AI & ML

How to Effectively Prepare Your Data for GenAI

Actian Corporation

March 20, 2024

Preparing your Data using Generative AI

Many organizations are prioritizing the deployment of Generative AI for a number of mission-critical use cases. This isn’t surprising. Everyone seems to be talking about GenAI, with some companies now moving forward with various applications.

While company leaders may be ready to unleash the power of GenAI, their data may not be as ready. That’s because a lack of proper data preparation is setting up many organizations for costly and time-consuming setbacks.

However, when approached correctly, proper data prep can help accelerate and enhance GenAI deployments. That’s why preparing data for GenAI is essential, just like for other analytics, to avoid the “garbage in, garbage out” principle and to prevent skewed results.

As Actian shared in our presentation at the recent Gartner Data & Analytics Summit, there are both promises and pitfalls when it comes to GenAI. That’s why you need to be skeptical about the hype and make sure your data is ready to deliver the GenAI results you’re expecting.

Data Prep is Step One

We noted in our recent news release that comprehensive data preparation is the key to ensuring generative AI applications can do their job effectively and deliver trustworthy results. This is supported by the Gartner “Hype Cycle for Artificial Intelligence, 2023” that says, “Quality data is crucial for generative AI to perform well on specific tasks.”

In addition, Gartner explains that “Many enterprises attempt to tackle AI without considering AI-specific data management issues. The importance of data management in AI is often underestimated, so data management solutions are now being adjusted for AI needs.”

A lack of adequately prepared data is certainly not a new issue. For example, 70% of digital transformation projects fail because of hidden challenges that organizations haven’t thought through, according to McKinsey. This is proving true for GenAI too—there are a range of challenges many organizations are not thinking about in their rush to deploy a GenAI solution. One challenge is data quality, which must be addressed before making data available for GenAI use cases.

What a New Survey Reveals About GenAI Readiness

To gain insights into companies’ readiness for GenAI, Actian commissioned research that surveyed 550 organizations in seven countries—70% of respondents were director level or higher. The survey found that GenAI is being increasingly used for mission-critical use cases:

  • 44% of survey respondents are implementing GenAI applications today.
  • 24% are just starting and will be implementing it soon.
  • 30% are in the planning or consideration stage.

The majority of respondents trust GenAI outcomes:

  • 75% say they have a good deal or high degree of trust in the outcomes.
  • 5% say they do not have very much or not much trust in them.

It’s important to note that 75% of those who trust GenAI outcomes developed that trust based on their use of other GenAI solutions such as ChatGPT rather than their own deployments. This level of undeserved trust has the potential to lead to problems because users do not fully understand the risk that poor data quality poses to GenAI outcomes in business.

It’s one issue if ChatGPT makes a typo. It’s quite another issue if business users are turning to GenAI to write code, audit financial reports, create designs for physical products, or deliver after-visit summaries for patients—these high value use cases do not have a margin for error. It’s not surprising, therefore, that our survey found that 87% of respondents agree that data prep is very or extremely important to GenAI outcomes.

Use Our Checklist to Ensure Data Readiness

While organizations may have a high degree of confidence in GenAI, the reality is that their data may not be as ready as they think. As Deloitte notes in “The State of Generative AI in the Enterprise,” organizations may become less confident over time as they gain experience with the larger challenges of deploying generative AI at scale. “In other words, the more they know, the more they might realize how much they don’t know,” according to Deloitte.

This could be why only four percent of people in charge of data readiness say they were ready for GenAI, according to Gartner’s “We Shape AI, AI Shapes Us: 2023 IT Symposium/Xpo Keynote Insights.” At Actian, we realize there’s a lot of competitive pressure to implement GenAI now, which can prompt organizations to launch it without thinking through data and approaches carefully.

In our experience at Actian, there are many hidden risks related to navigating and achieving desired outcomes for GenAI. Addressing these risks requires you to:

  • Ensure data quality and cleanliness.
  • Monitor the accuracy of training data and machine learning optimization.
  • Identify shifting data sets along with changing use case and business requirements over time.
  • Map and integrate data from outside sources, and bring in unstructured data.
  • Maintain compliance with privacy laws and security issues.
  • Address the human learning curve.

Actian can help your organization get your data ready to optimize GenAI outcomes. We have a “GenAI Data Readiness Checklist” that includes the results of our survey and also a strategic checklist to get your data prepped. You can also contact us and then our experts will help you find the fastest path to the GenAI deployment that’s right for your business.

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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

5 Reasons Why Organizations are Shifting Towards Data Mesh

Actian Corporation

March 19, 2024

data mesh red and orange grid

Data has become the lifeblood of organizations, driving innovation, efficiency, and competitiveness. Yet, amidst the exponential growth of data, organizations grapple with the challenge of deriving meaningful insights and value from their data assets. The complexities of data management exacerbate this struggle, as traditional approaches–which are often centralized and monolithic and anchored in a data lake or warehouse–fall short in addressing the diverse needs and dynamic nature of modern data ecosystems.

Against this backdrop, a new paradigm is emerging: data mesh. This innovative approach to data management represents a departure from traditional centralized models, offering a decentralized framework that empowers organizations to harness the full potential of their data assets.

In this article, we delve into the factors explaining the enthusiasm for data mesh and the decentralization of data management: those related to economic pressure and competitiveness weighing on organizations, and others related to the very birth of data mesh itself.

Reason 1: Economic Pressures

Executive teams are under increasing pressure to justify their investments in data infrastructure and management. Despite substantial resources allocated to these initiatives over the past decade, measuring tangible economic returns remains a significant challenge.

This frustration stems from the inability to correlate data investments with concrete financial outcomes, leading to uncertainty and dissatisfaction among stakeholders.

Reason 2: Competitiveness and the Impact of AI

The fear of losing competitiveness due to the inability to take advantage of the rapidly democratizing opportunities offered by artificial intelligence also creates frustration. Until recently, developing AI models was a long and risky process with uncertain outcomes. The rapid development of highly performant, inexpensive, and easy-to-integrate off-the-shelf models has changed the game entirely.

It’s now possible to prototype an AI application in a few days by adjusting and combining shelf models. However, scaling requires feeding these models with data that is of quality, traceable, secure, compliant, etc. In short, well-managed data adds additional pressure on centralized data management teams.

Reason 3: Flexibility in Implementation

Other factors are more directly related to the nature of the data mesh itself: it is not an architecture, a language, a method, or even a technology – all of which are often complex, controversial, and divisive subjects.

Data mesh simply lays out a few easy-to-understand principles, and these principles aren’t prescriptive – they can be implemented in a thousand different ways.

Reason 4: Endorsement and Enthusiasm

Data mesh principles are also not purely academic: they transpose to the world of analytical data the practices that allowed large software organizations to master the complexity of their systems while continuing to innovate at a rapid pace. Data mesh is based on strong theoretical and empirical foundations – it’s very hard to resist the argumentation developed by Dehghani.

It has the rare quality of easily gaining the support, even enthusiasm, of data teams, including at the decision-making level. This unanimity limits resistance to change, ensures strong sponsorship, and partly explains the speed of its adoption worldwide.

Reason 5: Accessibility and Cost-Effectiveness

The principles of data mesh are easy to implement, without significant investments, simply by reallocating existing resources. When transforming a monolithic software platform into a plethora of loosely coupled and tightly integrated distributed services, one knows that the operation will be lengthy, costly, and risky.

For data, the situation is very different. Data is already, by nature, distributed. And all organizations already have the necessary technologies to extract, process, store, and consume their data in higher-level applications. Implementing the basics of data mesh primarily involves transforming an organization and practices, not making new massive technological investments.

In their eagerness to reform their practices, data leaders have found in data mesh a convincing and accessible framework and have massively included it in their strategic roadmap. It goes without saying, however, that the transition from a centralized data management model to an operational data mesh can only be done gradually – there is no magic wand. And each organization begins this transition in its own context – its strategic challenges, personnel, organization, processes, culture, or even its technological stack.

The Practical Guide to Data Mesh: Setting up and Supervising an Enterprise-Wide Data Mesh

Written by Guillaume Bodet, our guide was designed to arm you with practical strategies for implementing data mesh in your organization, helping you:

  • Start your data mesh journey with a focused pilot project, leveraging an initial use case.
  • Discover efficient methods for scaling up your data mesh, enhancing the creation of data products.
  • Acknowledge the pivotal role an internal marketplace plays in facilitating the effective consumption of data products.
  • Learn how the Actian Data Intelligence Platform emerges as a robust supervision system, orchestrating an enterprise-wide data mesh.

Get the eBook here!

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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 Integration

Effective Data Integration and Automation in Digital Landscape

Actian Corporation

March 18, 2024

data cloud using effective data integration

In today’s data-driven world, the demand for seamless data integration and automation has never been greater. Various sectors rely heavily on data and applications to drive their operations, making it crucial to have efficient methods of integrating and automating processes. However, ensuring successful implementation requires careful planning and consideration of various factors.

Data integration refers to combining data from different sources and systems into a unified, standardized view. This integration gives organizations a comprehensive and accurate understanding of their data, enabling them to make well-informed decisions. By integrating data from various systems and applications, companies can avoid inconsistencies and fragmentations often arising from siloed data. This, in turn, leads to improved efficiency and productivity across the organization.

One of the primary challenges in data integration is the complexity and high cost associated with traditional system integration methods. However, advancements in technology have led to the availability of several solutions aimed at simplifying the integration process. Whether it’s in-house development or leveraging third-party solutions, choosing the right integration approach is crucial for achieving success. IT leaders, application managers, data engineers, and data architects play vital roles in this planning process, ensuring that the chosen integration approach aligns with the organization’s goals and objectives.

Before embarking on an integration project, thorough planning and assessment are essential. Understanding the specific business problems that need to be resolved through integration is paramount. This involves identifying the stakeholders and their requirements and the anticipated benefits of the integration. Evaluating different integration options, opportunities, and limitations is also critical. Infrastructure costs, deployment, maintenance efforts, and the solution’s adaptability to future business needs should be thoroughly considered before deciding on an integration approach.

Five Foundational Areas for Initiating any Data Integration Project

Establishing the Necessity

It is essential to understand the use cases and desired business outcomes to determine the necessity for an integration solution.

Tailoring User Experience

The integration solution should provide a unique user experience tailored to all integration roles and stakeholders.

Understanding Existing Business Systems and Processes

A detailed understanding of the existing business systems, data structures, scalability, dependencies, and regulatory compliance is essential.

Assessing Available Technologies

It is important to assess the available technologies and their potential to meet the organization’s integration needs and objectives.

Data Synchronization Management

Managing data synchronization is an ongoing process that requires careful planning, ownership, management, scheduling, and control.

Effective data integration and automation are crucial for organizations to thrive in today’s data-driven world. With the increasing demand for data and applications, it is imperative to prevent inconsistencies and fragmentations. By understanding the need for integration, addressing foundational areas, and leveraging solutions like Actian, organizations can streamline their data integration processes, make informed decisions, and achieve their business objectives. Embracing the power of data integration and automation will pave the way for future success in the digital age.

A Solution for Seamless Data Integration

Actian offers a suite of solutions to address the challenges associated with integration. Their comprehensive suite of products covers the entire data journey from edge to cloud, ensuring seamless integration across platforms. The Actian platform provides the flexibility to meet diverse business needs, empowering companies to effectively overcome data integration challenges and achieve their business goals. By simplifying how individuals connect, manage, and analyze data, Actian’s data solutions facilitate data-driven decisions that accelerate business growth. The data platform integrates seamlessly, performs reliably, and delivers at industry-leading speeds.

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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 Engineering

The Data Engineering Decision Guide to Data Integration Tools

Dee Radh

March 15, 2024

A team preparing to decide which path to take for successful data integration

With organizations using an average of 130 apps, the problem of data fragmentation has become increasingly prevalent. As data production remains high, data engineers need a robust data integration strategy. A crucial part of this strategy is selecting the right data integration tool to unify siloed data.

Assessing Your Data Integration Needs

Before selecting a data integration tool, it’s crucial to understand your organization’s specific needs and data-driven initiatives, whether they involve improving customer experiences, optimizing operations, or generating insights for strategic decisions.

Understand Business Objectives

Begin by gaining a deep understanding of the organization’s business objectives and goals. This will provide context for the data integration requirements and help prioritize efforts accordingly. Collaborate with key stakeholders, including business analysts, data analysts, and decision-makers, to gather their input and requirements. Understand their data needs and use cases, including their specific data management rules, retention policies, and data privacy requirements.

Audit Data Sources

Next, identify all the sources of data within your organization. These may include databases, data lakes, cloud storage, SaaS applications, REST APIs, and even external data providers. Evaluate each data source based on factors such as data volume, data structure (structured, semi-structured, unstructured), data frequency (real-time, batch), data quality, and access methods (API, file transfer, direct database connection). Understanding the diversity of your data sources is essential in choosing a tool that can connect to and extract data from all of them.

Define Data Volume and Velocity

Consider the volume and velocity of data that your organization deals with. Are you handling terabytes of data per day, or is it just gigabytes? Determine the acceptable data latency for various use cases. Is the data streaming in real-time, or is it batch-oriented? Knowing this will help you select a tool to handle your specific data throughput.

Identify Transformation Requirements

Determine the extent of data transformation logic and preparation required to make the data usable for analytics or reporting. Some data integration tools offer extensive transformation capabilities, while others are more limited. Knowing your transformation needs will help you choose a tool that can provide a comprehensive set of transformation functions to clean, enrich, and structure data as needed.

Consider Integration With Data Warehouse and BI Tools

Consider the data warehouse, data lake, and analytical tools and platforms (e.g., BI tools, data visualization tools) that will consume the integrated data. Ensure that data pipelines are designed to support these tools seamlessly. Data engineers can establish a consistent and standardized way for analysts and line-of-business users to access and analyze data.

Choosing the Right Data Integration Approach

There are different approaches to data integration. Selecting the right one depends on your organization’s needs and existing infrastructure.

Batch vs. Real-Time Data Integration

Consider whether your organization requires batch processing or real-time data integration—they are two distinct approaches to moving and processing data. Batch processing is suitable for scenarios like historical data analysis where immediate insights are not critical and data updates can happen periodically, while real-time integration is essential for applications and use cases like Internet of Things (IoT) that demand up-to-the-minute data insights.

On-Premises vs. Cloud Integration

Determine whether your data integration needs are primarily on-premises or in the cloud. On-premises data integration involves managing data and infrastructure within an organization’s own data centers or physical facilities, whereas cloud data integration relies on cloud service providers’ infrastructure to store and process data. Some tools specialize in on-premises data integration, while others are built for the cloud or hybrid environments. Choose a tool that depends on factors such as data volume, scalability requirements, cost considerations, and data residency requirements.

Hybrid Integration

Many organizations have a hybrid infrastructure, with data both on-premises and in the cloud. Hybrid integration provides flexibility to scale resources as needed, using cloud resources for scalability while maintaining on-premises infrastructure for specific workloads. In such cases, consider a hybrid data integration and data quality tool like Actian’s DataConnect or the Actian Data Platform to seamlessly bridge both environments and ensure smooth data flow to support a variety of operational and analytical use cases.

Evaluating ETL Tool Features

As you evaluate ETL tools, consider the following features and capabilities:

Data Source and Destination Connectivity and Extensibility

Ensure that the tool can easily connect to your various data sources and destinations, including relational databases, SaaS applications, data warehouses, and data lakes. Native ETL connectors provide direct, seamless access to the latest version of data sources and destinations without the need for custom development. As data volumes grow, native connectors can often scale seamlessly, taking advantage of the underlying infrastructure’s capabilities. This ensures that data pipelines remain performant even with increasing data loads. If you have an outlier data source, look for a vendor that provides Import API, webhooks, or custom source development.

Scalability and Performance

Check if the tool can scale with your organization’s growing data needs. Performance is crucial, especially for large-scale data integration tasks. Inefficient data pipelines with high latency may result in underutilization of computational resources because systems may spend more time waiting for data than processing it. An ETL tool that supports parallel processing can handle large volumes of data efficiently. It can also scale easily to accommodate growing data needs. Data latency is a critical consideration for data engineers, because it directly impacts the timeliness, accuracy, and utility of data for analytics and decision-making.

Data Transformation Capabilities

Evaluate the tool’s data transformation capabilities to handle unique business rules. It should provide the necessary functions for cleaning, enriching, and structuring raw data to make it suitable for analysis, reporting, and other downstream applications. The specific transformations required can include: data deduplication, formatting, aggregation, normalization etc., depending on the nature of the data, the objectives of the data project, and the tools and technologies used in the data engineering pipeline.

Data Quality and Validation Capabilities

A robust monitoring and error-handling system is essential for tracking data quality over time. The tool should include data quality checks and validation mechanisms to ensure that incoming data meets predefined quality standards. This is essential for maintaining data integrity and accuracy, and it directly impacts the accuracy, reliability, and effectiveness of analytic initiatives. High quality data builds trust in analytical findings among stakeholders. When data is trustworthy, decision-makers are more likely to rely on the insights generated from analytics. Data quality is also an integral part of data governance practices.

Security and Regulatory Compliance

Ensure that the tool offers robust security features to protect your data during transit and at rest. Features such as SSH tunneling and VPNs provide encrypted communication channels, ensuring the confidentiality and integrity of data during transit. It should also help you comply with data privacy regulations, such as GDPR or HIPAA.

Ease of Use and Deployment

Consider the tool’s ease of use and deployment. A user-friendly low-code interface can boost productivity, save time, and reduce the learning curve for your team, especially for citizen integrators that can come from anywhere within the organization. A marketing manager, for example, may want to integrate web traffic, email marketing, ad platform, and customer relationship management (CRM) data into a data warehouse for attribution analysis.

Vendor Support

Assess the level of support, response times, and service-level agreements (SLAs) provided by the vendor. Do they offer comprehensive documentation, training resources, and responsive customer support? Additionally, consider the size and activity of the tool’s user community, which can be a valuable resource for troubleshooting and sharing best practices.

A fully managed hybrid solution like Actian simplifies complex data integration challenges and gives you the flexibility to adapt to evolving data integration needs.

For a comprehensive guide to evaluating and selecting the right Data Integration tool, download the ebook Data Engineering Guide: Nine Steps to Select the Right Data Integration Tool.

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About Dee Radh

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

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ESG

How to Use Business Intelligence to Support Strategic Sustainability

Actian Corporation

March 13, 2024

An analyst researching ESG strategies for sustainability

In our modern business world, where new trends, demands, and innovation can happen at lightning-fast speed, sustainability has become a top focus for executives and customers. In response, forward-thinking organizations are looking for ways to minimize their global impact, reduce carbon emissions, and implement sustainability best practices to optimize efficiency without sacrificing profitability.

As noted in Harvard Business Review (HBR), consumers are viewing sustainability as a baseline requirement when making purchases. “Our research suggests we’re on the brink of a major shift in consumption patterns, where truly sustainable brands—those that make good on their promises to people and the planet—will seize the advantage from brands that make flimsy claims or that have not invested sufficiently in sustainability.”

One effective approach for strategically supporting and measuring sustainability, including environmental, social, and governance (ESG) efforts, is to use business intelligence (BI). BI is a trusted, powerful, and proven process that transforms data into actionable insights to enable confident and informed decision-making. The insights can be applied to sustainability goals.

The Crucial Role of BI in Improving Sustainability

BI can play a pivotal role in your sustainability efforts by analyzing data related to resource usage, energy consumption, and waste across business operations, supply chains, manufacturing processes, product design and lifecycle, and other areas. BI insights can uncover patterns such as spikes in energy usage, areas most prone to waste, or process inefficiencies that create barriers to achieving sustainability goals.

The power of BI stems from its ability to perform analytics on large volumes of data from a variety of sources. This capability enables you to monitor and report on sustainability efforts while identifying areas where you can improve processes to reduce your environmental impact.

For example, a manufacturer using BI can determine that a specific process run at a certain time of the day is causing the company to consume significantly more energy. The process could potentially be altered to improve efficiency, lowering energy usage. Another example is in the transportation and logistics industry. These companies can use real-time BI to optimize their deliveries based on traffic, weather, and other factors for the fastest route possible, which can reduce carbon emissions and use less energy.

When BI is used in conjunction with data visualization tools, the insights are put into charts, graphs, or maps. This makes the insights easy to understand, even for people without a technical or analytical background. You can look at data about your organization’s waste, for example, to find out at a glance where there are opportunities for recycling or waste reduction.

BI Demands Efficient Data Management Processes

One common challenge many organizations face when leveraging BI for sustainability, or for any other use case, is managing the expansive data sets that are available. Data management is a necessity for any BI or analytics project, but many organizations lack this essential capability. It can be due to a lack of scalability, an inability to easily add data pipelines, outdated integration tools that can’t easily ingest and share data, or information stuck in silos. This limits the data that can be used for BI, which in turn can lead to inaccurate or incomplete insights.

That’s why you need modern data processing and BI capabilities. You also need to ensure that your data is accurate, reliable, and trusted in order to have full confidence in the results. A modern data management strategy is required for effective BI. The strategy should equip your organization to handle the volume, variety, and velocity of data and make it accessible and available for BI.

Data management best practices also include cleansing, enriching, and aggregating data to ensure it has the quality you need. You must also determine if your BI requires real-time or near real-time data and if so, have a platform in place to deliver data at the speed you need. Data management, BI, and sustainability are intertwined—data management provides data quality and accessibility, while BI turns the data into strategic insights to inform and refine sustainable strategies.

BI is a Key Enabler for the Future of Sustainability

If your organization is placing an increasing focus on sustainability, you’ll realize the value of BI to help with these efforts. The power of BI and the evolution of BI technology will help you better anticipate resource needs, have the insights needed to proactively minimize your environmental impact, and forecast trends that could affect sustainability. You’ll also have the insights needed to align your business goals with ESG objectives.

BI is a powerful tool in your arsenal to implement and continually improve sustainability practices. With detailed and accurate data analysis, along with the ability to drill down into issues for granular details, you can identify new opportunities to drive efficiencies, make better use of your resources, and take meaningful actions that reduce your environmental footprint.

Moving forward, integrating BI processes into business sustainability strategies will become more common and sophisticated—and more necessary. BI is positioned to play an essential role in enabling data-driven decisions that promote ESG without compromising business performance. In fact, BI can help you strike an acceptable balance that encourages growth for both sustainability and the business.

Managing data and embracing BI are two steps needed to become more sustainable in our increasingly eco-conscious world. Likewise, data and BI can be instrumental in identifying areas that can benefit from increased efficiencies, pinpointing resources that are being underutilized, and determining where sustainability efforts can make the most impact.

Actian Offers the Ideal Platform to Support Sustainability

With consumers, business partners, and business stakeholders placing a strong emphasis on sustainability and ESG responsibility, BI stands out as a proven tool to guide you toward sustainable practices while also boosting the bottom line. Actian can help you integrate and manage your data for BI and analytics.

Our high-performance technologies can bring together large volumes of data for analysis. We can integrate data from various sources, including internet of things (IoT) devices, supply chains, manufacturing processes, and energy metrics for a comprehensive view of your ESG posture.

The scalable Actian Data Platform makes data easy to integrate, manage, and analyze to support your sustainability goals, regardless of the size or complexity of your data sets. You can also use the platform for predictive modeling to determine how proposed process changes will affect sustainability.

At Actian, we’re committed to data-driven sustainability and encourage our customers to also use data to make a positive environmental impact.

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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 Monetization?

Actian Corporation

March 12, 2024

data monetization depiction

In the modern digital landscape, data monetization has emerged as a pivotal concept driving economic growth and innovation. At its core, data monetization refers to the process of extracting economic value from data assets. It involves leveraging data resources to generate revenue, enhance decision-making processes, and create competitive advantages for businesses and organizations.

In this article, we provide a comprehensive overview of data monetization: what it entails, the various strategies and methods used, and the benefits and challenges for data-driven companies.

The Definition of Data Monetization

Data monetization refers to the process of converting raw data or data assets into tangible economic value. It involves identifying, extracting, and leveraging insights, patterns, and information contained within data assets to generate revenue, improve operational efficiencies, enhance decision-making processes, and create new business opportunities.

Data monetization can take different forms: Internal Data Monetization – Which focuses on leveraging data assets within an organization, and External Data Monetization – which involves the sale, licensing, or sharing of data assets with external parties such as partners, customers, or third-party vendors.

Moreover, we can distinguish two main strategies to monetize data: Direct Data Monetization – Which refers to selling data assets directly to external parties or customers for financial gain, and Indirect Data Monetization – which refers to using data internally to optimize operations, improve products or services, and enhance customer experiences.

Internal vs. External Data Monetization

What is Internal Data Monetization?

Internal data monetization involves leveraging data assets within an organization to enhance operational efficiency, inform strategic decision-making, and drive innovation. Essentially, it’s about extracting value from data generated and collected by the organization itself. This could include transaction records, customer interactions, and performance metrics captured from internal systems and processes. Internal data monetization focuses on optimizing internal operations and improving business outcomes through data-driven insights and analysis.

Examples of internal data monetization approaches include:

Data Optimization

Organizations utilize data analytics and business intelligence tools to optimize internal processes, streamline workflows, and improve resource allocation. By analyzing internal data sources, companies can identify inefficiencies, bottlenecks, and areas for improvement, leading to cost savings and operational efficiencies.

Product Development

Internal data can be instrumental in informing the development of new products or services tailored to meet customer needs and market demands. By analyzing market trends and performance metrics, organizations can identify opportunities for innovation and develop products that resonate with their target audience.

Operational Insights

By analyzing operational data, organizations can identify trends, patterns, and outliers that impact business performance, enabling them to make informed decisions and optimize operational processes.

What is External Data Monetization?

External data monetization refers to the strategic sale, licensing, or sharing of data assets with external parties outside the organization. This involves leveraging data assets to create revenue streams or strategic partnerships with external entities.

Examples of external data monetization approaches include:

Data Brokerage

Data brokerage is a strategic practice where organizations serve as intermediaries between data providers and consumers, facilitating the exchange and sale of data assets for financial gain. In essence, data brokers aggregate, package, and sell datasets containing valuable insights and information to external parties for various purposes, including market research, analytics, and targeted advertising.

Data Licensing

Data licensing involves organizations granting external parties the rights to access and utilize proprietary data assets for specific purposes, durations, or usage rights, typically in exchange for licensing fees or royalties. This strategic practice enables organizations to monetize their valuable data assets while providing external entities with access to valuable insights for various purposes, including marketing, research, and analytics.

Advertising and Marketing

External data monetization involves leveraging consumer data to target advertisements, promotions, and marketing campaigns more effectively to external audiences. Organizations can personalize messages, optimize ad targeting, and maximize the return on advertising investments by analyzing consumer behavior, preferences, and demographics.

Direct vs. Indirect Data Monetization

What is Direct Data Monetization?

Direct data monetization is a strategy that involves the immediate sale or licensing of raw or processed data to external parties for financial gain. This approach transforms data into a commodity, offering organizations the opportunity to capitalize on the valuable insights and information contained within their datasets. Direct data monetization strategies focus on extracting economic value from data assets by making them available for purchase or subscription by external entities.

Examples of direct Data Monetization strategies include:

Selling Raw or Processed Data

Organizations engage in direct data monetization by selling datasets to third parties. These datasets may encompass a wide range of information, including demographic profiles, consumer behavior data, market trends, and industry-specific insight.

Offering Data-as-a-Service (DaaS)

Data-as-a-Service (DaaS) is a direct monetization model where organizations provide access to their data assets on a subscription basis. DaaS offerings enable external entities to leverage data in real-time, either through Application Programming Interfaces (APIs) or cloud-based platforms, without the need for extensive infrastructure or data management capabilities.

What is Indirect Data Monetization?

On the other hand, indirect data monetization involves utilizing data internally to optimize operations and enhance products or services. Rather than selling the data itself, organizations leverage information derived from data analysis to drive internal improvements and create value within the organization itself.

Examples of indirect Data Monetization strategies include:

Improving Internal Processes and Operations

By analyzing internal data sources, organizations gain valuable insights into their operational processes and identify areas for improvement. This enables organizations to optimize supply chain management, improve resource allocation, and streamline business processes, leading to cost savings and increased operational efficiency.

Enhancing Product Development and Innovation

Data-driven product development is an indirect monetization strategy where organizations leverage valuable information from internal data to innovate and create new products or services. By analyzing internal data sources, including customer feedback, market trends, and performance metrics, organizations gain valuable insights into emerging needs and market demands.

Benefits of Data Monetization

Revenue Generation Opportunities

Data monetization serves as a catalyst for organizations to tap into additional revenue streams by capitalizing on their data assets. Through the strategic sale or licensing of data to external parties, businesses can diversify their income sources and bolster financial growth and stability.

Enhanced Customer Experiences

By harnessing insights derived from comprehensive data analysis, organizations gain an invaluable understanding of customer preferences, behaviors, and needs. Armed with this knowledge, businesses can tailor their products, services, and marketing endeavors to resonate more deeply with their target audience. The result is a heightened level of customer satisfaction and loyalty, as customers feel understood, valued, and catered to in a personalized manner that transcends generic approaches.

Challenges of Data Monetization

Data Security Risks

From data breaches to the threat of unauthorized access and cyberattacks, safeguarding data assets becomes critical when monetizing data. Organizations must implement robust cybersecurity measures, fortified with encryption protocols, access controls, and vigilant monitoring to uphold sensitive information.

Ethical Considerations

In their quest for data monetization, organizations encounter various ethical considerations. These include issues of data ownership, consent, and transparency. It’s crucial for organizations to proceed cautiously, ensuring that their data collection, usage, and sharing practices adhere to ethical standards. This involves respecting user privacy rights, obtaining informed consent, and promoting transparency in data handling.

In conclusion, data monetization represents more than just a revenue stream; it embodies a paradigm shift in how organizations leverage data to drive value, foster innovation, and shape the future of business.

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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.
AI & ML

Data Preparation Guide: 6 Steps to Deliver High Quality GenAI Models

Dee Radh

March 11, 2024

3D keyboard tile for AI

Data preparation is a critical step in the data analysis workflow and is essential for ensuring the accuracy, reliability, and usability of data for downstream tasks. But as companies continue to struggle with data access and accuracy, and as data volumes multiply, the challenges of data silos and trust become more pronounced.

According to Ventana Research, data teams spend a whopping 69% of their time on data preparation tasks. Data preparation might be the least enjoyable part of their job, but the quality and cleanliness of data directly impact analytics, insights, and decision-making. This also holds true for Generative AI. The quality of your training data affects the performance of GenAI models for your business.

High-Quality Data Preparation: The Foundation for Successful AI

Generative AI models, such as Generative Adversarial Networks (GANs) or Variational Autoencoders (VAEs), learn from patterns and structures present in the input data to generate new content. To train models effectively, data must be curated, transformed, and organized into a structured format, free from missing values, missing fields, duplicates, inconsistent formatting, outliers, and biases.

Without a doubt, data preparation tasks are a time-consuming and repetitive process. But, failure to adequately prepare data can result in suboptimal performance, biased outcomes, and ethical, legal, and practical challenges for Generative AI applications.

Generative AI models lacking sufficient data preparation may face several challenges and limitations. Here are three major consequences:

Poor Quality Outputs

Generative AI models often require data to be represented in a specific format or encoding in a way that’s suitable for the modeling task. Without proper data preparation, the input data may contain noise, errors, or biases that negatively impact the training process. As a result, Generative AI models may produce outputs that are of poor quality, lack realism, or contain artifacts and distortions.

Biased Outputs

Imbalanced datasets in which certain classes or categories are underrepresented, can lead to biased models and poor generalization performance. Data preparation ensures that the training data is free from noise, errors, and biases, which can adversely affect the model’s ability to learn and generate realistic outputs.

Compromised Ethics and Privacy

Generative AI models trained on sensitive or personal data must adhere to strict privacy and ethical guidelines. Data preparation involves anonymizing or de-identifying sensitive information to protect individuals’ privacy and comply with regulatory requirements, such as GDPR or HIPAA.

By following a systematic checklist for data preparation, data scientists can improve model performance, reduce bias, and accelerate the development of Generative AI applications. Here are six steps to follow:

  1. Project Goals

    • Clearly outline the objectives and desired outcomes of the Generative AI model so you can identify the types of data needed to train the model.
    • Understand how the model will be utilized in the business context.

  1. Data Collection

    • Determine and gather all potential sources of data relevant to the project.
    • Consider structured and unstructured data from internal and external sources.
    • Ensure data collection methods comply with relevant regulations and privacy policies (e.g. GDPR).

  1. Data Prep

    • Handle missing values, outliers, and inconsistencies in the data.
    • Standardize data formats and units for consistency.
    • Perform exploratory data analysis (EDA) to understand the characteristics, distributions, and patterns in the data.

  1. Model Selection and Training

    • Choose an appropriate Generative AI model architecture based on project requirements and data characteristics (e.g., GANs, VAEs, autoregressive models). Consider pre-trained models or architectures tailored to specific tasks.
    • Train the selected model using the prepared dataset.
    • Validate model outputs qualitatively and quantitatively. Conduct sensitivity analysis to understand model robustness.

  1. Deployment Considerations

    • Prepare the model for deployment in the business environment.
    • Optimize model inference speed and resource requirements.
    • Implement monitoring mechanisms to track model performance in production.

  1. Documentation and Reporting

    • Document all steps taken during data preparation, model development, and evaluation.
    • Address concerns related to fairness, transparency, and privacy throughout the project lifecycle.
    • Communicate findings and recommendations to stakeholders effectively for full transparency into processes.

Data preparation is a critical step for Generative AI because it ensures that the input data is of high quality, appropriately represented, and well-suited for training models to generate realistic, meaningful and ethically responsible outputs. By investing time and effort in data preparation, organizations can improve the performance, reliability, and ethical implications of their Generative AI applications.

Actian Data Preparation for GenAI

The Actian Data Platform comes with unified data integration, warehousing and visualization in a single platform. It includes a comprehensive set of capabilities for preprocessing, transformations, enrichment, normalization and serialization of structured, semi-structured and unstructured data such as JSON/XML, delimited files, RDBMS, JDBC/ODBC, HBase, Binary, ORC, ARFF, Parquet and Avro.

At Actian, our mission is to enable data engineers, data scientists and data analysts to work with high-quality, reliable data, no matter where it lives. We believe that when data teams focus on delivering comprehensive and trusted data pipelines, business leaders can truly benefit from groundbreaking technologies, such as GenAI.

Book a demo to see how Actian can help automate data preparation tasks in a robust, scalable, price-performant way.

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About Dee Radh

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

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Data Management

How to Modernize Your Data Management Strategy in the Auto Industry

Actian Corporation

March 5, 2024

Man pondering an automotive data management strategy in the auto industry

In the data-driven automotive industry, a modern data management strategy is needed to oversee and drive data usage to improve operations, spark innovation, meet customer demand for features and services, create designs and safety features, and inform decisions. Keeping the strategy up to date ensures it meets your current data needs and aligns with business priorities.

With so many data sources now available—and new ones constantly emerging—in addition to data volumes growing rapidly, companies in the automotive industry need a data management strategy that supports this modern reality. A vast range of data is available, including sensor, telematics, and customer data, and it all needs to be integrated and made easily accessible to analysts, engineers, marketers, and others.

Go Beyond Traditional Data Management Approaches

In today’s fast-changing data management environment, the ability to understand and solve data challenges is essential to becoming a true data-driven automotive company. As AWS explains, a robust strategy can help solve data management challenges, improve customer experience and loyalty, build future-proof apps, and deliver other benefits.

By contrast, not having a strategy or taking an outdated approach to data can have negative consequences. “When companies have ineffective strategies, they handle daily tasks less effectively,” according to Dataversity. “Data and data processes get duplicated between different departments, and data management gaps continue to exist.”

A modern data management strategy must go beyond traditional approaches to address present-day needs such as scalability, real-time data processing, building data pipelines to new sources, and integrating diverse data. The strategy should be supported by technology that delivers the capabilities your business needs, such as managing complex and large volumes of data.

Plan to Make Data Readily Available

Your data strategy should cover the variety and complexity of your data, how the data will be brought together, and how the integrated data will be shared in real-time, if necessary, with everyone who needs it. The strategy must ultimately ensure a unified, comprehensive view of the data in order to provide accurate and trusted insights.

Making data readily available with proper governance is essential to fostering a data-driven culture, enabling informed decision-making, and designing vehicles that meet customer wants and needs. The data can also help you predict market changes, gain insights into your supply chains, and better understand business operations.

As best practices and technologies for data management continue to evolve, your strategy and data management tools should also advance to ensure you’re able to optimize all of your data. A modern data management strategy designed to meet your business and IT needs can help you be better prepared for the future of the automotive industry. 

Align Your Data Strategy With Business Goals

Your data strategy should support current business priorities, such as meeting environmental, sustainability, and governance (ESG) mandates. As the automotive industry uses data for innovations such as autonomous driving vehicles and intelligent manufacturing processes, there is also a growing pressure to meet ESG goals.

As a result of ESG and other business objectives, your data strategy must address multiple business needs:

  • Deliver speed and performance to process and analyze data quickly for timely insights.
  • Offer scalability to ingest and manage growing data volumes without compromising performance.
  • Integrate technology to ensure data flows seamlessly to apps, platforms, and other sources and locations.
  • Ensure governance so data follows established processes for security, compliance, and usage.
  • Build trust in the data so all stakeholders have confidence in the insights for informed decision-making.
  • Improve sustainability by using data to lower your environmental impact and decrease energy consumption.
  • Future-proof your strategy with an approach that gives you the agility to meet shifting or new priorities.

The road ahead for the automotive industry requires businesses to continually explore new use cases for data to stay ahead of changing market dynamics, customer expectations, and compliance requirements. Your ability to innovate, accelerate growth, and maintain competitiveness demands a data strategy that reflects your current and future needs.

How Actian Can Support Your Strategy

Modernizing your data management strategy is essential to meet business and IT needs, achieve ESG mandates, and leverage the full value of your data. Actian can help. We have the expertise to help you build a customized strategy for your data, and we have the platform to make data easy to connect, manage, and analyze.

The Actian Data Platform is more than a tool. It’s a solution that enables you to navigate the complex data landscape in the automotive industry. The scalable platform can handle large-scale data processing to quickly deliver answers—which is key in an industry where decisions can have far-reaching implications—without sacrificing performance.

With Actian, you can meet your objectives faster, ensuring your future is data-driven, sustainable, clear, and more attainable. It’s why more than 10,000 businesses trust Actian with their data.

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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

Everything You Need to Know About Data Contracts

Actian Corporation

March 4, 2024

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Enterprises exchange vast volumes of data between different departments, services, and partner ecosystems from various applications, technologies, and sources. Ensuring that the data being exchanged is reliable, of high quality, and trustworthy is vital for generating tangible business value. This is where data contracts come in. Similar to traditional contracts that define expectations and responsibilities, data contracts serve as the framework for reliable data exchange.

In this article, learn everything you need to know about data contracts.

What is a Data Contract?

A data contract is essentially an agreement between two or more parties regarding the structure, format, and semantics of the data being exchanged. It serves as a blueprint that defines how information should be organized, encoded, and validated during the communication process. Moreover, a crucial aspect of a data contract involves specifying how and when it should be delivered to ensure data freshness. Ideally, they should be provided at the start of any data-sharing agreement, setting clear guidelines from the outset while ensuring alignment with the evolving regulatory landscape and technological advancements.

Data contracts typically serve as the bridge between data producers, such as software engineers, and data consumers, such as data engineers or scientists. These contracts meticulously outline how data should be structured and organized to facilitate its utilization by downstream processes, such as data pipelines. Accuracy in data becomes essential to prevent downstream quality issues and ensure the precision of data analyses.

Yet, data producers may lack insights into the specific requirements and essential information needed by each data team’s organization for effective data analysis. In response to this gap, data contracts have emerged as indispensable. They provide a shared understanding and agreement regarding data ownership, organization, and characteristics, facilitating smoother collaboration and more effective data utilization across diverse teams and processes.

It’s important to emphasize that data contracts are occasionally separated from data sharing agreements. While data contracts intricately outline the technical specifics and legal obligations inherent in data exchange, data sharing agreements provide a simplified version, often in formats like Word documents, specifically tailored for non-technical stakeholders like Data Protection Officers (DPOs) and legal counsels.

What is in a Data Contract?

A data contract typically includes agreements on:

Semantics

Semantics in a data contract clarify the meaning and intended usage of data elements and fields, ensuring mutual understanding among all parties. Clear documentation provides guidance on format, constraints, and requirements, promoting consistency and reliability across systems.

The Data Model (Schema)

The schema in a data contract defines the structure of datasets, including data types and relationships. It guides users in handling and processing data, ensuring consistency across systems for seamless integration and effective decision-making.

Service Level Agreements (SLA)

The SLAs component of a data contract sets out agreed standards for data-related services to ensure the freshness and availability of the data. It defines metrics like response times, uptime, and issue resolution procedures. SLAs assign accountability and responsibilities to both parties, ensuring service levels are met. Examples of delivery frequencies include in batch, e.g. once a week, on-demand as an API, or in real-time as a stream.

Data Governance

In the data contract, data governance establishes guidelines for managing data responsibly. It clarifies roles, responsibilities, and accountability, ensuring compliance with regulations and fostering trust among stakeholders. This framework helps maintain data integrity and reliability, aligning with legal requirements and organizational objectives.

Data Quality

The data quality section of a data contract ensures that exchanged data meets predefined standards, including criteria such as accuracy, completeness, consistency, and timeliness. By specifying data validation rules and error-handling protocols, the contract aims to maintain the integrity and reliability of the data throughout its lifecycle.

Data Security and Privacy

The data security and privacy part of a data contract outlines measures to protect sensitive information and ensure compliance with privacy regulations. It includes policies for encryption, access controls, and regular audits to safeguard data integrity and confidentiality. The contract emphasizes compliance with laws like GDPR, HIPAA, or CCPA to protect individuals’ privacy rights and build trust among stakeholders.

Here is an example of a data contract from PayPal’s open-sourced Data Contract:

Who is Responsible for Data Contracts?

Creating data contracts typically involves collaboration between all stakeholders within an organization, including data architects, data engineers, compliance experts, and business analysts.

Data Architects

Data architects play a key role in defining the technical aspects of the data contract, such as data structures, formats, and validation rules. They ensure that the data contract aligns with the organization’s data architecture principles and standards, facilitating interoperability and integration across different systems and applications.

Data Engineers

Data engineers are responsible for implementing the technical specifications outlined in the data contract. They develop data pipelines, integration processes, and data transformation routines to ensure that data is exchanged, processed, and stored according to the contract requirements. Their expertise in data modeling, database management, and data integration is essential for translating the data contract into actionable solutions.

Compliance Experts

Compliance experts also play a crucial role in creating data contracts by ensuring that the agreements comply with relevant laws, regulations, and contractual obligations. They review and draft contractual clauses related to data ownership, privacy, security, intellectual property rights, and liability, mitigating legal risks and ensuring that the interests of all parties involved are protected.

Business Analysts

Business analysts contribute by providing insights into the business requirements, use cases, and data dependencies that inform the design and implementation of the data contract. They help identify data sources, define data attributes, and articulate business rules and validation criteria that drive the development of the contract.

The Importance of Data Contracts

At the core of data contracts lies the establishment of clear guidelines, terms, and expectations governing data sharing activities. By outlining the rights, responsibilities, and usage parameters associated with shared data, data contracts help foster transparency and mitigate potential conflicts or misunderstandings among parties involved in data exchanges.

Data Quality

One of the primary importance of data contracts is their role in ensuring data quality and integrity throughout the data lifecycle. By defining standards, formats, and validation protocols for data exchange, contracts promote adherence to consistent data structures and quality benchmarks. This, in turn, helps minimize data discrepancies, errors, and inconsistencies, thereby enhancing the reliability and trustworthiness of shared data assets for downstream analysis and decision-making processes.

Data Governance and Regulatory Compliance

Data contracts serve as indispensable tools for promoting data governance and regulatory compliance within organizations. In an increasingly regulated environment, where data privacy laws and industry standards govern the handling and protection of sensitive information, contracts provide a framework for implementing robust data protection measures and ensuring adherence to legal requirements. By incorporating provisions for data security, privacy, and compliance with relevant regulations, contracts help mitigate legal risks, protect sensitive data, and uphold the trust and confidence of data subjects and stakeholders.

Data Collaboration

Data contracts facilitate effective collaboration and partnership among diverse stakeholders involved in data sharing initiatives. By articulating the roles, responsibilities, and expectations of each party, contracts create a shared understanding and alignment of objectives, fostering a collaborative environment conducive to innovation and knowledge exchange.

In conclusion, data contracts extend beyond mere legal instruments; they serve as foundational pillars for promoting data-driven decision-making, fostering trust and accountability, and enabling efficient data exchanging ecosystems.

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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 Security

Data Security: The Advantages of Hybrid vs. Public Clouds

Actian Corporation

February 29, 2024

Data Security

As an industry, we often discuss proper and effective data analysis, however data security is actually even more important. After all, what good is effective analysis without securing the foundational data? Additionally, in 2024 there are numerous clouds one can persist data within including public, private, and hybrid cloud environments. This raises the natural question of how to properly secure your data for the cloud.

Public, Multi-Cloud, and Hybrid Clouds

It helps to start with a baseline of common terminology used throughout the industry. Public clouds are publicly accessible compute and storage services provided by third-party cloud providers. Multi-cloud is simply an architecture composed of services originating from more than one public cloud.

A hybrid cloud is composed of different interconnected public and private clouds that work together sharing data and processing tasks. Interconnectivity between hybrid environments is established with local area networks, wide area networks, VPNs, and APIs. Like all cloud environments, hybrid environments leverage virtualization, containerization, and software-defined networking and storage technologies. And dedicated management planes allow users to allocate resources and scale on-demand.

Security Benefits of Hybrid Cloud Architecture

A hybrid cloud is ideal when you want to leverage both the scale of public cloud services while also securing and retaining a subset of your data on-premises. This helps an organization retain and secure compliance and to address data security policies. Sensitive datasets can be retained on-premises while less sensitive assets may be published to public cloud services.

Hybrid clouds provide the ability to scale on-demand public services during peak workloads. Organizations reap cost optimization by being able to leverage both on-premises and public cloud services and storage assets. And there are disaster recovery and geographic failover benefits to hybrid cloud solutions. Finally, a hybrid cloud enables businesses to gradually migrate legacy applications and datasets from on-premises to public cloud environments.

Actian Data Platform

Actian Data Platform coupled with DataConnect provides no-code, low-code and pro-code data integrations that enable hybrid cloud data solutions. Actian DataConnect provides enterprise-grade integration with connectivity support for both our public and private cloud data platforms. Public cloud data services can be provisioned using SOAP or REST API access with configurable authentication. Users are able to schedule and execute data integration jobs that securely move data across all Actian Data Platform environments. Both at-rest and in-flight data encryption can also be implemented.

Actian Data Platform’s data warehousing component can be scaled up and down in real-time, this helps greatly with right-sizing workload scale. The Actian public cloud data warehouse is built on decades of patented real-time query processing and optimizer innovations. In summary, the Actian Data Platform is unique in its ability to collect, manage, and analyze data in real-time, leveraging its native data integration, data quality, and data warehouse capabilities in an easy-to-use single 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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Databases

Easily Add Modern User Interfaces to Your Database Applications

Teresa Wingfield

February 29, 2024

Data Modernization with OpenRoad

Modernizing legacy database applications brings all the advantages of the cloud alongside benefits such as faster development, user experience optimization, staff efficiency, stronger security and compliance, and improved interoperability. In my first blog on legacy application modernization with OpenROAD, a rapid database application development tool, I drilled into the many ways it makes it easier to have data modernization in applications with low risk by retaining your existing business logic. However, there’s still another big part of the legacy modernization journey, the user experience.

Users expect modern, intuitive interfaces with rich features and responsive design. Legacy applications often lack these qualities, which can often require significant redesign and redevelopment during application modernization to meet modern user experience expectations. Not so with OpenROAD! It simplifies the process of creating modern, visually appealing user interfaces by providing developers with a range of tools and features discussed below.

The abf2or Migration Utility

The abf2or migration utility modernizes Application-By-Forms (ABF) applications to OpenROAD frames, including form layout, controls, properties, and event handlers. It migrates business logic implemented in ABF scripts to equivalent logic in OpenROAD. This may involve translating script code and ensuring compatibility with OpenROAD’s scripting language. The utility also handles the migration of data sources to ensure that data connections and queries function properly and can convert report definitions.

WebGen

WebGen is an OpenROAD utility that lets you quickly generate web and mobile applications in HTML5 and JavaScript from OpenROAD frames allowing OpenROAD applications to deployed on-line and on mobile devices.    

OpenROAD and Workbench IDE

The OpenROAD Workbench Integrated Development Environment (IDE) is a comprehensive toolset for software development, particularly for creating and maintaining applications built using the OpenROAD framework. It provides tools specifically designed to migrate partitioned ABF applications to OpenROAD frames. Developers can then use the IDE’s visual design tools to further refine and customize the programs.   

Platform and Device Compatibility

Multiple platform support, including Windows and Linux, lets developers create user interfaces that can run seamlessly across different operating systems without significant modification. Developers can deliver applications to a desktop or place them on a web server for web browser access; OpenROAD installs them automatically if not already installed. The runtime for Windows Mobile enables deploying OpenROAD applications to mobile phones and Pocket PC devices.

Visual Development Environment

OpenROAD provides a visual development environment where developers can design user interface components using drag-and-drop tools, visual editors, and wizards. This makes it easier for developers to create complex user interface layouts without writing extensive code manually.   

Component Library

OpenROAD offers a rich library of pre-built user interface components, such as buttons, menus, dialog boxes, and data grids. Developers can easily customize and integrate these components into applications, saving time and user interface design effort.

Integration With Modern Technologies

Integration with modern technologies and frameworks such as HTML5, CSS3, and JavaScript allows developers to incorporate modern user interface design principles, such as responsive design and animations, into their applications.

Scalability and Performance

OpenROAD delivers scalable and high-performance user interfaces capable of handling large volumes of data and complex interactions. It optimizes resource utilization and minimizes latency, ensuring a smooth and responsive user experience.

Modernize Your OpenROAD Applications

Your legacy database applications may be stable, but most will not meet the expectations of users who want modern user interfaces. You don’t have to settle for the status quo. OpenROAD makes it easy to deliver what your users are asking for with migration tools to convert older interfaces, visual design tools, support for web and mobile application development, an extensive library of pre-built user interface components, and much more.

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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 Management

The Link Between Trusted Data and Expanded Innovation

Actian Corporation

February 27, 2024

Enterprise Data, Expanded Innovation

One highlight of my job is being able to talk to customers and prospective customers throughout the year at various events. What I keep hearing is that data is hard, and this holds true for companies of all sizes. And they’re right. Data can be hard. It can be hard to integrate, manage, govern, secure, and analyze. Building pipelines to new data sources can also be hard.

Business and IT both need data to be accessible to all users and applications, cost-effective to store, and deliver real-time insights. Any data challenges will limit these capabilities and present major barriers to innovation. That’s why we’ve made it our mission to make data easy and trustworthy.

Actian exists to provide the most trusted, flexible, and easy-to-use data platform on the market. We know that’s a bold promise and requires solving a lot of your data pain points. Yet we also know that to be truly data-driven, you must have uninterrupted access to trusted data.

Overcoming the Trust Barrier

At Actian, we’ve been saying for a long time that you need to be able to trust your data. For too many companies, that’s not happening, or it’s not happening promptly. For example, nearly half—48%—of CEOs worry about data accuracy, according to IBM, while Gartner found that less than half of data and analytics teams—just 44%—are effectively providing value to their organization.

These numbers are unacceptable, especially in the age of technology. Everyone who uses data should be able to trust it to deliver ongoing value. So, we have to pause and ask ourselves why this isn’t happening. The answer is that common barriers often get in the way of reaching data goals, such as:

  • Silos that create isolated, outdated, and untrustworthy data.
  • Quality issues, such as incomplete, inaccurate, and inconsistent data.
  • Users do not have the skills needed to connect and analyze data, so they rely on IT.
  • Latency issues prevent real-time data access, which limits timely insights.
  • Data management problems that existed on-premises were migrated to the cloud.

Organizations know they have some or all of these problems, but they often don’t know what steps are needed to resolve them. Actian can help. We have the technology and expertise to enable data confidence—regardless of where you are on your data journey.

Innovation Starts With Trustworthy Data

What if you could swiftly go from data to decision with full confidence and ease? It doesn’t have to be a pipe dream. The solution is readily available now. It ensures you’re using high-quality, accurate data so you have full confidence in your decision-making. It simplifies data transformations, empowering you to get the data you want, when and how you want it, regardless of your skill level, and without relying on IT. Plus, you won’t have to wait for data because it gets delivered in real-time.

The Actian Data Platform makes data easy-to-use, allowing you to meet the needs of more business users, analysts, and data-intensive applications. You can collect, manage, and analyze data in real-time with our transactional database, data integration, data quality, and data warehouse capabilities working together in a single, easy-to-use platform.

The platform lets you manage data from any public cloud, multi- or hybrid cloud, and on-premises environment through a single pane of glass. The platform’s self-service data integration lowers costs while enabling you to perform more use cases without needing multiple data products.

What does all of this mean for your business? It means that data integration, access, and quality are easier than ever. It also means that you can trust your data to make confident decisions that accelerate your organization’s growth, foster new levels of innovation, support your digital transformation, and deliver other business value.

Enabling a Data-Driven Culture

With data volumes becoming more robust, having immediate access to high-quality data is essential, but challenging. Any problems with quality, latency, or integration will compound as data volumes grow, leading to potentially misinformed decision-making and mistrust in the data. Establishing data quality standards, making integration and access easy, and putting data in the hands of everyone who needs it advances the business, promotes a data-driven culture, and drives innovation. And this is where Actian can play a critical role.

What makes the Actian Data Platform unique, at a high level, is its ability to consolidate various data functions into a single platform, making data readily available and easy to use across your organization.

The platform handles extract, transform, and load (ETL), data transformation, data quality checks, and data analytics all in one place. Bringing everything and everyone together on a single platform lowers costs and reduces the resources needed to manage your data system. You benefit from real-time, trustworthy data across the entire organization, giving you full confidence in your data.

When you trust your data, you have the ability—and the confidence—to explore more use cases, increase revenues, reduce costs, fast-track innovation, win market share, and more for a strategic edge in your industry. Our customers are using data to drive new successes everyday!

Additional Resources:

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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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