Actian Life

Actian Life: Celebrating Our Author and Our SEO Award Winner

Actian Corporation

May 20, 2024

Headshots of Ron Weber and Thomas Schweser representing the joy of being a part of Actian Life

Actian employees Thomas Schweser coauthored a book on graph theory and Ron Weber earned a 2023 SEO Evangelist Edgie award, exemplifying Actian’s culture of innovation.

At Actian, we believe that our employees’ achievements are a strong reflection of our vibrant culture and innovative spirit. That holds true whether employees are making breakthroughs and delivering value in their day jobs or finding success in work-adjacent activities.

Today, we’re proud to shine a spotlight on Thomas Schweser, who co-wrote a book on graph theory called “Brooks’ Theorem,” and Ron Weber, who received BrightEdge’s 2023 SEO Evangelist Edgie award. They showcase employee achievements in two highly competitive areas.

Graph Theory Guru and SEO Maestro

Schweser is a research engineer on Actian’s Vector team based in Ilmenau, Germany. His book, published by Springer, focuses on graph coloring and critical graphics, which are a niche but important area of discrete mathematics.

While graph theory isn’t his primary focus at work, he appreciates its ubiquitous presence in the technology world. “Graphs are everywhere, especially in computer science,” he explains. “They make complex information digestible and help visualize relationships clearly.”

His book offers a valuable resource for those studying or utilizing Brooks’ Theorem—which states a relationship between the maximum degree of a graph and its chromatic number. “The book gives an overview of all the important graph coloring theorems and trends that have occurred over the last decades,” Schweser points out. “It should serve as a nice book if you want to give a college lecture on graph coloring.”

On the U.S. side of the business, Weber is the Senior Director, Web Communications and SEO, for Actian. Based in San Diego, he leads efforts to enable coworkers across the organization to succeed with and leverage SEO-driven content. As soon as he joined Actian a couple years ago, he went right to work on a complete website redesign while creating an aggressive content development schedule and building a formal SEO program from scratch.

The results were immediate and impressive:

  • A whopping 96% improvement in Actian content that comes up on the first page in search results because of strong keywords and robust content.
  • 33% improvement in second page results, and 51% increase in third page results.
  • 7% improvement in website traffic.
  • 1% boost in conversion rates.
  • Overall increases in website traffic, lead volume, and qualified leads.

“We grew our website traffic exponentially from 2022 to 2023, and again in 2024,” Weber notes. “A lot of it was the content strategy, like insisting that we start developing a lot of reader-friendly content. This is not a surprise—you can’t have SEO without content, and I’ve been advocating for SEO since I got here.”

Pursuing Passions Leads to Successes

Schweser and Weber’s successes are the direct result of pursuing their passions. Weber’s journey into SEO began in the early days of the internet when he was helping clients with website optimization and using paid search engines to drive results. His passion for search engine optimization has only grown since then, which mirrors the importance for Actian to place near the top in internet search results.

“If we think about how we want companies to migrate to Actian, we have to know what they’re searching for and we need to have content around that part of the journey,” he explains. “More than 90% of the customer journey involves companies using search engines, so we need to meet them at every step.”

Weber continues to stay ahead of changes in search engine algorithms that impact page rankings. He enjoys seeing Actian place high in search results that feature specific keywords. “We’re number four right now in a search term against 23 billion results,” he notes. “That to me is a thrill—you get to number four or even number one against millions and millions of index pages—and that excitement never gets old.”

Schweser’s journey to having a book published began in 2015 when he was finishing his bachelor’s thesis. A professor, Michael Stiebitz, shared an early version of the book that he was working on with his colleague Bjarne Toft. That draft served as the starting point for Schweser’s master’s thesis and later his Ph.D. thesis. The three collaborated, gathered examples and papers about the theorem from across decades, and co-authored the book together.

“In 2020, I was asked to join the book as a coauthor, and of course I accepted,” he relates. “A lot of the research that I was dealing with in my PhD thesis also made it into the book.”

Commitments to End Goals Are Validated

The SEO award is particularly gratifying for Weber because it validates his ongoing efforts at Actian. “It’s meaningful because it shows that our strategy works and that our team’s hard work pays off,” he notes.

He challenges himself and his team to continue evolving their strategy to engage and retain website visitors. “Our play is, ‘How do we bring people to our site? How do we engage them with good content? How do we get them to do the thing that we want them to do?’” he explains. “We have to understand how to acquire, convert, and then retain them over time.”

Weber credits the Actian leadership team, especially CMO Jennifer Jackson, for supporting his efforts, including investing in the tools needed to build and measure the success of a modern website. “This is very much their award too,” he says. “When I see our CMO showcase our site, it makes our work very meaningful.”

For Schweser, the book was a culmination of his ongoing interest and research in Brooks’ Theorem. “There was no comprehensive overview of all the recent trends in graph coloring theory,” he points out. “A lot of people were writing papers, but nobody tried to collect all of them, and nobody was trying to figure out the large trends that exist there. That’s what we did with our book.”

Helping the Next Generation of Employees

One area that Schweser and Weber have in common is their enthusiasm for helping students who are about to enter the workforce. Schweser, along with coworkers, works with interns on Actian projects, while Weber is an adjunct professor for content marketing at the University of California, San Diego.

Schweser is excited about mentoring the next generation of tech talent and has helped guide numerous students through hands-on projects that actively contributed to Actian goals and product releases. Over the last year, his office has mentored about 10 students.

“Along with my colleague Steffen Kläbe, I’m responsible for the German student program at Actian,” he says. “We try to find students from the universities who want to do an internship with us or want to write their thesis in collaboration with Actian. I have always enjoyed working with students, and it’s great that Actian offers us the opportunity to continue doing that here.”

Weber also has experience mentoring college students by serving as an adjunct professor. He teaches students about the value of SEO and how to optimize SEO platforms to drive results. In addition, he has experience working with interns and supporting them as they transition to full-time careers.

Many Paths to Innovation

Actian prides itself on innovation. As Schweser and Weber have demonstrated, there are many ways to innovate and drive success. Having a clear strategy, the right resources, and strong backing leads to exceptional results.

Their achievements reflect Actian’s culture of supporting and valuing all employees’ contributions. Employees’ diverse backgrounds and ability to combine different perspectives ultimately enable outstanding solutions. Whether it’s writing and researching graph theory or creating award-winning SEO strategies, Actian employees show how to achieve innovation in their fields.

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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

Top 5 Data Integration Use Cases for Data Leaders

Dee Radh

May 13, 2024

Data Integration code

Summary

Chief Data Officers (CDOs) and Chief Information Officers (CIOs) play critical roles in navigating the complexities of modern data environments. As data grows exponentially and spans across cloud, on-premises, and various SaaS applications, the challenge of integrating and managing this data becomes increasingly daunting. In the guide Top 5 Data Integration Use Cases, we explore five key data integration use cases that empower business users by enabling seamless access, consolidation, and analysis of data. These use cases highlight the significance of robust data integration solutions in driving efficiency, informed decision-making, and overall business success.


Modern organizations face significant data integration challenges due to the exponential growth of cloud-based data. With the surge in projects fueled by cloud computing, IoT, and sophisticated ecosystems, there is an intensified pressure on data integration initiatives. Effective data integration strategies are necessary to leverage data and other technologies across multiple platforms such as SaaS applications, cloud-based data warehouses, and internal systems.

As digital transformations advance, the need for efficient data delivery methods grows, encompassing both on-premises and cloud-based endpoints. Integration capabilities provided as a service have emerged as a robust solution to meet the evolving demands of modern data integration. The widespread adoption of SaaS applications among line-of-business (LOB) users is a significant driver for cloud-based integration solutions. Business users require a straightforward way to exchange data across various SaaS applications, often without IT’s involvement.

However, integrating data stored in apps and enterprise systems typically necessitates IT assistance, creating barriers to data access and causing blind spots in both on-premises and cloud data. Enterprise systems hold crucial data that can provide insights into customer interactions, payments, support issues, and other business areas. Yet, this data is often isolated and safeguarded as mission-critical assets.

For effective integration, a solution is needed that enables secure information sharing with all users, independent of engineering or IT resources. Empowering LOBs to access and integrate data securely and independently helps avoid delays and bottlenecks associated with traditional integration methods. Ensuring critical information is easily accessible to all employees is essential for maintaining a competitive advantage, adapting swiftly to evolving business conditions, and building a data-driven culture.

Below are five typical use cases that can benefit from a modern data platform with self-service data integration:

Data Consolidation and Access

Data platforms with integration capabilities empower business users to access and leverage data stored in data warehouses, as well as on-premises and cloud-based data. Equipped with pre-built connectors, data quality features, and scheduling functions, these platforms minimize IT involvement. Business users can create tailored integration scenarios, effortlessly retrieving pertinent data from various sources, leading to improved decision-making and valuable insights tailored to user needs.

Process Automation

Integration and automation enhance efficiency and streamline operations. Through system integration and task automation, companies can accelerate data processing and analysis, enabling faster access to information. This saves significant time and allows business users to focus on more strategic endeavors. Automation optimizes workflows, minimizes errors, and ultimately improves operational efficiency.

Sales and Marketing Alignment

Integrating CRM systems with marketing automation platforms ensures seamless data flow between sales and marketing teams, optimizing lead management and customer engagement. This integration enhances revenue generation processes and facilitates informed decision-making through real-time tracking and analysis of customer data. By aligning sales and marketing efforts, businesses boost productivity and achieve cohesive goals faster, driving growth and delivering exceptional customer experiences.

Customer 360

Integrating customer data from various touchpoints, such as website interactions, support tickets, and sales interactions, offers a comprehensive understanding of each customer. This holistic view allows marketing teams to personalize activities based on individual customer preferences and behaviors. Integrated data helps identify patterns and trends, maximizing marketing efforts and better controlling budgets. It also enhances customer service, enabling businesses to anticipate and address customer needs effectively.

Real-Time Reporting and Analytics

Integrating operational systems with business intelligence (BI) tools empowers business users to access real-time insights and reports, facilitating data-driven decision-making. Real-time reporting and analytics are indispensable for competitiveness in today’s fast-paced market, allowing businesses to react quickly to market changes and improve customer service with up-to-date information.

Data integration is a strategic necessity for organizations aiming to leverage their data effectively. For CDOs and CIOs, investing in robust data integration solutions is not just about addressing immediate challenges but also about laying the foundation for long-term success. By embracing the use cases outlined above, organizations can empower their teams, streamline operations, and drive sustainable growth. Ultimately, a well-integrated data environment enables leaders to make informed decisions, adapt swiftly to changes, and maintain a competitive edge in the marketplace.

For data leaders dealing with data that resides on-premises, in the cloud, and in hybrid environments, downloading the Top 5 Data Integration Use Cases guide is an essential step towards eliminating data silos. 

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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.
Data Management

Modernizing Data Architectures in the Public Sector

Tim Williams

May 7, 2024

Modernizing Data Architectures

Trusted and integrated data plays an increasingly critical role in business success; the public sector is facing a significant challenge—how to modernize its data architecture to connect and share data. Strategic modernization is needed to manage the ever-growing volumes of diverse data while ensuring quality, efficient service delivery to meet the changing needs of government employees, citizens, and other stakeholders.

Relying on legacy systems in the public sector can lead to problems such as:

  • An inability to scale to meet current and future data needs.
  • A lack of integration capabilities creates barriers to data sharing.
  • Manual processes cause inefficiencies and increase the risk of errors.
  • Limited data accessibility leads to delays in data-driven processes.
  • Analysts don’t trust siloed data, hindering decision-making.
  • An increased risk of cybersecurity threats and breaches.

To solve these challenges and foster a data-driven culture, public sector organizations must move away from antiquated technologies to a modern, agile infrastructure. This will allow every person and every application that needs timely and accurate data to easily access it.

Embrace Hybrid Cloud Solutions as a First Step

One proven solution to data challenges is to implement hybrid cloud technologies. These technologies span third-party cloud services and on-premises infrastructure. Organizations benefit from the ultra-fast scalability, cost advantages, and efficiency of the cloud while also optimizing on-prem investments.

A hybrid approach lets organizations transition to the cloud at their own pace as part of their modernization efforts, while benefitting from apps or systems that run best on-premises. A gradual migration also helps minimize disruption and maintains data integrity.

For example, in the UK, local councils and even large government organizations are accustomed to siloed systems that require manual input and ongoing employee intervention to bring the silos together. These fragmented systems cause inefficiencies compared to modern and automated processes. This necessitates a shift to responsive systems that can handle organizations’ modern data needs.

Moving to the cloud can be complex due to legacy systems being deeply entrenched in operational processes and storing essential data. To make the migration as smooth as possible, organizations need to use a hybrid cloud data platform and work with an experienced vendor that has experience in data integration.

Make Data Integration and Data Access Completely Seamless

To be a modern and digital-first organization, public sector agencies must have the ability to integrate disparate data sources from a myriad of systems and bring data out of organizational silos. The data must then be made available to employees at all skill levels. Select data also needs to be made available to citizens and other organizations. The data can then be utilized for everything from informing decision-making to forming policies.

Modernizing systems and infrastructure can be more economical, too. Legacy systems may seem financially advantageous in the short term, but over time, maintenance costs, downtime, and barriers to using data will quickly increase the total cost of ownership (TCO). A strategic and well-executed modernization plan supported by advanced data management technologies can reduce overall operational costs, automate processes, gain public trust, and accelerate digital transformation initiatives.

Ongoing modernization efforts should include a plan to integrate advanced technologies such as machine learning, artificial intelligence (AI), and generative AI. This helps public organizations bring together systems and technologies to build a fully connected ecosystem that makes it easy to integrate, manage, and share data, and support new use cases.

It’s worth noting that for AI and GenAI initiatives to be successful, organizations must first ensure their data is ready. This means the data is prepared and has the quality needed to drive trusted outcomes. Training an AI model on inaccurate, untrustworthy data will produce unreliable results.

Take a Future-Looking Approach to Connecting Data

A comprehensive data management strategy enables public sector organizations to predict and quickly respond to changes, make integrated data actionable, and better meet the needs of the public. Like their counterparts in the private sector, public organizations need to prioritize their modernization efforts. They also need to stay current on technological advancements and integrate the ones that meet the specific needs of their organization.

By adopting scalable, secure, and integrated data management solutions, the public sector can pave the way for a more efficient, responsive, connected, and data-driven future. Actian can help with these efforts. The Actian Data Platform allows organizations to easily connect data and build new pipelines. The platform can integrate into an organization’s existing infrastructure to meet their changing needs, including providing real-time data access at scale.

The platform simplifies today’s complex data environment by breaking down siloes, providing a unified approach to data, and bringing together data from diverse sources. In addition, the modern platform helps future-proof organizations by offering comprehensive data services spanning data integration, management, and accessibility. These capabilities facilitate a data-driven approach, enabling quick, reliable decisions across the public sector.

Tim Williams

About Tim Williams

Tim Williams is an Account Director at Actian, advising organizations on data governance, quality, and real-time analytics. He has a broad range of expertise from enterprise to SMB, with a special focus on public sector challenges. Tim offers best practices on unifying data across systems, presenting at government tech seminars to share success stories. Check out his Actian blog posts for advice on modern data governance and continuous analytics at scale.
Data Intelligence

The Journey to Data Mesh – Part 4 – Federated Computational Governance

Actian Corporation

May 6, 2024

While the literature on data mesh is extensive, it often describes a final state, rarely how to achieve it in practice. The question then arises:

What approach should be adopted to transform data management and implement a data mesh?

In this series of articles, get an excerpt from our Practical Guide to Data Mesh where we propose an approach to kick off a data mesh journey in your organization, structured around the four principles of data mesh (domain-oriented decentralized data ownership and architecture, data as a product, self-serve data infrastructure as a platform, and federated computational governance) and leveraging existing human and technological resources.

Throughout this series of articles, and in order to illustrate this approach for building the foundations of a successful data mesh, we will rely on an example: that of the fictional company Premium Offices – a commercial real estate company whose business involves acquiring properties to lease to businesses.

In the previous articles of the series, we’ve identified the domains, defined an initial use case, assembled the team responsible for its development, and created our first data products. Now, it’s time to move on to the final data mesh principle, federated computational governance.

What is Federated Computational Governance?

Federated computational governance refers to a system of governance where decision-making processes are distributed across multiple entities or organizations, using computational algorithms and distributed technologies. In this system, decision-making authority is decentralized, with each participating entity retaining a degree of autonomy while collaborating within a broader framework. Federated computational governance’s key characteristics are:

  • Decentralization: Decision-making authority is distributed among multiple entities rather than concentrated in a single central authority.
  • Computational Algorithms: Algorithms play a significant role in governing processes, helping to automate decision-making, enforce rules, and ensure transparency and fairness.
  • Collaborative Framework: Entities collaborate within a broader framework, sharing resources, data, and responsibilities to achieve common goals.
  • Transparency and Accountability: Using computational algorithms and distributed ledgers can enhance transparency by providing a clear record of processes and ensuring accountability among participating entities.
  • Adaptability and Resilience: Federated computational governance systems are designed to be adaptable and resilient, capable of evolving and responding to changes in the environment or the needs of participants.

The Challenges of a Federated Governance in a Data Mesh

The fourth data mesh principle, federated computational governance, implies that a central body defines the rules and standards that domains must adhere to. Local leaders are responsible for implementing these rules in their domain and providing the central body with evidence of their compliance – usually in the form of reporting.

Although the model is theoretically simple, its implementation often faces internal cultural challenges. This is particularly the case in heavily regulated sectors, where centralized governance teams are reluctant to delegate all or part of the controls they historically had responsibility for.

Federated governance also faces a rarely favorable ground reality: data governance is closely linked to risk management and compliance, two areas that rarely excite operational teams.

Consequently, it becomes difficult to identify local responsible parties or to transfer certain aspects of governance to data product owners – who, for the most part, must already learn a new profession. Therefore, in most large organizations, the federated structure will likely be emulated by the central body and then gradually implemented in the domains as their maturity progresses.

To avoid an explosion of governance costs or fragmentation, Dehghani envisions that the data platform could eventually automatically support entire aspects of governance.

The Aspects of Governance That Can be Automated

We firmly believe in harnessing automation to address this challenge on multiple fronts:

  • Quality controls – Many solutions already exist.
  • Traceability – Development teams can already automatically extract complete lineage information from their data products and document transformations.
  • Fine-grained access policy management – There are already solutions, all of which rely at least on tagging information.

With a little imagination, one could even imagine generative AI analyzing transformation SQL queries and translating them into natural language (solutions exist).The road is long, of course, but decentralization allows for iterative progress, domain by domain, product by product. And let’s also remember that any progress in automating governance, in whatever aspect, relies on the production and processing of metadata.

Premium Offices Example:

At Premium Offices, the Data Office has a very defensive governance culture – as the company operates in the capital market, it is subject to strict regulatory constraints.

As part of the pilot, it was decided not to impact the governance framework. Quality and traceability remain the responsibility of the Data Office and will be addressed retroactively with their tools and methods. Access control will also be its responsibility – a process is already in place, in the form of a ServiceNow workflow (setting permissions on BigQuery requires several manual operations and reviews). The only concession is that the workflow will be modified so that access requests are verified by the Data Product Owner before being approved and processed by the Data Office. In other words, a small step toward federated governance.

Regarding metadata, the new tables and views in BigQuery must be documented, at both the conceptual and physical levels, in the central data catalog (which is unaware of the concept of data product). It is a declarative process that the pilot team already knows. Any column tagging will be done by the Data Office after evaluation.

For the rest, user documentation for data products will be disseminated in a dedicated space on the internal wiki, organized by domain, which allows for very rich and structured documentation and has a decent search engine.

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.
  • Discover efficient methods for scaling up your data mesh.
  • 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.

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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 Analytics

Real-Time Analytics for Smarter Decision-Making in Public Services

Tim Williams

April 30, 2024

Real-Time Analytics for Smarter Decision

Consumers and citizens are accustomed to getting instant answers and results from businesses. They expect the same lightning-fast responses from the public sector, too. Likewise, employees at public sector organizations need the ability to quickly access and utilize data—including employees without advanced technical or analytics skills—to identify and address citizens’ needs.

Giving employees the information to meet citizen demand and answer their questions requires public sector organizations to capture and analyze data in real-time. Real-time data supports intelligent decision-making, automation, and other business-critical functions.

Easily accessible and trusted data can also increase operational effectiveness, predict risk with greater accuracy, and ultimately increase satisfaction for citizens. That data must be secure while still enabling frictionless sharing between departments for collaboration and use cases.

This naturally leads to a pressing question—How can your organization achieve real-time analytics to benefit citizens and staff alike? The answer, at a foundational level, is to implement a modern, high-performance data platform.

Make Efficient Data Utilization a Priority

Achieving a digital transformation in the public sector involves more than upgrading technology. It entails rethinking how services are delivered, how data is shared, and how your infrastructure handles current and future workloads. Too often in public service organizations, just like with their counterparts in the private sector, legacy systems are limiting the effectiveness of data.

These systems lack the scalability and integration needed to support digital transformation efforts. They also face limitations making trusted data available when and where it’s needed, including availability for real-time data analytics. Providing the data, analytics, and IT capabilities required by modern organizations is only possible with a modern and scalable data platform. This type of platform is designed to integrate systems and operations, capture and share all relevant data to predict and respond quickly to changes, and improve service delivery to citizens.

At the same time, modernization efforts that include a cloud migration can be complex. This is often due to the vast amounts of data that need to be moved to the cloud and the legacy systems entrenched in organizational processes. That’s why you need a clear and proven strategy and to work with an experienced vendor to make the transition seamless while ensuring data quality.

Meet Demand for Real-Time Analytics

Hybrid cloud data platforms have emerged as a proven solution for integrating and sharing data in the public sector. By combining on-premises infrastructure with cloud-based services, these platforms offer the flexibility, scalability, and capability to manage, integrate, and share large data volumes.

Another benefit of hybrid solutions is that they allow organizations to optimize their on-premises investments while keeping costs from spiraling out of control in the cloud—unlimited scaling in the cloud can have costs associated with it. Public sector organizations can use a hybrid platform to deliver uninterrupted service, even during peak times or critical events, while making data available in real time for analytics, apps, or other needs.

Smart decision-making demands accurate, trustworthy, and integrated data. This means that upstream, you need a platform capable of seamlessly integrating data and adding new data pipelines—without relying on IT or advanced coding.

Likewise, manual processes and IT intervention will quickly bog down an organization. For example, when a social housing team needs data from multiple systems to ensure buildings meet safety regulations, accessing and analyzing the information might take days or weeks—with no guarantee the data is trustworthy. Automating the pipelines reduces time to insights and ensures data quality measures are in place to catch errors and duplication.

Data integration is essential to breaking down data silos, providing deeper context and relevancy to data, and ensuring the most informed decisions possible. For example, central government agencies can use the data to drive national policies while identifying issues and needs, and strategically allocating resources.

Expect New Value and Use Cases With Real-Time Analytics

Moving from legacy systems to a modern platform and migrating to the cloud at a pace your organization is comfortable with enables a range of benefits:

  • Lower long-term costs and total cost of ownership (TCO).
  • Enhanced service delivery.
  • Gain the trust of data users and the public.
  • Have confidence in the data and analytic insights.
  • Immediate scalability coupled with increased flexibility.

With a solution like the Actian Data Platform, you can do even more. For example, the platform lets you easily connect, transform, and manage data. The data platform enables real-time data access at scale along with real-time analytics. Public sector organizations can benefit, for instance, by using the data to craft employee benefits programs, housing policies, tax guidelines, and other government programs.

The Actian Data Platform can integrate into your existing infrastructure and easily scale to meet changing needs. The platform makes data easy to use so you can better predict citizen needs, provide more personalized services, identify potential problems, and automate operations.

Taking a modern approach to data management, integration, and quality, along with having the ability to process, store, and analyze even large and complex data sets, allows you to digitally transform faster and be better positioned for intelligent decision-making. As the public sector strives to effectively serve the needs of the public in a cost-effective, sustainable, and responsible way, data-driven decision-making will play a greater role for all stakeholders.

Tim Williams

About Tim Williams

Tim Williams is an Account Director at Actian, advising organizations on data governance, quality, and real-time analytics. He has a broad range of expertise from enterprise to SMB, with a special focus on public sector challenges. Tim offers best practices on unifying data across systems, presenting at government tech seminars to share success stories. Check out his Actian blog posts for advice on modern data governance and continuous analytics at scale.
Data Intelligence

The Journey to Data Mesh – Part 3 – Creating Your First Data Products

Actian Corporation

April 22, 2024

journey to data mesh

While the literature on data mesh is extensive, it often describes a final state, rarely how to achieve it in practice. The question then arises:

What approach should be adopted to transform data management and implement a data mesh?

In this series of articles, get an excerpt from our Practical Guide to Data Mesh where we propose an approach to kick off a data mesh journey in your organization, structured around the four principles of data mesh (domain-oriented decentralized data ownership and architecture, data as a product, self-serve data infrastructure as a platform, and federated computational governance) and leveraging existing human and technological resources.

Throughout this series of articles, and in order to illustrate this approach for building the foundations of a successful data mesh, we will rely on an example: that of the fictional company Premium Offices – a commercial real estate company whose business involves acquiring properties to lease to businesses.

In the initial articles of the series, we’ve identified the domains, defined an initial use case, and assembled the team responsible for its development. Now, it’s time to move on to the second data mesh principle, “data as a product,” by developing the first data products.

The Product-Thinking Approach of the Mesh

Over the past decade, domains have often developed a product culture around their operational capabilities. They offer their products to the rest of the organization as APIs that can be consumed and composed to develop new services and applications. In some organizations, teams strive to provide the best possible experience to developers using their domain APIs: search in a global catalog, comprehensive documentation, code examples, sandbox environments, guaranteed and monitored service levels, etc.

These APIs are then managed as products that are born, evolve over time (without compatibility breaks), enriched, and are eventually deprecated, usually replaced by a newer, more modern, more performant version.

The data mesh proposes to apply this same product-thinking approach to the data shared by the domains.

Data Products Characteristics

In some organizations, this product-oriented culture is already well established. In others, it will need to be developed or introduced. But let’s not be mistaken:

A data product is not a new digital artifact requiring new technical capabilities (like an API Product). It is simply the result of a particular data management approach exposed by a domain to the rest of the organization.

Managing APIs as a product did not require a technological breakthrough: existing middleware did the job just fine. Similarly, data products can be deployed on existing data infrastructures, whatever they may be. Technically, a data product can be a simple file in a data lake with an SQL interface; a small star schema, complemented by a few views facilitating querying, instantiated in a relational database; or even an API, a Kafka stream, an Excel file, etc.

A data product is not defined by how it is materialized but by how it is designed, managed, and governed; and by a set of characteristics allowing its large-scale exploitation within the organization.

These characteristics are often condensed into the acronym DATSIS (Discoverable, Addressable, Trustworthy, Self-describing, Interoperable, Secure).

In addition, obtaining a DATSIS data product does not require significant investments. It involves defining a set of global conventions that domains must follow (naming, supported protocols, access and permission management, quality controls, metadata, etc.). The operational implementation of these conventions usually does not require new technological capabilities – existing solutions are generally sufficient to get started.

An exception, however, is the catalog. It plays a central role in the deployment of the data mesh by allowing domains to publish information about their data products, and consumers to explore, search, understand, and exploit these data products.

Best Practices for Data Product Design

Designing a data product is certainly not an exact science – there could be only one product, or three or four. To guide this choice, it is once again useful to leverage some best practices from distributed architectures – a data product must:

  • Have a single and well-defined responsibility.
  • Have stable interfaces and ensure backward compatibility.
  • Be usable in several different contexts and therefore support polyglotism.

Data Products Developer Experience

Developer experience is also a fundamental aspect of the data mesh, with the ambition to converge the development of data products and the development of services or software components. It’s not just about being friendly to engineers but also about responding to a certain economic rationality:

The decentralization of data management implies that domains have their own resources to develop data products. In many organizations, the centralized data team is not large enough to support distributed teams. To ensure the success of the data mesh, it is essential to be able to draw from the pool of software engineers, which is often larger.

The state of the art in software development relies on a high level of automation: declarative allocation of infrastructure resources, automated unit and integration testing, orchestrated build and deployment via CI/CD tools, Git workflows for source and version management, automatic documentation publishing, etc.

The development of data products should converge toward this state of the art – and depending on the organization’s maturity, its teams, and its technological stack, this convergence will take more or less time. The right approach is to automate as much as possible using existing and mastered tools, then identify operations that are not automated to gradually integrate additional tooling.

In practice, here is what constitutes a data product:

  1. Code first – For pipelines that feed the data product with data from different sources or other data products; for any consumption APIs of the data product; for testing pipelines and controlling data quality; etc.
  2. Data, of course – But most often, the data exists in systems and is simply extracted and transformed by pipelines. Therefore, it is not present in the source code (excluding exceptions).
  3. Metadata – Some of which document the data product: schema, semantics, syntax, quality, lineage, etc. Others are intended to ensure product governance at the mesh scale – contracts, responsibilities, access policies, usage restrictions, etc.
  4. Infrastructure – Or more precisely, the declaration of the physical resources necessary to instantiate the data product: deployment and execution of code, deployment of metadata, resource allocation for storage, etc.

On the infrastructure side, the data mesh does not require new capabilities – the vast majority of organizations already have a data platform. The implementation of the data mesh also does not require a centralized platform. Some companies have already invested in a common platform, and it seems logical to leverage the capabilities of this platform to develop the mesh.But others have several platforms, some entities, or certain domains having their infrastructure. It is entirely possible to deploy the data mesh on these hybrid infrastructures: as long as the data products respect common standards for addressability, interoperability, and access control, the technical modalities of their execution are of little importance.

Premium Offices Example:

To establish an initial framework for the governance of its data mesh, Premium Offices has set the following rules:

  • A data product materializes as a dedicated project in BigQuery – this allows setting access rules at the project level, or more finely if necessary. These projects will be placed in a “data products” directory and a sub-directory bearing the name of the domain to which they belong (in our example, “Brokerage”).
  • Data products must offer views to access data – these views provide a stable consumption interface and potentially allow evolving the internal model of the product without impacting its consumers.
  • All data products must identify data using common references for common data (Clients, Products, Suppliers, Employees, etc.) – this simplifies cross-referencing data from different data products (LEI, product code, UPC, EAN, email address, etc.).
  • Access to data products requires strong authentication based on GCP’s IAM capabilities – using a service account is possible, but each user of a data product must then have a dedicated service account. When access policies depend on users, the end user’s identity must be used via OAuth2 authentication.
  • The norm is to grant access only to views – and not to the internal model.
  • Access requests are processed by the Data Product Owner through workflows established in ServiceNow.
  • DBT is the preferred ETL for implementing pipelines – each data product has a dedicated repository for its pipeline.
  • A data product can be consumed either via the JDBC protocol or via BigQuery APIs (read-only).
  • A data product must define its contract – data update frequency, quality levels, information classification, access policies, and usage restrictions.
  • The data product must publish its metadata and documentation in a marketplace – in the absence of an existing system, Premium Offices decides to document its first data products in a dedicated space on its company’s wiki.

This initial set of rules will of course evolve, but it sets a pragmatic framework to ensure the DATSIS characteristics of data products by exclusively leveraging existing technologies and skills. For its pilot, Premium Offices has chosen to decompose the architecture into two data products:

  • Tenancy Analytics – This first data product offers analytical capabilities on lease contracts – entity, parent company, property location, lease start date, lease end date, lease type, rent amount, etc. It is modeled in the form of a small star schema allowing analysis along 2 dimensions: time and tenant – these are the analysis dimensions needed to build the first version of the dashboard. It also includes one or two views that leverage the star schema to provide pre-aggregated data – these views constitute the public interface of the data product. Finally, it includes a view to obtain the most recent list of tenants.
  • Entity Ratings – This second data product provides historical ratings of entities in the form of a simple dataset and a mirror view to serve as an interface, in agreement with common rules. The rating is obtained from a specialized provider, which distributes them in the form of APIs. To invoke this API, a list of entities must be provided, obtained by consuming the appropriate interface of the Tenancy analytics product.

In conclusion, adopting the mindset of treating data as a product is essential for organizations undergoing data management decentralization. This approach cultivates a culture of accountability, standardization, and efficiency in handling data across different domains. By viewing data as a valuable asset and implementing structured management frameworks, organizations can ensure consistency, reliability, and seamless integration of data throughout their operations.

In our final article, we will go over the fourth and last principle of data mesh: federated computational governance.

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.
  • Discover efficient methods for scaling up your data mesh.
  • 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.

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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

The Journey to Data Mesh – Part 2 – Building a Team & Data Platform

Actian Corporation

April 15, 2024

Young Stock Trader Shows to the Executive Managers Cryptocurrency and Trade Market Correlation Pointing at the Wall TV

While the literature on data mesh is extensive, it often describes a final state, rarely how to achieve it in practice. The question then arises:

What approach should be adopted to transform data management and implement a data mesh?

In this series of articles, get an excerpt from our Practical Guide to Data Mesh where we propose an approach to kick off a data mesh journey in your organization, structured around the four principles of data mesh (domain-oriented decentralized data ownership and architecture, data as a product, self-serve data infrastructure as a platform, and federated computational governance) and leveraging existing human and technological resources.

Throughout this series of articles, and in order to illustrate this approach for building the foundations of a successful data mesh, we will rely on an example: that of the fictional company Premium Offices – a commercial real estate company whose business involves acquiring properties to lease to businesses.

In the previous article, we discussed the essential prerequisites for defining the scope of your data management decentralization pilot project, by identifying domains and selecting a use case. In this article, we will explain how to establish its development team and data platform.

Building the Pilot Development Team

As mentioned, the first step in our approach is to identify an initial use case and, more importantly, to develop it by implementing the 4 principles of data mesh with existing resources. Forming the team responsible for developing the pilot project will help implement the first principle of data mesh, domain-oriented decentralized data ownership.

PREMIUM OFFICES EXAMPLE

The data required for the pilot belongs to the Brokerage domain, where the team responsible for developing the pilot will be created. This multidisciplinary team includes:

  • A Data Product Owner
    • Should have both a good understanding of the business and a strong data culture to fulfill the following responsibilities: designing data products and managing their lifecycle, defining and enforcing usage policies, ensuring compliance with internal standards and regulations, and measuring and overseeing the economic performance and compliance of their product portfolio.
  • Two Engineers
    • One from the Brokerage domain teams – bringing knowledge of operational systems and domain software engineering practices, and the other from the data team – familiar with DBT, GCP, and BigQuery.
  • A visualization developer
    • Who can design and build the dashboard.

Domain Tooling: The Data Platform of the Data Mesh

One of the main barriers to decentralization is the risk of multiplying the efforts and skills required to operate pipelines and infrastructures in each domain. But in this regard, there is also a solid state-of-the-art inherited from distributed architectures.

The solution is to structure a team responsible for providing domains with the technological primitives and tools needed to extract, process, store, and serve data from their domain.

This model has existed for several years for application infrastructures and has gradually become generalized and automated through virtualization, containerization, DevOps tools, and cloud platforms. Although data infrastructure tooling is not as mature as software infrastructure, especially in terms of automation, most solutions are transferable, and capabilities are already present in organizations as a result of past investments. Therefore, nothing is preventing the establishment of a data infrastructure team, setting its roadmap, and gradually improving its service offering: simplification and automation being the main axes of this progression.

The Three Planes of the Data Mesh Platform

The data platform for data mesh covers a wide range of capabilities, broader than infrastructure services. This platform is divided into three planes:

  1. The Data infrastructure provisioning plane – Provides low-level services to allocate the physical resources needed for big data extraction, processing, storage, real-time or non-distributed distribution, encryption, caching, access control, network, co-location, etc.
  2. The Data product developer experience plane – Provides the tools needed to develop data products: declaration of data products, continuous build and deployment, testing, quality controls, monitoring, securing, etc. The idea is to provide abstractions above the infrastructure to hide its complexity and automate the conventions adopted on the mesh scale.
  3. The Data mesh supervision plane – Provides a set of global capabilities for discovering data products, lineage, governance, compliance, global reporting, policy control, etc.

On the infrastructure side, the data mesh does not require new capabilities – the vast majority of organizations already have a data platform. The implementation of the data mesh also does not require a centralized platform. Some companies have already invested in a common platform, and it seems logical to leverage the capabilities of this platform to develop the mesh.But others have several platforms, some entities, or certain domains having their infrastructure. It is entirely possible to deploy the data mesh on these hybrid infrastructures: as long as the data products respect common standards for addressability, interoperability, and access control, the technical modalities of their execution are of little importance.

PREMIUM OFFICES EXAMPLE

Premium Offices has invested in a shared cloud platform – specifically, GCP (Google Cloud Platform). The platform includes experts in a central team who understand its intricacies. For its pilot project, Premium Offices simply chose to integrate one of these experts into the project team. This individual will be responsible for finding solutions to automate the deployment of data products as much as possible and identifying manual steps that can be automated later, as well as any missing tools

In conclusion, establishing a dedicated development team is essential for the success of your data management decentralization pilot project. By bringing together individuals with diverse skills and expertise, organizations can effectively implement the principles of data mesh and drive meaningful insights from their data. Moreover, leveraging existing platforms and investing in automation facilitates the development process, paving the way for scalability and long-term success.

In our next article, learn how to execute your data mesh pilot project through the design and development of your first data products.

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.
  • Discover efficient methods for scaling up your data mesh.
  • 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.
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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

Actian Data Platform Receives Data Breakthrough Award

Actian Corporation

April 11, 2024

Data Breakthrough Award

Data integration is a critical capability for any organization looking to connect their data—in an era when there’s more data from more sources than ever before. In fact, data integration is the key to unlocking and sustaining business growth. A modern approach to data integration elevates analytics and enables richer, more contextual insights by bringing together large data sets from new and existing sources.

That’s why you need a data platform that makes integration easy. And the Actian Data Platform does exactly that. It’s why the platform was recently honored with the prestigious “Data Integration Solution of the Year” award from Data Breakthrough. The Data Breakthrough Aware program recognizes the top companies, technologies, and products in the global data technology market.

Whether you want to connect data from cloud-based sources or use data that’s on-premises, the integration process should be simple, even for those without advanced coding or data engineering skill sets. Ease of integration allows business analysts, other data users, and data-driven applications to quickly access the data they need, which reduces time to value and promotes a data-driven culture.

Access the Autonomy of Self-Service Data Integration

Being recognized by Data Breakthrough, an independent market intelligence organization, at its 5th annual awards program highlights the platform’s innovative capabilities for data integration and our comprehensive approach to data management. With the platform’s modern API-first integration capabilities, organizations in any industry can connect and leverage data from diverse sources to build a more cohesive and efficient data ecosystem.

The platform provides a unified experience for ingesting, transforming, analyzing, and storing data. It meets the demands of your modern business, whether you operate across cloud, on-premises, or in hybrid environments, while giving you full confidence in your data.

With the platform, you can leverage a self-service data integration solution that addresses multiple use cases without requiring multiple products—one of the benefits that Data Breakthrough called out when giving us the award. The platform makes data easy to use for analysts and others across your organization, allowing you to unlock the full value of your data.

Making Data Integration Easy

Actian Data Platform offers integration as a service while making data integration, data quality, and data preparation easier than you may have ever thought possible. The recently enhanced platform also assists in lowering costs and actively contributes to better decision-making across the business.

The platform is unique in its ability to collect, manage, and analyze data in real time with its transactional database, data integration, data quality, and data warehouse capabilities. It manages data from any public cloud, multi or hybrid cloud, and on-premises environments through a single pane of glass.

All of this innovation will be increasingly needed as more organizations—more than 75% of enterprises by 2025—will have their data in data centers across multiple cloud providers and on-premises. Having data in various places requires a strategic investment in data management products that can span multiple locations and have the ability to bring the data together.

This is another area where the Actian Data Platform delivers value. It lets you connect data from all your sources and from any environment to break through data silos and streamline data workflows, making trusted data more accessible for all users and applications.

Try the Award-Winning Platform With a Guided Experience

Actian Data Platform also enables you to prep your data to ensure it’s ready for AI and also help you use your data to train AI models effectively. The platform can automate time-consuming data preparation tasks, such as aggregating data, handling missing values, and standardizing data from various sources.

One of our platform’s greatest strengths is its extreme performance. It offers a nine times faster speed advantage and 16 times better cost savings over alternative platforms. We’ve also made recent updates to improve user friendliness. In addition to using pre-built connectors, you can easily connect data and applications using REST- and SOAP-based APIs that can be configured with just a few clicks.

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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

Transform Your Approach to Data: How Cloud Migration Drives Modernization

Actian Corporation

April 9, 2024

Transform your approach to data in this data cloud

Whether you’re looking for cloud migration or have already completed the move, you’re probably looking to the cloud as a way to modernize your approach to data and analytics. It’s a common reason to migrate—many organizations view their cloud journey as a prime opportunity to implement new technologies that support their business and IT goals.

Like data-driven companies across all industries, you’re undoubtedly experiencing an explosion in data volumes while the number of data sources is also quickly expanding. That’s why you need the ability to immediately scale to manage large data volumes and handle changing workload demands. The cloud meets these requirements.

If you’re curious about how your peers plan to leverage the cloud for data and analytics, the technologies they’re using, and how they’re choosing cloud vendors, our survey results deliver the insights. The results, gleaned from 450 businesses, are featured in our eBook “The Top Data and Analytics Capabilities Every Modern Business Should Have.” We’ll cover some of the highlights in this blog.

Turn Your Migration into a Modernization Opportunity

As organizations realize the myriad of benefits that come with digital transformation, they want to accelerate their use of digital tools and implement technologies that advance their modernization goals. This naturally leads to company mandates to move data and analytics technologies from on-premises environments to cloud or hybrid infrastructures. Cloud migration enables instant scalability, business agility, more efficient operations, and other benefits.

Some of the top use cases in the cloud, according to our eBook, are:

  • Financial risk management at 48%.
  • Supply chain and inventory optimization at 46%.
  • Product 360 and product analytics at 43%.

The shift to the cloud for use cases and data optimization is not surprising. Organizations like yours need the ability to ingest and manage large data sets and get answers fast—even in real-time—using modern data and analytics capabilities. The cloud supports this. And if you want to optimize on-premises investments, you can leverage a hybrid data platform that spans the cloud and on-prem.

For example, the Actian Data Platform offers ease of use, scalability, and robust data integration and management capabilities across any environment, including hybrid and multi-cloud. As the need for data simplicity grows, with more data sources and analytics use cases constantly emerging, you need a data platform that enables users at all skill levels to access and utilize data without IT bottlenecks. Empowering more business analysts and others with data accelerates the speed and efficiency of business decisions, operations, and data-driven processes.

Make a Deliberate Decision When Migrating

Selecting a cloud provider for your organization involves careful considerations that go beyond the technology. For instance, it’s essential to look at deployment flexibility, support, and cost-effectiveness, among other factors. You also need to be aware of potential challenges, such as vendor lock-in and unexpected costs.

Likewise, the right cloud migration strategy is critical. Lift-and-shift migrations are common because they’re usually fast, but they can have a downside too—they may not leverage the full potential of cloud capabilities, and they can move on-prem problems with data to the cloud. Your business may benefit from a cloud-native approach, which can offer resiliency and cost-effectiveness.

According to our eBook, the top reasons companies transition to the cloud are:

  • 57% want to make managing data privacy, security, and compliance easier.
  • 51% want to improve scalability and performance, and remove capacity constraints.
  • 48% view the cloud as a better option for applications.

The eBook also notes that 72% of companies are using cloud platforms for all new analytics projects. 47% of organizations are taking a lift-and-shift approach to migrating data and analytics capabilities to the cloud, and 40% are evaluating each project independently to decide how to migrate.

Embrace Modern Data Capabilities

Having access to data in real-time is becoming essential for making the most informed decisions, identifying trends, and predicting problems before they happen, among other benefits. A modern cloud data platform like the one from Actian can deliver real-time data to the analysts and applications that need it.

The Actian Data Platform offers real-time data capabilities, superior price performance, and data integration capabilities that make it an ideal choice for businesses. You can use it to support and advance the data processes you need. As highlighted in our eBook, the top cloud technologies organizations are using include:

  • Cloud data security and data privacy at 54%.
  • Cloud data integration and data operations also at 54%.
  • Cloud data quality and data mastering at 52%.
  • Cloud data streaming and real-time analytics at 52%.

To find out more about the data and analytics capabilities your peers want in the cloud, the challenges they’re experiencing, what they look for in a cloud vendor, and other insights, “The Top Data and Analytics Capabilities Every Modern Business Should Have” help you get more value from your cloud journey.

actian avatar logo

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

The Journey to Data Mesh – Part 1 – Scoping Your Pilot Project

Actian Corporation

April 9, 2024

While the literature on data mesh is extensive, it often describes a final state, rarely how to achieve it in practice. The question then arises:

What approach should be adopted to transform data management and implement a data mesh?

In this series of articles, get an excerpt from our Practical Guide to Data Mesh where we propose an approach to kick off a data mesh journey in your organization, structured around the four principles of data mesh (domain-oriented decentralized data ownership and architecture, data as a product, self-serve data infrastructure as a platform, and federated computational governance) and leveraging existing human and technological resources.

Throughout this series of articles, and in order to illustrate this approach for building the foundations of a successful data mesh, we will rely on an example: that of the fictional company Premium Offices – a commercial real estate company whose business involves acquiring properties to lease to businesses.

The initial step to transforming data management and implementing data mesh within your organization involves building a pilot project, an embryo of mesh. This will be developed based on the 4 principles of data mesh, using existing resources, meaning without impacting the organization.

To ensure a successful start to your data management decentralization journey, you must focus on two essential prerequisites: well-defined domains and the choice of an initial use case.

Domain Identification

The primary prerequisite for launching the pilot project is the identification of domains – the federation of autonomous domains being at the core of the data mesh.

This step generally poses no difficulty. Indeed, the concept of domains is already widely understood, and the division into domains is often stable – whether structured according to value chains, major business processes, or organizational operational capabilities. Domains sometimes have their own technical teams and operational systems that generate the majority of the data. The transition often involves reallocating data ownership according to an existing structure.

PREMIUM OFFICES EXAMPLE

Premium Offices is already structured around domains that reflect its major capabilities. Here are three examples of domains:

  • Asset
    • A domain responsible for acquiring and managing real estate assets. It primarily relies on asset management software.
  • Brokerage
    • A domain that manages the commercialization of properties for rent and tenant management. It utilizes Tenant Management software and is responsible for the commercial website and posting offers on specialized marketplaces.
  • Capital Markets
    • A domain responsible for loans to finance purchases and optimize the loan portfolio. It uses another specialized software.

Premium Offices already has a modern data platform, based on DBT, Google BigQuery, and Tableau. It is managed by a centralized team supported by a centralized Data Office.

Organisation En Domaines Chez Premium Offices

Organization by domains – From the main capacities of Premium Offices

Choosing an Initial Use Case

The choice of a use case for the pilot project is relatively arbitrary – it could involve revamping an existing dashboard, creating a new dashboard, adding AI capabilities to an application, or even commercializing certain data. However, this first use case must possess specific characteristics to facilitate optimal learning conditions:

  • It must focus on usage, not just one or more data products – the intrinsic value of a data product is null, and its value is realized through its uses.
  • It should not be overly cross-cutting and should consume data from one or two domains at most – ideally, just one.
  • It should not be overly simplistic and should consume more than one data product; two or three are sufficient.
  • It should not be overly experimental – the goal is to achieve concrete results quickly.

PREMIUM OFFICES EXAMPLE

For the pilot project, Premium Offices has chosen to build a credit risk dashboard for its tenants to better anticipate and prevent potential defaults. This dashboard must combine tenant data from its software and credit data acquired from a specialized provider. These data are already used operationally in the process of evaluating a new tenant.

In conclusion, initiating a data mesh transformation and launching a pilot project begins with key prerequisites: identifying domains and choosing an initial use case. By defining a scope upfront, organizations can lay a solid foundation for decentralized data management, all without impacting the organization.

In the next article, we delve into the establishment of a development team and a robust data platform to support the data mesh pilot project.

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.
  • Discover efficient methods for scaling up your data mesh.
  • 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.
actian avatar logo

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 Architecture

Strategies for Midsize Enterprises to Overcome Cloud Adoption

Dee Radh

March 22, 2024

Hybrid Cloud Strategies

While moving to the cloud is transformative for businesses, the reality is that midsize enterprise CIOs and CDOs must consider a number of challenges associated with cloud adoption. Here are the three most pressing challenges we hear about – and how you can work to solve them.

  • Leveraging existing data infrastructure investments.
  • Closing technical skills gap.
  • Cloud cost visibility and control.

Recommendations

  • Innovate with secure hybrid cloud solutions.
  • Choose managed services that align with the technical ability of your data team.
  • Maintain cost control with a more streamlined data stack.

Innovate With Secure Hybrid Cloud Solutions for Cloud Adoption

There is no denying that cloud adoption is cheaper in the long run. The elimination of CapExcosts enables CIOs to allocate resources strategically, enhance financial predictability, and align IT spending with business goals. This shift toward OpEx-based models is integral to modernizing IT operations and supporting organizational growth and agility in today’s digital economy.

Data pyramid on the data cloud in 2028

But migrating all workloads to the cloud in a single step carries inherent risks including potential disruptions. Moreover, companies with strict data sovereignty requirements or regulatory obligations may need to retain certain data on-premises due to legal, security, or privacy considerations. Hybrid cloud mitigates these risks by enabling companies to migrate gradually, validate deployments, and address issues iteratively, without impacting critical business operations. It offers a pragmatic approach for midsize enterprises seeking to migrate to the cloud while leveraging their existing data infrastructure investments.

How Actian Hybrid Data Integration Can Help

The Actian Data Platform combines the benefits of on-premises infrastructure with the scalability and elasticity of the cloud for analytic workloads. Facilitating seamless integration between on-premises data sources and the cloud data warehouse, the platform enables companies to build hybrid cloud data pipelines that span both environments. This integration simplifies data movement, storage and analysis, enabling organizations to extend the lifespan of existing assets and deliver a cohesive, unified and resilient data infrastructure.

Choose Managed Services that Align With the Technical Ability of Your Data Team

Cloud adoption brings an array of new opportunities to the table, but the cloud skills gap remains a problem. High demand means there’s fierce market competition for skilled technical workers. Midsize enterprises across industries and geos are struggling to hire and retain top talent in the areas of cloud architecture, operations, security, and governance, which in turn severely delays their cloud adoption, migration, and maturity. This carries the potential greater risk of falling behind competitors.

Data Analytics on cloud skills

Bridging this skills gap requires strategic investments in HR and Learning and Development (L&D), but the long-term solution has to go simply beyond upskilling employees. One such answer is managed services that are typically low- or no-code, thus enabling even non-IT users to automate key BI, reporting, and analytic workloads with proper oversight and accountability. Managed solutions are typically designed to handle large volumes of data and scale seamlessly as data volumes grow—perfect for midsize enterprises. They often leverage distributed processing frameworks and cloud infrastructure to ensure high performance and reliability, even with complex data pipelines.

Actian’s Low-Code Solutions

The Actian Data Platform was built for collaboration and governance midsize enterprises demand. The platform comes with more than 200 fully managed pre-built connectors to popular data sources such as databases, cloud storage, APIs, and applications. These connectors eliminate the need for manual coding to interact with different data systems, speeding up the integration process and reducing the likelihood of errors. The platform also includes built-in tools for data transformation, cleansing, and enrichment. Citizen integrators and business analysts can apply various transformations to the data as it flows through the pipeline, such as filtering, aggregating and cleansing, ensuring data quality and reliability—all without code.

Maintain Cost Control With a More Streamlined Data Stack

Midsize enterprises are rethinking their data landscape to reduce cloud modernization complexity and drive clear accountability for costs across their technology stack. This complexity arises due to various factors, including the need to refactor legacy applications, integrate with existing on-premises systems, manage hybrid cloud environments, address security and compliance requirements, and ensure minimal disruption to business operations.

Point solutions, while helpful for specific problems, can lead to increased operational overhead, reduced data quality, and potential points of failure, increasing the risk of data breaches and regulatory violations. Although the cost of entry is low, the ongoing support, maintenance, and interoperability cost of these solutions are almost always high.

Data Analytics on Top Cloud Challenges

A successful journey to cloud requires organizations to adopt a more holistic approach to data management, with a focus on leveraging data across the entire organization’s ecosystem. Data platforms can simplify data infrastructure, thus enabling organizations to migrate and modernize their data systems faster and more effectively in cloud-native environments all while reducing licensing costs and streamlining maintenance and support.

How Actian’s Unified Platform Can Help

The Actian Data Platform can unlock the full potential of the cloud and offers several advantages over multiple point solutions with its centralized and unified environment for managing all aspects of the data journey from collection through to analysis. The platform reduces the learning curve for users, enabling them to derive greater value from their data assets while reducing complexity, improving governance, and driving efficiency and cost savings.

Getting Started

Book a demo to see how Actian can accelerate your journey to the cloud in a governed, scalable, and price-performant way.

dee radh headshot

About Dee Radh

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

Cloud Data Migration: Migrate Your Mission-Critical Database

Teresa Wingfield

March 20, 2024

Cloud Data Migration on a Critical Mission

The Path to Successful Cloud Data Migration

Is your company contemplating moving its mission-critical database through cloud data migration? If so, you may have concerns around the cloud’s ability to provide the performance, security, and privacy required to adequately support your database applications. Fortunately, it’s a new day in cloud computing that allows you to migrate to the cloud with confidence! Here are some things to keep in mind that will bring you peace of mind for cloud migration.

Optimized Performance

You may enjoy faster database performance through cloud data migration. Cloud service providers (CSPs) offer varying processing power, memory, and storage capacity options to meet your most demanding workload performance requirements. Frequently accessed data can be stored in high-speed caches closer to users, minimizing latency and improving response times. Load balancers distribute processing across servers within the cloud infrastructure to prevent server overload and bottlenecks. Some CSPs also have sophisticated monitoring tools to track resource usage and identify performance bottlenecks.

Enhanced Security

Data isn’t necessarily more secure in your on-premises data center than in the cloud. This is because CSPs invest heavily in advanced security controls to protect their infrastructure and have deep security expertise. They constantly update and patch their systems, often addressing vulnerabilities faster than on-premises deployments. Some CSPs also offer free vulnerability scanning and penetration testing.

However, it’s important to keep in mind that you are also responsible for security in the cloud. The Shared Responsibility Model (SRM) is a cloud security approach that states that CSPs are responsible for securing their service infrastructure and customers are responsible for securing their data and applications within the cloud environment. This includes tasks such as:

  • Patching and updating software.
  • Properly configuring security settings.
  • Implementing adequate access controls.
  • Managing user accounts and permissions.

Improved Compliance

Organizations with strict data privacy requirements have understandably been reluctant to operate their mission-critical databases with sensitive data in the cloud. But with the right CSP and the right approach, it is possible to implement a compliant cloud strategy. CSPs offer infrastructure and services built to comply with a wide range of global security and compliance standards such as GDPR, PCI DSS, HIPAA, and others, including data sovereignty requirements:

Data Residency Requirements

You can choose among data center locations for where to store your data to meet compliance mandates. Some CSPs can prevent data copies from being moved outside of a location.

Data Transfer Requirements

These include the legal and regulatory rules that oversee how personal data can be moved across different jurisdictions, organizations, or systems. CSPs often offer pre-approved standard contractual clauses (SCCs) and support Binding Corporate Rules (BCRs) to serve compliance purposes for data transfers. Some CSPs let their customers control and monitor their cross-border data transfers.

Sovereign Controls

Some CSPs use hardware-based enclaves to ensure complete data isolation.

Additionally, many CSPs, as well as database vendors, offer features to help customers with compliance requirements to protect sensitive data. These include:

  • Data encryption at rest and in transit protects data from unauthorized access.
  • Access controls enforce who can access and modify personal data.
  • Data masking and anonymization de-identify data while still allowing analysis.
  • Audit logging: tracks data access and activity for improved accountability.

Microsoft Cloud for Sovereignty provides additional layers of protection through features like Azure Confidential Computing. This technology utilizes hardware-based enclaves to ensure even Microsoft cannot access customer data in use.

Cloud Data Migration Made Easy

Ingres NeXt delivers low-risk database migration from traditional environments to modern cloud platforms with web and mobile client endpoints. Since no two journeys to the cloud are identical, Actian provides the infrastructure and tooling required to take customers to the cloud regardless of what their planned journey may look like.

Here are additional articles on database modernization benefits and challenges that you may find helpful:

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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.