In today’s digital era, as technology is maneuvering all aspects of businesses, the importance of a carefully designed data management system is more evident than ever. Companies are drowning in an avalanche of data, which makes real-time data management vital. Undoubtedly, companies have come a long way from traditional spreadsheets to relational databases, cloud-based data banks and more recently, augmented data analytics. Augmented solutions have proven to improve the efficiency of repetitive, labor-intensive tasks and derive more meaningful business insights that fuel the company’s competitive advantage.
To cope with the ever-dynamic business environment and extract maximum value from all available data, businesses must harness the technological boon in the form of Artificial Intelligence, Data Streaming Tools, and Graph Databases.
Solutions such as, BangDB specialize in offering NoSQL-based database solutions to speed up the data management process of businesses.
AI in Data Management
Artificial Intelligence, including Machine Learning and deep learning, is among the most transformational technologies seen in human history. The wonders and benefits of AI, when applied to data management, produce synergetic results for companies. AI can be harnessed at all levels of data management by data administrators and engineers. It enables the automation of repetitive data-related tasks like data identification and tagging, prioritizing, managing, and optimizing queries. Consequently, it also speeds up the training, testing, deployment, and inference stages of the data management pipeline. At a higher level, incorporating AI in databases accelerates the development of machine learning models and the deployment of AI applications.
BangDB’s AI-enabled database reduces the time consumed and improves the quality of business insights, thereby resulting in enhanced decision-making. Such automation also frees database administrators and data engineers from monotonous tasks to focus on higher-impact functions. Consequently, AI can improve an organization’s data management performance, making it more robust and flexible to cater to dynamic data needs.
Streaming Data
Streaming Data refers to the continuous, never-ending flow of data from infinite sources such as e-commerce purchases, financial trading floor transactions or social networks’ data, and so on. Using Data Streaming Tools, this data can be stored, processed, analyzed, and acted upon as and when streams are generated in real-time. Data Streaming Tools challenge the traditional batch-processing tools that require businesses to wait for data collection, processing, and analysis before the data becomes usable for decision-making.
Data Streaming Tools are lately gaining popularity as it becomes almost impossible to manage, regulate and analyze the large volumes of data generated at an extremely high velocity using traditional processing methods. It enables modern organizations to act on up-to-the-millisecond data before it becomes stale.
Data Streaming opens up myriads of use-cases for data streaming tools across several industries. Therefore, Data Streaming is a boon for any industry that feeds on big data and analyses it to make spontaneous decisions.
Graph Database
A Graph database is a specialized database designed to provide the relationships between data- equal importance as the data itself. Unlike relational databases, graphs hold data through subject, object, and relationships. This highly-scalable concept of a Graph database is based on a NoSQL system. The system is designed to efficiently translate data into actionable insights using graphical interconnections. In comparison to traditional databases, graphs are more user-friendly as they display relationships through simple arrows. In contrast, traditional databases require the creation of time-consuming expensive joins.
Graph Databases also help manage highly connected data and complex queries. They can access nodes and relationships with high efficiency, thereby allowing users to quickly traverse millions of connections in a short period. Graphs also provide an effective method for training complex AI and ML Applications. Graphs also support greater transparency in AI’s decision-making process.
Conclusion
In essence, all these technologies will enable businesses to achieve a higher level of data management prowess. Automation and augmentation of the data management processes using Artificial Intelligence, Data Streaming Tools, and Graph Databases will not only make data more easily accessible but also help make businesses more responsive to the outside environment.
If your company is looking to sharpen its competitive advantage by automating and augmenting its data management systems new generation DB solutions such as BangDB can help your company become future-ready!
