest data management trends. Some intriguing benefits of database management systems include:

Maintaining Reliability

A database management system maintains the data efficiently, mitigating the risks of potential errors. Since only reliable and up-to-date data is stored and processed, companies can make the best use of it in alignment with market trends and customer needs.

Improving Data Visibility

The visibility of organizational data assets can be enhanced using data management systems. With these systems, users can easily and seamlessly find valuable data needed for analysis. Companies often use automated solutions to increase end-to-end visibility across different data management systems.

Enhancing Security

Data security is the first and foremost priority for any organization and its employees. The right data management solution ensures to protect your data from unauthorized access, thefts, breaches, and other cybersecurity challenges. Besides this, it also makes sure that the sensitive information available in the databases is backed up and retrievable.

Promoting Scalability

With the right management of data, companies can easily and effectively analyze data and use it to scale their businesses. Insightful data can be used and integrated into different products, such as mobile apps, web applications, etc. to promote scalability.

Effective Data Sharing and Data Integration

An efficient database management solution provides businesses with quick solutions to share and integrate data across the organization. Moreover, by incorporating the right functionalities, you can also automate the process of resolving data-related queries.

Recent Database Management Trends: How is the data landscape changing?

With business intelligence and data analytics, companies can easily survive even the worst phases, be it the COVID-19 pandemic or economic slowdown.

Gartner has reported that data and analytics are the key factors that promote digital transformation. It is further reported that by the end of this year, 90% of industrial strategies will emphasize information as a crucial business asset and analytics as an essential competency.

Data – the fundamental atomic unit is managed, refined, analyzed, and utilized with the help of several database technologies, bringing out the potential information beneficial to businesses. The following section will help you understand the most popular database management trends that revolutionized the sphere of data.

• Automation in Database Management
• Data Democratization
• Data Mesh and Data Fabrication
• Data Observability
• Data Analytics with Governance Capabilities
• Data Transformation Using Advanced Data Stack
• Evolution of Containerized Apps and Cloud-native Technologies

Automation in Database Management

Gone are the days when automation was just used in quality assurance and testing. Today, the domain of data management has turned out to be one of the major industries that observed real progress in terms of automation. But why?

Well, the primary reason is reducing time. In fact, it is reported that for every five data consumers, there is only one data engineer. The business world is observing a scarcity of data engineers and related resources, especially during times of recession.

Due to this, businesses are looking for out-of-the-box solutions to automate as many tasks as possible. Presently, there are several operations where the collaborative effort of AI and metadata is doing wonders, such as:

Monitoring data quality – Leveraging data observability to understand the data’s health and finding ways to improve its quality.

Onboarding of data source – Using data identifiers, offline user data can be easily uploaded to an online environment 

Data discovery and matching – With AI-based technologies, finding specific data fields and data matching becomes seamless. It not only reduces data duplication but also increases data accuracy in a data source.

Since a large amount of data is document- or graph-based, database automation can also be put under the category of NoSQL database trends and applications. Some of the major industries that leverage automation in data management include insurance, accounting, marketing, and recruitment.

Data Democratization

In the current landscape when the business world is ever-changing, collective participation is needed to achieve a diverse thought process crucial for making informed business decisions.

This inspires businesses to move towards a decentralized approach while managing and making data accessible within an organization. This decentralization of data focuses more on shifting responsibility from a centralized administrating unit to individual teams. It’s like blockchain technology – every member (or node) of a chain has access to the data and complete transparency of transactions allows better functioning of the entire system.

In the domain of database management, this decentralization of data is often known as data democratization. But what is it exactly?

Well, businesses that implement data democratization enable every single person in the organization to access and work with data comfortably, irrespective of whatever department they belong to. Consequently, it enhances data literacy, data culture, and the decision-making process. Apart from this, the other major benefits of data democratization are given below:

Data Mesh and Data Fabrication

Legacy database architectures hinder business agility, making it challenging for companies to adapt to the new changes in the database landscape. Some of the issues with them restricting scalability include:

Data complexity and proliferation – Managing the increased amount of data from different domains and sources.

Lack of collaboration – Siloing of data developers from the places where data actually originates.

Lack of agility – Unable to meet data demands timely

In order to overcome these challenges, solutions like data mesh and data fabrication can prove to be useful. These trends are based on the principle of “data as a product”, which further shifts the responsibility of data management from just the data analyst team to the entire business.

Data Mesh

Data mesh primarily focuses on the concept of organizational change. It enables the core data domain teams to own and manage the delivery of data products for the reason that they understand it better than others.

This type of architecture embraces the omnipresence of data within an organization by employing a self-serve design. It helps data consumers to formulate data-driven strategies and decisions. It breaks the traditional monolithic kind of data ownership and shifts the focus to the decentralization of data.

Although data lakes and warehouses still exist in the mesh database structure, they just act as nodes in the mesh.

Data Fabric

Data fabric, as the name suggests, is a database management trend that connects or ‘fabricates’ all the data sources and components through metadata. This design architecture mainly addresses the data complexity associated with the availability of data on different platforms or locations.

When integrated accurately, data fabric can develop user-friendly and autonomous database interface solutions. Besides data democratization, both data fabrication and data mesh are the trends in new database technology that follow the decentralization approach.

Data Observability

Data observability is one of the emerging trends in the database management system domain that deals with determining data quality and monitoring data health. It basically helps your organization to analyze the state of your data based on the information collected by the database system. For this, your system is monitored using automation along with a little intervention of manual methods.

In order to represent the health of data, there are five pillars of data observability that need to be analyzed:

Freshness – It deals with the aspect of whether data is up-to-date or not. In case a freshness issue persists, it can lead to the breakage of the data pipelines, which can further result in multiple loopholes in the data.

Distribution – It primarily focuses on identifying the data’s field-level health. Simply put, it checks whether the data is within an accepted range.

Volume – Volume, as the name suggests, signifies the amount of data in a database. This factor indicates whether the data intake fulfills the required threshold values.

Lineage – Data lineage offers insights into which teams have generated data and which ones have accessed it. When a data pipeline breaks, the lineage also shares information about the affected upstream sources and downstream consumers.

Schema – The schema is basically the data structure supported by a DBMS. It must be created in a way the DBMS complies with.

How does data observability benefit businesses?

Data observability helps businesses in several ways. Besides maintaining data health, it also deals with fixing data-related issues, and even preventing them from happening in the first place. Here are some major benefits of data observability for businesses:

1. Data quality monitoring even with little to no domain knowledge at all
2. Easy management and analysis of most complicated data systems
3. Recognize the problems that even domain experts unknowingly skip
4. Proactively identify errors and alert users downstream
5. Allow users to prevent issues with the least effort

Data Analytics with Governance Capabilities

Today, companies need effective governance capabilities at multiple levels to address the current challenges concerning flexibility, scalability, and changing market dynamics. However, in order to overcome these challenges, companies need multiple tools that serve specific purposes.

The recent trends in the database management system space focus on a more comprehensive and efficient approach. Businesses having expertise in one domain are stepping their foot in multiple directions. For instance, a business intelligence vendor who mainly deals with data preparation now incorporates data integration and catalog as well in their solutions.

In a nutshell, database management systems and tools that are used to focus only on a single functionality are becoming obsolete, and the ones that offer multiple functionalities are favored. For this, it is crucial to adopt advanced web technologies offering multiple features to the users. By incorporating local databases with top-notch web technologies like React Native, you can enhance the performance to an entirely new level.

Data Transformation Using Advanced Data Stack

Similar to data quality assurance and analytics, the latest database trends can also witness improvement in data integration with the help of advanced data stacks. A modern-day data stack can be referred to as a set of tools that enhance the process of data engineering.

A modern data stack supports automatic scaling and allows data engineers and analysts to perform more complex activities. Here are some features offered by a modern data stack:

Cloud-based data management: Storing and managing data using cloud-based solutions, either with or without on-premises storage.

Cloud warehousing: Cloud data warehousing is a process in which a data warehouse (or database) is stored as a managed service and delivered in a public cloud. This database can also be optimized for improved accessibility, scalable BI, and data analytics. Businesses often prefer developing an enterprise data warehouse for structuring data and scaling performance.

Data visualization and transformation: Data visualization helps make data more comprehensible for analytics by translating it into a visual representation, such as a graph or map. This process of translating and structuring data into a usable and understandable format is known as data transformation.

Reverse ETL of data: Reverse ETL (Extraction, Transformation, and Loading) of data involves data copying from the warehouse and integrating it into other business applications, such as market automation, analytics, and customer relationship management. 

Automating ETL pipeline: By automating ETL pipelines, data integration specialists can easily design and monitor the performance of data workflows using graphical interfaces. Dataform is one of the major companies making waves in this domain.

One of the significant differences between a modern and conventional data stack is that the former is faster and offers a better user experience than the latter. In addition, it also provides users with easy integration and works favorably with other cloud-based apps. 

Evolution of Containerized Apps and Cloud-native Technologies

As mentioned earlier, the recent trends in database management systems promote the adoption of cloud-native technologies to manage data. In fact, it is reported that cloud DBMS accounted for more than 93% of DBMS revenue hikes. Leveraging cloud-native technologies in database management systems can help organizations with:

Improving scalability: By implementing cloud technologies, such as cloud computing, organizations can store and manage a vast amount of data easily on cloud servers. Therefore, in times when a company expands its business, storing data will not be an issue, hence improving the scalability.

Reducing costs: Cloud-native technologies employ consumption-based pricing models, offering businesses an option to pay only for the services they have opted for. Moreover, since the deployment of software and hardware is not needed while integrating a cloud-native technology, costs are significantly reduced.

Enhancing user experience: Apps based on cloud-native technologies are based on a mobile-first approach. Such data architectures prioritize user experience by providing customers with customized features and improved performance.

Besides this, another player in the list of emerging trends in the database management system is containerized applications. With these, users can install an app on any hardware without changing its code base. However, they might need to use tools like Kubernetes or Docker. Observing the benefits and popularity of containerized apps, their expected growth in the next four years can take a giant leap from 40% to 90%.

How Can Appventurez Help Businesses with Database Management Systems?

Since data analysis and business intelligence are at their peak now, finding and developing a suitable database system becomes a necessity. But the issue is, building a robust and highly efficient database system is easier said than done. However, a professional database development company can make things easier for you.

At Appventurez, our software development team provides you with an advanced database platform that empowers your data analyst team, further helping them get valuable insights and form better decisions.