Data Management

Analysts Say Data Processing Across Hybrid Environments is Important

Actian Corporation

February 6, 2024

data processing with actian zen and apache kafka

Insights from Matt Aslett, VP Research Director at Ventana Research: Would you be surprised to know that by 2026, eight in 10 enterprises will have data spread across multiple cloud providers and on-premises data centers? This prediction by Ventana Research’s Matt Aslett is based, at least in part, on the trend of organizations increasingly using more than one cloud service in addition to their on-premises infrastructure.

Optimizing all of this data—regardless of where it lives—requires a modern data platform capable of accessing and managing data in hybrid environments. “As such, there is a growing requirement for cloud-agnostic data platforms, both operational and analytic, that can support data processing across hybrid IT and multi-cloud environments,” Aslett explains.

For many organizations, managing data while ensuring quality in any environment is a struggle. New data sources are constantly emerging and data volumes are growing at unprecedented rates. When you couple this with an increase in the number of data-intensive applications and analysts who need quality data, it’s easy to see why data management is more complex but more necessary than ever before.

As organizations are finding, data management and data quality problems can and will scale—challenges, silos, and inefficient data processes that exist on-premises or in one cloud will compound as you migrate across multiple clouds or hybrid infrastructures. That’s why it’s essential to fix those issues now and implement effective data management strategies that can scale with you. 

Replacing Complexity With Simplicity

Ventana research also says that traditional approaches to data processing rely on a complex and often “brittle” architecture. This type of architecture uses a variety of specialized products cobbled together from multiple vendors, which in turn require specialized skill sets to use effectively.

As additional technologies are bolted onto the architecture, processes and data sharing become even more complex. In fact, one problem we see at Actian is that organizations continue to add new data and analytics products into ecosystems that are bogged down with legacy technologies. This creates a complicated tech stack of disparate tools, programming languages, frameworks, and technologies that create barriers to integrating, managing, and sharing data.

For a company to be truly data-driven, data must be easily accessible and trusted by every analyst and data user across the enterprise. Any obstacles to tapping into new data sources or accessing quality data, such as requiring ongoing IT help, encourage data silos, and shadow IT—common problems that can lead to misinformed decision-making and will cause stakeholders to lose confidence in the data.

A modern data platform that makes data easy to access, share, and trust with 100% confidence is needed to encourage data use, automate processes, inform decisions, and feed data-intensive applications. The platform should also deliver high performance and be cost-effective to appeal to everyone from data scientists and analysts who use the data to the CFO who’s focused on the IT budget.

Manageability and Usability Are Critical Platform Capabilities

Today’s data-driven environment demands an easy-to-use cloud data platform. Choosing the best platform to meet your business and IT needs can be tricky. Recognized industry analyst research can help by identifying important platform capabilities and identifying which vendors lead in those categories.

For example, Ventana Research’s “Data Platforms Value Index” is an assessment you can use to evaluate vendors and products. One capability the assessment evaluated is product manageability, which is how well the product can be managed technologically and by the business, and how well it can be governed, secured, licensed, and supported in a service level agreement (SLA).

The assessment also looked at the usability of the product—how well it meets the various business needs of executives, management, workers, analysts, IT, and others. “The importance of usability and the digital experience in software utilization has been increasing and is evident in our market research over the last decade,” the assessment notes. “The requirements to meet a broad set of roles and responsibilities across an organization’s cohorts and personas should be a priority for all vendors.”

The Actian Data Platform ranked second for manageability and third for usability, which reflects the platform’s ease of use by making data easy to connect, manage, and analyze. These key capabilities are must-haves for data-driven companies.

Cut Prep Time While Boosting Data Quality

According to Ventana Research, 69% of organizations cite data prep as consuming the most time in analytics initiatives, followed by reviewing data for quality issues at 64%. This is consistent with what we hear from our customers

This is due to data silos, data quality concerns, IT dependency, data latency, and not knowing the steps to optimize data to intelligently grow the business. Organizations must remove these barriers to go from data to decision with confidence and ease.

The Actian Data Platform’s native data integration capabilities can help. It allows you to easily unify data from different sources to gain a comprehensive and accurate understanding of all data, allowing for better decision-making, analysis, and reporting. The platform supports any source and target data, offers elastic integration and cloud-automated scaling, and provides tools for data integration management in hybrid environments.

You benefit from codeless API and application integration, flexible design capabilities, integration templates, and the ability to customize and re-use integrations. Our integration also includes data profiling capabilities for reliable decision-making and a comprehensive library of pre-built connectors.

The platform is unique in its ability to collect, manage, and analyze data in real-time with its transactional database, data integration, data quality, and data warehouse capabilities. It manages data from any public cloud, multi- or hybrid cloud, and on-premises environments through a single pane of glass. In addition, the platform offers self-service data integration, which lowers costs and addresses multiple use cases, without needing multiple products.

As Ventana Research’s Matt Aslett noted in his analyst perspective, our platform reduces the number of tools and platforms needed to generate data insights. Streamlining tools is essential to making data easy and accessible to all users, at all skill levels. Aslett also says, “I recommend that all organizations that seek to deliver competitive advantage using data should evaluate Actian and explore the potential benefits of unified data platforms.”

At Actian, we agree. That’s why I encourage you to experience the Actian Data Platform for yourself or join us at upcoming industry events to connect with us in person.

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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.

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Actian Life

The Trend Continues: Actian Once Again Named a Top Workplace

Actian Corporation

February 6, 2024

hands representing teamwork for Actian being a top workplace

At Actian, we’re about enabling customers to trust their data. But, within our company, we also trust each other—our highly skilled, talented, and personable employees have confidence in each other and in our leadership team. That’s one of the reasons why Actian Careers stands out as a top choice for employment opportunities.

Our dedicated staff and employee-first approach to business make a significant difference in the services and technologies we provide to customers. They’re also why Actian is recognized by our employees for our culture and also why we just earned another award for being a Top Workplace.

Elevating the Employee Experience in the Virtual Workspace

Actian was recognized by Monster—a global leader in connecting people and jobs—with a 2024 Top Workplaces for Remote Work award. “These awards underscore the importance of listening to employees about where and when they can be their most productive and happiest selves,” explains Monster CEO Scott Gutz. “We know that this flexibility is essential to helping both employers and candidates find the right fit.”

The 2024 Top Workplaces for Remote Work award celebrates organizations with 150 or more employees that provide an exceptional remote working environment. The Top Workplaces employer recognition program has a 17-year history of researching, surveying, and celebrating people-first organizations nationally and across 65 regional markets.

The company Energage determines the awards through an employee survey. This means we received the award based on direct and honest employee feedback. Results of a confidential employee engagement survey were evaluated by comparing responses to research-based statements that predict high performance against industry benchmarks.

Proven History of Offering an Inclusive, Supportive, and Flexible Workplace

Actian offers a culture where people belong, are enabled to innovate, and can reach their full potential. It’s not just a place to work—it’s a place to thrive, belong, and make a difference.

Being honored with a Top Workplace award demonstrates that when we say we place employees first, we mean it and employees experience it every day. Some of the ways we engage and reward our staff include:

  • A Rewards and Recognition Program that showcases an individual’s work and contributions.
  • Professional development to empower employees to grow their skill set.
  • Seasonal events and regular gatherings—including some that are virtual.
  • A commitment to work-life flexibility.
  • Time off to volunteer and give back to communities.
  • Quarterly peer nominations to recognize colleagues for their work.

People feel welcome at Actian, which is why we’ve seen a pattern of being recognized for our workplace and culture. This includes receiving 10 Top Workplace awards for Culture Excellence in 2023, seven in 2022, and one each in 2021 and 2020.

These awards span Innovation, Work-Life Balance, Leadership, Cross-Team Collaboration, Meaningful Work, Employee Appreciation, and more. We’ve also been named a Top Workplace by other organizations based on employee feedback.

Join Us

It is the highest honor to have employees give us high marks for our workplace. If this sounds like an environment where you’d like to work, and you’re interested in bringing your talent to Actian, view our open career opportunities.

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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.

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Data Intelligence

What is a Data Product Owner? Role, Skills and Responsibilities

Actian Corporation

February 5, 2024

In our previous article on Data Products, we discussed the definition, characteristics, and examples of data products as well as the necessity to switch to a product-thinking mindset to truly transform your datasets into viable data products. Amid this shift towards a Data Mesh architecture, it is important to highlight a very important part of data product management – data product ownership. Indeed, it is crucial to appoint the right people as stakeholders for your enterprise data products.

In this article, we go over the human side of data products – the role, responsibilities, and required skills of a Data Product Owner.

What are the Role and Skills of a Data Product Owner?

As the name suggests, Data Product Owners are the guarantors of the development and success of data products within an organization. They act as a bridge between data teams, stakeholders, and end-users, translating complex data concepts into actionable insights that drive value and innovation. To do so, Data Product Owners have unique sets of technical skills, including the ability to extract insights from data & identify patterns, understand programming languages such as Python or R, and have a strong foundation in data technologies such as data warehouses, databases, data lakes, etc.

In addition to technical skills, a Data Product Owner has great business acumen, with the ability to understand the business context, objectives, trends, and overall landscape and develop data strategies that are aligned with said context. They therefore use data for decision making by correctly collecting and analyzing data.

Lastly, Data Product Owners have great communication skills, with the ability to convey data insights to the different stakeholders in the company such as data scientists and developers but also non-technical roles such as business users and analysts. They usually also have experience in agile methodologies and problem-solving skills to deliver successful data products on time.

What are a Data Product Owner’s Core Responsibilities?

The multifaceted nature of a Data Product Owner as described above makes them have a variety of responsibilities. In Data Mesh in Action, by J. Majchrzak et al., they list Data Product Owners’ tasks as:

  • Vision definition: They are responsible for determining the purpose of creating a data product, understanding its users, and capturing their expectations through the lens of product thinking.
  • Strategic planning of product development: They are in charge of creating a comprehensive roadmap for the data product’s development journey, as well as defining key performance indicators (KPIs).
  • Ensuring satisfaction requirements: Ensuring the data product meets all requirements is a critical responsibility. This includes providing a detailed metadata description and ensuring compliance with accepted standards and data governance rules.
  • Backlog Management & Prioritization: The Data Product Owner makes tactical decisions regarding the management of the data product backlog. This involves prioritizing requirements, clarifying them, splitting stories, and approving implemented items.
  • Stakeholder Management: They must gather information to understand expectations and clarify any inconsistencies or conflicting requirements to ensure alignment.
  • Collaboration With Development Teams: Engaging with the data product development team is essential for clarifying requirements and making informed decisions on challenges affecting development and implementation.
  • Participation in Data Governance: The Data Product Owner actively contributes to the data governance team, influencing the introduction of rules within the organization and providing valuable feedback on the practical implementation of data governance rules.

While the principle dictates one Data Product Owner for a specific data product, a single owner may oversee multiple products, especially if they are smaller or require less attention. The size and complexity of data products vary, leading to differences in the specific responsibilities shouldered by Data Product Owners.

What are the Differences Between a Data Product Owner and a Product Owner?

The relationship between a Product Owner and a Data Product Owner can vary based on specific characteristics and requirements. While in some instances, these roles overlap, in others, they distinctly diverge. In the book Data Mesh in Action, they distinguish between three different scenarios:

Case 1: The Dual Role

In this scenario, the Data Product Owner also serves as a Product Owner, and the development teams for both the data product and the overall product alignment. This configuration is most fitting when the data product extends from the source system, and the complexity is manageable, not requiring separate development efforts.

An example would be a subscription purchase module providing data on purchases seamlessly integrated into the source system.

Case 2: Dual Ownership, Separate Teams

Here, the Data Product Owner holds a dual role as a Product Owner, but the teams responsible for the data product and the overall product development are distinct. This setup is applied when analytical data derived from the application is extensive, requiring a distinct backlog and a specialized team for execution.

An example would be a subscription purchase module offering analytical data supported by a ML model, enabling predictions of purchase behavior.

Case 3: Independent Entities

In this scenario, the roles of the Data Product Owner and Product Owner are distinct, and the teams responsible for the data product and the overall product development operate independently. This configuration is chosen when the data product is a complex solution demanding independent development efforts.

An example would be building a data mart supported by an ML model for predicting purchase behavior.

In essence, the interplay between the roles of Product Owner and Data Product Owner is contingent upon the intricacies of the data product and its relationship with the overarching system. Whether they converge or diverge, the configuration chosen aligns with the specific demands posed by the complexity and integration requirements of the data product in question.

Conclusion

In conclusion, as organizations increasingly adopt Data Product Management within a Data Mesh architecture, the effectiveness of dedicated Data Product Owners becomes essential. Their capacity to connect technical intricacies with business goals, combined with a deep understanding of evolving data technologies, positions them as central figures in guiding the journey toward unleashing the full potential of enterprise Data Products.

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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 Management

How to Develop a Multi-Cloud Approach to Data Management

Teresa Wingfield

January 26, 2024

multi cloud data management

A recent 451 Research survey found that an astonishing 98% of companies are using more than one cloud provider. Two-thirds of organizations use services from 2 or 3 public cloud providers and nearly one-third of organizations use four or more providers. Using a multi-cloud strategy involves using the services of multiple cloud providers simultaneously. It’s the dominant data management strategy for most organizations.

Top Multi-Cloud Advantages

There’s a long list of reasons why organizations choose to adopt a multi-cloud approach versus just being tied to a single provider.  Here’s a look at some of the top reasons.

You Can Match the Right Cloud to the Right Job

The features and capabilities of cloud vendors vary greatly, so using a multi-cloud approach can let you select the best providers for your specific workload requirements. Differences in services for analytics, machine learning, big data, transactions, enterprise applications, and more are factors to consider when deciding where to run in the cloud. Product integrations, security, compliance, development tools, management tools, and geographic locations unique to a cloud provider may also influence your choice.

You Can Save Money

Pricing between providers can differ significantly. These are just a few examples of what you need to take into account when comparing costs:

  • Providers price the same services differently.
  • Resources such as compute, memory, storage, and networks have different configurations and pricing tiers.
  • The geographic location of a data center can lead to differences in the cost of cloud provider services.
  • Discounts for reserved instances, spot instances, and committed use can save you dollars depending on your usage patterns.
  • Data transfer costs between regions, data centers, and the internet can add up quickly and you should factor these into your costs.
  • The cost of support services can also impact overall expenses.

You Can Enhance Business Continuity

Multi-cloud strategies can enhance business continuity so your cloud processing can resume quickly in the face of disruptions. Below are some aspects of multi-cloud business continuity:

  • There’s no single point of failure.
  • Geographic redundancy enhances resilience against adverse regional events.
  • Cloud provider diversification mitigates the impact of vendor-specific issues such as a service outage or a security breach. Traffic can be redirected to another provider to avoid service disruption.
  • Data storage redundancy and backup across clouds can help prevent data loss and data corruption.
  • Redundant network connectivity across multiple clouds can prevent network-related disruptions.

You Can Avoid Vendor Lock-In

Using multiple cloud providers prevents organizations from being tied to a single provider. This avoids vendor lock-in, giving organizations more freedom to switch providers or negotiate better terms as needed.

You Can Strengthen Your Compliance

Different cloud providers may offer different compliance certifications and different geographic locations for where data is stored. A choice of options helps improve compliance with industry standards and regulations as well as compliance with data residency and data sovereignty-specific regulations.

Some organizations choose to operate a hybrid cloud environment with capabilities stratified across multiple clouds, private and public. Sensitive data applications may be on a private cloud where an organization has more control over the deployment infrastructure.

Actian in a Multi-Cloud World

Despite these advantages, it’s essential for organizations to carefully plan and manage their multi-cloud data management strategy to ensure seamless integration, efficient resource utilization, and strong security.

The Actian Data Platform is a platform that meets multi-cloud data management requirements with features such as a universal data fabric and built-in data integration tools to process and transform data across clouds. You will also benefit from cloud economics, paying only for what you use, having the ability for the service to shut down or go to sleep after a pre-defined period of inactivity, and scheduling starting, stopping, and scaling the environment to optimize uptime and cost. Security such as data plane network isolation, industry-grade encryption, including at-rest and in-flight, IP allow lists, and modern access controls handle the complexities of multi-cloud security.

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About Teresa Wingfield

Teresa Wingfield is Director of Product Marketing at Actian, driving awareness of the Actian Data Platform's integration, management, and analytics capabilities. She brings 20+ years in analytics, security, and cloud solutions marketing at industry leaders such as Cisco, McAfee, and VMware. Teresa focuses on helping customers achieve new levels of innovation and revenue with data. On the Actian blog, Teresa highlights the value of analytics-driven solutions in multiple verticals. Check her posts for real-world transformation stories.

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Data Intelligence

Enhance Your Data Catalog With an Enterprise Data Marketplace (EDM)

Actian Corporation

January 25, 2024

data catalog data marketplace

Over the past decade, Data Catalogs have emerged as important pillars in the landscape of data-driven initiatives. However, many vendors on the market fall short of expectations with lengthy timelines, complex and costly projects, bureaucratic Data Governance models, poor user adoption rates, and low-value creation. This discrepancy extends beyond metadata management projects, reflecting a broader failure at the data management level.

The present situation reveals a disconnection between technical proficiency and business knowledge, a lack of collaboration between data producers and consumers, persistent data latency and quality issues, and unmet scalability of data sources and use cases. Despite substantial investments in both personnel and technology, companies find themselves grappling with a stark reality – the failure to adequately address business needs.

The good news, however, is that this predicament can be reversed by embracing an Enterprise Data Marketplace (EDM) and leveraging existing investments.

Introducing the Enterprise Data Marketplace

An EDM is not a cure-all but rather a transformative solution. It necessitates that companies to reframe their approach to data, introducing a new entity – Data Products. A robust Data Mesh, as advocated by Zhamak Dehghani in her insightful blog post, becomes imperative, with the EDM serving as the experiential layer of the Data Mesh.

However, the landscape has evolved with a new breed of EDM – a Data Sharing Platform integrated with a robust federated Data Catalog:

EDM = Data Sharing Platform + Strong Data Catalog

This is precisely what the Actian Data Intelligence Platform accomplishes, and plans to enhance further, with our definition of an EDM:

An Enterprise Data Marketplace is an e-commerce-like solution, where Data Producers publish their Data Products, and Data Consumers explore, understand, and acquire these published Data Products.

The Marketplace operates atop a Data Catalog, facilitating the sharing and exchange of the most valuable Domain Data packaged as Data Products.

Why Complement Your Data Catalog With an Enterprise Data Marketplace?

We’ve compiled 5 compelling reasons to enhance your Data Catalog with an Enterprise Data Marketplace.

Reason #1: Streamline the Value Creation Process

By entrusting domains with the responsibility of creating Data Products, you unlock the wealth of knowledge possessed by business professionals and foster a more seamless collaboration with Data Engineers, Data Scientists, and Infrastructure teams. Aligned with shared business objectives, the design, creation, and maintenance of valuable, ready-to-use Data Products will collectively adopt a Product Design Thinking mindset.

Within this framework, teams autonomously organize themselves, streamlining ceremonies for the incremental delivery of Data Products, bringing fluidity to the creation process. As Data Products incorporate fresh metadata to guide Data Consumers on their usage, an EDM assumes a pivotal role in shaping and exploring metadata related to Data Products – essentially serving as the Experience Plane within the Data Mesh framework.

By adhering to domain-specific nuances, there is a notable reduction in both the volume and type of metadata, alongside a more efficient curation process. In such instances, a robust EDM, anchored by a potent Data Catalog like the Actian Data Intelligence Platform, emerges as the core engine. This EDM not only facilitates the design of domain-specific ontologies but also boasts automated harvesting capabilities from both on-premises and cloud-based data sources. Moreover, it empowers the federation of Data Catalogs to implement diverse Data Mesh topologies and grants end-users an effortlessly intuitive eCommerce-like Data Shopping experience.

Reason #2: Rationalize Existing Investments

By utilizing an EDM (alongside a powerful Data Catalog), existing investments in modern data platforms and people can be significantly enhanced. Eliminating intricate data pipelines, where data often doesn’t need to be moved, results in substantial cost savings. Similarly, cutting down on complex, numerous, and unnecessary synchronization meetings with cross-functional teams leads to considerable time savings.

Therefore, a focused approach is maintained by the federated governance body, concentrating solely on Data Mesh-related activities. This targeted strategy optimizes resource allocation and accelerates the creation of incremental, delegated Data Products, reducing the Time to Value.

To ensure measurable outcomes, closely monitoring the performance of Data Products with accurate KPIs becomes paramount – This proactive measure enhances decision-making and contributes to the delivery of tangible results.

Reason #3: Achieve Better Adoption Than With a Data Catalog Only

An EDM, coupled with a powerful Data Catalog, plays a pivotal role in facilitating adoption. At the domain level, it aids in designing and curating domain-specific metadata easily understood by Domain Business Users. This avoids the need for a “common layer”, a typical pitfall in Data Catalog adoption. At the Mesh Level, it offers means to consume Data Products effectively, providing information on location, version, quality, state, provenance, platform, schema, etc. A dynamic domain-specific metamodel, coupled with strong search and discovery capabilities, makes the EDM a game-changer.

The EDM’s added value lies in provisioning and access rights, integrating with ticketing systems, dedicated Data Policy enforcement platforms, and features from Modern Data platform vendors – a concept termed Computational Data Governance.

Reason #4: Clarify Accountability and Monitor Value Creation Performance

Applying Product Management principles to Data Products and assigning ownership to domains brings clarity to responsibilities. Each domain becomes accountable for the design, production, and life cycle management of its Data Products. This focused approach ensures that roles and expectations are well-defined.

The EDM then opens up Data Products to the entire organization, setting standards that domains must adhere to. This exposure helps maintain consistency and ensures that Data Products align with organizational goals and quality benchmarks.

In the EDM framework, companies establish tangible KPIs to monitor the business performance of Data Products. This proactive approach enables organizations to assess the effectiveness of their data strategies. Additionally, it empowers Data Consumers to contribute to the evaluation process through crowd-sourced ratings, fostering a collaborative and inclusive environment for feedback and improvement.

Reason #5: Apply Proven Lean Software Development Principles to Data Strategy

The creation of Data Products follows a similar paradigm to the Lean Software Development principles that revolutionized digital transformation. Embracing principles like eliminating waste, amplifying learning, deciding late, delivering fast, and building quality is integral to the approach that a Data Mesh can enable.

In this context, the EDM acts as a collaborative platform for teams engaged in the creation of Data Products. It facilitates:

  • Discovery Features: Offering automatic technical curation of data types, lineage information, and schemas, enabling the swift creation of ad hoc products.
  • Data Mesh-Specific Metadata Curation: The EDM incorporates automatic metadata curation capabilities specifically tailored for Data Mesh, under the condition that the Data Catalog has federation capabilities.
  • 360 Coverage of Data Products Information: Ensuring comprehensive coverage of information related to Data Products, encompassing their design and delivery aspects.

In essence, the collaboration between an Enterprise Data Marketplace and a powerful Data Catalog not only enhances the overall data ecosystem but also brings about tangible benefits by optimizing investments, reducing unnecessary complexities, and improving the efficiency of the data value creation process.

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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 Management

Digital Transformation: Database Modernization in Applications

Teresa Wingfield

January 18, 2024

digital transformation and database modernization

In my previous blog on digital transformation, I wrote about the benefits of migrating mission-critical databases to the cloud. This time, I’m focusing on database modernization in applications. Application modernization can involve modernizing an application’s code, features, architecture and/or infrastructure. It’s a growing priority according to The 2023 Gartner CIO and Technology Executive Survey that places it in the top 4 technology areas in spending, with 46% of organizations increasing their spending on application modernization. Further, Foundry, an IDG company, reports that 87% of its survey respondents cite modernizing critical applications as a key success driver.

7 Benefits of Database Application Modernization

Why all the recent interest in transitioning to modern applications? Application modernization and database modernization are closely intertwined processes that work together to enhance the overall agility, efficiency, performance, security, innovation, and capabilities of an organization’s business. Here’s how application modernization complements database modernization:

Accelerated Time to Market

Monolithic legacy applications are time-consuming to update. Modernized applications with a loosely coupled architecture can enable faster development cycles, reducing the time it takes to bring new features or products to market. Agile development methodologies often accompany application modernization, enabling incremental and iterative development so that teams can respond rapidly to changing business requirements.

Cloud-Enabled Opportunities

Moving applications to the cloud as part of an application modernization initiative provides an extensive list of advantages over on-premises deployments, including elasticity, scalability, accessibility, business continuity, environmental sustainability, and more.

Optimized User Experience

Modernizing applications offers many ways to increase user satisfaction, and productivity, including more intuitive interfaces, personalization, improved response times and better accessibility.  Multi-channel support such as mobile and web and cross-platform compatibility extend reach while advanced search and navigation, rich media incorporation, and third-party integrations add value for users.

Stronger Security and Compliance

Legacy applications built on outdated technologies may lack security features and defenses against contemporary threats and may not comply with regulatory compliance requirements. Modernizing applications allows for the implementation of the latest security measures and compliance standards, reducing the likelihood of security breaches and non-compliance.

Staff Productivity

Legacy systems can be difficult to maintain and may require significant technical resources for updates and support. Modern applications can improve staff efficiency, reduce maintenance expenses, and lead to better utilization of resources for strategic initiatives that deliver greater value to the business.

Easier Integration

Application modernization supports integration with technologies and architectural best practices that enhance interoperability, flexibility, and efficiency. Using technologies such as microservices, APIs, containers, standardized protocols, and/or cloud services, it’s easier to integrate modernized applications within complex IT environments.

Support for Innovation

Legacy applications often make it difficult to incorporate newer technologies, hindering innovation. Modernizing applications allows organizations the ability to leverage emerging technologies, such as machine learning and Internet of Things (IoT) for competitive advantage.

Database Application Modernization With Ingres NeXt

In summary, database application modernization is a strategic digital transformation initiative that can help organizations stay ahead in the digital age.  However, application modernization can be expensive and risky without the right approach.

Ingres NeXt is designed to protect existing database application investments in OpenROAD while leveraging them in new ways to add value to your business, without costly and lengthy rewrites. Flexible options to modernize your OpenROAD applications include:

  • ABF and Forms-Based Applications – Modernize ABF applications to OpenROAD frames using the abf2or migration utility and extend converted applications to mobile and web applications.
  • OpenROAD and Workbench IDE – Migrate partitioned ABF applications to OpenROAD frames.
  • OpenROAD Server – Deploy applications securely in the OpenROAD Server to retain and use application business logic.

In addition, The Ingres NeXt Readiness Assessment offers a pre-defined set of professional services that can lower your risk for application modernization and increase your confidence for a successful cloud journey. The service is designed to assist you with understanding the requirements to modernize Ingres and ABF or OpenROAD applications and to impart recommendations important to your modernization strategy formulation, planning, and implementation.

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About Teresa Wingfield

Teresa Wingfield is Director of Product Marketing at Actian, driving awareness of the Actian Data Platform's integration, management, and analytics capabilities. She brings 20+ years in analytics, security, and cloud solutions marketing at industry leaders such as Cisco, McAfee, and VMware. Teresa focuses on helping customers achieve new levels of innovation and revenue with data. On the Actian blog, Teresa highlights the value of analytics-driven solutions in multiple verticals. Check her posts for real-world transformation stories.

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Data Intelligence

Everything You Need to Know About Data Products

Actian Corporation

January 18, 2024

data products blue cubes

In recent years, the data management and analytics landscape has witnessed a paradigm shift with the emergence of the Data Mesh framework. Coined by Zhamak Dehghani in 2019, Data Mesh is a framework that emphasizes a decentralized and domain-oriented approach to managing data. One notable discipline in the Data Mesh architecture is to treat data as a product, introducing the concept of “data products”. However, the term “data product” is often tossed around without a clear understanding of its essence. In this article, we will shed light on everything you need to know about data products and data product thinking.

Shifting to Product Thinking

For organizations to treat data as products and transform their datasets as data products, it is essential for teams to first shift to a product-thinking mindset. According to J. Majchrzak et al. in Data Mesh in Action,

Product thinking serves as a problem-solving methodology, prioritizing the comprehensive understanding of user needs and the core problem at hand before delving into the product creation process. The primary objective is to narrow the gap between user requirements and the proposed solution.

In their book, they highlight two main principles:

  • Love the problem, not the solution: Before embarking on the design phase of a product, it is imperative to gain an understanding of the users and the specific problem being addressed.
  • Think in terms of products, not features: While there is a natural inclination to concentrate on adding new features and customizing assets, it is crucial to view data as a product that directly satisfies user needs.

Therefore, before unveiling a dataset, adhering to product thinking involves posing essential questions:

  • What is the problem that you want to solve?
  • Who will use your data product?
  • Why are you doing this? What is the vision behind it?
  • What is your strategy? How will you do it?
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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 Management

How Engineers Can Improve Database Reliability

Actian Corporation

January 16, 2024

database reliability

Database reliability is broadly defined as a database that performs consistently and correctly, without interruptions or failures, to ensure accurate and consistent data is readily available for all users. As your organization becomes increasingly data-driven and realizes the importance of using data for decision-making, stakeholders must be able to trust your data. Building trust and having confidence requires complete, accurate, and easily accessible data, which in turn requires a reliable database.

For data to be considered reliable, it should be timely, accurate, consistent, and recoverable. Yet as data processes become more complex, data sources expand, data volumes grow, and data errors have a more significant impact, more attention is given to data quality. It’s also why the role of the database reliability engineer (DBRE) becomes more important.

Preventing data loss and delivering uninterrupted data are increasingly important for modern businesses. Today’s data users expect to be able to access data at any time, from virtually any location. If that doesn’t happen, analysts and other business users lose trust in the database—and database downtime can be extremely expensive. Some estimates put the cost of downtime at approximately $9,000 per minute, with some large organizations losing hundreds of thousands of dollars per hour.

Enable a Highly Functioning and Reliable Database

It’s best to think of a DBRE as an enabler. That’s because the database reliability engineer enables a resilient, scalable, and functional database to meet the demands of users and data-intensive applications. Engineers can ensure database reliability by following a strategy that includes these essential components and capabilities:

Optimize Database Performance

Use tuning tools to gain maximum performance for fast, efficient processing of queries and transactions. Following best practices to optimize performance for your particular database keeps applications running correctly, provides good user experiences, uses resources effectively, and scales more efficiently.

Provide Fault Tolerance

Keep the database operating properly even when components fail. This ensures data is always available to enable business continuity. In addition to offering high availability, fault tolerance delivers uninterrupted database services while assisting with disaster recovery and data integrity. For some industries, fault tolerance may be needed to meet regulatory compliance requirements.

Replicate Data

Create and manage multiple copies of data in different locations or on different servers. Data replication ensures a reliable copy of data is available if another copy becomes damaged or inaccessible due to a failure—organizations can switch to the secondary or standby server to access the data. This offers high availability by making sure a single point of failure does not prevent data accessibility.

Have a Backup and Restore Strategy

Back up data regularly and store it in a secure location so you can quickly recover it if data is lost or corrupted. The data backup process can be automated, and the restoration process must be tested to ensure it works flawlessly when needed. Your backup and restore strategy is critical for protecting valuable data, meeting compliance regulations in some industries, and mitigating the risk of lost data, among other benefits.

Keep Data Secure

Make sure data is safe from breaches and unauthorized access, while making it readily available to anyone across the organization who needs it. Well-established database security protocols and access controls contribute to keeping data safe from internal and external threats.

Balance Workloads

Implement a load-balancing strategy to improve query throughput speed for faster response times, while also preventing a single server from becoming overloaded. Load balancing distributes workloads across multiple database services, which minimizes latency and better utilizes resources to handle more workloads faster.

Improve and Monitor Your Database

Once you have the technologies, processes, and strategy in place for a reliable database, the next step is to keep it running like a finely tuned machine. These approaches help sustain database reliability:

Use Database Metrics

Determine what database reliability looks like for your organization, then identify the metrics needed to ensure you’re meeting your baseline. You can implement database alerts to notify database administrators of issues, such as performance falling below an established metric. Having insights into metrics, including resource utilization and query response speed, allows you to make informed decisions about scaling, capacity planning, and resource allocation.

Monitor the Database

Track the database’s performance and usage to uncover any issues and to ensure it meets your performance goals. Monitoring efforts also help you proactively identify and prevent problems that could slow down the database or cause unexpected downtime.

Continually Use Optimization Techniques

Performance tuning, data partitioning, index optimization, caching, and other tasks work together to achieve a highly optimized database. Performing regular maintenance can also prevent issues that negatively impact the database. Consider database optimization a critical and ongoing process to maintain a responsive and reliable database.

Establish Data Quality Standards

Quality data is a must-have, which requires data that is timely, integrated, accurate, and consistent. Data quality tools and a data management strategy help maintain data quality to meet your compliance needs and usability standards.

Reliable Databases to Meet Your Business and IT Needs

Taking an engineering approach to improve database reliability gives you the data quality, availability, and performance needed to become a truly data-driven organization. A high-functioning, easy-to-use database encourages data integration to eliminate data silos and offer a single source of truth.

Actian offers a range of modern databases to meet your specific business and IT needs. These databases enable you to make confident, data-driven decisions that accelerate your organization’s growth. For example:

  • Actian Ingres offers powerful and scalable transactional processing capabilities.
  • Zen databases are a family of low-maintenance, high performance, and small footprint databases.
  • NoSQL has high-availability, replication, and agile development capabilities, and makes application development fast and easy.

We also have the Actian Data Platform, which is unique in its ability to collect, manage, and analyze data in real-time, with its transactional database, data integration, data quality, and data warehouse capabilities in an easy-to-use platform.

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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.

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Data Intelligence

What is Data Engineering?

Actian Corporation

January 16, 2024

Computer, Woman Programmer And Man Training For Coding, Cyber Security Or Software On Computer. Female It Specialist, Male Coder Or Talking To Connect Internet, Information Update And Cloud Computing

Data engineering is the practice of designing and constructing large-scale systems for collecting, storing, and analyzing data. While companies can amass vast amounts of data, they require the right expertise and technology to ensure the data is in optimal condition upon reaching data scientists and analysts. Ensuring this exploitability is the role of data engineering. Let’s delve into the explanations.

Data engineering is a discipline focused on designing, implementing, and managing data architectures. Its purpose? To cater to a company’s specific requirements regarding information analysis and processing. Data engineers are responsible for creating robust and efficient pipelines and integrating extraction, transformation, and loading (ETL) processes to ensure the quality, consistency, and availability of data. To achieve this, they work closely with data scientists and analysts to ensure the data is relevant, accessible, and exploitable.

Data engineering encompasses not only database management, distributed storage, real-time data flow management, and performance optimization but also its essential mission is to ensure a strong and scalable infrastructure, a fundamental foundation for the development of a genuine data culture within a company.

What do Data Engineers Do?

Behind the term data engineering are data engineers who are responsible for designing, implementing, and maintaining the infrastructure necessary for effective data management within a company. Data quality management, indexing, partitioning, and replication are all part of their responsibilities. They implement monitoring and error management systems while collaborating with data science teams to design data models that meet the company’s objectives.

Benefits of Data Engineering

Within your company, integrating data engineering into your data strategy offers four main advantages.

Optimization of the Data Lifecycle Management

Data engineering ensures the Extraction, Transformation, and Loading (ETL) of data, facilitating consolidation from various sources into centralized warehouses.

Maximum Scalability

Thanks to the use of technologies like Hadoop and Spark, data engineering offers horizontal scalability, allowing companies to efficiently process massive volumes of data in real time.

Improvement of Data Quality

ETL pipelines inherently integrate data cleaning, normalization, and validation processes, thereby strengthening the reliability of analyses.

Access to the Best of Innovation

Data engineering promotes innovation by enabling the seamless integration of new technologies such as machine learning and artificial intelligence, stimulating the creation of advanced analytical solutions for informed decision-making.

Differences Between Data Engineering and Data Science

Far from being opposed, data science and data engineering are complementary disciplines. Data engineering focuses on the design, deployment, and management of data infrastructures, playing a key role in data quality and reliability.

On the other hand, data science focuses more on advanced data analysis. For this, data science teams use different statistical techniques, machine learning algorithms, and artificial intelligence to extract insights and create predictive models.

While data engineering builds the foundations, data science explores these data to generate meaningful knowledge and forecasts. When the former contributes to building your long-term data strategy, the latter is responsible for implementing and applying it sustainably.

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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

Data Mesh 101: Best Practices for Metadata Management

Actian Corporation

January 14, 2024

dot graphic depicting data mesh and metadata management

In the ever-evolving landscape of data management, organizations are shifting towards new innovative approaches to tackle the complexities of their data landscapes. One such notable trend gaining substantial momentum is the concept of Data Mesh – a decentralized approach to data architecture, emphasizing autonomous, domain-oriented data products.

As we embark on this journey of decentralized data, let’s dig into the vital role of metadata and the importance of effectively managing it in the context of Data Mesh.

The Role of Metadata

Metadata, often referred to as ‘data about data,’ plays a fundamental role in shaping a functional data ecosystem. It extends beyond the simple task of describing datasets; rather, it involves understanding the data’s origins, quality, transformations, etc. The different types of metadata include:

  • Technical Metadata: Focuses on the technical aspects of data, such as data formats, schema, data lineage, and storage details.
  • Business Metadata: Business metadata revolves around the business context of data. It includes information about data ownership, business rules, data definitions, and any other details that help align data assets with business objectives.
  • Operational Metadata: Operational metadata provides insights into the day-to-day operations related to data. This includes information about data processing workflows, data refresh schedules, and any operational dependencies.
  • Collaborative Metadata: Collaborative metadata captures information about user interactions, annotations, and comments related to data assets.

In the decentralized framework of Data Mesh, metadata serves as the link, bridging different data domains with these different types of metadata. As data moves among different teams, metadata becomes the guide, assisting everyone in navigating the diverse data landscape. Metadata, therefore, acts as a valuable aid by providing insights into the structure and content of their assets. It facilitates data discovery for users, making it easier to discern and locate specific data that aligns with their needs.

Additionally, metadata forms the basis for data governance, providing a framework for enforcing quality standards, security protocols, and compliance measures uniformly across diverse domains. It plays a critical role in access control and ensures that users are not only informed but also adhere to the defined access policies.

Challenges of Managing Metadata in Data Mesh

One significant challenge stems from the decentralized nature of a Data Mesh. In a traditional centralized data architecture, metadata management is often handled by a dedicated team or department, ensuring consistency and standardization. However, in a Data Mesh, each domain team is responsible for managing its own metadata. This decentralized approach can lead to variations in metadata practices across different domains, making it challenging to maintain uniform standards and enforce data governance policies consistently.

The diversity of data sources and domains within a Data Mesh is another notable challenge in metadata management. Different domains may use various tools, schemas, and structures for organizing and describing their data. Managing metadata across these diverse sources requires establishing common metadata standards and ensuring compatibility, which can be a complex and time-consuming task. The heterogeneity of data sources adds a layer of intricacy to the creation of a cohesive and standardized metadata framework.

Ensuring consistency and quality across metadata is an ongoing challenge in a Data Mesh environment. With multiple domain teams independently managing their metadata, maintaining uniformity becomes a constant effort – Inconsistencies in metadata can lead to misunderstandings, misinterpretations, and errors in data analysis.

Best Practices for Managing Data in Data Mesh

To overcome these challenges, here are some best practices for managing metadata for your organization.

First, establishing clear and standardized metadata definitions across diverse domains is essential for ensuring consistency, interoperability, and a shared understanding of data elements. Clear definitions provide a common language and framework that ensures consistency in how data is described and understood across the organization.

Furthermore, standardized metadata definitions play a pivotal role in data governance. They provide a basis for uniformly enforcing data quality standards, security protocols, and compliance measures across diverse domains. This ensures that data is not only described consistently but also adheres to organizational policies and regulatory requirements, contributing to a robust and compliant data ecosystem.

However, it’s equally important to empower domain teams with ownership of their metadata. This decentralized approach fosters a sense of responsibility and expertise among those who know the data best. By giving domain teams control over their metadata, organizations leverage their specific knowledge to ensure accuracy, consistency, and trustworthiness across all data domains. This approach promotes adaptability within individual domains, contributing to a more reliable and informed data management strategy.

This dual strategy allows for both centralized governance, ensuring organization-wide standards, and decentralized ownership, promoting agility and domain-specific knowledge within the landscape of a Data Mesh.

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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 Management

Is Your Data Management Strategy Ready for Manufacturing?

Traci Curran

January 12, 2024

data management manufacturing

In the rapidly evolving landscape of the manufacturing industry, data has become a cornerstone of innovation. From robotics and connected factories to operational efficiency, the potential for leveraging data is boundless. However, to harness the full power of data, manufacturers must ensure that their data management strategies are not only current but also future-ready. For this reason, organizations must consider critical needs when creating a robust data management strategy. They must ensure that this strategy aligns with manufacturing priorities and carefully consider the value of digital transformation.

Defining the Manufacturing Data Management Strategy

A data management strategy is the backbone of successful data utilization in manufacturing. It encompasses the integration, standardization, and secure data storage, ensuring it is governed and trusted. In the context of the future of manufacturing, this strategy must align seamlessly with industry priorities, such as enhancing efficiency, maintaining quality control, predicting delays, and fostering innovation while simultaneously reducing costs.

The Role of Data Strategy in Digital Transformation for Manufacturing

A forward-thinking data management strategy is indispensable for any manufacturer looking to embark on a digital transformation journey. As the manufacturing landscape becomes increasingly digital and automated, selecting the right platform is crucial. A well-crafted data strategy, as often stated, is at the center of every successful digital transformation. This ensures not just immediate gains but also future-proofs the business against evolving technological landscapes.

Technology is a catalyst for digital transformation in manufacturing, enhancing efficiency, agility, and innovation. Integrating advanced technologies empowers manufacturers to optimize processes, improve product quality, and respond more effectively to market demands. By leveraging technology, manufacturers can not only optimize operations but also get ahead of any disruptions to suppliers or supply chains.

Key Metrics for Measuring Manufacturing Digital Transformation

Measuring the success of digital transformation in manufacturing requires defined metrics that should be an integral part of any data management strategy. These metrics serve as benchmarks, allowing manufacturers to gauge the impact of their digital initiatives. According to Gartner, “36% of manufacturing enterprises realize above-average business value from IT spending in digitalization at a reasonable cost compared with peers.”

Other metrics to consider include:

Customer Engagement

Track metrics such as website traffic, social media interactions, and customer feedback to assess the level of engagement with digital platforms.

Customer Satisfaction (CSAT) Scores

Use surveys and feedback mechanisms to measure customer satisfaction with digital services, products, and overall experiences.

Operational Efficiency

Assess improvements in operational efficiency through metrics like reduced process cycle times, decreased manual intervention, and streamlined workflows.

Employee Productivity

Monitor changes in employee productivity resulting from digital tools and automation. This can include metrics like tasks completed per hour or efficiency gains in specific processes.

Cost Reduction

Measure the cost savings achieved through digital optimization, such as reduced manual processes, lower maintenance costs, and improved resource utilization.

Data Quality and Accuracy

Evaluate the quality and accuracy of data, ensuring that digital transformation initiatives contribute to improved data integrity.

Customer Lifetime Value (CLV)

Evaluate the long-term value generated from each customer, factoring in repeat business, upsells, and customer loyalty influenced by digital initiatives.

Net Promoter Score (NPS)

Measure the likelihood of customers recommending your products or services as an indicator of overall satisfaction and loyalty.

Contextualized Data in the Fourth Industrial Revolution

Industry 4.0 represents a paradigm shift in manufacturing, characterized by integrating advanced technologies, digitalization, and data-driven decision-making. Entering the era of Industry 4.0 necessitates manufacturers to have clear, concise, and contextualized data.

Real-time decision-making is a cornerstone of Industry 4.0, and clear data ensures that manufacturers can swiftly respond to dynamic conditions, optimize processes, and troubleshoot issues in real-time. Predictive maintenance, a key aspect of this industrial revolution, relies on contextualized data to anticipate equipment needs and minimize downtime. By harnessing clear and contextualized data, manufacturers can optimize production processes, implement robust quality control measures, and achieve end-to-end visibility in the supply chain. This level of data clarity facilitates customization and personalization in production, enhances energy efficiency, and supports the integration of connected ecosystems within the manufacturing environment.

Additionally, manufacturers can identify potential risks through clear data insights and implement strategies to mitigate uncertainties. Clear data is crucial for ensuring compliance with regulatory standards, a necessity in Industry 4.0, given the increasing focus on stringent regulations.

Actian’s Role in Manufacturing Data Management

Actian has decades of experience helping manufacturers create and implement robust data management strategies. Actian’s solutions enable data-driven decision-making processes, ensuring manufacturers not only stay competitive in the present but also remain agile and prepared for the future.

In the dynamic landscape of manufacturing, a well-crafted data management strategy is not just a necessity, it’s a roadmap to success. As the industry hurdles towards an era of unprecedented technological advancement, manufacturers must ensure their strategies are not only current but also forward-looking. It’s time to embrace the future of manufacturing by putting data at the forefront of operations, and Actian is here to guide that transformative journey.

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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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Data Management

7 Reasons to Move Mission-Critical Databases to the Cloud Using Migration

Teresa Wingfield

January 10, 2024

cloud migration

Digital transformation refers to the integration and application of digital technologies, processes, and strategies across an organization to fundamentally improve how it operates and delivers value to its customers, the business, and others. It involves leveraging digital tools, data, and technologies to automate business processes, improve customer experiences, drive innovation, and support data-driven decision-making.

The adoption and growth of digital transformation are massive, according to Statista Research. “In 2022, digital transformation spending reached USD 1.6 trillion. By 2026, global spending on digital transformation is anticipated to reach USD 3.4 trillion.” A recent Gartner study reported that digital change is an organizational priority for 87% of senior executives and CIOs, requiring additional focus on data modernization.

Why Move Mission-Critical Databases With Cloud Migration?

Which digital transformation project(s) to pursue depends on your specific situation and goals to ensure successful outcomes. Moving mission-critical databases to the cloud is an untapped opportunity for many organizations, and it can offer tremendous advantages if they do not face compliance and confidentiality restrictions for cloud use or do not have complex legacy entanglements that make moving too hard. Explore these key benefits of cloud migration:

Faster Business Agility

The cloud removes the need for infrastructure procurement, setup, management, and maintenance. Cloud migration helps organizations respond quickly to market changes, customer demands, and competitive pressures by quickly scaling to meet business needs. This agility can lead to a competitive advantage in dynamic industries.

On-Demand Scalability

Cloud elasticity is useful in environments that require the ability to scale storage and compute up or down to match demand. Cloud-native deployments that offer cloud elasticity can ensure optimal performance even during peak usage periods, such as retail during busy holiday shopping or government at tax time.

Greater Sustainability

The shared infrastructure of cloud providers allows for better economies of scale because they can distribute the cost of data centers, cooling systems, and other resources efficiently among many customers. And, instead of over-provisioning infrastructure in their own data center to handle occasional spikes in demand, organizations can dynamically adjust resources in the cloud, minimizing waste.

Optimized User Experience

Regional data centers of cloud providers allow organizations to host databases closer to end users for better performance, responsiveness, and availability of applications. This is especially useful when low latency and quick data access are critical, such as electronic commerce, online gaming, live streaming, and interactive web applications.

Stronger Security and Compliance

Major cloud providers invest significantly in physical security, network security, and data center protections that should keep mission-critical databases safe, provided organizations adhere to best practices for implementation and security. In addition, cloud providers often offer compliance certifications for various regulatory standards that can help simplify the process of adhering to industry regulations and requirements.

Improved Backup and Recovery

The cloud allows organizations to create backups and implement disaster recovery more efficiently. This is crucial for safeguarding mission-critical data against unforeseen events such as data corruption, cyber-attacks, and natural disasters.

Staff Optimization

Many database vendors offer managed or co-managed services in the cloud. These may include taking care of tasks such as provisioning, scaling, hardware maintenance, software patches, upgrades, backups, query optimization, table and index management, application tuning, and more. This leaves teams more time to focus on strategic initiatives with higher business value.

How Actian Supports Migration of Mission-Critical Databases to the Cloud

Actian recognizes that mission-critical database migration to the cloud represents one of the most exciting digital transformation opportunities, making a high impact on business agility, scalability, sustainability, data protection, and staff optimization. This is why we developed the Ingres NeXt Initiative to provide businesses with flexible options for infrastructure, software, and management of the Ingres database or Actian X running on Google Cloud, Microsoft Azure, and Amazon Web Services. Our cloud migration approach minimizes risk, protects business logic, lowers costs, reduces time and effort, and decreases business disruption.

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About Teresa Wingfield

Teresa Wingfield is Director of Product Marketing at Actian, driving awareness of the Actian Data Platform's integration, management, and analytics capabilities. She brings 20+ years in analytics, security, and cloud solutions marketing at industry leaders such as Cisco, McAfee, and VMware. Teresa focuses on helping customers achieve new levels of innovation and revenue with data. On the Actian blog, Teresa highlights the value of analytics-driven solutions in multiple verticals. Check her posts for real-world transformation stories.

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