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The world generated 120 zettabytes of data in 2023, on track for a 1.5x growth over two years to exceed 180 zettabytes in 2025. Unfortunately, data management strategies have not kept pace with the evolution and expansion of data, largely continuing to work with old-world processes and structured information stored in historical databases. There is an […]
The post Smart Data Fingerprinting: The Answer to Data Management Challenges appeared first on DATAVERSITY.
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In the healthcare industry, surgical capacity management is one of the biggest issues organizations face. Hospitals and surgery centers must be efficient in handling their resources. The margins are too small for waste, and there are too many patients in need of care. Data, particularly real-time data, is an essential asset. But it is only […]
The post Overcoming Real-Time Data Integration Challenges to Optimize for Surgical Capacity appeared first on DATAVERSITY.
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Author: Jeff Robbins
Data integration is a critical capability for any organization looking to connect their data—in an era when there’s more data from more sources than ever before. In fact, data integration is the key to unlocking and sustaining business growth. A modern approach to data integration elevates analytics and enables richer, more contextual insights by bringing together large data sets from new and existing sources.
That’s why you need a data platform that makes integration easy. And the Actian Data Platform does exactly that. It’s why the platform was recently honored with the prestigious “Data Integration Solution of the Year” award from Data Breakthrough. The Data Breakthrough Aware program recognizes the top companies, technologies, and products in the global data technology market.
Whether you want to connect data from cloud-based sources or use data that’s on-premises, the integration process should be simple, even for those without advanced coding or data engineering skill sets. Ease of integration allows business analysts, other data users, and data-driven applications to quickly access the data they need, which reduces time to value and promotes a data-driven culture.
Being recognized by Data Breakthrough, an independent market intelligence organization, at its 5th annual awards program highlights the Actian platform’s innovative capabilities for data integration and our comprehensive approach to data management. With the platform’s modern API-first integration capabilities, organizations in any industry can connect and leverage data from diverse sources to build a more cohesive and efficient data ecosystem.
The platform provides a unified experience for ingesting, transforming, analyzing, and storing data. It meets the demands of your modern business, whether you operate across cloud, on-premises, or in hybrid environments, while giving you full confidence in your data.
With the Actian platform, you can leverage a self-service data integration solution that addresses multiple use cases without requiring multiple products—one of the benefits that Data Breakthrough called out when giving us the award. The platform makes data easy to use for analysts and others across your organization, allowing you to unlock the full value of your data.
The Actian Data Platform offers integration as a service while making data integration, data quality, and data preparation easier than you may have ever thought possible. The recently enhanced platform also assists in lowering costs and actively contributes to better decision making across the business.
The Actian platform is unique in its ability to collect, manage, and analyze data in real time with its transactional database, data integration, data quality, and data warehouse capabilities. It manages data from any public cloud, multi or hybrid cloud, and on-premises environments through a single pane of glass.
All of this innovation will be increasingly needed as more organizations—more than 75% of enterprises by 2025—will have their data in data centers across multiple cloud providers and on-premises. Having data in various places requires a strategic investment in data management products that can span multiple locations and have the ability to bring the data together.
This is another area where the Actian Data Platform delivers value. It lets you connect data from all your sources and from any environment to break through data silos and streamline data workflows, making trusted data more accessible for all users and applications.
The Actian Data Platform also enables you to prep your data to ensure it’s ready for AI and also help you use your data to train AI models effectively. The platform can automate time-consuming data preparation tasks, such as aggregating data, handling missing values, and standardizing data from various sources.
One of our platform’s greatest strengths is its extreme performance. It offers a nine times faster speed advantage and 16 times better cost savings over alternative platforms. We’ve also made recent updates to improve user friendliness. In addition to using pre-built connectors, you can easily connect data and applications using REST- and SOAP-based APIs that can be configured with just a few clicks.
Are you interested in experiencing the platform for yourself? If so, we invite you to participate in a guided free trial. For a limited time, we’re offering a 30-day trial with our team of technical experts. With your data and our expertise, you’ll see firsthand how the platform lets you go from data source to decision quickly and with full confidence.
The post Actian Platform Receives Data Breakthrough Award for Innovative Integration Capabilities appeared first on Actian.
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Author: Actian Corporation
At some point in your life, you’ve probably joined a gym. In doing so, you had to decide what type of membership was right for your fitness goals and at the right price point for your budget. Maybe it was an all-access pass, billed monthly or annually, with unlimited use of the facility, including the […]
The post Managing Software Entitlements and Billing: How Usage Data Supports Streamlined Processes appeared first on DATAVERSITY.
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Author: Victor DeMarines
If you’ve been keeping an eye on the evolution of generative AI (GenAI) technology recently, you’re likely familiar with its core concepts: how GenAI models function, the art of crafting prompts, and the types of data GenAI models rely on. While these fundamental components within GenAI remain constant, the way they’re applied is transforming. The […]
The post Why It’s Time to Rethink Generative AI in the Enterprise appeared first on DATAVERSITY.
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Author: Eamonn O’Neill
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In today’s fast-moving investment world, corporate and insurance asset owners are operating in the dark, hindered by the absence of a standardized industry benchmark for an overall asset performance assessment. Asset owners usually have many other responsibilities beyond managing portfolio strategies, affecting their ability to allocate time to comprehensively evaluate and optimize the performance of […]
The post Unveiling the ROI Dilemma: How Data Blind Spots Impact Asset Owners’ Bottom Line appeared first on DATAVERSITY.
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Author: Bryan Yip
Is your company contemplating moving its mission-critical database to the cloud? If so, you may have concerns around the cloud’s ability to provide the performance, security, and privacy required to adequately support your database applications. Fortunately, it’s a new day in cloud computing that allows you to migrate to the cloud with confidence! Here are some things to keep in mind that will bring you peace of mind for cloud migration.
You may enjoy faster database performance in the cloud. Cloud service providers (CSPs) offer varying processing power, memory, and storage capacity options to meet your most demanding workload performance requirements. Frequently accessed data can be stored in high-speed caches closer to users, minimizing latency and improving response times. Load balancers distribute processing across servers within the cloud infrastructure to prevent server overload and bottlenecks. Some CSPs also have sophisticated monitoring tools to track resource usage and identify performance bottlenecks.
Data isn’t necessarily more secure in your on-premises data center than in the cloud. This is because CSPs invest heavily in advanced security controls to protect their infrastructure and have deep security expertise. They constantly update and patch their systems, often addressing vulnerabilities faster than on-premises deployments. Some CSPs also offer free vulnerability scanning and penetration testing.
However, it’s important to keep in mind that you are also responsible for security in the cloud. The Shared Responsibility Model (SRM) is a cloud security approach that states that CSPs are responsible for securing their service infrastructure and customers are responsible for securing their data and applications within the cloud environment. This includes tasks such as:
Organizations with strict data privacy requirements have understandably been reluctant to operate their mission-critical databases with sensitive data in the cloud. But with the right CSP and the right approach, it is possible to implement a compliant cloud strategy. CSPs offer infrastructure and services built to comply with a wide range of global security and compliance standards such as GDPR, PCI DSS, HIPAA, and others, including data sovereignty requirements:
Data Residency Requirements: You can choose among data center locations for where to store your data to meet compliance mandates. Some CSPs can prevent data copies from being moved outside of a location.
Data Transfer Requirements: These include the legal and regulatory rules that oversee how personal data can be moved across different jurisdictions, organizations, or systems. CSPs often offer pre-approved standard contractual clauses (SCCs) and support Binding Corporate Rules (BCRs) to serve compliance purposes for data transfers. Some CSPs let their customers control and monitor their cross-border data transfers.
Sovereign Controls: Some CSPs use hardware-based enclaves to ensure complete data isolation.
Additionally, many CSPs, as well as database vendors, offer features to help customers with compliance requirements to protect sensitive data. These include:
Microsoft Cloud for Sovereignty provides additional layers of protection through features like Azure Confidential Computing. This technology utilizes hardware-based enclaves to ensure even Microsoft cannot access customer data in use.
Ingres NeXt delivers low-risk database migration from traditional environments to modern cloud platforms with web and mobile client endpoints. Since no two journeys to the cloud are identical, Actian provides the infrastructure and tooling required to take customers to the cloud regardless of what their planned journey may look like.
Here are additional articles on database modernization benefits and challenges that you may find helpful:
The post Migrate Your Mission-Critical Database to the Cloud with Confidence appeared first on Actian.
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Author: Teresa Wingfield
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With organizations using an average of 130 apps, the problem of data fragmentation has become increasingly prevalent. As data production remains high, data engineers need a robust data integration strategy. A crucial part of this strategy is selecting the right data integration tool to unify siloed data.
Before selecting a data integration tool, it’s crucial to understand your organization’s specific needs and data-driven initiatives, whether they involve improving customer experiences, optimizing operations, or generating insights for strategic decisions.
Begin by gaining a deep understanding of the organization’s business objectives and goals. This will provide context for the data integration requirements and help prioritize efforts accordingly. Collaborate with key stakeholders, including business analysts, data analysts, and decision-makers, to gather their input and requirements. Understand their data needs and use cases, including their specific data management rules, retention policies, and data privacy requirements.
Next, identify all the sources of data within your organization. These may include databases, data lakes, cloud storage, SaaS applications, REST APIs, and even external data providers. Evaluate each data source based on factors such as data volume, data structure (structured, semi-structured, unstructured), data frequency (real-time, batch), data quality, and access methods (API, file transfer, direct database connection). Understanding the diversity of your data sources is essential in choosing a tool that can connect to and extract data from all of them.
Consider the volume and velocity of data that your organization deals with. Are you handling terabytes of data per day, or is it just gigabytes? Determine the acceptable data latency for various use cases. Is the data streaming in real-time, or is it batch-oriented? Knowing this will help you select a tool to handle your specific data throughput.
Determine the extent of data transformation logic and preparation required to make the data usable for analytics or reporting. Some data integration tools offer extensive transformation capabilities, while others are more limited. Knowing your transformation needs will help you choose a tool that can provide a comprehensive set of transformation functions to clean, enrich, and structure data as needed.
Consider the data warehouse, data lake, and analytical tools and platforms (e.g., BI tools, data visualization tools) that will consume the integrated data. Ensure that data pipelines are designed to support these tools seamlessly. Data engineers can establish a consistent and standardized way for analysts and line-of-business users to access and analyze data.
There are different approaches to data integration. Selecting the right one depends on your organization’s needs and existing infrastructure.
Consider whether your organization requires batch processing or real-time data integration—they are two distinct approaches to moving and processing data. Batch processing is suitable for scenarios like historical data analysis where immediate insights are not critical and data updates can happen periodically, while real-time integration is essential for applications and use cases like Internet of Things (IoT) that demand up-to-the-minute data insights.
Determine whether your data integration needs are primarily on-premises or in the cloud. On-premises data integration involves managing data and infrastructure within an organization’s own data centers or physical facilities, whereas cloud data integration relies on cloud service providers’ infrastructure to store and process data. Some tools specialize in on-premises data integration, while others are built for the cloud or hybrid environments. Choose a tool that depends on factors such as data volume, scalability requirements, cost considerations, and data residency requirements.
Many organizations have a hybrid infrastructure, with data both on-premises and in the cloud. Hybrid integration provides flexibility to scale resources as needed, using cloud resources for scalability while maintaining on-premises infrastructure for specific workloads. In such cases, consider a hybrid data integration and data quality tool like Actian’s DataConnect or the Actian Data Platform to seamlessly bridge both environments and ensure smooth data flow to support a variety of operational and analytical use cases.
As you evaluate ETL tools, consider the following features and capabilities:
Ensure that the tool can easily connect to your various data sources and destinations, including relational databases, SaaS applications, data warehouses, and data lakes. Native ETL connectors provide direct, seamless access to the latest version of data sources and destinations without the need for custom development. As data volumes grow, native connectors can often scale seamlessly, taking advantage of the underlying infrastructure’s capabilities. This ensures that data pipelines remain performant even with increasing data loads. If you have an outlier data source, look for a vendor that provides Import API, webhooks, or custom source development.
Check if the tool can scale with your organization’s growing data needs. Performance is crucial, especially for large-scale data integration tasks. Inefficient data pipelines with high latency may result in underutilization of computational resources because systems may spend more time waiting for data than processing it. An ETL tool that supports parallel processing can handle large volumes of data efficiently. It can also scale easily to accommodate growing data needs. Data latency is a critical consideration for data engineers, because it directly impacts the timeliness, accuracy, and utility of data for analytics and decision-making.
Evaluate the tool’s data transformation capabilities to handle unique business rules. It should provide the necessary functions for cleaning, enriching, and structuring raw data to make it suitable for analysis, reporting, and other downstream applications. The specific transformations required can include: data deduplication, formatting, aggregation, normalization etc., depending on the nature of the data, the objectives of the data project, and the tools and technologies used in the data engineering pipeline.
A robust monitoring and error-handling system is essential for tracking data quality over time. The tool should include data quality checks and validation mechanisms to ensure that incoming data meets predefined quality standards. This is essential for maintaining data integrity and accuracy, and it directly impacts the accuracy, reliability, and effectiveness of analytic initiatives. High quality data builds trust in analytical findings among stakeholders. When data is trustworthy, decision-makers are more likely to rely on the insights generated from analytics. Data quality is also an integral part of data governance practices.
Ensure that the tool offers robust security features to protect your data during transit and at rest. Features such as SSH tunneling and VPNs provide encrypted communication channels, ensuring the confidentiality and integrity of data during transit. It should also help you comply with data privacy regulations, such as GDPR or HIPAA.
Consider the tool’s ease of use and deployment. A user-friendly low-code interface can boost productivity, save time, and reduce the learning curve for your team, especially for citizen integrators that can come from anywhere within the organization. A marketing manager, for example, may want to integrate web traffic, email marketing, ad platform, and customer relationship management (CRM) data into a data warehouse for attribution analysis.
Assess the level of support, response times, and service-level agreements (SLAs) provided by the vendor. Do they offer comprehensive documentation, training resources, and responsive customer support? Additionally, consider the size and activity of the tool’s user community, which can be a valuable resource for troubleshooting and sharing best practices.
A fully managed hybrid solution like Actian simplifies complex data integration challenges and gives you the flexibility to adapt to evolving data integration needs.
The best way for data engineers to get started is to start a free trial of the Actian Data Platform. From there, they can load their own data and explore what’s possible within the platform. You can also book a demo to see how Actian can help automate data pipelines in a robust, scalable, price-performant way.
For a comprehensive guide to evaluating and selecting the right Data Integration tool, download the ebook Data Engineering Guide: Nine Steps to Select the Right Data Integration Tool.
The post The Data Engineering Decision Guide to Data Integration Tools appeared first on Actian.
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In the data-driven automotive industry, a modern data management strategy is needed to oversee and drive data usage to improve operations, spark innovation, meet customer demand for features and services, create designs and safety features, and inform decisions. Keeping the strategy up to date ensures it meets your current data needs and aligns with business priorities.
With so many data sources now available—and new ones constantly emerging—in addition to data volumes growing rapidly, companies in the automotive industry need a data management strategy that supports this modern reality. A vast rangxe of data is available, including sensor, telematics, and customer data, and it all needs to be integrated and made easily accessible to analysts, engineers, marketers, and others.
In today’s fast-changing data management environment, the ability to understand and solve data challenges is essential to becoming a true data-driven automotive company. As AWS explains, a robust strategy can help solve data management challenges, improve customer experience and loyalty, build future-proof apps, and deliver other benefits.
By contrast, not having a strategy or taking an outdated approach to data can have negative consequences. “When companies have ineffective strategies, they handle daily tasks less effectively,” according to Dataversity. “Data and data processes get duplicated between different departments, and data management gaps continue to exist.”
A modern data management strategy must go beyond traditional approaches to address present-day needs such as scalability, real-time data processing, building data pipelines to new sources, and integrating diverse data. The strategy should be supported by technology that delivers the capabilities your business needs, such as managing complex and large volumes of data.
Your data strategy should cover the variety and complexity of your data, how the data will be brought together, and how the integrated data will be shared in real-time, if necessary, with everyone who needs it. The strategy must ultimately ensure a unified, comprehensive view of the data in order to provide accurate and trusted insights.
Making data readily available with proper governance is essential to fostering a data-driven culture, enabling informed decision-making, and designing vehicles that meet customer wants and needs. The data can also help you predict market changes, gain insights into your supply chains, and better understand business operations.
As best practices and technologies for data management continue to evolve, your strategy and data management tools should also advance to ensure you’re able to optimize all of your data. A modern data management strategy designed to meet your business and IT needs can help you be better prepared for the future of the automotive industry.Â
Your data strategy should support current business priorities, such as meeting environmental, sustainability, and governance (ESG) mandates. As the automotive industry uses data for innovations such as autonomous driving vehicles and intelligent manufacturing processes, there is also a growing pressure to meet ESG goals.
As a result of ESG and other business objectives, your data strategy must address multiple business needs:
The road ahead for the automotive industry requires businesses to continually explore new use cases for data to stay ahead of changing market dynamics, customer expectations, and compliance requirements. Your ability to innovate, accelerate growth, and maintain competitiveness demands a data strategy that reflects your current and future needs.
Modernizing your data management strategy is essential to meet business and IT needs, achieve ESG mandates, and leverage the full value of your data. Actian can help. We have the expertise to help you build a customized strategy for your data, and we have the platform to make data easy to connect, manage, and analyze.
The Actian Data Platform is more than a tool. It’s a solution that enables you to navigate the complex data landscape in the automotive industry. The scalable platform can handle large-scale data processing to quickly deliver answers—which is key in an industry where decisions can have far-reaching implications—without sacrificing performance.
With Actian, you can meet your objectives faster, ensuring your future is data-driven, sustainable, clear, and more attainable. It’s why more than 10,000 businesses trust Actian with their data.
The post How to Modernize Your Data Management Strategy in the Auto Industry appeared first on Actian.
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Author: Actian Corporation
Companies are currently in the midst of a significant paradigm shift, where they are faced with the decision to either over-complicate their data architecture or opt for a single cloud solution. It’s imperative to assess which cloud option aligns best with the needs of an organization; however, neither option allows organizations to optimize their data. […]
The post Data-First Architectures Unlock the Power of Data appeared first on DATAVERSITY.
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Author: Jeff Heller