If data is the oil of the 21st century [Source: CIO], then efficient data management is the refinery that transforms raw information into valuable insights.
With data volumes growing at an unprecedented rate (global annual data creation volume to reach 180 zettabytes by 2025) [Source: Statista] and technological advancements pushing boundaries, the future of data management brings both challenges and opportunities for businesses. To remain at the forefront of this rapidly evolving landscape, it becomes crucial to stay abreast of the latest trends and innovations in data management. Let’s explore some of them.
Top 7 Data Management Trends to Watch Out for in 2023
1. More Organizations will be Outsourcing Data Management Tasks
Businesses are heavily investing in third-party data management, with estimated investment reaching $11 billion by 2023 [Source: Gitnux]. As the volume and complexity of data continue to grow, more organizations will outsource data management tasks to third-party providers. Main reason? To free up internal resources and focus on the core areas of business.
2. Increased Use of Cloud Migration
Cloud migration is becoming even more prevalent in 2023 as organizations seek to capitalize on the benefits of scalable and flexible data storage and processing. Nearly 70% of businesses reported to have migrated their data to the cloud [Source: Channelfutures].
Cloud storage allows organizations to store and process vast amounts of data without having to invest in and maintain on-premises infrastructure. As data volumes grow, the cloud provides the necessary expansion to accommodate seamlessly.
3. More Focus on Data Quality & Security
Data breaches and cyber threats continue to make headlines. During the first quarter of 2023, more than 6 million data records were exposed globally as a result of data breaches [Source: Statista]. Organizations are investing heavily in cybersecurity measures to protect their confidential data from cyberattacks and data breaches.
As far as data quality is concerned, businesses now realize that data-driven decisions can be made only if they have quality data. But, what really constitutes quality data? Completeness, accuracy, consistency, usability, and validity are the five pillars of good data. Data-dependent organizations are going to look for these qualities in their data management process.
4. More Enforced Data Governance(DG) Policies
The increasing awareness of data privacy regulations, such as GDPR(General Data Protection Regulation) and CCPA(California Consumer Privacy Act), has made businesses more cautious than ever to implement robust data governance policies that ensure compliance and mitigate the risk of data breaches.
5. Increased Adoption of AI and ML
Organizations are leveraging AI and ML technologies to automate data cleansing, data validation, data analysis, and other data-related tasks. The continuous advancements in AI/ML algorithms are making them more accessible and effective for various data-related tasks, leading to efficient and effective decision-making.
Google’s Cloud Data Fusion, AWS Glue, and IBM’s Watson Data platform are some of the examples of major tech giants adopting machine learning for data management.
6. Data Virtualization Will Gain Traction
Data Virtualization is becoming a trend in data management due to its ability to provide a unified and integrated view of data from various sources without physically moving or replicating it. This approach is helping organizations deal with the ever-growing volumes of data from multiple sources. This will also help in improving data security and compliance.
7. The Rise of New Options for Data Storage
As data continues to grow, new data storage options will emerge to cater to specific needs. This might include innovative storage technologies, such as quantum storage or DNA-based data storage, holographic, and hybrid cloud storage. It’s because they offer higher capacity and more durable solutions for long-term data retention. It’s obvious that popular cloud service providers like Amazon AWS, Microsoft Azure, and IBM Cloud will face some tough competition in the near future.
Innovative Technologies That Are Being Developed to Improve Data Management
New technologies are being created to keep up with the increasing demand for data management. Let us explore some of them and see how they will make it easier to store, manage, and analyze data on a large scale.
- Data Mesh & Data Fabrics
Traditional data architectures are often siloed, which makes it difficult to share data across different departments or teams. Data Mesh is an architectural paradigm that advocates decentralized and domain-oriented data ownership. Data Fabrics refer to the platforms that facilitate seamless data sharing and collaboration across the organization. Both concepts encourage data democratization and self-serve access.
- Robotic Process Automation(RPA)
Data management often involves repetitive and time-consuming tasks, such as data entry, validation, and cleansing. To automate these tasks, RPA bots are going to become an integral part of the data management process.
Future RPA advancements will lead to more sophisticated AI-driven bots that can handle complex data processing, integration, and analysis. These advancements will significantly improve data management efficiency,
- Quantum Data Storage
Quantum data storage has the potential to revolutionize data management by significantly increasing data storage capacities and improving data retrieval speeds. Storing larger volumes of data in tiny physical spaces reduces the need for massive data centers and enhances overall data efficiency. However, it’s worth noting that quantum data storage is still in its early stages of development and is yet to become widely available. It is considered as the most secure option but it is also very expensive.
- Blockchain for Data Security
Blockchain is not just about cryptocurrencies. Inventive technologies are being developed that focus on using blockchain to enhance data security [Source: Forbes] by providing a decentralized and cryptographically secure platform for data storage and sharing. It reduces the risk of data tampering or unauthorized access as any changes to the data are recorded and visible to all participants. This level of transparency and security is especially valuable in industries dealing with sensitive data like healthcare and finance.
- Human-in-the-loop Approach for Increased Data Accuracy
Automation tools have revolutionized the data management process by saving time, costs, and resources but the accuracy of these tools is still questionable. And accuracy is one of the major concerns for data-driven businesses.
Looking ahead, the future of data management lies in the Human-in-the-loop (HITL) approach, which will revolutionize AI-powered data processing. Involving human expertise in AI/ML-assisted data management ensures data accuracy, context-awareness, and quality improvement. Human input helps in refining data processing pipelines, reducing errors, and enhancing the overall reliability.
Final Thoughts
The future of data management holds immense promise, fueled by cutting-edge technologies and rapidly evolving trends that are reshaping how organizations handle their data. However, as data complexity and volume continue to soar, relying solely on machines to make sense of it becomes increasingly challenging. To bridge this gap, businesses must embrace a harmonious collaboration between automated tools (AI) and human data experts to ensure accurate, efficient, and secure data management.
To get a more practical view, check out this success story demonstrating how a leading IT outsourcing company uses the HITL approach in data management.
