In the technology sector, data management trends are constantly evolving. As we look to 2023, the industry is expected to see a shift toward more sophisticated and comprehensive systems for handling data. Companies of all sizes have begun to prioritize data management, investing in platforms and tools to ensure they remain competitive. Cloud-based technologies are increasingly being adopted in the field of data management, leading to decentralised teams that prioritise collaborative work applications and expand the number of users with data access. As a company that collects data, it is essential explore the most important trends expected to shape the data management and master data management industry in 2023. According to our survey, the most influential factors for data management success were automation and infrastructure, which are also related to new trends in data management in 2023. From data warehouses to AI-driven automation, a focus on security and compliance, and the proliferation of cloud-based solutions, we’ll take a closer look at how these trends are impacting businesses today.
Read our complete article below to learn about the latest trends in data management
Trends in Information Management, Big Data Management trends, Master data management industry trends
Decentralized Data Management: Data Democratization, Data Fabric, and Data Mesh
Decentralized data management refers to a system where data is managed on a domain basis, with individual departments or teams taking more responsibility instead of a centralized body.
This approach is supported by three key terms: data democratization, data fabric, and data mesh.
Data democratization is a philosophy where everyone in the organization is held responsible for the production, use, and quality of data, rather than just a single role or department. The focus is on providing users with greater access to data, data literacy, and data culture.
Data fabric is a data management solution that connects all data sources and management components through metadata, creating a frictionless web that provides access to enterprise data to all relevant stakeholders. Data fabric has the potential to develop a user-friendly and largely autonomous enterprise-wide data coverage interface when it is fully integrated.
Data mesh is a decentralized architecture and governance concept that puts responsibility for data on the teams that produce and own the data. While still incorporating some centralized governance principles to prevent data from becoming siloed, data mesh empowers teams to be more autonomous in managing their data.
Decentralized approaches offer benefits such as:
- More immediate decision-making2
- More power to end-users
- Creation of data products that require no preparation.
However, they also present challenges in data management a to achieve a decentralized data management landscape, organizations need tools that can
- Collect metadata
- Provide data access to everyone
- Combine multiple tools into a single, user-friendly platform.
This is one of the top trends in data management in 2023.
The Emergence of Data Observability and AI-Driven Data Quality
Data quality is an ever-evolving field, and organizations have adopted different approaches to tackle it. In the past, a rule-based approach was common, and later, AI and machine learning techniques were used to identify low-quality data.
However, new big data management trends are emerging, which take a holistic approach to data quality by employing various techniques to monitor data health. Data observability refers to one of the many master data management industry trends.
Data observability is the ability of an organization to understand the state of its data based on the information collected by monitoring the system via automation with minimal manual intervention. With data observability, organizations can recognize:
- Data quality issues
- Anomalies Schema changes
- Schema changes
- More about their entire data systems
The benefits of data observability include the
- Ability to monitor data systems with little to no domain knowledge
- Proactively detect issues
- Handle more complex data systems.
AI-driven data quality is also gaining traction. AI algorithms can automate the identification of data quality issues and provide recommendations for resolution. AI-driven data quality can help organizations identify patterns and make predictions that may not be apparent to humans. By automating data quality processes, organizations can detect issues faster and with more accuracy.
This is one of the new trends in data management in 2023.
Towards Modern Data Stack
As the demand for data integration continues to grow, a modern data stack offers a set of tools that save time and allow for higher-value activities. The modern data stack is defined by several features, including
- Cloud-based infrastructure
- Automated ETL pipeline
- Cloud warehouse
- Data Visualization
- Data transformation
- Reverse ETL
One of the significant advantages of a modern data stack and what makes it one of the key trends in information management is its ease of use, which makes it faster, more self-service, and offers a better user experience. By being cloud-native, the modern data stack offers several benefits, such as
- Easier integration
- Lower barriers to entry
- Ability to work well with other cloud-based applications.
- It doesn’t require technical configuration, making it approachable for users.
The modern data stack’s ability to scale with unique organizational data needs makes it a flexible solution for managing data. With the modern data stack, users can improve their data quality and integration processes while freeing up time for more valuable activities.
Rise of Data and Analytics Governance Platforms
In the past, companies had to integrate multiple tools to achieve the same goal in their data systems. This created challenges such as time-consuming individual integrations, poorly integrated tools, and hand-built solutions for specific use cases. Additionally, there were challenges with performance, change management, and user adoption.
Modern data and analytics efforts, according to Gartner, need a balanced set of governance capabilities, yet stand-alone technologies frequently fall short. As a result, the data management industry is shifting towards a more comprehensive approach.
Specialized companies are expanding their products to meet the need for tool consolidation. For example, a BI vendor that primarily focused on data preparation may now include data integration and a catalog in their solutions. Meanwhile, a governance vendor may expand into data quality or data observability options. Overall, individual tools are becoming obsolete in favor of customer data management platforms that offer multiple functionalities.
At Posidex, we offer platforms that unify
- Data quality
- Reference and master
- data management
- Data integration
- Data visualization
Cloud-Native Technologies and Containerized Applications in Data Management
Cloud-native technologies and containerized applications are transforming the data management landscape not to be missed master data management industry trends. The benefits of cloud-native technologies, such as scalability, low upfront costs, ease of use, and consumption-based pricing, have led to a surge in cloud adoption across industries. In fact, Gartner’s 2021 magic quadrant found that cloud DBMS accounted for 93% of DBMS revenue growth and predicts that they will account for 50% of total DBMS revenue in 2022.
In addition to cloud-native technologies, the use of containerized applications is also on the rise. Containerized applications allow apps to be deployed on any hardware without requiring changes to the code base and require fewer resources to maintain.
According to Gartner, the number of organizations with containerized apps is expected to increase from 40% to 90% between 2021 and 2027, and 25% of all enterprise apps are predicted to run in containers by 2027.
Containerized apps offer many benefits, including.
- Greater flexibility
As a result, companies are increasingly adopting containerized apps to support their data management needs, making them one of the key big data management trends.
In the data management industry, this is one of the most growing master data management industry trends and that is towards automation, which is proving to be beneficial in various ways. With only one data engineer for every five data consumers, companies are turning to out-of-the-box solutions that can automate some of their tasks, resulting in significant time savings.
AI and metadata are increasingly being used to automate processes such as
- Data discovery
- Data source onboarding
- Data quality monitoring
- Data matching
- Golden record creation in MDM.
Data democratization will require companies to automate many data management processes and provide business users with simple controls.
These trends in information management are making data management more accessible and less resource-intensive. Companies can free up their data engineers to focus on higher-value activities, while also improving the speed and accuracy of their data management processes. As such, automation is becoming an essential component of the modern data stack, enabling organizations to be more agile, efficient, and competitive.
The Rise of Low-Code/No-Code Data Apps in Data Management
With the emergence of low-code and no-code data management applications, data management processes are becoming more accessible to a wider range of users and roles. This approach simplifies app creation, which helps businesses save time and resources while empowering users to manage data more effectively.
In the case of low-code/no-code applications, tools like MS PowerApps, Airtable, and Notion can be mastered by almost anyone. Similarly, Posidex provides an easy way for users to monitor entire data systems such as Snowflake, without complex setup requirements.
Additionally, ONE Data is a low-code data management application that enables business users to onboard, improve, and check the quality of data in an automated and governed environment without taking up the precious time of data engineers. Organizations are also developing localized applications with simple workflows to manage minor local issues, leading to localized databases that can be prioritized on a team-by-team basis, catering to individual user preferences.
All these developments and enhancements are undoubtedly exciting, but how can you be a part of this movement? At Posidex, we are committed to providing innovative solutions in data management, enabling businesses to make better decisions. We encourage you to explore our offerings and get in touch with us to discuss how we can assist you in meeting your requirements.