Data Architecture

Top 7 Benefits Enabled By an On-Premises Data Warehouse

Actian Corporation

July 8, 2024

light streaks to represent on-premises data warehouse

In the ever-evolving landscape of database management, with new tools and technologies constantly hitting the market, the on-premises data warehouse remains the solution of choice for many organizations. Despite the popularity of cloud-based offerings, the on-premises data warehouse offers unique advantages that meet a variety of use cases.

A modern data warehouse is a requirement for data-driven businesses. While cloud-based options seemed to be the go-to trend over the last few years, on-premises data warehouses offer essential capabilities to meet your needs. Here are seven common benefits:

Ensure Data Security and Compliance

In industries such as finance and healthcare, data security and regulatory compliance are critical. These sectors manage sensitive information that must be protected, which is why they have strict protocols in place to make sure their data is secure.

With the ever-present risk of cyber threats—including increasingly sophisticated attacks—and stringent regulations such as General Data Protection Regulation (GDPR), Health Insurance Portability and Accountability Act (HIPAA), and Payment Card Industry Data Security Standard (PCI-DSS), you face significant risk and likely penalties for non-compliance. Relying on external cloud providers for data security can potentially leave you vulnerable to breaches and complicate your compliance efforts.

An on-prem data warehouse gives you complete control over your data. By housing data within your own infrastructure, you can implement robust security measures tailored to your specific needs. This control ensures compliance with regulatory requirements and minimizes the risk of data breaches.

Deliver High Performance With Low Latency

High-performance applications and databases, including those used for real-time analytics and transactional processing, require low-latency access to data. In scenarios where speed and responsiveness are critical, cloud-based solutions may introduce latency that can hinder performance, while on-premises offerings do not.

Latency issues can lead to slower decision-making, reduced operational efficiency, and poor user experiences. For businesses that rely on real-time insights, any delay can result in missed opportunities and diminished competitiveness. On-premises data warehouses offer the advantage of proximity.

By storing data locally, within your organization’s physical building, you can achieve near-instantaneous access to critical information. This setup is particularly advantageous for real-time analytics in which every millisecond counts. The ability to process data quickly and efficiently enhances overall performance and supports rapid decision-making.

Customize Your Infrastructure

Standardized cloud solutions may not always align with the unique requirements of your organization. Customization and control over the infrastructure can be limited in a cloud environment, making it difficult to tailor solutions to specific business or IT needs.

Without the ability to customize, you may face data warehouse constraints that limit your operational effectiveness. Plus, a lack of flexibility can result in suboptimal performance and increased operational costs if you need to work around the limitations of cloud services. For example, an inability to fine-tune your data warehouse infrastructure to handle high-velocity data streams can lead to performance bottlenecks that slow down critical operations.

An on-premises data warehouse gives you control over your hardware and software stack. You can customize your infrastructure to meet specific performance and security requirements. This customization extends to the selection of hardware components, storage solutions, and network configurations, enabling you to fully optimize your data warehouse for unique workloads and applications.

Manage Costs With Visibility and Predictability

Cloud services often operate on a pay-as-you-go model with unlimited scalability, which can lead to unpredictable costs. While a cloud data warehouse can certainly be cost-effective, expenses can escalate quickly with increased data volume and usage. Costs can fluctuate significantly based on how much the system is used, the amount of data transferred into and out of the cloud, the number and complexity of workloads, and other factors.

Unpredictable costs can strain budgets and make financial planning difficult, which makes the CFO’s job more challenging. On-premises data warehouses solve that problem by offering greater cost predictability.

By investing in your on-prem data warehouse upfront, you avoid the variable costs associated with cloud services. This approach leads to better budget planning and cost control, making it easier to allocate resources effectively. Over the long term, on-premises solutions can be cost-efficient with a favorable total cost of ownership and strong return on investment, especially if you have stable and predictable data usage patterns.

Meet Data Sovereignty Regulations

In some regions, data sovereignty laws mandate how data is collected, stored, used, and shared within specific borders. Similarly, data localization laws may require data about a region’s residents or data that was produced in a specific area to be stored inside a country’s borders. This means data collected in one country cannot be transferred and stored in a data warehouse in another country.

Navigating complex data sovereignty requirements can be challenging, especially when you’re dealing with international operations. An on-premises data warehouse helps ensure compliance with local data sovereignty laws by keeping data within a physical building.

This approach simplifies adherence to regional regulations and mitigates the risks associated with cross-border data transfers in the cloud. You can confidently operate within legal frameworks using an on-prem data warehouse, safeguarding your reputation and avoiding legal problems.

Integrate Disparate Systems

Many organizations operate by using a mix of legacy and modern systems. Integrating these disparate technologies into a cohesive ecosystem to manage, store, and analyze data can be complex, especially when using cloud-based solutions.

Legacy systems often contain critical data and processes that are essential to daily business operations. Migrating these systems to the cloud can be risky and disruptive, potentially leading to data loss or downtime, or require significant recoding or refactoring.

On-premises data warehouses enable integration with legacy systems. You also have the assurance of maintaining continuity by leveraging your existing infrastructure while gradually adding, integrating, or modernizing your data management capabilities in the warehouse.

Enable High Data Ingestion Rates

Industries such as telecommunications, manufacturing, and retail typically generate massive amounts of data at high velocities. Efficiently ingesting and processing this data in real time is crucial for maintaining operational effectiveness and gaining timely insights.

On-premises data warehouses are well suited to handle high data ingestion rates. By keeping data ingestion processes local, you can be ensured that data is captured, processed, and analyzed with minimal delay. This capability is essential for industries where real-time data is critical for optimizing operations and identifying emerging trends.

Choosing the Most Optimal Environment for Your Data Warehouse

While cloud-based data warehouses offer many benefits, the on-premises data warehouse continues to play a vital role in addressing specific database management challenges. From ensuring data security and compliance to providing low-latency access and the ability to customize, the on-premises data warehouse remains a powerful tool to meet your organization’s data needs.

By understanding the benefits enabled by an on-premises data warehouse, you can make informed decisions about your database management strategy. Whether it’s for meeting your regulatory requirements, optimizing performance, or controlling costs, the on-premises data warehouse stands as a robust and reliable option in the diverse landscape of database management solutions.

As we noted in a previous blog, the on-prem data warehouse is not dead. At the same time, we realize the unique benefits of a cloud approach. If you want on-prem and the cloud, you can have both with a hybrid approach.

For example, the Actian Data Platform offers data warehousing, integration, and trusted insights on-prem, in the public cloud, or in hybrid environments. A hybrid approach can minimize disruption, preserve critical data, and ensure that legacy systems continue to function effectively alongside new technologies, allowing you to make decisions and drive outcomes with confidence.

About Actian Corporation

Actian empowers enterprises to confidently manage and govern data at scale. Actian data intelligence solutions help streamline complex data environments and accelerate the delivery of AI-ready data. Designed to be flexible, Actian solutions integrate seamlessly and perform reliably across on-premises, cloud, and hybrid environments. Learn more about Actian, the data division of HCLSoftware, at actian.com.

Data Intelligence

Harnessing the Power of AI in Data Cataloging

Actian Corporation

July 8, 2024

Businessman Working On Laptop With Virtual Screen. Process Automation To Efficiently Manage Files.online Documentation Database And Document Management System Concept.

In today’s era of expansive data volumes, AI stands at the forefront of revolutionizing how organizations manage and extract value from diverse data sources. Effective data management becomes paramount as businesses grapple with the challenge of navigating vast amounts of information. At the heart of these strategies lies data cataloging—an essential tool that has evolved significantly with the integration of AI, with promises of efficiency, accuracy, and actionable insights. Let’s see how in this article.

The Benefits of AI in Data Cataloging

AI revolutionizes data cataloging by automating and enhancing traditionally manual processes, thereby accelerating efficiency and improving data accuracy across various functions:

Automated Metadata Generation

AI algorithms autonomously generate metadata by analyzing and interpreting data assets. This includes identifying data types, relationships, and usage patterns. Machine learning models infer implicit metadata, ensuring comprehensive catalog coverage. Automated metadata generation reduces the burden on data stewards and ensures consistency and completeness in catalog entries. This capability is precious in environments with rapidly expanding data volumes where manual metadata creation could be more practical.

Simplified Data Classification and Tagging

AI facilitates precise data classification and tagging using natural language processing (NLP) techniques. By understanding contextual nuances and semantics, AI enhances categorization accuracy, which is particularly beneficial for unstructured data formats such as text and multimedia. Advanced AI models can learn from historical tagging decisions and user feedback to improve classification accuracy. This capability simplifies data discovery processes and enhances data governance by consistently and correctly categorizing data.

Enhanced Search Capabilities

AI-powered data catalogs feature advanced search capabilities that enable swift and targeted data retrieval. AI recommends relevant data assets and related information by understanding user queries and intent. Through techniques such as relevance scoring and query understanding, AI ensures that users can quickly locate the most pertinent data for their needs, thereby accelerating insight generation and reducing time spent on data discovery tasks.

Robust Data Lineage and Governance

AI is crucial in tracking data lineage by tracing its origins, transformations, and usage history. This capability ensures robust data governance and compliance with regulatory standards. Real-time lineage updates provide a transparent view of data provenance, enabling organizations to maintain data integrity and traceability throughout its lifecycle. AI-driven lineage tracking is essential in environments where data flows through complex pipelines and undergoes multiple transformations, ensuring all data usage is documented and auditable.

Intelligent Recommendations

AI-driven recommendations empower users by suggesting optimal data sources for analyses and identifying potential data quality issues. These insights derive from historical data usage patterns. Machine learning algorithms analyze past user behaviors and data access patterns to recommend datasets that are likely to be relevant or valuable for specific analytical tasks. By proactively guiding users toward high-quality data and minimizing the risk of using outdated or inaccurate information, AI enhances the overall effectiveness of data-driven operations.

Anomaly Detection

AI-powered continuous monitoring detects anomalies indicative of data quality issues or security threats. Early anomaly detection facilitates timely corrective actions, safeguarding data integrity and reliability. AI-powered anomaly detection algorithms utilize statistical analysis and machine learning techniques to identify deviations from expected data patterns.

This capability is critical in detecting data breaches, erroneous data entries, or system failures that could compromise data quality or pose security risks. By alerting data stewards to potential issues in real-time, AI enables proactive management of data anomalies, thereby mitigating risks and ensuring data consistency and reliability.

The Challenges and Considerations of AI in Data Cataloging

Despite its advantages, AI-enhanced data cataloging presents challenges requiring careful consideration and mitigation strategies.

Data Privacy and Security

Protecting sensitive information requires robust security measures and compliance with data protection regulations such as GDPR. AI systems must ensure data anonymization, encryption, and access control to safeguard against unauthorized access or data breaches.

Scalability

Implementing AI at scale demands substantial computational resources and scalable infrastructure capable of handling large volumes of data. Organizations must invest in robust IT frameworks and cloud-based solutions to support AI-driven data cataloging initiatives effectively.

Data Integration

Harmonizing data from disparate sources into a cohesive catalog remains complex, necessitating robust integration frameworks and data governance practices. AI can facilitate data integration by automating data mapping and transformation processes. However, organizations must ensure compatibility and consistency across heterogeneous data sources.

In conclusion, AI’s integration into data cataloging represents a transformative leap in data management, significantly enhancing efficiency and accuracy. AI automates critical processes and provides intelligent insights to empower organizations to exploit their data assets fully in their data catalog. Furthermore, overcoming data privacy and security challenges is essential for successfully integrating AI. As AI technology advances, its role in data cataloging will increasingly drive innovation and strategic decision-making across industries.

About Actian Corporation

Data Analytics

Will AI Take Data Analyst Jobs?

Dee Radh

July 3, 2024

Summary

This blog explores whether AI poses a threat to data analysts by automating core tasks and argues that AI elevates their roles, enabling deeper strategic analysis, storytelling, and ethical oversight.

AI automates routine work—such as cleaning data, querying databases, and generating basic reports—freeing analysts to focus on high-value tasks.
Human analysts remain essential for contextual insight, critical thinking, bias detection, and ethical considerations that AI cannot replicate.
The rising demand for analytics skills and AI-savvy professionals suggests data analyst roles will grow—not decline—as AI augments, not replaces, their work.

The rise of Artificial Intelligence (AI) has sparked a heated debate about the future of jobs across various industries. Data analysts, in particular, find themselves at the heart of this conversation. Will AI render human data analysts obsolete?

Contrary to the doomsayers’ predictions, the future is not bleak for data analysts. AI will empower data analysts to thrive, enhancing their ability to provide more insightful and impactful business decisions. Let’s explore how AI, and specifically large language models (LLMs), can work in tandem with data analysts to unlock new levels of value in data and analytics.

The Role of Data Analysts: More Than Number Crunching

First, it’s essential to understand that the role of a data analyst extends far beyond mere number crunching. Data analysts are storytellers, translating complex data into actionable insights that all decision makers can easily understand. They possess the critical thinking skills to ask the right questions, interpret results within the context of business objectives, and communicate findings effectively to stakeholders. While AI excels at processing vast amounts of data and identifying patterns, it lacks the nuanced understanding of business context and the ability to interpret data that are essential capabilities unique to human analysts.

AI as an Empowering Tool, Not a Replacement

Automating Routine Tasks

AI can automate many routine and repetitive tasks that occupy a significant portion of a data analyst’s time. Data cleaning, integration, and basic statistical analysis can be streamlined using AI, freeing analysts to focus on more complex and value-added activities. For example, AI-powered tools can quickly identify and correct data inconsistencies, handle missing values, and perform preliminary data exploration. This automation increases efficiency and allows analysts to delve deeper into data interpretation and strategic analysis.

Enhancing Analytical Capabilities

AI and machine learning algorithms can augment the analytical capabilities of data analysts. These technologies can uncover hidden patterns, detect anomalies, and predict future trends with greater accuracy and speed than legacy approaches. Analysts can use these advanced insights as a foundation for their analysis, adding their expertise and business acumen to provide context and relevance. For instance, AI can identify a subtle trend in customer behavior, which an analyst can then explore further to understand underlying causes and implications for marketing strategies.

Democratizing Data Insights

Large language models (LLMs), such as GPT-4, can democratize access to data insights by enabling non-technical stakeholders to interact with data in natural language. LLMs can interpret complex queries and generate understandable explanations very quickly, making data insights more accessible to everyone within an organization. This capability enhances collaboration between data analysts and business teams, fostering a data-driven culture where decisions are informed by insights derived from both human and AI analysis.

How LLMs Can Be Used in Data and Analytics Processes

Natural Language Processing (NLP) for Data Querying

LLMs can simplify data querying through natural language processing (NLP). Instead of writing complex SQL queries, analysts and business users can ask questions in plain English. For example, a user might ask, “What were our top-selling products last quarter?” and the LLM can translate this query into the necessary database commands and retrieve the relevant data. This capability lowers the barrier to entry for data analysis, making it more accessible and efficient.

Automated Report Generation

LLMs can assist in generating reports by summarizing key insights from data and creating narratives around them. Analysts can use these auto generated reports as a starting point, refining and adding their insights to produce comprehensive and insightful business reports. This collaboration between AI and analysts ensures that reports are both data-rich and contextually relevant.

Enhanced Data Visualization

LLMs can enhance data visualization by interpreting data and providing textual explanations. For instance, when presenting a complex graph or chart, the LLM can generate accompanying text that explains the key takeaways and trends in the data. This feature helps bridge the gap between data visualization and interpretation, making it easier for stakeholders to understand and act on the insights.

The Human Element: Context, Ethics, and Interpretation

Despite the advancements in AI, the human element remains irreplaceable in data analysis. Analysts bring context, ethical considerations, and nuanced interpretation to the table. They understand the business environment, can ask probing questions, and can foresee the potential impact of data-driven decisions on various areas of the business. Moreover, analysts are crucial in ensuring that data usage adheres to ethical standards and regulatory requirements, areas where AI still has limitations.

Contextual Understanding

AI might identify a correlation, but it takes a human analyst to understand whether the correlation is meaningful and relevant to the business. Analysts can discern whether a trend is due to a seasonal pattern, a market anomaly, or a fundamental change in consumer behavior, providing depth to the analysis that AI alone cannot achieve.

Ethical Oversight

AI systems can inadvertently perpetuate biases present in the data they are trained on. Data analysts play a vital role in identifying and mitigating these biases, ensuring that the insights generated are fair and ethical. They can scrutinize AI-generated models and results, applying their judgment to avoid unintended consequences.

Strategic Decision-Making

Ultimately, data analysts are instrumental in strategic decision-making. They can synthesize insights from multiple data sources, apply their industry knowledge, and recommend actionable strategies. This strategic input is crucial for aligning data insights with business goals and driving impactful decisions.

The End Game: A Symbiotic Relationship

The future of data analysis is not a zero-sum game between AI and human analysts. Instead, it is a symbiotic relationship where each complements the other. AI, with its ability to process and analyze data at unprecedented scale, enhances the capabilities of data analysts. Analysts, with their contextual understanding, critical thinking, and ethical oversight, ensure that AI-driven insights are relevant, accurate, and actionable.

By embracing AI as a tool rather than a threat, data analysts can unlock new levels of productivity and insight, driving smarter business decisions and better outcomes. In this collaborative future, data analysts will not only survive but thrive, leveraging AI to amplify their impact and solidify their role as indispensable assets in the data-driven business landscape.

About Dee Radh

As Senior Director of Product Marketing, Dee Radh heads product marketing for Actian. Prior to that, she held senior PMM roles at Talend and Formstack. Dee has spent 100% of her career bringing technology products to market. Her expertise lies in developing strategic narratives and differentiated positioning for GTM effectiveness. In addition to a post-graduate diploma from the University of Toronto, Dee has obtained certifications from Pragmatic Institute, Product Marketing Alliance, and Reforge. Dee is based out of Toronto, Canada.

Data Management

Streamlining the Chaos: Conquering Manufacturing With Data

Kasey Nolan

July 2, 2024

depiction of conquering manufacturing with data

The Complexity of Modern Manufacturing

Manufacturing today is far from the straightforward assembly lines of the past; it is chaos incarnate. Each stage in the manufacturing process comes with its own set of data points. Raw materials, production schedules, machine operations, quality control, and logistics all generate vast amounts of data, and managing this data effectively can be the difference between smooth operations and a breakdown in the process.

Data integration is a powerful way to conquer the chaos of modern manufacturing. It’s the process of combining data from diverse sources into a unified view, providing a holistic picture of the entire manufacturing process. This involves collecting data from various systems, such as Enterprise Resource Planning (ERP) systems, Manufacturing Execution Systems (MES), and Internet of Things (IoT) devices. When this data is integrated and analyzed cohesively, it can lead to significant improvements in efficiency, decision-making, and overall productivity.

The Power of a Unified Data Platform

A robust data platform is essential for effective data integration and should encompass analytics, data warehousing, and seamless integration capabilities. Let’s break down these components and see how they contribute to conquering the manufacturing chaos.

1. Analytics: Turning Data into Insights

Data without analysis is like raw material without a blueprint. Advanced analytics tools can sift through the vast amounts of data generated in manufacturing, identifying patterns and trends that might otherwise go unnoticed. Predictive analytics, for example, can forecast equipment failures before they happen, allowing for proactive maintenance and reducing downtime.

Analytics can also optimize production schedules by analyzing historical data and predicting future demand. This ensures that resources are allocated efficiently, minimizing waste and maximizing output. Additionally, quality control can be enhanced by analyzing data from different stages of the production process, identifying defects early, and implementing corrective measures.

2. Data Warehousing: A Central Repository

A data warehouse serves as a central repository where integrated data is stored. This centralized approach ensures that all relevant data is easily accessible, enabling comprehensive analysis and reporting. In manufacturing, a data warehouse can consolidate information from various departments, providing a single source of truth.

For instance, production data, inventory levels, and sales forecasts can be stored in the data warehouse. This unified view allows manufacturers to make informed decisions based on real-time data. If there’s a sudden spike in demand, the data warehouse can provide insights into inventory levels, production capacity, and lead times, enabling quick adjustments to meet the demand.

 3. Integration: Bridging the Gaps

Integration is the linchpin that holds everything together. It involves connecting various data sources and ensuring data flows seamlessly between them. In a manufacturing setting, integration can connect systems like ERP, MES, and Customer Relationship Management (CRM), creating a cohesive data ecosystem.

For example, integrating ERP and MES systems can provide a real-time view of production status, inventory levels, and order fulfillment. This integration eliminates data silos, ensuring that everyone in the organization has access to the same accurate information. It also streamlines workflows, as data doesn’t need to be manually transferred between systems, reducing the risk of errors and saving time.

Case Study: Aeriz

Aeriz is a national aeroponic cannabis brand that provides patients and enthusiasts with the purest tasting, burning, and feeling cultivated cannabis. They needed to be able to connect, manage, and analyze data from several systems, both on-premises and in the cloud, and access data that was not easy to gather from their primary tracking system.

By leveraging the Actian Data Platform, Aeriz was able to access data that wasn’t part of the canned reports provided by their third-party vendors. They were able to easily aggregate this data with Salesforce to improve inventory visibility and accelerate their order-to-cash timeline.

The result was an 80%-time savings of a full-time employee responsible for locating and aggregating data for business reporting. Aeriz can now focus resources on analyzing data to find improvements and efficiencies to accommodate rapid growth.

The Actian Data Platform for Manufacturing

Imagine having the ability to foresee equipment failures before they happen? Or being able to adjust production lines based on live demand forecasts? Enter the Actian Data Platform, a powerhouse designed to tackle the complexities of manufacturing data head-on. The Actian Data Platform transforms your raw data into actionable intelligence, empowering manufacturers to make smarter, faster decisions.

But it doesn’t stop there. The Actian Data Platform’s robust data warehousing capabilities ensure that all your critical data is centralized, accessible, and ready for deep analysis. Coupled with seamless integration features, this platform breaks down data silos and ensures a cohesive flow of information across all your systems. From the shop floor to the executive suite, everyone operates with the same up-to-date information, fostering collaboration and efficiency like never before. With Actian, chaos turns to clarity and complexity becomes a competitive advantage.

Embracing the Future of Manufacturing

Imagine analytics that predict the future, a data warehouse that’s your lone source of truth, and integration that connects it all seamlessly. This isn’t just about managing chaos—it’s about turning data into a well-choreographed dance of efficiency and productivity. By embracing the power of data, you can watch your manufacturing operations transform into a precision machine that’s ready to conquer any challenge!

About Kasey Nolan

Kasey Nolan is Solutions Product Marketing Manager at Actian, aligning sales and marketing in IaaS and edge compute technologies. With a decade of experience bridging cloud services and enterprise needs, Kasey drives messaging around core use cases and solutions. She has authored solution briefs and contributed to events focused on cloud transformation. Her Actian blog posts explore how to map customer challenges to product offerings, highlighting real-world deployments. Read her articles for guidance on matching technology to business goals.

Data Intelligence

The Role of Data Catalogs in Accelerating AI Initiatives

Actian Corporation

July 2, 2024

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In today’s data-driven landscape, organizations increasingly rely on AI to gain insights, drive innovation, and maintain a competitive edge. Indeed, AI technologies, including machine learning, natural language processing, and predictive analytics, transform businesses’ operations, enabling them to make smarter decisions, automate processes, and uncover new opportunities. However, the success of AI initiatives depends significantly on the quality, accessibility, and efficient management of data.

This is where the implementation of a data catalog plays a crucial role.

By facilitating data governance, discoverability, and accessibility, data catalogs enable organizations to harness the full potential of their AI projects, ensuring that AI models are built on a solid foundation of accurate and well-curated data.

First: What is a Data Catalog?

A data catalog is a centralized repository that stores metadata—data about data—allowing organizations to manage their data assets more effectively. This metadata, collected by various data sources, is automatically scanned to enable catalog users to search for their data and get information such as the availability, freshness, and quality of a data asset.

Therefore, by definition, a data catalog has become a standard for efficient metadata management and data discovery. We broadly define a data catalog as being:

A detailed inventory of all data assets in an organization and their metadata, designed to help data professionals quickly find the most appropriate data for any analytical business purpose.

How Does Implementing a Data Catalog Boost AI Initiatives in Organizations?

Now that we’ve briefly defined what a data catalog is, let’s discover how data catalogs can significantly boost AI initiatives in organizations:

Enhanced Data Discovery

The success of AI models is determined by the ability to access and utilize large, diverse datasets that accurately represent the problem domain. A data catalog enables this success by offering robust search and filtering capabilities, allowing users to quickly find relevant datasets based on criteria such as keywords, tags, data sources, and any other semantic information provided. These Google-esque search features enable data users to efficiently navigate the organization’s data landscape and find the assets they need for their specific use cases.

For example, a data scientist working on a predictive maintenance model for manufacturing equipment can use a data catalog to locate historical maintenance records, sensor data, and operational logs. This enhanced data discovery is crucial for AI projects, as it enables data scientists to identify and retrieve the most appropriate datasets for training and validating their models.

The Difference: Get highly personalized discovery experiences with the Actian Data Intelligence Platform. Our platform enables data consumers to enjoy a unique discovery experience via personalized exploratory paths by ensuring that the user profile is taken into account when ranking the results in the catalog. Our algorithms also give smart recommendations and suggestions on your assets day after day.

View our data discovery features.

Improved Data Quality and Trustworthiness

The underlying data must be of high quality for AI models to deliver accurate and reliable results. High-quality data is crucial because it directly impacts the model’s ability to learn and make predictions that reflect real-world scenarios. Poor-quality data can lead to incorrect conclusions and unreliable outputs, negatively affecting business decisions and outcomes.

A data catalog typically includes features for data profiling and data quality assessment. These features help identify data quality issues such as missing values, inconsistencies, and outliers, which can skew AI model results. By ensuring that only clean and trustworthy data is used in AI initiatives, organizations can enhance the reliability and performance of their AI models.

The Difference: Actian Data Intelligence Platform uses GraphQL and knowledge graph technologies to provide a flexible approach to integrating best-of-breed data quality solutions into our catalog. Sync the datasets of your third-party DQM tools via simple API operations. Our powerful Catalog API capabilities will automatically update any modifications made in your tool directly within our platform.

View our data quality features.

Improved Data Governance and Compliance

Data governance is critical for maintaining data integrity, security, and compliance with regulatory requirements. It involves the processes, policies, and standards that ensure data is managed and used correctly throughout its lifecycle. Regulatory requirements such as the GDPR in Europe and the CCPA in California, United States are examples of stringent laws that organizations must adhere to.

In addition, data governance promotes transparency, accountability, and traceability of data, making it easier for stakeholders to spot errors and mitigate risks associated with flawed or misrepresented AI insights before they negatively impact business operations or damage the organization’s reputation. Data catalogs support these governance initiatives by providing detailed metadata, including data lineage, ownership, and usage policies.

For AI initiatives, robust data governance means data can be used responsibly and ethically, minimizing data breaches and non-compliance risks. This protects the organization legally and ethically and builds trust with customers and stakeholders, ensuring that AI initiatives are sustainable and credible.

The Difference: Actian Data Intelligence Platform guarantees regulatory compliance by automatically identifying, classifying, and managing personal data assets at scale. Through smart recommendations, our solution detects personal information. It suggests which assets to tag – ensuring that information about data policies and regulations is well communicated to all data consumers within the organization in their daily activities.

View our data governance features.

Collaboration and Knowledge Sharing

AI projects often involve cross-functional teams, including data scientists, engineers, analysts, and business stakeholders. Data catalogs are pivotal in promoting collaboration by serving as a shared platform where team members can document, share, and discuss data assets. Features such as annotations, comments, and data ratings enable users to contribute their insights and knowledge directly within the data catalog. This functionality fosters a collaborative environment where stakeholders can exchange ideas, provide feedback, and iterate on data-related tasks.

For example, data scientists can annotate datasets with information about data quality or specific characteristics functional for machine learning models. Engineers can leave comments regarding data integration requirements or technical considerations. Analysts can rate the relevance or usefulness of different datasets based on their analytical needs.

The Difference: Actian Data Intelligence Platform provides discussion tabs for each catalog object, facilitating effective communication between Data Stewards and data consumers regarding their data assets. Shortly, data users will also be able to provide suggestions regarding the content of their assets, ensuring continuous improvement and maintaining the highest quality of data documentation within the catalog.

Common Understanding of Enterprise-Wide AI Terms

Data catalogs often incorporate a business glossary, a centralized repository for defining and standardizing business terms and data & AI definitions across an organization. A business glossary enhances alignment between business stakeholders and data practitioners by establishing clear definitions and ensuring consistency in terminology.

This clarity is essential in AI initiatives, where precise understanding and interpretation of data are critical for developing accurate models. For example, a well-defined business glossary allows data scientists to quickly identify and utilize the right data sets for training AI models, reducing the time spent on data preparation and increasing productivity. By facilitating a common understanding of data across departments, a business glossary accelerates AI development cycles and empowers organizations to derive meaningful insights from their data landscape.

The Difference: Actian Data Intelligence Platform provides data management teams with a unique place to create their categories of semantic concepts, organize them in hierarchies, and configure the way glossary items are mapped with technical assets.

View our Business Glossary features.

In Conclusion

In the rapidly evolving landscape of AI-driven decision-making, data catalogs have emerged as indispensable tools for organizations striving to leverage their data assets effectively. They ensure that AI initiatives are built on a foundation of high-quality, well-governed, well-documented data, which is essential for achieving accurate insights and sustainable business outcomes.

As organizations continue to invest in AI capabilities, adopting robust data catalogs will play a pivotal role in maximizing the value of data assets, driving innovation, and maintaining competitive advantage in an increasingly data-centric world.

About Actian Corporation

Data Management

Getting Started With Actian Zen and BtrievePython

Johnson Varughese

July 1, 2024

Welcome to the world of Actian Zen, a versatile and powerful edge data management solution designed to help you build low-latency embedded apps. This is Part 1 of the quickstart blog series that focuses on helping embedded app developers get started with Actian Zen. In this blog, we’ll explore how to leverage BtrievePython to run Btrieve2 Python applications, using the Zen 16.0 Enterprise/Server Database Engine.

But before we dive in, let’s do a quick introduction.

What is Btrieve?

Actian Zen Btrieve interface is a high-performance, low-level, record-oriented database management system (DBMS) developed by Pervasive Software, now part of Actian Corporation. It provides efficient and reliable data storage and retrieval by focusing on record-level operations rather than complex queries. Btrieve is known for its speed, flexibility, and robustness, making it a popular choice for applications that require high-speed data access and transaction processing.

What is BtrievePython?

BtrievePython is a modern Python interface for interacting with Actian Zen databases. It allows developers to leverage the powerful features of Btrieve within Python applications, providing an easy-to-use and efficient way to manage Btrieve records. By integrating Btrieve with Python, BtrievePython enables developers to build high-performance, data-driven applications using Python’s extensive ecosystem and Btrieve’s reliable data-handling capabilities.

This comprehensive guide will walk you through the setup on both Microsoft Server 2019 and Ubuntu V20, ensuring you have all the tools you need for success.

Getting Started With Actian Zen

Actian Zen offers a range of data access solutions compatible with various operating systems, including Android, iOS, Linux, Raspbian, and Windows (including IoT and Nano Server). For this demonstration, we’ll focus on Microsoft Server 2019, though the process is similar across different platforms.

Before we dive into the setup, ensure you’ve downloaded and installed the Zen 16.0 Enterprise/Server Database Engine for Windows or Linux on Ubuntu. Detailed installation instructions can be found on Actian’s Academy channel.

Setting Up Your Environment

Installing Python and BtrievePython on Windows:

- - Download and Install Python: Visit Python’s official website and download the latest version (we’re using Python v3.12).
  - Open Command Prompt as Administrator: Ensure you have admin rights to proceed with the installation.
  - Install BtrievePython: Execute pip install btrievePython. Note that this step requires an installed ZEN 16.0 client or Engine. If the BtrievePython installation fails, ensure you have Microsoft Visual C++ 14.0 or greater by downloading the Visual C++ Build Tools.
  - Verify Installation: Run pip list to check if BtrievePython is listed.
  - Run a Btrieve2 Python Sample: Download the sample program from the Actian documentation and run it using python btr2sample.py 9 from an admin command prompt.

Installing Python and BtrievePython on Linux (Ubuntu):

- - Install PIP: Use sudo apt install python3-pip to get PIP, the Python package installer.
  - Open a terminal window as a non-“root” user and export PATH=$PATH:/usr/local/actianzen/bin
  - Install BtrievePython: Execute sudo pip install btrievePython, ensuring a ZEN 16.0 client or Engine is present.
  - Verify Installation: Run pip show btrievePython to confirm the installation.
  - Run a Btrieve2 Python Sample: After downloading the sample from the Actian documentation, run the sample with python3 btr2sample.py 9.

Visual Guide

The setup process includes several steps that are best followed with visual aids. Here are some key screenshots to help guide you through the setup:

For the Windows Setup:

Downloading and setting up Python.

Python Download Site:

python download site

Command Prompt Operations: Steps to install BtrievePython.

command prompt operations for btrieve

Code snippet:

code snippet btrieve

Verification and Execution: verifying the installation and running the Btrieve2 sample application.

verification and execution btrieve

For the Linux Setup:

Installation Commands:

Install Python3-pip

install python3 linux btrieve

BtrievePython Setup: BtrievePython installation.

btrieve python setup

Open a terminal window as a non-“root” user and export PATH=$PATH:/usr/local/actianzen/bin

BtrievePython Installed

btrieve python installed

Sample Execution: running the Btrieve2 sample app.

sample execution btrieve

Conclusion

This guide has provided a thorough walkthrough on using BtrievePython with Actian Zen to run Btrieve2 Python applications. Whether you’re working on Windows or Linux, these steps will help you set up your environment efficiently and get your applications running smoothly. Actian Zen’s compatibility with multiple platforms ensures that you can manage your data seamlessly, regardless of your operating system.

For further details and visual guides, refer to the Actian Academy and the comprehensive documentation. Happy coding!

About Johnson Varughese

Johnson Varughese manages Support Engineering at Actian, assisting developers leveraging ZEN interfaces (Btrieve, ODBC, JDBC, ADO.NET, etc.). He provides technical guidance and troubleshooting expertise to ensure robust application performance across different programming environments. Johnson's wealth of knowledge in data access interfaces has streamlined numerous development projects. His Actian blog entries detail best practices for integrating Btrieve and other interfaces. Explore his articles to optimize your database-driven applications.

Data Management

Edge Computing With Actian Zen: Paving the Way for the Future

Kasey Nolan

June 26, 2024

Edge Computing with Actian Zen: Paving the Way for a Sustainable Future

Consider your morning commute–taking your kids to school, your morning coffee run, hurrying to the office–how much time do you spend in the car? And how much money are you spending filling up your tank? Or maybe you’re like me and desperately trying not to think about your carbon footprint every time you drive 20 minutes (each way!) to the grocery store.

Now imagine how it would be if your office was just a block or two down the street, daycare right next door, and your grocery store in-between. Imagine the time savings, cost savings, and reduction in your personal carbon emissions if you could do everything you need, but without having to travel as far. If you could snap your fingers to make it happen, would you?

That’s the question being asked across the world of data processing. Businesses are increasingly seeking efficient and sustainable ways to manage and process their data across the world. End-users are less patient and the sheer volume of data being transferred from one endpoint to the next has massive implications for energy consumption and overall latency.

One solution to this is edge computing, which is the data processing equivalent of reducing your commute from an hour to two minutes. Not only does edge computing use fewer resources and energy, but it’s faster and more efficient, making it a greener choice for managing data.

Understanding Edge Computing

Before delving into the sustainability benefits, it’s crucial to understand what edge computing is. Edge computing is a distributed computing framework where data is processed closer to where it is generated, rather than relying on a centralized data center or the cloud. If you’ve ever used the Target App for shopping, you may notice it’ll give a little warning for items that are low in stock. “Only 2 left at your store!” Retailers like Target use edge enabled sensors to track products on shelves in real-time, automating inventory management for a more reliable picture of what’s available locally.

If not for IoT sensors and edge computing, you likely wouldn’t get a real-time view of inventory– data would be collected via barcode scans, transferred to a centralized data center that could be states away, batch processed, and then synchronized with inventory systems. This could take minutes, hours, or even days depending on the company, and this process is rife with problems like latency, network reliability, bandwidth constraints, and high infrastructure costs. Not to mention that a central server represents a single point of failure–meaning if one store is down, they all are. Not a great experience for shoppers, and a great case for moving to the edge.

The Sustainability Edge

Because it’s 2024, sustainability and environmental, social, and governance (ESG) initiatives are paramount. For example, 90% of S&P 500 companies release ESG reports, and ESG initiatives are considered by 89% of investors when making investment decisions. Sticking with those high numbers, 89% of executives plan to increase their overall technology budget, and 28% say that at least one-fifth of their workforce is involved in emerging tech as part of their primary job function. That’s a huge amount of people who are actively considering both sustainability and emerging technologies in their day-to-day work, in their projections, and in their strategic initiatives.

Edge computing marries these two initiatives beautifully. For instance, 60% of companies are using edge to some degree today, and half of those have deeply integrated edge into their digital core. In fact, Forbes predicts a mass migration from the cloud to the edge in 2024. The sustainability advantages perfectly complement the cost savings and consumer benefits of edge computing as opposed to the traditional cloud.

Here are three primary ways edge computing supports ESG:

Reduced Energy Consumption: Traditional data centers and cloud computing require substantial energy to power and cool the vast arrays of servers. This energy consumption not only translates into high operational costs but also contributes significantly to carbon emissions. Edge computing, on the other hand, decentralizes data processing, distributing it across multiple edge devices that are often located closer to the data source. This decentralization reduces the load on central data centers, leading to lower overall energy consumption.
Optimized Bandwidth Usage: Transmitting large volumes of data to and from centralized data centers or the cloud can be bandwidth-intensive. This not only increases operational costs but also places a strain on network infrastructure. By processing data at the edge, organizations can significantly reduce the amount of data that needs to be transmitted over the network. This not only optimizes bandwidth usage but also reduces the associated energy consumption and emissions.
Decreased Latency and Improved Efficiency: One of the inherent advantages of edge computing is the reduction in latency. By processing data closer to the source, edge computing eliminates the delays associated with transmitting data to distant data centers. This not only enhances the speed and responsiveness of applications but also improves overall system efficiency.

Actian Zen: A Sustainable Edge Solution

Edge computing doesn’t exist in a vacuum, and it takes the right toolkit to take advantage of all the benefits. You need to be sure you have the right database and a database management system (DBMS) that’s edge compatible.

Enter Actian Zen, a high-performance, embedded, and zero-administration DBMS designed for edge computing, IoT applications, and mobile environments. Known for its small footprint, low resource consumption, and ability to operate efficiently on a wide range of devices, Actian Zen provides a versatile and powerful DBMS that meets the needs of modern business across various industries.

Three main benefits Zen delivers include:

Optimizing IT and Cloud Expenditures: Actian Zen is designed to operate efficiently on a wide range of devices, from IoT sensors to industrial gateways. Its compact size means it can be deployed on low-power devices, reducing the need for energy-intensive hardware. Additionally, by processing data locally at the edge, Actian Zen significantly reduces the need for extensive data transmission to central servers or cloud environments. This local processing minimizes bandwidth usage and decreases the load on centralized data centers, leading to lower operational costs associated with data storage and cloud services. Furthermore, the reduced reliance on large, energy-intensive data centers aligns with sustainability goals by lowering overall energy consumption and carbon emissions.
Ensuring Compliance With Internal Policies and External Regulations: By enabling data processing at the edge, Actian Zen reduces the need for data transmission to centralized servers, thus saving bandwidth and energy. This local processing aligns with sustainability initiatives aimed at reducing energy consumption and emissions. Actian Zen also features role-based access, which allows for granular control over who can access and manipulate data, aligning with internal security policies and regulatory standards.
Enabling Scalability and Flexibility to Accommodate Future Growth: With Actian Zen, developers can scale from a core set of libraries capable of single-user client data management to a full-fledged, enterprise-grade server. It’s capable of supporting thousands of users on multicore, VM cloud environments, or in Docker containers with Kubernetes orchestration and Helm chart deployment configuration.

Zen: The Sustainable Database Solution

As the demand for sustainable computing solutions grows, edge computing with Actian Zen emerges as a game-changer. By reducing energy consumption, optimizing bandwidth usage, and decreasing latency, Actian Zen not only enhances operational efficiency but also contributes to a greener future. If you’re looking to balance performance with sustainability, you’ll find Actian Zen’s edge computing capabilities to be a compelling choice. Embrace the power of edge computing with Actian Zen and take a step toward a more sustainable, efficient, and environmentally friendly future.

About Kasey Nolan

Data Architecture

Is the On-Premises Data Warehouse Dead?

Actian Corporation

June 26, 2024

As organizations across all industries grapple with ever-increasing amounts of data, the traditional on-premises data warehouse is facing intense scrutiny. Data and IT professionals, analysts, and business decision-makers are questioning its viability in our modern data landscape where agility, scalability, and real-time insights are increasingly important.

Data warehouse stakeholders are asking:

How do on-prem costs compare to a cloud-based data warehouse?
Can our on-premises warehouse meet data growth and business demands?
Do we have the flexibility to efficiently integrate new data sources and analytics tools?
What are the ongoing maintenance and management needs for our on-prem warehouse?
Are we able to meet current and future security and compliance requirements?
Can we integrate, access, and store data with a favorable price performance?

Addressing these questions enables more informed decision making about the practicality of the on-premises data warehouse and whether a migration to a cloud-based warehouse would be beneficial. As companies like yours also look to answer the question of whether the on-premises data warehouse is truly a solution of the past, it’s worth looking at various warehouse offerings. Is one model really better for transforming data management and meeting current business and IT needs for business intelligence and analytics?

Challenges of Traditional On-Premises Data Warehouses

Data warehouses that serve as a centralized data repository on-premises, within your physical environment, have long been the cornerstone of enterprise data management. These systems store vast amounts of data, enabling you to integrate and analyze data to extract valuable insights.

Many organizations continue to use these data warehouses to store, query, and analyze their data. This allows them to get a return on their current on-prem warehouse investment, meet security and compliance requirements, and perform advanced analytics. However, the downside is that these warehouses increasingly struggle to meet the demands of modern business environments that need to manage more data from more sources than ever before, while making the data accessible and usable to analysts and business users at all skill levels.

These are critical challenges faced by on-premises data warehouses:

Scalability Issues. A primary drawback of on-premises data warehouses is their limited scalability—at least in a fast and efficient manner. Growing data volumes and increased workloads require you to invest in additional hardware and infrastructure to keep pace. This entails significant costs and also requires substantial time. The rigidity of on-premises systems makes it difficult to quickly scale resources based on fluctuating needs such as seasonal trends, marketing campaigns, or a business acquisition that brings in large volumes of new data.
Limited Flexibility. As new data sources emerge, you need the ability to quickly build data pipelines and integrate the information. On-premises data warehouses often lack the flexibility to efficiently handle data from emerging sources—integrating new data sources is typically a cumbersome, time-consuming process, leading to delays in data analytics and business insights.
High Operational Costs. Maintaining an on-premises data warehouse can involve considerable operational expenses. That means you must allocate a budget for hardware, software licenses, electricity, and cooling the data warehouse environment in addition to providing the physical space. You must also factor in the cost of skilled IT staff to manage the warehouse and troubleshoot problems.
Performance Restrictions. You can certainly have high performance on-premises, yet as data volumes surge, on-prem data warehouses can experience performance bottlenecks. This results in slower query processing times and delayed insights, restricting your ability to make timely decisions and potentially impacting your competitive edge in the market.

These are some of the reasons why cloud migrations are popular—they don’t face these same issues. According to Gartner, worldwide end-user spending on public cloud services is forecast to grow 20.4% to $675.4 billion in 2024, up from $561 billion in 2023, and reach $1 trillion before the end of this decade.

Yet it’s worth noting that on-prem warehouses continue to meet the needs of many modern businesses. They effectively store and query data while offering customization options tailored to specific business needs.

On-Prem is Not Even on Life Support

Despite the drawbacks to on-premises data warehouses, they are alive and doing fine. And despite some analysts predicting their demise for the last decade or so, reality and practicality tell a different story.

Granted, while many organizations have mandates to be cloud-first and have moved workloads to the cloud, the on-prem warehouse continues to deliver the data and analytics capabilities needed to meet the requirements of today’s businesses, especially those with stable workloads. In fact, you can modernize in place, or on-prem, with the right data platform or database.

You also don’t have to take an either-or approach to on-premises data warehouses vs. the cloud. You can have them both with a hybrid data warehouse that offers a modern data architecture combining the benefits of on-premises with cloud-based data warehousing. This model lets you optimize both environments for data storage, processing, and analytics to ensure the best performance, cost, security, and flexibility.

Data Warehouse Options Cut Across Specific Needs

It’s important to remember that your organization’s data needs and strategy can be uniquely different from your peers and from businesses in other industries. For example, you may be heavily invested in your on-prem data warehouse and related tools, and therefore don’t want to move away from these technologies.

Likewise, you may have a preference to keep certain workloads on-prem for security or low latency reasons. At the same time, you may want to take advantage of cloud benefits. A modern warehouse lets you pick your option—solely on-premises, completely in the cloud, or a hybrid that effectively leverages on-prem and cloud.

One reason to take a hybrid approach is that it helps to future-proof your organization. Even if your current strategy calls for being 100% on-premises, you may want to keep your options open to migrate to the cloud later, if or when you’re ready. For instance, you may want a data backup and recovery option that’s cloud based, which is a common use case for the cloud.

Is On-Prem Right For You?

On-premises data warehouses are alive and thriving, even if they don’t receive the amount of press as their cloud counterparts. For many organizations, especially those with stringent regulatory requirements, the on-prem warehouse continues to play an essential role in data and analytics. It allows predictable cost management along with the ability to customize hardware and software configurations to fit specific business demands.

If you’re curious about the best option for your business, Actian can help. Our experts will look at your current environment along with your data needs and business priorities to recommend the most optimal solution for you.

We offer a modern product portfolio, including data warehouse solutions, spanning on-prem, the cloud, and hybrid to help you implement the technology that best suits your needs, goals, and current investments. We’re always here to help to ensure you can trust your data and your buying choices.

About Actian Corporation

Data Platform

Buyers Guide for Data Platforms 2024

Actian Corporation

June 26, 2024

Actian 2024 Ventana-Analytic Data Platforms Ranked Exemplary

Data Platforms Buyers Guide

The process of choosing the right technology for your specific business and IT needs can be complex, yet making the right decision is critical. So, how do you make an informed choice?

The product landscape changes fast, meaning the products you looked at even a few months ago may have changed significantly. And let’s face it – proof of concepts (POCs) are limited deployments with vendors showcasing their solutions for a brief period of time. You don’t want to find out later, after you’ve invested significant time and money, that a product won’t handle your specific workloads, or give you the security, scalability and price-performance you need.

You need to know upfront how it performs from both a customer and a product experience in essential categories such as performance, reliability, manageability, and validation. Likewise, you want to know that the product has a strong roadmap for your future and peer use cases are available.

The Need for Unbiased Assessments

Independent analyst reports and buying guides can help you make informed decisions. They offer unbiased, critical insights into the advantages and drawbacks of vendors’ products. The information cuts through marketing claims to help you understand how technologies, such as data platforms, truly perform to help you choose a solution with confidence.

These reports are typically based on thorough research and analysis, considering various factors such as product capabilities, customer satisfaction, and market performance. This objectivity can help you avoid the pitfalls of biased or incomplete information.

For example, the 2024 Ventana Research Buyers Guide for Data Platforms evaluated 25 data platform software providers, detailing their strengths and weaknesses. This broad perspective enables you to understand the competitive landscape and identify potential technology partners that align with your strategic goals.

The Buyers Guide is meticulously curated and structured into seven in-depth categories across Product and Customer Experience. A vendor’s overall placement is assessed through a weighted score and is only awarded to companies that meet a strict set of criteria, with the aim to streamline and aid vendor selection.

Ventana’s Market View on Data Platforms

A modern data platform allows businesses to stay competitive and innovative in a data-driven world. They manage the storage, integration, and analysis of data, ensuring a single source of truth.

Data platforms should empower all users, especially non-technical users, with actionable insights. As Ventana Research stated in its 2024 Buyers Guide for Data Platforms, “Data platforms provide an environment for organizing and managing the storage, processing, analysis, and presentation of data across an enterprise. Without data platforms, enterprises would be reliant on a combination of paper records, time-consuming manual processes, and huge libraries of physical files to record, process and store business information.”

Today’s data platforms are typically designed to be scalable and flexible, accommodating the growing and evolving data needs of your business. They support a variety of data from new and emerging sources. This versatility ensures that you can continue to leverage your data as you expand and innovate.

2024 Ventana Research Data Platforms Exemplary

Ventana’s Criteria for Choosing Data Platforms

Ventana notes that buying decisions should be based on research. “We believe it is important to take a comprehensive, research-based approach, since making the wrong choice of data platforms technology can raise the total cost of ownership, lower the return on investment and hamper an enterprise’s ability to reach its full performance potential,” according to Ventana.

Three key evaluation criteria from the 2024 Ventana Buyers Guide for Data Platforms are:

Assess Your Primary Workload Needs and Future-Proof Them for GenAI. Determine whether your primary focus is on operational or analytic workloads, or both. Operational workloads include finance, supply chain, and marketing applications, whereas analytical workloads include business intelligence (BI) and data science. Ventana predicts that by 2027, personalized experiences driven by GenAI will increase the demand for data platforms capable of supporting hybrid operational and analytical processing.
Evaluate Your Main Data Storage and Management Criteria. Determine the capabilities you need, then evaluate data platforms that align with those requirements. Criteria often includes the core database management system, performance and query functionality, the ability to integrate data and ensure quality, whether the platform offers simple platform usability and manageability, and if it meets cost, price performance, and return on investment requirements.
Consider Support for Data Workers in Multiple Roles. Consider the types of data you need to manage along with the key functionalities required by your users, from database administrators to data engineers to data scientists. According to Ventana, data platforms must support a range of users with different needs – across technology and business teams.

Have Confidence in Your Data Platform

In the rapidly evolving tech landscape, making informed choices is more important than ever. Analyst reports are invaluable resources that provide objective, comprehensive insights to guide those decisions.

Actian is providing complimentary access to the 2024 Ventana Research Data Platforms Buyers Guide. Read the report to learn more about what Ventana has to say about Actian and our positioning as Exemplary.

If you’re in the market for a single, unified data platform that’s recognized by an analyst firm as handling both operational and analytic workloads, let’s talk so you can have confidence in your buying decision.

About Actian Corporation

Databases

The Rise of Embedded Databases in the Age of IoT

Kunal Shah

June 24, 2024

The Rise of Embedded Databases in the Age of IoT

The Internet of Things (IoT) is rapidly transforming our world. From smart homes and wearables to industrial automation and connected vehicles, billions of devices are now collecting and generating data. According to a recent analysis, the number of Internet of Things (IoT) devices worldwide is forecasted to almost double from 15.1 billion in 2020 to more than 29 billion IoT devices in 2030. This data deluge presents both challenges and opportunities, and at the heart of it all lies the need for efficient data storage and management – a role increasingly filled by embedded databases.

Traditional Databases vs. Embedded Databases

Traditional databases, designed for large-scale enterprise applications, often struggle in the resource-constrained environment of the IoT. They require significant processing power, memory, and storage, which are luxuries most IoT devices simply don’t have. Additionally, traditional databases are complex to manage and secure, making them unsuitable for the often-unattended nature of IoT deployments.

Embedded databases, on the other hand, are specifically designed for devices with limited resources. They are lightweight, have a small footprint, and require minimal processing power. They are also optimized for real-time data processing, crucial for many IoT applications where decisions need to be made at the edge, without relaying data to a cloud database.

Why Embedded Databases are Perfect for IoT and Edge Computing

Several key factors make embedded databases the ideal choice for IoT and edge computing:

Small Footprint: Embedded databases require minimal storage and memory, making them ideal for devices with limited resources. This allows for smaller form factors and lower costs for IoT devices.
Low Power Consumption: Embedded databases are designed to be energy-efficient, minimizing the power drain on battery-powered devices, a critical concern for many IoT applications.
Fast Performance: Real-time data processing is essential for many IoT applications. Embedded databases are optimized for speed, ensuring timely data storage, retrieval, and analysis at the edge.
Reliability and Durability: IoT devices often operate in harsh environments. Embedded databases are designed to be reliable and durable, ensuring data integrity even in case of power failures or device malfunctions.
Security: Security is paramount in the IoT landscape. Embedded databases incorporate robust security features to protect sensitive data from unauthorized access.
Ease of Use: Unlike traditional databases, embedded databases are designed to be easy to set up and manage. This simplifies development and deployment for resource-constrained IoT projects.

Building complex IoT apps shouldn’t be a headache. Let us show you how our embedded edge database can simplify your next IoT project.

Benefits of Using Embedded Databases in IoT Applications

The advantages of using embedded databases in IoT applications are numerous:

Improved Decision-Making: By storing and analyzing data locally, embedded databases enable real-time decision making at the edge. This reduces reliance on cloud communication and allows for faster, more efficient responses.
Enhanced Functionality: Embedded databases can store device configuration settings, user preferences, and historical data, enabling richer functionality and a more personalized user experience.
Reduced Latency: Processing data locally eliminates the need for constant communication with the cloud, significantly reducing latency and improving responsiveness.
Offline Functionality: Embedded databases allow devices to function even when disconnected from the internet, ensuring uninterrupted operation and data collection.
Cost Savings: By reducing reliance on cloud storage and processing, embedded databases can help lower overall operational costs for IoT deployments.

Use Cases for Embedded Databases in IoT

Embedded databases are finding applications across a wide range of IoT sectors, including:

Smart Homes: Embedded databases can store device settings, energy usage data, and user preferences, enabling intelligent home automation and energy management.
Wearables: Fitness trackers and smartwatches use embedded databases to store health data, activity logs, and user settings.
Industrial Automation: Embedded databases play a crucial role in industrial IoT applications, storing sensor data, equipment settings, and maintenance logs for predictive maintenance and improved operational efficiency.
Connected Vehicles: Embedded databases are essential for connected car applications, storing vehicle diagnostics, driver preferences, and real-time traffic data to enable features like self-driving cars and intelligent navigation systems.
Asset Tracking: Embedded databases can be used to track the location and condition of assets in real-time, optimizing logistics and supply chain management.

The Future of Embedded Databases in the IoT

As the IoT landscape continues to evolve, embedded databases are expected to play an even more critical role. Here are some key trends to watch:

Increased Demand for Scalability: As the number of connected devices explodes, embedded databases will need to be scalable to handle larger data volumes and more complex workloads.
Enhanced Security Features: With growing security concerns in the IoT, embedded databases will need to incorporate even more robust security measures to protect sensitive data.
Cloud Integration: While embedded databases enable edge computing, there will likely be a need for seamless integration with cloud platforms for data analytics, visualization, and long-term storage.

The rise of the IoT has ushered in a new era for embedded databases. Their small footprint, efficiency, and scalability make them the perfect fit for managing data at the edge of the network. As the IoT landscape matures, embedded databases will continue to evolve, offering advanced features, enhanced security, and a seamless integration with cloud platforms.

At Actian, we help organizations run faster, smarter applications on edge devices with our lightweight, embedded database – Actian Zen. And, with the latest release of Zen 16.0, we are committed to helping businesses simplify edge-to-cloud data management, boost developer productivity and build secure, distributed IoT applications.

Additional Resources:

[Datasheet] Actian Zen – Embedded Database for the Intelligent Edge.
Read the Actian Zen 16.0 Launch Blog for more detailed information about the latest release.

About Kunal Shah

Kunal Shah is a product marketer with 15+ years in data and digital growth, leading marketing for Actian Zen Edge and NoSQL products. He has consulted on data modernization for global enterprises, drawing on past roles at SAS. Kunal holds an MBA from Duke University. Kunal regularly shares market insights at data and tech conferences, focusing on embedded database innovations. On the Actian blog, Kunal covers product growth strategy, go-to-market motions, and real-world commercial execution. Explore his latest posts to discover how edge data solutions can transform your business.

Data Intelligence

Data Shopping Part 2 – The Data Shopping Experience

Actian Corporation

June 24, 2024

Just as shopping for goods online involves selecting items, adding them to a cart, and choosing delivery and payment options, the process of acquiring data within organizations has evolved in a similar manner. In the age of data products and data mesh, internal data marketplaces enable business users to search for, discover, and access data for their use cases.

In this series of articles, get an excerpt from our Practical Guide to Data Mesh and discover all there is to know about data shopping as well as the platform’s Data Shopping experience in its Enterprise Data Marketplace:

How to shop for data products.
The Data Shopping experience.

In our previous article, we discussed the concept of data shopping within an internal data marketplace, addressing elements such as data product delivery and access management. In this article, we will explore the reason behind the Actian Data Intelligence Platform’s decision to extend its data shopping experience beyond internal boundaries, as well as how our interface, Actian Studio, enables the analysis of the overall performance of your data products.

Data Product Shopping

In our previous article, we discussed the complexities of access rights management for data products due to the inherent risks of data consumption. In a decentralized data mesh, the data product owner assesses risks, grants access, and enforces policies based on the data’s sensitivity, the requester’s role, location, and purpose. This may involve data transformation or additional formalities, with delivery ranging from read-only access to fine-grained controls.

In a data marketplace, consumers trigger a workflow by submitting access requests, which data owners evaluate and determine access rules for, sometimes with expert input. For the marketplace, we have chosen not to integrate this workflow directly into the solution but rather to interface with external solutions.

The idea is to offer a uniform experience for triggering an access request but to accept that the processing of this request may be very different from one environment to another, or even from one domain to another within the same organization – This principle is inherited from classical marketplaces. Most marketplaces offer a unique experience for making a purchase but connect to other systems for the operational implementation of delivery – the modalities of which can vary widely depending on the product and the seller.

This decoupling between the shopping experience and the operational implementation of delivery seems essential to us for several reasons.

The main reason is the extreme variability of the processes involved. Some organizations already have operational workflows, relying on a larger solution (data access requests are integrated into a general access request process, supported, for example, by a ticketing tool such as ServiceNow or Jira). Others have dedicated solutions supporting a high level of automation but whose deployment is not yet widespread. Still, others rely on the capabilities of their data platform, and some even on nothing at all – access is obtained through direct requests to the data owner, who handles them without a formal process. This variability is evident from one organization to another but also within the same organization – structurally, when different domains use different technologies, or temporally when the organization decides to invest in a more efficient or secure system and must gradually migrate access management to this new system.

Decoupling, therefore, allows offering a consistent experience to the consumer while adapting to the variability of operational methods.

For a data marketplace customer, the shopping experience is very simple. Once the data product(s) of interest is identified, they trigger an access request by providing the following information:

Who they are – This information is already available.
Which data product they want to access – This information is also already available, along with the metadata needed for decision-making.
What they intend to use the data for – This is crucial since it drives risk management and compliance requirements.

With the Actian Data Intelligence Platform, once the access request is submitted, it is processed in another system, and its status can be tracked from the marketplace – this is the direct equivalent of order tracking found on e-commerce sites.

From the consumer’s perspective, the data marketplace provides a catalog of data products (and other digital products) and a simple, universal system for gaining access to these products.

For the producer, the marketplace plays a fundamental role in managing their product portfolio.

Enhance Data Product Performance With Actian Studio

As mentioned earlier, in addition to the e-commerce system, which is intended for consumers, a classical marketplace also offers tools dedicated to sellers, allowing them to supervise their products, respond to buyer inquiries, and monitor the economic performance of their offerings. And other tools, intended for marketplace managers, to analyze the overall performance of products and sellers.

Actian Data Intelligence Platform’s Enterprise Data Marketplace integrates these capabilities into a dedicated back-office tool, Actian Studio. It allows for managing the production, consolidation, and organization of metadata in a private catalog and deciding which objects will be placed in the marketplace – which is a searchable space accessible to the widest audience.

These activities primarily fall under the production process – metadata are produced and organized together with the data products. However, it also allows for monitoring the use of each data product, notably by providing a list of all its consumers and the uses associated with them.

This consumer tracking helps establish the two pillars of data mesh governance:

Compliance and risk management – By conducting regular reviews, certifications, and impact analyses during data product changes.
Performance management – The number of consumers, as well as the nature of the uses made of them, are the main indicators of a data product’s value. Indeed, a data product that is not consumed has no value.

As a support tool for domains to control the compliance of their products and their performance, the the Actian Data Intelligence Platform’s Enterprise Data Marketplace also offers comprehensive analysis capabilities of the mesh – the lineage of data products, scoring, and evaluation of their performance, control of overall compliance and risks, regulatory reporting elements, etc.

This is the magic of the federated graph, which allows for exploiting information at all scales and provides a comprehensive representation of the entire data landscape.

About Actian Corporation

Data Management

The Consequences of Poor Data Quality: Uncovering the Hidden Risks

Traci Curran

June 23, 2024

Costly Consequences of Poor Data Quality

Summary

Poor data quality quietly drains millions in revenue, productivity, and trust. This blog outlines the hidden financial, operational, and compliance risks that stem from inaccurate or incomplete data.

The average business loses $15 million annually due to poor data quality; in the U.S., this impact reaches $3.1 trillion across the economy.
Employees spend up to 27% of their time correcting bad data, slowing decision-making, and increasing operational costs.
Poor data undermines compliance efforts, damages brand reputation, and leads to missed market opportunities.

The quality of an organization’s data has become a critical determinant of its success. Accurate, complete, and consistent data is the foundation upon which crucial decisions, strategic planning, and operational efficiency are built. However, the reality is that it is a pervasive issue, with far-reaching implications that often go unnoticed or underestimated.

Defining Poor Data Quality

Before delving into the impacts of poor data quality, it’s essential to understand what constitutes subpar data. Inaccurate, incomplete, duplicated, or inconsistently formatted information can all be considered poor data quality. This can stem from various sources, such as data integration challenges, data capture inconsistencies, data migration pitfalls, data decay, and data duplication.

The Hidden Costs of Poor Data Quality

Loss of Revenue
Poor data quality can directly impact a business’s bottom line. Inaccurate customer information, misleading product details, and incorrect order processing can lead to lost sales, decreased customer satisfaction, and damaged brand reputation. Gartner estimates that poor data quality costs organizations an average of $15 million per year.
Reduced Operational Efficiency
When employees waste time manually correcting data errors or searching for accurate information, it significantly reduces their productivity and the overall efficiency of business processes. This can lead to delayed decision-making, missed deadlines, and increased operational costs.
Flawed Analytics and Decision-Making
Data analysis and predictive models are only as reliable as the data they are based on. Incomplete, duplicated, or inaccurate data can result in skewed insights, leading to poor strategic decisions that can have far-reaching consequences for the organization.
Compliance Risks
Stringent data privacy regulations, such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), require organizations to maintain accurate and up-to-date personal data. Failure to comply with these regulations can result in hefty fines and reputational damage.
Missed Opportunities
Poor data quality can prevent organizations from identifying market trends, understanding customer preferences, and capitalizing on new product or service opportunities. This can allow competitors with better data management practices to gain a competitive edge.
Reputational Damage
Customers are increasingly conscious of how organizations handle their personal data. Incidents of data breaches, incorrect product information, or poor customer experiences can quickly erode trust and damage a company’s reputation, which can be challenging to rebuild.

Measuring the Financial Impact of Poor Data Quality

Annual Financial Loss: Organizations face an average annual loss of $15 million due to poor data quality. This includes direct costs like lost revenue and indirect costs such as inefficiencies and missed opportunities (Data Ladder).
GDP Impact: Poor data quality costs the US economy approximately $3.1 trillion per year. This staggering figure reflects the extensive nature of the issue across various sectors, highlighting the pervasive economic burden (Experian Data Quality) (Anodot).
Time Wasted: Employees can waste up to 27% of their time dealing with data issues. This includes time spent validating, correcting, or searching for accurate data, significantly reducing overall productivity (Anodot).
Missed Opportunities: Businesses can miss out on 45% of potential leads due to poor data quality, including duplicate data, invalid formatting, and other errors that hinder effective customer relationship management and sales efforts (Data Ladder).
Audit and Compliance Costs: Companies may need to spend an additional $20,000 annually on staff time to address increased audit demands caused by poor data quality. This highlights the extra operational costs that come with maintaining compliance and accuracy in financial reporting (CamSpark).

Strategies for Improving Data Quality

Addressing poor data quality requires a multi-faceted approach encompassing organizational culture, data governance, and technological solutions.

Fostering a Data-Driven Culture
Developing a workplace culture that prioritizes data quality is essential. This involves establishing clear data management policies, standardizing data formats, and assigning data ownership responsibilities to ensure accountability.
Implementing Robust Data Governance
Regularly auditing data quality, cleaning and deduplicating datasets, and maintaining data currency are crucial to maintaining high-quality data. Automated data quality monitoring and validation tools can greatly enhance these processes.
Leveraging Data Quality Solutions
Investing in specialized data quality software can automate data profiling, cleansing, matching, and deduplication tasks, significantly reducing the manual effort required to maintain data integrity.

The risks and costs associated with poor data quality are far-reaching and often underestimated. By recognizing the hidden impacts, quantifying the financial implications, and implementing comprehensive data quality strategies, organizations can unlock the true value of their data and position themselves for long-term success in the digital age.

About Traci Curran

Traci Curran is Director of Product Marketing at Actian, focusing on the Actian Data Platform. With 20+ years in tech marketing, Traci has led launches at startups and established enterprises like CloudBolt Software. She specializes in communicating how digital transformation and cloud technologies drive competitive advantage. Traci's articles on the Actian blog demonstrate how to leverage the Data Platform for agile innovation. Explore her posts to accelerate your data initiatives.

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