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ACTIAN Data Platform

Sep 9 2024

Actian’s Benchmark Dominance: A Price-Performance Powerhouse

Actian Shines in TPC-H Benchmark, Outperforming Major Competitors

In August of this year, Actian conducted a TPC-H benchmark test utilizing the services of McKnight Consulting Group. While some companies perform and publish their own benchmarks, Actian prefers to utilize the services of a third party for true, reliable and unbiased testing. Based in Plano, Texas, the McKnight Consulting Group has helped over 100 companies with analytics, big data, master data management strategies and implementations, including benchmarking.

Actian conducted a similar TPC-H benchmark test last year, validating that it indeed was faster than some of its key competitors such as Google BigQuery and Snowflake, with a performance of 11 times and three times faster than each vendor, respectively. Since then, the Actian engineering team has continued to enhance the performance capabilities of the Actian Data Platform with the understanding that it needs to meet the requirements of its existing and prospective customer base.

This is especially important given the growth in business use cases and the sources of data used in day-to-day operations. Actian is always striving to keep ahead of the curve for its customers, and its ability to provide both rapid data processing capabilities and, in turn, unparalleled price-performance, have been key factors in its product roadmap.

In this recent TPC-H benchmark test, Actian decisively outperformed its competitors Snowflake, Databricks, and Google BigQuery.

Key Benchmark Findings

Raw Performance: Actian Data Platform’s execution speed was significantly faster than all three competitors tested in the benchmark. It achieved nearly eight times the performance of Databricks, over six times that of Snowflake, and an impressive 12 times the performance of BigQuery.
Concurrency: Even with five concurrent users, Actian Data Platform maintained its performance advantage, outperforming Databricks by three times, Snowflake by over seven times, and BigQuery by 9.6 times.
Price-Performance: Actian Data Platform’s combination of speed and affordability was unmatched. It offered a price-performance ratio that was over eight times better than both Snowflake and BigQuery.

This is a significant improvement over last year’s fantastic results and is a testament to Actian’s commitment to database performance and price performance. Actian, with over 50 years of experience in data and database models, continues to show its prowess in the market.

What Does This Mean for Actian’s Current and Future Customers?

For businesses seeking a high-performance, cost-effective data warehouse or analytics platform, the benchmark results are a compelling reason to consider the Actian Data Platform. Here’s why:

Faster Insights: Actian’s superior performance means that businesses can get answers to their most critical questions faster. Actian has always aimed to provide REAL real-time analytics, and these results prove that we can get customers there. This can lead to improved decision-making, increased operational efficiency, and better customer experiences.
Lower Costs: Actian Data Platform’s favorable price-performance ratio translates into significant cost savings for businesses. By choosing Actian, organizations can avoid the high and sometimes unpredictable costs associated with other data platforms while still achieving exceptional results. This leads to long-term total cost of ownership benefits that other vendors cannot provide.
Scalability: Actian Data Platform’s ability to handle concurrent users and large datasets demonstrates its scalability. This is essential for businesses that need to support growing data volumes and user demands – two business needs that every organization is facing today.

Price Performance is Top of Mind

Today, CFOs and technical users alike are trying to find ways to get the best price performance possible from their database management systems (DBMS). Not only are CFOs interested in up-front acquisition and implementation costs, but also all costs downstream that are associated with utilization and maintenance of whichever system they choose.

Technical users of DBMS offerings are also looking for alternative ways to utilize their systems to save costs. In the back alleys of the internet (places like Reddit and other forums) users of various DBMS platforms are talking with others about how to effectively “game” their DBMS platforms to get the best price performance possible, sometimes leading to the development of shadow database solutions to try to save costs.

With the latest TPC-H benchmark results showing that the Actian Data Platform performs over eight times better than both Snowflake and BigQuery, companies looking for outstanding price performance in their future and, indeed, current DBMS systems need to consider Actian.

Take the Next Step

Actian Data Platform’s dominance in the TPC-H benchmark is a clear indication of its exceptional capabilities. By delivering superior performance, affordability, and scalability, Actian offers a compelling solution for businesses seeking a powerful and cost-effective data platform. If organizations are looking to unlock the full potential of their data with confidence, Actian is worth a closer look.

To download the complete TPC-H report from McKnight, click here.

The post Actian’s Benchmark Dominance: A Price-Performance Powerhouse appeared first on Actian.

Using a Data Platform to Power Your Data Strategy

In today’s fast-paced digital landscape, organizations are increasingly recognizing the critical role that data plays in driving business success. The ability to harness data effectively can lead to significant competitive advantages, making it essential for businesses to adopt robust data management strategies.

Understanding the Importance of Data Management

Data management involves collecting, storing, organizing, and analyzing data to inform business decisions. As the volume and complexity of data continue to grow, traditional data management methods are becoming inadequate. Organizations often find themselves dealing with data silos, where information is trapped in isolated systems, making it difficult to access and analyze. According to the McKinsey Global Institute, data-driven organizations are 23 times more likely to acquire customers, six times more likely to retain them, and 19 times more likely to be profitable than their less data-savvy counterparts. This statistic underscores the necessity for businesses to implement effective data management practices.

The Evolution of Data Platforms

Historically, data management relied heavily on on-premises solutions, often requiring significant infrastructure investment and specialized personnel. However, the advent of cloud computing has transformed the data landscape. Modern data platforms offer a unified approach that integrates various data management solutions, enabling organizations to manage their operational and analytical needs efficiently. A data platform is a comprehensive solution combining data ingestion, transformation, and analytics. It allows users across the organization to access and visualize data easily, fostering a data-driven culture.

Key Features of a Modern Data Platform

When selecting a data platform, organizations should consider several critical features:

Unified Architecture: A data platform should provide a centralized data warehouse that integrates various data sources, facilitating easier access and analysis.
Data Integration Capabilities: The ability to connect and transform data from disparate sources is essential for creating a single source of truth.
Real-Time Analytics: Modern platforms support streaming data, enabling organizations to analyze information as it arrives, which is crucial for timely decision-making.
Data Quality Management: Features that ensure data accuracy and consistency are vital to maintain trust in the insights derived from the data.
User-Friendly Analytics Tools: Built-in visualization and reporting tools allow users to generate insights without extensive technical expertise.

Overcoming Modern Data Challenges

Despite the advantages of modern data platforms, organizations still face challenges such as:

Data Overload: The exponential growth of data can overwhelm traditional systems, making it difficult to extract meaningful insights.
Cost Management: As organizations move to the cloud, managing operating costs becomes a top concern.
Skill Shortages: The demand for data professionals often exceeds supply, hindering organizations’ ability to leverage their data effectively.

To address these challenges, businesses must adopt innovative technologies that facilitate rapid insights and scalability while ensuring data quality. If you’re looking to advance your use of data to improve your competitive advantage and operational efficiency, we invite you to read our new Gorilla Guide® To… Using a Data Platform to Power Your Data Strategy for a deep dive into the benefits of a unified data platform.

The post Using a Data Platform to Power <br>Your Data Strategy appeared first on Actian.

How Data is Revolutionizing Transportation and Logistics

In today’s fast-paced world, the transportation and logistics industry is the backbone that keeps the global economy moving. Logistics is expected to be the fastest-growing industry by 2030. As demand for faster, more efficient, and cost-effective services grows, you’ll need to be able to connect, manage, and analyze data from all parts of your business to make fast, efficient decisions that improve your supply chain, logistics, and other critical areas.

Siloed data, poor data quality, and a lack of integration across systems can hinder you from optimizing your operations, forecasting demand accurately, and providing top-tier customer service. By leveraging advanced data integration, management, and analytics, you can transform these challenges into opportunities, driving efficiency, reliability, and customer satisfaction.

The Challenges: Harnessing Data in Transportation and Logistics

One of the most significant hurdles in the transportation and logistics sector is accessing quality data across departments. Data is often scattered across multiple systems—such as customer relationship management (CRM), enterprise resource planning (ERP), telematics systems, and even spreadsheets—without a unified access point. This fragmentation creates data silos, where crucial information is isolated across individuals and business units, making it difficult for different departments to access the data they need. For instance, the logistics team might not have access to customer data stored in the CRM, which can hinder their ability to accurately plan deliveries, personalize service, proactively address potential issues, and improve overall communication.

Furthermore, the lack of integration across these systems exacerbates the problem of fragmented data. Different data sources often store information in varied and incompatible formats, making it challenging to compare or combine data across systems. This leads to inefficiencies in several critical areas, including demand forecasting, route optimization, predictive maintenance, and risk management. Without a unified view of operations, companies struggle to leverage customer behavior insights from CRM data to improve service quality or optimize delivery schedules, and face other limitations.

The Impact: Inefficiencies and Operational Risks

The consequences of these data challenges are far-reaching. Inaccurate demand forecasts can lead to stockouts, overstock, and poor resource allocation, all of which directly impact your bottom line. Without cohesive predictive maintenance, operational downtime increases, negatively impacting delivery schedules and customer satisfaction. Inefficient routing, caused by disparate data sources, results in higher fuel costs and delayed deliveries, further eroding profitability and customer trust.

Additionally, the lack of a unified customer view can hinder your ability to provide personalized services, reducing customer satisfaction and loyalty. In the absence of integrated data, risk management becomes reactive rather than proactive, with delayed data processing increasing exposure to risks and limiting your ability to respond quickly to emerging threats.

The Solution: A Unified Data Platform

Imagine a scenario where your transportation and logistics operations are no longer bogged down by data fragmentation and poor integration. With a unified view across your entire organization, you can access accurate, real-time insights across the end-to-end supply chain, enabling youto make data-driven decisions that reduce delays and improve overall efficiency.

A unified data platform integrates fragmented data from multiple sources into a single, accessible system. This integration eliminates data silos, ensuring that all relevant information—whether from CRM, ERP, telematics, or GPS tracking systems—is available in real-time to decision-makers across your organization.

For example, predictive maintenance becomes significantly more effective when historical data, sensor data, and telematics are integrated and analyzed consistently. This approach minimizes unplanned downtime, extends the lifespan of assets, and ensures that vehicles and equipment are always operating at peak efficiency, leading to substantial cost savings.

Similarly, advanced route optimization algorithms that utilize real-time traffic data, weather conditions, and historical delivery performance can dynamically adjust routes for drivers. The result is consistently on-time deliveries, reduced fuel costs, and enhanced customer satisfaction through reliable and efficient service.

A unified data platform also enables the creation of a 360-degree customer view by consolidating customer data from various touchpoints—such as transactions, behaviors, and support interactions—into a comprehensive and up-to-date profile. This holistic view allows you to offer personalized services and targeted marketing, leading to higher customer satisfaction, increased loyalty, and more successful sales strategies.

Proactive risk management is another critical benefit of a unified data platform. By analyzing real-time data from multiple sources, you can identify potential risks before they escalate into critical issues. Whether you’re experiencing supply chain disruptions, regulatory compliance challenges, or logistical issues, the ability to respond swiftly to emerging risks reduces potential losses and ensures smooth operations, even in the face of unforeseen challenges.

Face the Future of Transportation and Logistics With Confidence

As the transportation and logistics industry continues to evolve, the role of data will only become more critical. The Actian Data Platform can help you overcome the current challenges of data fragmentation, poor quality, and lack of integration in addition to helping you position yourself at the forefront of innovation in the industry. By leveraging data to optimize operations, improve customer service, and proactively manage risks, you will achieve greater efficiency, cost-effectiveness, and customer satisfaction—driving greater success in a competitive and dynamic market.

The post How Data is Revolutionizing Transportation and Logistics appeared first on Actian.

5 Misconceptions About Data Quality and Governance

The quality and governance of data has never been more critical than it is today.

In the rapidly evolving landscape of business technology, advanced analytics and generative AI have emerged as game-changers, promising unprecedented insights and efficiencies. However, as these technologies become more sophisticated, the adage GIGO or “garbage in, garbage out” has never been more relevant. For data and IT professionals, understanding the critical role of data quality in these applications is not just important—it’s imperative for success.

Going Beyond Data Processing

Advanced analytics and generative AI don’t just process data; they amplify its value. This amplification can be a double-edged sword:

Insight Magnification: High-quality data leads to sharper insights, more accurate predictions, and more reliable AI-generated content.

Error Propagation: Poor quality data can lead to compounded errors, misleading insights, and potentially harmful AI outputs.

These technologies act as powerful lenses—magnifying both the strengths and weaknesses of your data. As the complexity of models increases, so does their sensitivity to data quality issues.

Effective Data Governance is Mandatory

Implementing robust data governance practices is equally important. Governance today is not just a regulatory checkbox—it’s a fundamental requirement for harnessing the full potential of these advanced technologies while mitigating associated risks.

As organizations rush to adopt advanced analytics and generative AI, there’s a growing realization that effective data governance is not a hindrance to innovation, but rather an enabler.

Data Reliability at Scale: Advanced analytics and AI models require vast amounts of data. Without proper governance, the reliability of these datasets becomes questionable, potentially leading to flawed insights.

Ethical AI Deployment: Generative AI in particular raises significant ethical concerns. Strong governance frameworks are essential for ensuring that AI systems are developed and deployed responsibly, with proper oversight and accountability.

Regulatory Compliance: As regulations like GDPR, CCPA, and industry-specific mandates evolve to address AI and advanced analytics, robust data governance becomes crucial for maintaining compliance and avoiding hefty penalties.

But despite the vast mines of information, many organizations still struggle with misconceptions that hinder their ability to harness the full potential of their data assets.

As data and technology leaders navigate the complex landscape of data management, it’s crucial to dispel these myths and focus on strategies that truly drive value.

For example, Gartner offers insights into the governance practices organizations typically follow, versus what they actually need:

Source: Gartner

5 Data Myths Impacting Data’s Value

Here are five common misconceptions about data quality and governance, and why addressing them is essential.

Misconception 1: The ‘Set It and Forget It’ Fallacy

Many leaders believe that implementing a data governance framework is a one-time effort. They invest heavily in initial setup but fail to recognize that data governance is an ongoing process that requires continuous attention and refinement mapped to data and analytics outcomes.

In reality, effective data governance is dynamic. As business needs evolve and new data sources emerge, governance practices must adapt. Successful organizations treat data governance as a living system, regularly reviewing and updating policies, procedures, and technologies to ensure they remain relevant and effective for all stakeholders.

Action: Establish a quarterly review process for your data governance framework, involving key stakeholders from across the organization to ensure it remains aligned with business objectives and technological advancements.

Misconception 2: The ‘Technology Will Save Us’ Trap

There’s a pervasive belief that investing in the latest data quality tools and technologies will automatically solve all data-related problems. While technology is undoubtedly crucial, it’s not a silver bullet.

The truth is, technology is only as good as the people and processes behind it. Without a strong data culture and well-defined processes, even the most advanced tools will fall short. Successful data quality and governance initiatives require a holistic approach that balances technology with human expertise and organizational alignment.

Action: Before investing in new data quality and governance tools, conduct a comprehensive assessment of your organization’s data culture and processes. Identify areas where technology can enhance existing strengths rather than trying to use it as a universal fix.

Misconception 3:. The ‘Perfect Data’ Mirage

Some leaders strive for perfect data quality across all datasets, believing that anything less is unacceptable. This pursuit of perfection can lead to analysis paralysis and a significant resource drain.

In practice, not all data needs to be perfect. The key is to identify which data elements are critical for decision-making and business operations, and focus quality efforts there. For less critical data, “good enough” quality that meets specific use case requirements may suffice.

Action: Conduct a data criticality assessment to prioritize your data assets. Develop tiered quality standards based on the importance and impact of different data elements on your business objectives.

Misconception 4: The ‘Compliance is Enough’ Complacency

With increasing regulatory pressures, some organizations view data governance primarily through the lens of compliance. They believe that meeting regulatory requirements is sufficient for good data governance.

However, true data governance goes beyond compliance. While meeting regulatory standards is crucial, effective governance should also focus on unlocking business value, improving decision-making, and fostering innovation. Compliance should be seen as a baseline, not the end goal.

Action: Expand your data governance objectives beyond compliance. Identify specific business outcomes that improved data quality and governance can drive, such as enhanced customer experienced or more accurate financial forecasting.

Misconception 5: The ‘IT Department’s Problem’ Delusion

There’s a common misconception that data quality and governance are solely the responsibility of the IT department or application owners. This siloed approach often leads to disconnects between data management efforts and business needs.

Effective data quality and governance require organization-wide commitment and collaboration. While IT plays a crucial role, business units must be actively involved in defining data quality standards, identifying critical data elements, and ensuring that governance practices align with business objectives.

Action: Establish a cross-functional data governance committee that includes representatives from IT, business units, and executive leadership. This committee should meet regularly to align data initiatives with business strategy and ensure shared responsibility for data quality.

Move From Data Myths to Data Outcomes

As we approach the complexities of data management in 2025, it’s crucial for data and technology leaders to move beyond these misconceptions. By recognizing that data quality and governance are ongoing, collaborative efforts that require a balance of technology, process, and culture, organizations can unlock the true value of their data assets.

The goal isn’t data perfection, but rather continuous improvement and alignment with business objectives. By addressing these misconceptions head-on, data and technology leaders can position their organizations for success in an increasingly competitive world.

The post 5 Misconceptions About Data Quality and Governance appeared first on Actian.

Buyers Guide for Data Platforms 2024

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.

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.

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