Data integration stands as a critical first step in constructing any artificial intelligence (AI) application. While various methods exist for starting this process, organizations accelerate the application development and deployment process through data virtualization.
Data virtualization empowers businesses to unlock the hidden potential of their data, delivering real-time AI insights for cutting-edge applications like predictive maintenance, fraud detection and demand forecasting.
Despite heavy investments in databases and technology, many companies struggle to extract further value from their data. Data virtualization bridges this gap, allowing organizations to use their existing data sources with flexibility and efficiency for AI and analytics initiatives.
Virtualizing data acts as a bridge, enabling the platform to access and display data from external source systems on demand. This innovative approach centralizes and streamlines data management without requiring physical storage on the platform itself. A virtual layer establishes itself between data sources and users, enabling organizations to access and manage their data without replication or movement from its original location.
Why choose data virtualization?
- Data virtualization streamlines the merging of data from diverse sources by eliminating the need for physical movement or duplication. This significantly reduces data integration time and expense, while also minimizing the potential for inaccuracies or data loss.
- Organizations can achieve a centralized perspective of their data, regardless of its storage source. This serves as a single point of reference for analytics, reporting and data-based decisions, resulting in increased accuracy and quicker generation of valuable insights.
- Organizations gain the ability to effortlessly modify and scale their data in response to shifting business demands, leading to greater agility and adaptability.
Breaking down data silos: Fueling machine learning success with data virtualization
AI has significantly transformed large companies, reshaping business operations and decision-making processes through advanced analytics solutions. This transformation heavily relies on data virtualization, which serves as a central hub, connecting real-time data streams from various sources, such as sensor data and equipment logs, and eliminating data silos and fragmentation.
Data virtualization not only integrates real-time data but also historical data from comprehensive software suites used for various functions, such as enterprise resource planning or customer relationship management. This historical data provides valuable insights into areas like maintenance schedules, asset performance or customer behavior, depending on the suite.
By combining real-time and historical data from diverse sources, data virtualization creates a comprehensive and unified view of an organization’s entire operational data ecosystem. This holistic view empowers businesses to make data-driven decisions, optimize processes and gain a competitive edge.
With the rise of generative AI chatbots, foundation models now use this rich data set. These algorithms actively sift through the data to uncover hidden patterns, trends and correlations, providing valuable insights that enable advanced analytics to predict a range of outcomes. These predictions can identify potential business opportunities like market shifts and customer needs, proactively detect and prevent system issues and failures, and optimize maintenance schedules for maximum uptime and efficiency.
Design considerations for virtualized data platforms
1. Latency and real-time analysis
Challenge:
Accessing stored data directly typically incurs less latency compared to virtualized data retrieval, which can impede real-time predictive maintenance analyses, where timely insights are crucial.
Design considerations:
We need a two-pronged approach to ensure real-time insights and minimize delays in accessing virtualized data. First, we’ll analyze the network infrastructure and optimize data transfer protocols. This can involve techniques like network segmentation to reduce congestion or using faster protocols like UDP for certain data types. By optimizing data transfer, we decrease the time it takes to retrieve the information you need. Second, we’ll implement data refresh strategies to maintain a reasonably up-to-date dataset for analysis. This might involve using batch jobs to perform incremental data updates at regular intervals, balancing the update frequency with the resources required. Striking this balance is crucial: too frequent updates can strain resources, while infrequent updates can lead to outdated data and inaccurate predictions. By combining these strategies, we can achieve both minimal latency and a fresh data set for optimal analysis.
2. Balancing update frequency and source system strain
Challenge:
Continuously querying virtualized data for real-time insights can overload the source systems, impacting their performance. This poses a critical concern for predictive analysis or AI, which depends on frequent data updates.
Design considerations:
To optimize query frequency for your predictive analysis and reporting, need to carefully design how it accesses data. This includes focusing on retrieving only critical data points and potentially using data replication tools for real-time access from multiple sources. Additionally, consider scheduling or batching data retrievals for specific crucial points instead of constant querying, reducing strain on data systems and improving overall model performance.
3. Virtualization layer abstraction and developer benefits
Advantage:
The virtualization layer in the data platform acts as an abstraction layer. This means developers building AI/ML or data mining applications for business once the abstraction layer is ready without worrying about where the data is physically stored or its specific storage details. They can focus on designing the core logic of their models without getting bogged down in data management complexities. This leads to faster development cycles and quicker deployment of these applications.
Benefits for developers:
By utilizing an abstraction layer, developers working on data analytics can focus on the core logic of their models. This layer acts as a shield, hiding the complexities of data storage management. This translates to faster development times as developers don’t need to get bogged down in data intricacies, ultimately leading to quicker deployment of the predictive maintenance models.
4. Storage optimization considerations
Storage optimization techniques like normalization or denormalization might not directly apply to all functions of a specific data analysis application, but they play a significant role when adopting a hybrid approach. This approach involves integrating both ingested data and data accessed through virtualization within the chosen platform.
Assessing the tradeoffs between these techniques helps ensure optimal storage usage for both ingested and virtualized data sets. These design considerations are crucial for building effective ML solutions using virtualized data on the data platform.
Data virtualization: A strategic powerhouse for modern applications
Data virtualization has evolved beyond mere innovation. It serves as a strategic tool for enhancing the capabilities of various applications. A prime example is a data virtualization platform. This platform facilitates the development of a wide range of applications by using data virtualization, thereby significantly improving their efficiency, adaptability and capacity to deliver near real-time insights.
Let’s explore some compelling use cases that showcase the transformative power of data virtualization.
1. Optimizing supply chains for a globalized world
In today’s interconnected global economy, vast networks with complex dependencies characterize supply chains. Data virtualization streamlines these intricate systems crucially. A data virtualization platform unifies data from numerous sources, including production metrics, logistics tracking details and market trend data. This comprehensive view empowers businesses, offering a complete picture of their entire supply chain operations.
Imagine having unimpeded visibility across all aspects. You can proactively identify potential bottlenecks, optimize logistics processes and adapt to shifting market dynamics in real time. The result is an optimized and agile value chain delivering significant competitive advantages.
2. Deep dive into customer behavior: Customer analytics
The digital revolution has rendered understanding your customers critical for business success. A data virtualization platform breaks down data silos by using data virtualization. It seamlessly integrates customer data from various touchpoints, such as sales records, customer service interactions and marketing campaign performance metrics. This unified data landscape fosters a comprehensive understanding of customer behavior patterns and preferences.
Armed with these profound customer insights, businesses can create highly personalized experiences, target promotions and innovate products that resonate more effectively with their target audience. This data-driven approach promotes customer satisfaction and cultivates enduring loyalty, a key element for thriving in today’s competitive environment.
3. Proactive fraud detection in the digital age
Financial fraud constantly evolves, presenting a challenging detection task addressed proactively by data virtualization platforms. The platform identifies potential fraud attempts in real time by virtualizing and analyzing data from various sources, such as transaction logs, user behavior patterns and demographic details. This approach not only protects businesses from financial losses but also fosters trust with their customer base, a crucial asset in today’s digital age.
The transformative potential of data virtualization is exemplified by these impactful applications. IBM Cloud Pak® for Data platform and IBM watsonx empowers businesses to unlock the full power of their data, driving innovation and gaining a significant competitive edge across diverse industries. IBM also offers IBM Data Virtualization as a common query engine and IBM Knowledge Catalog for data governance.
We are here to help you at every step of your data virtualization journey.
Predict outcomes faster by using a platform built with a data fabric architecture
Was this article helpful?
YesNo
