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Didn&#8217;t get a chance to attend <a href="https://coalesce.getdbt.com/" target="_blank" rel="noopener">dbt Coalesce</a>?
Hear from Datatonic’s Lead Data Architect, Ash Sultan, DevOps Lead, Alex Peissel, and Senior Cloud Architect, Stan Hill, as they explore using modern data analytics at scale with dbt!
Learn how you can reduce your time to insights, increase the productivity of your data teams, and optimise your costs.
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Datatonic, 5x Google Cloud Partner of the year, is the leading cloud data + AI consultancy for the world’s most ambitious brands, challenging traditions to deliver tangible innovation at the leading edge of Google Cloud. Learn about our experience with <a href="https://datatonic.com/partners/dbt/" target="_blank" rel="noopener">dbt</a>, and <a href="https://datatonic.com/contact/" target="_blank" rel="noreferrer noopener">contact us</a> to find out more.
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<h1>Download 
the guide</h1>
A guide by data leaders, for data leaders, looking to turn Data Mesh principles into practice on Google Cloud.

Enterprise MDS Deployment at Scale: dbt + DevOps

Generative AI is rapidly transforming the marketing landscape, moving beyond theoretical discussions to become an indispensable tool for innovative campaigns. While the initial buzz might have focused on its creative potential, the real power of GenAI lies in its ability to drive tangible business results and optimize marketing efforts at scale.
This blog post will explore some practical, business-oriented use cases for GenAI in marketing, demonstrating how it’s being leveraged for emerging applications that deliver significant return on investment.
<h3>Generative AI Use Cases for Marketing</h3>
GenAI offers a suite of capabilities that can revolutionize how marketers approach various tasks, from ad creation to personalization.
<ul>
<li style="font-weight: 400;" aria-level="1">Automatically Generating A/B Test Variations for Ad Copy: Imagine the time and effort saved if you could instantly generate dozens, even hundreds, of different ad copy variations for A/B testing. GenAI can analyze your target audience, product features, and marketing objectives to produce diverse, compelling ad copy. This allows marketers to test a much wider range of messages, quickly identify what resonates best, and optimize campaigns for higher conversion rates without manual brainstorming and drafting.</li>
<li style="font-weight: 400;" aria-level="1">Creating Personalized Email Subject Lines at Scale: Personalization is key to engaging email marketing, but crafting unique subject lines for every segment or individual can be a monumental task. GenAI can analyze customer data, past interactions, and email content to generate highly personalized and attention-grabbing subject lines. This not only improves open rates but also fosters a stronger connection with the recipient, making emails feel more relevant and less like mass communications.</li>
<li style="font-weight: 400;" aria-level="1">Generating Product Descriptions at Scale: For businesses with extensive product catalogs, creating unique and compelling product descriptions for each item can be a significant bottleneck. GenAI can take basic product information – such as features, specifications, and target audience – and automatically generate engaging, SEO-friendly product descriptions. This accelerates time-to-market for new products, ensures consistent branding, and frees up human writers to focus on more strategic content.</li>
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<div class="p-rich_text_section">Brand-consistent Asset Creation: By training GenAI models on your company&#8217;s brand guidelines, such as approved color palettes, fonts, and stylistic elements, your business can rapidly create consistent images, graphics, and videos. This allows users to generate content freely, yet remain confidently within the bounds of the brand. By implementing size guidelines, multiple outputs can   be quickly developed for different channels. For example, a user could input a text prompt for a new product video, and the model would automatically generate the footage while adhering to its specific aspect ratio for social media, and incorporating the approved logo placement, eliminating the risk of off-brand output and dramatically accelerating content production. (edited) </div>
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<h3>Measuring the ROI of AI in Marketing</h3>
Once you have implemented GenAI use cases into your marketing processes, you can measure the return on investment within your marketing department. By automating repetitive and time-consuming tasks, GenAI can increase your marketing ROI:
<ul>
<li style="font-weight: 400;" aria-level="1">Increases Efficiency: Marketers can achieve more with fewer resources, allowing them to focus on strategic planning and creative oversight.</li>
<li style="font-weight: 400;" aria-level="1">Improves Performance: Data-driven insights and rapid iteration through GenAI-powered tools lead to optimized campaigns and higher conversion rates.</li>
<li style="font-weight: 400;" aria-level="1">Enhances Personalization: Deeper personalization fosters stronger customer relationships and drives engagement.</li>
<li style="font-weight: 400;" aria-level="1">Scales Operations: Businesses can expand their marketing efforts and reach larger audiences without an increase in manual effort.</li>
</ul>
Measuring the Return on Investment (ROI) of GenAI spending in marketing primarily involves quantifying the reduction in cost or time and the increase in revenue or performance metrics. For example, to calculate the ROI of the increase in efficiency, the time or effort cost saved on specific tasks (e.g., content generation, ad creation) can be compared against the GenAI tool cost, using metrics like FTE (Full-Time Equivalent) hours saved or cost per marketing asset produced. 
Additionally, improved performance is quantifiable through traditional marketing KPIs like conversion rates, click-through rates (CTR), and campaign revenue before and after GenAI implementation.
<h3>Conclusion</h3>
In conclusion, Generative AI is far from just a passing trend in marketing. Its ability to automate, personalize, and optimize at scale is creating tangible business value and reshaping the future of how brands connect with their customers. By embracing these innovative applications, marketers can unlock new levels of efficiency, effectiveness, and ultimately, a significant return on their investment.
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<a href="https://datatonic.com/contact/">Get in touch</a> to find out how GenAI can scale your marketing efforts.

The Rise of Generative AI in Marketing: More Than Just Hype

Creative transformation is the strategic fusion of human creativity with the power of data and artificial intelligence (AI), enabling businesses to unlock new levels of efficiency, personalization, and impact. In the last few years, there has been a fundamental shift in how businesses approach innovation, marketing, and customer engagement. In a world saturated with digital content, the ability to stand out and connect with audiences is paramount. 
For too long, data and creativity were seen as separate, almost opposing forces. Data was the domain of analytics and spreadsheets, while creativity belonged to the realm of brainstorming and artistic vision. But today, the most successful companies are those that have recognized that these two elements are inextricably linked. Data and AI provides the insights and the tools to amplify and scale human creativity in ways we never thought possible.
<h3>How Data and AI are Driving Creative Transformation</h3>
Data and AI are not here to replace human creativity, but to supercharge it. Here are some examples of how they are enabling this transformation:
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<ul>
<li style="font-weight: 400;" aria-level="1">Hyper-Personalization: AI can analyze vast datasets to understand individual customer preferences, behaviors, and purchase history. This allows businesses to create highly personalized content and experiences that resonate with each customer. For example, a retail company can use AI to dynamically place those products in a setting that aligns with that audience preferences e.g. A backpack in the mountains or on a workers commute and tailored ad copy based on a customer&#8217;s browsing habits, creating a more engaging and effective marketing campaign.</li>
<li style="font-weight: 400;" aria-level="1">Content Generation at Scale: Generative AI models can create variations of text, images, and even videos in a matter of seconds. This capability is revolutionizing content creation by allowing marketing teams to rapidly produce different versions of ad creative, email subject lines, and social media posts. This not only saves time but also allows for extensive A/B testing to identify the most effective creative elements.</li>
<li style="font-weight: 400;" aria-level="1">Predictive Analytics for Creative Strategy: Data analytics can help businesses understand which creative concepts are likely to perform best before they even launch a campaign. By analyzing historical data on audience engagement and campaign performance, AI can predict which images, headlines, or calls-to-action will be most successful, allowing creative teams to focus their efforts on the most promising ideas.</li>
<li style="font-weight: 400;" aria-level="1">Automating Repetitive Tasks: AI can handle the repetitive, administrative tasks associated with creative work, such as resizing images for different platforms, scheduling social media posts, or organizing creative assets. This frees up human creatives to focus on high-level strategic thinking, ideation, and the  unique, impactful creative work that only humans can do. WPP recently shared that through the adoption of their AI-powered platform, WPP Open, employees are saving <a href="https://www.linkedin.com/posts/wpp_wppopen-ai-marketinginnovation-activity-7381339177130287104-HiYL?utm_source=share&amp;utm_medium=member_desktop&amp;rcm=ACoAACbnK_cBnw7PxaxFrz6ykLuZ6N-jRF4ijVM">20 hours per week</a>, unlocking the equivalent of 6 months of extra capacity per year. </li>
</ul>
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These examples are just the beginning. Creative transformation, powered by a robust data foundation and intelligent AI solutions, is the future of business, enabling companies to build a seamless connection between creative vision and measurable results.
<h3>Why to Start Now</h3>
First Mover Advantage: Today&#8217;s AI pioneers are setting tomorrow&#8217;s customer expectations. Those who embrace AI early will define the standards of convenience, personalization, and efficiency that customers come to expect. This early adoption creates a lasting impression and builds strong brand loyalty, making it harder for competitors to catch up.
Technology Accelerates Faster Than Adoption: Staying ahead requires continuous monitoring of AI advancements and proactive strategy development. Businesses that anticipate future trends can seamlessly integrate new technologies, avoiding the scramble to adapt when a new standard emerges.
Learning Compounds Over Time: Every campaign is a learning opportunity. You and your ML systems improve with experience. Each data point gathered and every outcome observed contributes to a more sophisticated understanding of customer behavior and market dynamics. This iterative process allows for constant refinement of strategies, leading to increasingly effective and efficient AI applications.
Economies of Scale: Falling costs and quality improvements are making AI more attractive over time. As AI technology matures, the barriers to entry for businesses will decrease, making sophisticated tools accessible to a wider range of organizations. This widespread adoption will further drive innovation and starting now helps your company to get ahead.
<h3>Conclusion</h3>
In upcoming blogs, we&#8217;ll dive deeper into how these concepts are being applied in the real world, exploring topics like the practical uses of generative AI in marketing, the importance of a solid data infrastructure, and how to bridge the gap between marketing and sales data. Stay tuned to learn how your business can harness the power of creative transformation.

Creative Transformation 101: A New Era for Business and Creativity

At its core, MLOps is the intersection of Machine Learning, Development, and Operations. It&#8217;s a practice designed to streamline the entire ML lifecycle, from experimentation and development to deployment, monitoring, and governance. Without a mature MLOps approach, scaling ML initiatives becomes a significant bottleneck, preventing organizations from realizing the full potential of their data science investments.
To understand the path to truly scalable ML, we can break down the journey into three key stages of MLOps maturity: the Manual Era, Automated MLOps, and the ultimate goal of Self-Serve MLOps. This blog post will explore each stage, its characteristics, benefits, and challenges, and highlight why reaching &#8220;Self-Serve&#8221; is the ultimate goal for any enterprise serious about industrializing machine learning.
<h3>Stage 1: The Manual Era – Building from Scratch</h3>
The first stage of MLOps maturity is characterized by a hands-on, ad-hoc approach. In this phase, infrastructure for each ML use case is created manually. This process heavily relies on manual steps for setup and deployment and often depends on &#8220;tribal knowledge&#8221; of specific cloud platforms held by a few key individuals. A typical scenario involves a few initial ML use cases, each built almost independently, without a repeatable blueprint.
Challenges of the Manual Era:
<ul>
<li style="font-weight: 400;" aria-level="1">Time-Consuming: Onboarding a new use case can take a significant amount of time, often extending for weeks. This creates a long lead time from idea to production.</li>
<li style="font-weight: 400;" aria-level="1">Resource Intensive: Every deployment requires deep expertise from cloud engineers or data scientists with ops knowledge, tying up valuable resources.</li>
<li style="font-weight: 400;" aria-level="1">Lack of Standardization: Inconsistent environments across different projects lead to the dreaded &#8220;works on my machine&#8221; issues and complicate troubleshooting.</li>
<li style="font-weight: 400;" aria-level="1">Poor Scalability: Scaling is a major challenge. Doubling the number of use cases effectively doubles the manual work and bottlenecks, making it impossible to grow efficiently.</li>
</ul>
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While this approach can help to get some initial models into production, this process is hard to replicate efficiently as you expand.
<h3>Stage 2: Automated MLOps – Gaining Momentum with Blueprints</h3>
The second stage represents a significant leap forward. It’s a transition from ad-hoc processes to standardized infrastructure blueprints. Organizations at this level leverage automation scripts or Infrastructure as Code (IaC) tools to create repeatable deployment processes. This allows teams to move beyond manual steps and focus on making the deployment process consistent and reliable.
Benefits of Automated MLOps:
<ul>
<li style="font-weight: 400;" aria-level="1">Faster Onboarding: With standardized blueprints, new use cases can be onboarded in minutes or hours, dramatically reducing the time-to-production.</li>
<li style="font-weight: 400;" aria-level="1">Reduced Human Error: Automation minimizes the potential for manual mistakes, leading to more reliable and reproducible deployments.</li>
<li style="font-weight: 400;" aria-level="1">Consistency: Standardized environments improve the overall reliability and make it easier to manage a growing portfolio of models.</li>
</ul>
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Despite these benefits, this stage still has limitations. The process is often still centralized, with operations teams remaining a bottleneck as they manage and maintain the automated pipelines for each new use case. While individual deployments are faster, scaling still requires the team to set up and manage pipelines for each additional use case. Doubling the number of use cases still requires more management effort, even if the individual tasks are quicker.
<h3>Stage 3: Self-Serve MLOps – The Path to Hyper-Scalability</h3>
Self-Serve MLOps is the ultimate goal for mature MLOps practices. This stage represents a fundamental shift in responsibility and workflow. Operations teams move away from building individual pipelines and instead focus on creating and maintaining a robust, secure, and flexible &#8220;landing zone.&#8221; Within this landing zone, ML teams are empowered to create and manage their own infrastructure and pipelines with full autonomy, guided by pre-defined guardrails.
Key Characteristics:
<ul>
<li style="font-weight: 400;" aria-level="1">Decentralized Deployment: ML teams have the autonomy to deploy their models, removing the bottleneck of a centralized ops team.</li>
<li style="font-weight: 400;" aria-level="1">High Scalability: This model can support any number of use cases without a linear increase in the ops burden.</li>
<li style="font-weight: 400;" aria-level="1">Focus on Throughput: Key performance indicators (KPIs) shift from individual deployment speed to &#8220;use case throughput,&#8221; directly demonstrating the business value of the platform.</li>
</ul>
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Benefits of Self-Serve MLOps:
<ul>
<li style="font-weight: 400;" aria-level="1">Accelerated Innovation: ML teams can iterate and deploy faster, getting new ideas into production and generating value in a fraction of the time.</li>
<li style="font-weight: 400;" aria-level="1">Maximized Business Value: More ML models reach production quickly, driving business impact and a stronger ROI.</li>
<li style="font-weight: 400;" aria-level="1">Optimized Resource Allocation: Ops teams are freed from repetitive tasks, allowing them to focus on platform stability, security, and innovation.</li>
</ul>
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This allows the teams to focus on what they do best: developing and refining the products that will drive the most value.
<h3>Conclusion</h3>
The journey from a manual, ad-hoc approach to a truly self-serve MLOps framework is a strategic imperative for any organization aiming to industrialize and scale its machine learning efforts. By moving from a centralized bottleneck to a decentralized, empowered model, enterprises can unlock the full potential of their data science teams and accelerate the delivery of business value.
Where does your organization stand on the MLOps maturity curve? Assessing your current stage is the first step toward building a roadmap for advancement. Embracing the MLOps journey is not just about adopting new tools; it&#8217;s about a fundamental shift in culture and process to build a future where machine learning innovation is limited only by imagination, not operational friction.
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The Journey to Hyper-Scalability: Navigating MLOps Maturity

From predictive maintenance in manufacturing to fraud detection in financial services, the potential of AI is limitless. However, the secret to a successful AI project is far more simple, yet often overlooked: the quality of your data. AI models are only as good as the data they&#8217;re trained on. Without a robust data strategy, you&#8217;re building a house on a shaky foundation.
<h3>Why Data Preparation is Non-Negotiable for AI</h3>
The old adage &#8220;Garbage In, Garbage Out&#8221; is more relevant than ever in the world of AI. If your data is incomplete, inconsistent, or has errors, your AI model will learn to make poor predictions.
But data preparation goes far beyond just cleaning up messy data. It&#8217;s about making sure your data is:
<ul>
<li style="font-weight: 400;" aria-level="1">Interpretable: Is the data in a format that your model can understand?</li>
<li style="font-weight: 400;" aria-level="1">Relevant: Does the data actually help the model solve the problem you&#8217;re trying to address?</li>
<li style="font-weight: 400;" aria-level="1">Scalable: Can your data pipeline handle the volume and velocity of data you&#8217;ll be dealing with in the future?</li>
</ul>
Neglecting these aspects can lead to a host of painful problems down the line, including slow production cycles, model degradation over time, and high maintenance costs.
<h3>Key Steps to AI Data Readiness</h3>
Achieving AI readiness requires a systematic approach. Here are the key steps to follow to ensure a solid data foundation:
<ol>
<li style="font-weight: 400;" aria-level="1">Define Your AI Strategy + Data Needs: Before you touch any data, you need to know what you want to achieve. What business problem are you trying to solve? What data do you need to solve it?</li>
<li style="font-weight: 400;" aria-level="1">Ingest + Centralise Your Data: Gather data from various sources and consolidate it into a single, accessible location. You can leverage the power of Google Cloud to build scalable and reliable data lakes and warehouses.</li>
<li style="font-weight: 400;" aria-level="1">Cleanse + Standardise for Quality: This is where you address inconsistencies, correct errors, and fill in missing values. The goal is to create a clean, standardized dataset that your model can work with.</li>
<li style="font-weight: 400;" aria-level="1">Transform + Engineer Features: This step is about getting creative. Transform raw data into features that will give your model the best chance of success. This could involve creating new variables, aggregating data, or normalizing values.</li>
<li style="font-weight: 400;" aria-level="1">Data Labeling + Annotation (for Supervised Learning): For supervised learning, you need to label your data so your model knows what to learn. This can be a time-consuming but critical step.</li>
<li style="font-weight: 400;" aria-level="1">Implement Robust Data Governance + Monitoring: Data is not a static asset. You need to have processes in place to ensure data quality is maintained over time. This includes monitoring data pipelines for errors and ensuring data privacy and security.</li>
</ol>
<h3>Conclusion</h3>
In the race to build your next great AI application, it&#8217;s easy to get caught up in the latest models and frameworks. But the reality is that the most impactful AI projects are built on a strong data foundation. Data preparation is not a one-time task; it&#8217;s an ongoing commitment to ensuring the quality and integrity of your most valuable asset.
Ready to build your strong data foundation and unlock the full potential of AI? <a href="https://datatonic.com/contact/">Get in touch today</a>.

The Key to AI Success: A Robust Data Strategy

The immense potential of data and AI to transform businesses is clear. Many businesses are exploring how to harness the power of machine learning (ML) to unlock new insights, automate processes, and create unparalleled customer experiences. However, building a powerful ML model is only half the battle. The real challenge, and the real value, comes from getting those models into production, keeping them running smoothly, and ensuring they continue to deliver results. 
<h3>The Untapped Value of Machine Learning</h3>
Before delving into MLOps, it&#8217;s worth appreciating the significant impact machine learning models are already having on businesses.
<ul>
<li style="font-weight: 400;" aria-level="1">Driving Revenue: Machine learning models are at the forefront of personalized recommendations, fraud detection, predictive maintenance, and dynamic pricing, all directly contributing to increased revenue.</li>
<li style="font-weight: 400;" aria-level="1">Boosting Efficiency: From automating customer service with chatbots to optimizing supply chains and forecasting demand, ML streamlines operations, leading to significant cost savings.</li>
<li style="font-weight: 400;" aria-level="1">Enhancing Decision-Making: ML models provide data-driven insights, allowing businesses to make more informed and strategic decisions, reducing guesswork and risk.</li>
</ul>
The value of ML is undeniable, but it&#8217;s a value that can only be fully realized when models are consistently deployed, monitored, and improved.
<h3>Introducing MLOps</h3>
MLOps, or Machine Learning Operations, is a set of practices that combines Machine Learning, DevOps, and Data Engineering to reliably and efficiently deploy and maintain ML models in production. 
Traditionally, there&#8217;s been a significant &#8220;chasm&#8221; between data science teams who build models and operations teams who are responsible for deploying and maintaining them. This often leads to:
<ul>
<li style="font-weight: 400;" aria-level="1">Slow Deployment: Months of effort to get a model from development to live production.</li>
<li style="font-weight: 400;" aria-level="1">Model Drift: Models degrading in performance over time due to changes in real-world data, leading to inaccurate predictions.</li>
<li style="font-weight: 400;" aria-level="1">Lack of Reproducibility: Difficulty in replicating model results or understanding how a model was built, hindering auditing and troubleshooting.</li>
<li style="font-weight: 400;" aria-level="1">Limited Scalability: Struggling to manage a growing number of models and complex ML pipelines.</li>
</ul>
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<h3>The Impact of a Robust MLOps Platform</h3>
Adopting a comprehensive MLOps strategy and developing a robust platform addresses these challenges head-on, delivering tangible benefits:
<ul>
<li style="font-weight: 400;" aria-level="1">Accelerated Time-to-Value: MLOps can dramatically shorten the production timeframe for ML applications. Some reports suggest it can reduce this time by as much as 90%, allowing organizations to unlock business value much faster. This means innovations reach customers sooner, and a competitive edge is gained.</li>
<li style="font-weight: 400;" aria-level="1">Improved Model Performance and Reliability: With continuous monitoring and automated retraining, MLOps ensures models remain accurate and perform optimally, even as data patterns change. This proactive approach also helps to prevent model degradation.</li>
<li style="font-weight: 400;" aria-level="1">Increased Efficiency and Reduced Costs: By automating tasks such as data preparation, model training, deployment, and monitoring, MLOps frees up valuable time for data scientists and engineers to focus on higher-value activities like model development and innovation. This also helps in lowering overall costs across the ML lifecycle.</li>
</ul>
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Some surveys indicate that <a href="https://www.fortunebusinessinsights.com/mlops-market-108986" target="_blank" rel="noopener">97% of users</a> who have implemented MLOps have observed a significant improvement in automation, robustness, and productivity.
<h3>Conclusion</h3>
MLOps includes changes to people, processes, and a strategic approach to maximising ML investments. As a leading Google Cloud partner with extensive experience in MLOps, we help organizations build scalable, reliable, and efficient ML platforms. 
We&#8217;ve worked with leading businesses such as <a href="https://datatonic.com/case-studies/genops-vodafone-google-cloud/" target="_blank" rel="noopener">Vodafone</a> and <a href="https://datatonic.com/case-studies/reliable-predictive-models-mlops-delivery-hero/" target="_blank" rel="noopener">Delivery Hero</a> to implement centralized MLOps platforms that automate the ML lifecycle, reduce deployment times from months to days, and empower their data scientists to focus on innovation.
Whether your organization is just starting its AI journey or looking to optimize existing ML operations, MLOps is the key to unlocking the full potential of machine learning models and transforming them into continuous drivers of business value. <a href="https://datatonic.com/contact/" target="_blank" rel="noopener">Get in touch</a> to explore our MLOps solutions and see how Datatonic can help bridge the gap from lab to live.

MLOps 101: Deploying AI at Scale

London, 25th April 2025 — Datatonic, a leading cloud data and AI solutions provider, today announced the acquisition of <a href="https://www.syntio.net/" target="_blank" rel="noopener">Syntio</a>, a specialist in data engineering. 
The strategic acquisition significantly bolsters Datatonic’s enterprise service offerings and global delivery capabilities with deep data engineering skills including data ingestion, data transformation and business intelligence. Syntio also adds a customer base which covers telcos, central banks and retailers across Europe to Datatonic’s growing global client list. 
Syntio was listed in the Financial Times as one of Europe’s fastest-growing companies for three years in a row. Datatonic’s acquisition brings together two highly complementary organizations, working on the toughest of data challenges, who share values and a commitment to delivering exceptional client outcomes. 
For customers, combining Datatonic’s expertise in Generative AI and AI Platforms with Syntio’s deep technical specialization in data engineering, means the combined company now offers the broadest range of high-impact solutions and deep expertise to its clients.
&#8220;We are thrilled to welcome Syntio to the Datatonic family,&#8221; said Scott Eivers, CEO at Datatonic. &#8220;This acquisition is a key step in our strategy to expand our global reach and enhance our service capabilities. Syntio’s talented team and specialized expertise will be invaluable as we continue to deliver innovative data and AI solutions to our clients.&#8221;
&#8220;Joining Datatonic at this stage in the evolution of AI and data engineering is an exciting opportunity for Syntio,&#8221; said Davorin Cetto, CEO of Syntio. &#8220;The scale of offerings, the geographic coverage and our combined expertise mean our customers now have the best technology options as they navigate towards a cloud data and AI-centric future. &#8220;
Datatonic is backed by Perwyn, a leading European private equity investor, which transforms businesses through its value-added, hands-on engagement with management teams.
Mike Rothwell, Investment Manager at Perwyn added: “We are very proud of his deal. The acquisition of Syntio expands Datatonic’s global footprint as well as adding the high quality team that Davorin and Tomislav have built. This will augment the business’ capabilities across generative AI, machine learning and data analytics. We will continue to support the growth of the business and its position as a market leading data and AI consultancy.”
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<img loading="lazy" decoding="async" class="alignnone wp-image-24695" src="https://cms.datatonic.com/app/uploads/2025/04/014A9401-300x200.jpg" alt="" width="677" height="451" srcset="https://cms.datatonic.com/app/uploads/2025/04/014A9401-300x200.jpg 300w, https://cms.datatonic.com/app/uploads/2025/04/014A9401-1024x683.jpg 1024w, https://cms.datatonic.com/app/uploads/2025/04/014A9401-768x512.jpg 768w, https://cms.datatonic.com/app/uploads/2025/04/014A9401-1536x1025.jpg 1536w, https://cms.datatonic.com/app/uploads/2025/04/014A9401.jpg 2048w" sizes="auto, (max-width: 677px) 100vw, 677px" />
Left to Right: Scott Eivers, CEO at Datatonic, Tomislav Domanovac, Co-Founder of Syntio, Andy Powell, MD, UKI at Datatonic, Martina Mandic, MD at Syntio, Louis Decuypere, Founder and Chairman at Datatonic, Davorin Cetto, Co-Founder and CEO at Syntio.
About Datatonic
Datatonic, 10 X Google Cloud Partner of the Year, is the leading cloud data and AI consultancy for industry-leading organizations, delivering tangible business impact at the cutting edge of Google Cloud.
As leaders in Generative AI, Datatonic provides high-impact, ROI-driven solutions that enhance decision-making, optimize operations, and revolutionize customer experiences. Our expertise extends beyond AI, offering comprehensive cloud data migration and marketing analytics solutions, training and managed services that empower businesses to unlock the full potential of their data.
For more information, visit <a href="https://datatonic.com/">https://datatonic.com/</a>.
About Syntio
Syntio is a specialist in data engineering, providing expert services to help organizations modernize their data infrastructure and develop advanced data platforms.

Datatonic Announces Acquisition of Syntio to Expand Global Services and Delivery Capabilities

Google Cloud Next 2025 has wrapped up, but the energy is still buzzing. This year, we were impressed by the groundbreaking announcements that are set to transform the landscape of data analytics, machine learning, and cloud infrastructure. In this post, we&#8217;ll dive into the key highlights and share our expert perspectives on how these developments will empower businesses to unlock new levels of efficiency, agility, and intelligence.
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Gemini 2.5 Pro and Gemini 2.5 Flash
The unveiling of the next generation Gemini models is a game-changer. Gemini 2.5 Pro, now in public preview, is engineered for maximum quality and complex tasks, already ranking #1 on Chatbot Arena. This level of reasoning and coding expertise will empower businesses to build even more sophisticated and accurate AI solutions. 
Furthermore, the upcoming Gemini 2.5 Flash, designed for low latency and cost-efficiency, opens up possibilities for real-time AI applications and more accessible AI deployments. These new models, available on Vertex AI, AI Studio, and in the Gemini app, provide a powerful foundation for AI-driven use cases. Keep an eye on Imagen 3 for high-quality text-to-image generation, Veo 2 for advanced video creation, Lyria for AI music generation, and Chirp 3 for custom AI voices.
&nbsp;
Advancements in Vertex AI for Building and Managing AI Agents
Google Cloud is doubling down on making AI agents a reality for enterprises. The introduction of the Agent Development Kit (ADK), an open-source framework, simplifies the creation of complex multi-agent systems while providing precise control. 
The ADK&#8217;s support for the Model Context Protocol (MCP) offers a unified way for AI models to interact with diverse data sources, streamlining integration. The introduction of the open Agent2Agent (A2A) protocol marks a significant step towards interoperable multi-agent ecosystems, fostering collaboration between different AI systems.
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Google Agentspace: Empowering Every Employee with AI
The launch of Google Agentspace is a pivotal moment in democratizing access to enterprise search and AI agents. By integrating Agentspace with Chrome Enterprise, Google is embedding AI directly into employees&#8217; existing workflows, making information discovery seamless and secure. 
The Agent Gallery provides a central hub for discovering and using agents, while the Agent Designer, a no-code interface, empowers employees to create custom agents for their specific needs. Features like the Idea Generation agent and Deep Research agent highlight the potential of Agentspace to foster innovation and knowledge synthesis within organizations. 
Additionally, ​​BigQuery is becoming an even more powerful data platform with deeper Gemini integration for multimodal analysis. The introduction of specialized agents for data engineers, data scientists, and data analysts promises to accelerate data workflows, for all industries. 
&nbsp;
Vertex AI Live API for Real-time Multimodal Applications
The new Live API in Vertex AI enables streaming audio and video directly into Gemini, allowing AI agents to process and respond to rich media in real time. This opens up exciting possibilities for immersive, multimodal applications, such as enhanced customer service agents that can understand and respond to visual and auditory cues, or intelligent video analytics solutions. 
&nbsp;
Grounding Agents with Google Maps
For businesses that rely on location-based data, the new Grounding with Google Maps feature in Vertex AI is a significant advancement. This allows AI agents to provide responses with geospatial information. This capability is particularly relevant for companies in industries like logistics, retail, and real estate, enabling the development of smarter, location-aware AI applications. Businesses can leverage this to build solutions that provide contextually relevant and geographically accurate insights to their users.
&nbsp;
AI in BI to Enhance Analytics
The general availability of Gemini in Looker introduces capabilities like Conversational Analytics for natural language data interaction, Visualization Assistant for automated chart generation, Formula Assistant for easier calculations, Automated Slide Generation for streamlined presentations, and LookML Code Assistant to aid engineers. 
These changes enable users to use their analytics and visualizations beyond the dashboard, creating actionable insights and improving decision-making. 
&nbsp;
Conclusion
Google Cloud Next 2025 has ushered in a new era of AI-powered possibilities for businesses. Organizations are now equipped with the technology to drive innovation, efficiency, and intelligence across their operations. These developments mark a significant leap forward in democratizing AI and empowering every employee with intelligent tools, paving the way for a future where data-driven insights and AI-powered automation become the norm across all industries.
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Feeling inspired to start your next data + AI project? <a href="https://datatonic.com/contact/" target="_blank" rel="noopener">Get in touch today</a>.

Google Cloud Next 2025: The Future of AI

London, 8th April 2025 — Datatonic, a global leader in Cloud Data + AI, announced today that it has been named 2025 Google Cloud Services Partner of the Year for Data &amp; Analytics (EMEA), bringing its total to 10 awards across Marketing Analytics, Machine Learning, and industries including Gaming, Telecommunications, and Retail.
Together with Google Cloud, Datatonic has helped businesses solve their most pressing challenges with powerful data analytics and AI solutions – from strengthening their customer experience and uncovering new sources of revenue, to migrating to Google Cloud and building best-in-class data platforms.
“Google Cloud’s partner awards recognize the significant impact and customer success that our partners have driven over the past year,” said Kevin Ichhpurani, President, Global Partner Ecosystem at Google Cloud. “We’re delighted to recognize Datatonic as a 2025 Google Cloud Partner Award winner, and look forward to a continued strong partnership in support of our mutual customers.”
Throughout the year, Datatonic has worked with industry-leading clients, such as <a href="https://datatonic.com/case-studies/genops-vodafone-google-cloud/" target="_blank" rel="noopener">Vodafone</a>, <a href="https://datatonic.com/case-studies/hedvig-marketing-analytics/" target="_blank" rel="noopener">Hedvig</a>, and <a href="https://datatonic.com/case-studies/training-telia-upskilling-in-ai/" target="_blank" rel="noopener">Telia</a>, to remain competitive in the rapidly evolving data analytics landscape, scale their operations, and bring better services to customers through data-driven decision-making and continuous innovation, such as its new Pathfinder approach to accelerate and ensure success for Vodafone’s enterprise-scale cloud migration.
“We’re honoured to be recognised for our global impact in data and analytics this year, which will be our tenth Partner of the Year award from Google Cloud. This award celebrates Datatonic’s work with industry leaders, where we’ve driven substantial and sustained business impact over the years for clients such as <a href="https://datatonic.com/case-studies/generative-ai-wizards-of-the-coast/" target="_blank" rel="noopener">Wizards of the Coast</a>, <a href="https://datatonic.com/case-studies/marketing-analytics-asos/" target="_blank" rel="noopener">ASOS</a>, and <a href="https://datatonic.com/case-studies/slashing-invoice-processing-costs-bigquery-docai-genai/" target="_blank" rel="noopener">Liberty Global</a>. We’re excited to continue this in the year ahead.” – Scott Eivers, CEO, Datatonic
As well as delivering projects with tangible business impact to help scale clients’ businesses, Datatonic has provided ongoing support to business leaders on the best use of data and AI with <a href="https://datatonic.com/services/managed-services/" target="_blank" rel="noopener">Managed Services</a> to maximise business value as well as upskilling client teams in collaboration with <a href="https://datatonic.academy/" target="_blank" rel="noopener">Datatonic Academy</a>.
&nbsp;
About Datatonic
Datatonic, 10 X Google Cloud Partner of the Year, is the leading cloud data and AI consultancy for industry-leading organizations, delivering tangible business impact at the cutting edge of Google Cloud.
As leaders in Generative AI, Datatonic provides high-impact, ROI-driven solutions that enhance decision-making, optimize operations, and revolutionize customer experiences. Our expertise extends beyond AI, offering comprehensive cloud data migration and marketing analytics solutions, training and managed services that empower businesses to bring the future to today with cutting-edge, industry-first solutions.
For more information, visit <a href="https://datatonic.com/" target="_blank" rel="noopener">https://datatonic.com/</a>.

Datatonic Wins Google Cloud Partner of the Year Award for Data + Analytics in EMEA

7TH APRIL, LONDON, MONTREAL AND WORLDWIDE &#8211; <a href="https://datatonic.com/" target="_blank" rel="noopener">Datatonic,</a> a leader in driving high business impact with Enterprise Data &amp; AI solutions, today announced Scott Eivers as its new global Chief Executive Officer, covering all Datatonic operations in UK, Europe and North America, spanning across all business divisions in Consulting, Managed Services and Academy.
Datatonic’s services are at the leading edge of modern data and AI expertise, blending strategy and deep technical expertise to speed up innovation where talent is scarce and deadlines short. A key differentiator is the team&#8217;s experience in achieving tangible value with real-world GenAI, such as building a large-scale GenOps platform to deploy 600+ models for <a href="https://datatonic.com/case-studies/genops-vodafone-google-cloud/" target="_blank" rel="noopener">Vodafone</a>, and reducing customer churn by 15% for <a href="https://datatonic.com/events/cineverse-webinar/" target="_blank" rel="noopener">Cineverse</a>, alongside becoming a launch partner for Google Cloud’s new <a href="https://datatonic.com/services/generative-ai/agentspace/">Agentspace</a>, to help customers catapult productivity with GenAI Agents. 
Scott Eivers joins from services partner <a href="https://nordcloud.com/" target="_blank" rel="noopener">Nordcloud</a>, an IBM Company, where he served as the UKI Region Director, responsible for rapidly scaling its business from a small challenger to a leader in the market. As part of his wider Chief Growth Officer role, he also helped Nordcloud set up its marketing and product functions. Prior to this, he also scaled a small business consulting function in Gartner Consulting to become a leader of technology and business consulting services across EMEA. Before this, he held a variety of senior roles growing functions and teams at global companies, such as Vodafone and BT.
Datatonic’s current CEO &amp; Founder, Louis Decuypere, will continue to serve as Chairman of Datatonic: “The journey we have been on has been exhilarating to date and we’re excited to take the next step. After nearly 10 incredible years building this company, and with Datatonic stronger than ever, I believe now is the perfect moment to transition leadership for our next phase of growth. My commitment to Datatonic and belief in our future remains stronger than ever, and with Scott’s leadership and our amazing team, I am incredibly excited about the unprecedented success ahead.”
The Datatonic team is rapidly growing to meet the global demand for strategic technical partners. Its proven AI skills address a wide range of business issues and growth is outpacing the demand for all other professional and technical service markets; it has recently been named as one of the <a href="https://itchanneloxygen.com/oxygen-fast-growth-50-2025-50-high-growth-uk-channel-partners-revealed/" target="_blank" rel="noopener">fastest-growing MSPs in the UK</a>.
Commenting on his appointment, Eivers noted &#8220;I have been truly inspired by the leading data and AI tech talent at Datatonic. I am looking forward to working beside such transformational thinkers at this incredible AI market crossroad”.
&nbsp;
About Datatonic
Datatonic, 10 X Google Cloud Partner of the Year, is the leading cloud data and AI consultancy for the industry-leading organizations, delivering tangible business impact at the cutting edge of Google Cloud.
As leaders in Generative AI, Datatonic provides high-impact, ROI-driven solutions that enhance decision-making, optimize operations, and revolutionize customer experiences. Our expertise extends beyond AI, offering comprehensive cloud data migration and marketing analytics solutions, training and managed services that empower businesses to unlock the full potential of their data.
For more information, visit <a href="https://datatonic.com/">https://datatonic.com</a>

New role underlines Google Cloud’s 9x Partner of the Year global growth ambitions

Datatonic Appoints Scott Eivers as Chief Executive Officer

The financial services and insurance (FSI) industry is increasingly leveraging advanced analytics and machine learning to extract insights from vast datasets, <a href="https://datatonic.com/case-studies/hedvig-marketing-analytics/" target="_blank" rel="noopener">improve marketing campaigns</a>, and automate processes. AI-powered tools are being used for fraud detection, risk assessment, and compliance monitoring, enhancing efficiency and security. This shift towards data-driven decision-making and AI-powered automation is reshaping the competitive landscape, driving innovation, and improving customer satisfaction in the FSI sector.
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What is Google Agentspace?
Agentspace allows users to have a single platform for Google-quality search and agents, across all enterprise systems, while maintaining the highest levels of compliance and data protections.
With deep research and assistant capabilities built-in, Agentspace helps employees across roles and industries get their jobs done. A financial analyst researching investment options for a client, a lawyer researching a case, an engineer troubleshooting a system issue, or a marketer building a PR campaign, all can save hours a day.
Agentspace allows users to interact with data in natural language, meaning users can ask questions about their data by simply typing their questions. This makes it easy for users to get the information they need without having to learn how to use more complex data analysis tools. With this in mind, let’s explore how different roles in the financial services and insurance industry can make the most of Agentspace.
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Policy management
Insurance companies can leverage Agentspace to simplify policy management, process claims efficiently, and manage client data securely. Agentspace can help claims handlers to quickly access the required information on policies, as well as customer data, enabling them to reduce errors and speed up processing. Agentspace’s multimodel capabilities mean policy managers will be able to access a huge amount of data previously inaccessible to support with decision making. 
&nbsp;
Marketing 
The shift of physical first to digital first across all of FSI means distribution channels are changing and marketing teams are having to adapt. Agentspace allows for marketing teams to access previous campaign information with ease and build more personalized experiences for customers. Users can also work collaboratively using <a href="https://gemini.google/overview/deep-research/?hl=en-GB" target="_blank" rel="noopener">Gemini Deep Research</a> to explore how new marketing channels can activate their growing online audience.
&nbsp;
Compliance teams
Compliance teams can use Agentspace for monitoring regulatory changes, generating auditing transactions and data, and automating compliance workflows. With regulatory updates, Agentspace can provide automated alerts and summaries of new regulations, ensuring compliance and reducing the friction for all employees to engage with regulatory information. 
For audit trail generation, it can be used to help automate the tracking and documentation of transactions, simplifying auditing and improving transparency. Additionally, for reporting, Agentspace can automate report generation for regulatory bodies, saving time and ensuring accuracy and timely submission.
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Risk managers
Risk Managers can also benefit from Agentspace by analyzing market trends and risks, developing risk mitigation strategies, monitoring risk indicators, and rapidly generating risk reports with custom built Agents that sit within Agentspace. 
When it comes to risk assessment, Agentspace can be used to accelerate data analysis and risk modeling, providing comprehensive risk insights and improving decision-making. For monitoring, it can provide real-time alerts and notifications of risk events, enabling proactive risk management and reducing potential losses. Furthermore, for reporting, Agentspace can help to speed up the creation of risk reports and dashboards, simplifying reporting and providing clear visualization of risk exposure.
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Conclusion
Agentspace is revolutionizing the financial services industry by providing a unified platform for data access, analysis, and automation. It empowers various roles, from policy management and customer support to compliance and risk management, with enhanced efficiency, accuracy, and speed. With strong privacy and security built-in, Agentspace has the ability to scale across the business, significantly improving decision-making, reducing risks, and enhancing customer satisfaction.

Agentspace in Finance: Streamline Compliance, Manage Risk, and Enhance Customer Experiences

The retail industry is constantly changing due to evolving consumer demands, new technologies, and economic shifts. These factors are reshaping job roles in the sector, emphasizing the need for improved efficiency, better customer experiences, and data-driven decision-making, in order to gain a competitive advantage.
&nbsp;
What is Google Agentspace?
Agentspace allows users to have a single platform for Google-quality search and agents, across all enterprise systems, while maintaining the highest levels of compliance and data protections. 
With deep research and assistant capabilities built-in, Agentspace helps employees across roles and industries get their jobs done. A financial analyst researching investment options for a client, a lawyer researching a case, an engineer troubleshooting a system issue, or a marketer building a PR campaign can all save hours a day.
Agentspace allows users to interact with data in natural language, meaning users can ask questions about their data by simply typing their questions. This makes it easy for users to get the information they need without having to learn how to use more complex data analysis tools. With this in mind, let’s explore how different roles in the retail industry can make the most of Agentspace. 
&nbsp;
Store Managers
Store managers can use Google Agentspace to optimize operations, through enhanced decision-making, and improve overall store performance. For example, Agentspace can support planning staffing schedules, as well as providing easy access to sales data and inventory levels all in one place.
These would enable Store Managers to focus on providing better service to customers as less time is spent doing manual planning, both through ensuring available customer service at all times, and products are sold out at any one time.
&nbsp;
Marketing Professionals
Marketing professionals in retail can use Google Agentspace to create and activate more targeted and personalised marketing campaigns. 
With personalization becoming more and more important for online retailers, marketing teams can use Agentspace to quickly answer questions about current or target customers, from first- and third-party date sources. Teams can also build research reports on new industry trends to inform campaign strategy and create new image, video and text to feed into that campaign. Agentspace for marketing is powered by connectors to marketing analytics platforms, such as the one Datatonic developed with <a href="https://datatonic.com/case-studies/marketing-analytics-asos/">ASOS</a>. 
&nbsp;
Buyers + Merchandisers
Agentspace will enable buyers and merchandisers to easily and quickly analyze vast datasets of sales information, identify emerging trends in consumer preferences, and predict future buying behaviors with greater ease and accuracy. 
This means buyers and merchandisers can make more informed purchasing decisions, selecting merchandise that is likely to resonate with their target customers and minimizing the risk of investing in slow-moving or unpopular items. By leveraging Agentspace and AI Agents to predict retail trends through better analysis of historical data, social media sentiment, and various market signals, buyers and merchandisers can deliver the right product at the right time to their customers.
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Inventory + Supply Chain Managers
Inventory and Supply Chain managers can use Google Agentspace to easily track inventory levels, manage orders and transport all in place without having to rely on multiple tools to get a clear picture of the supply chain.
Users can gain knowledge faster on what needs to be restocked, without needing to understand the more complex systems to gain the data, accelerating decision-making, and improving customer service by avoiding low stock and delayed delivery. Agentspace will allow everyone to easily interact with their supply chain data in a natural language, removing data bottlenecks, allowing the data to quickly be acted upon. 
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Conclusion
Google Agentspace is enabling retailers to rapidly unlock insights and knowledge from their data, accelerating the time to make decisions, and ultimately leading to better customer experiences, and increased revenues. 
Datatonic has a wealth of experience in helping retail businesses to implement new data + AI solutions, and Google Agentspace has a wide range of impactful use cases across the business for retailers. Request a free <a href="https://datatonic.com/services/generative-ai/agentspace-discovery/?utm_source=website&amp;utm_medium=organic&amp;utm_campaign=Blog_AgentspaceRetail_202503&amp;utm_term=" target="_blank" rel="noopener">Agentspace Use Case Discovery</a> workshop today to understand how it can be used in your business.

Revamping Retail: How Agentspace is Changing the Industry

London, 27th February 2025 &#8211; Datatonic, a leading cloud data and AI consultancy, is delighted to announce that we have been selected as a launch partner for Google Agentspace, the tool that will revolutionize Enterprise search for all businesses.
With experience delivering over 100 Generative AI projects on Google Cloud, Datatonic will be applying its extensive AI knowledge to implementing Agentspace and building custom agents for customers. Previous GenAI projects include:
<ul>
<li style="font-weight: 400;" aria-level="1">Developing an invoice processing tool to reduce invoice processing costs by 70% for <a href="https://datatonic.com/case-studies/slashing-invoice-processing-costs-bigquery-docai-genai/">Liberty Global</a>.</li>
<li style="font-weight: 400;" aria-level="1">Building an innovative GenOps platform for <a href="https://datatonic.com/case-studies/genops-vodafone-google-cloud/">Vodafone</a> that will support the delivery of 600+ AI and GenAI use cases.</li>
<li style="font-weight: 400;" aria-level="1">Collaborating with <a href="https://datatonic.com/case-studies/cineverse-generative-ai/">Cineverse</a> to reduce content search and discovery times by 90% with an AI assistant.</li>
</ul>
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Agentspace is transforming GenAI hype into the ultimate employee productivity platform; Agentspace tackles challenges that nearly all enterprises are facing, by offering:
<ul>
<li style="font-weight: 400;" aria-level="1">Streamlined workflows across a range of connectors.</li>
<li style="font-weight: 400;" aria-level="1">Enterprise-grade security, privacy, and compliance.</li>
<li style="font-weight: 400;" aria-level="1">Multi-modal features through integration with Gemini, NotebookLM, Imagen and Veo.</li>
<li style="font-weight: 400;" aria-level="1">A single unified Google-quality search experience across all enterprise data.</li>
</ul>
&nbsp;
To learn more about Agentspace, visit <a href="https://datatonic.com/services/generative-ai/agentspace/">our website</a>, and request a one-hour <a href="https://datatonic.com/services/generative-ai/agentspace-discovery/">Agentspace Use Case Discovery</a> session. 
&nbsp;
About Datatonic
Datatonic, 9 X Google Cloud Partner of the Year, is the leading cloud data and AI consultancy for industry-leading organizations, delivering tangible business impact at the cutting edge of Google Cloud.
As leaders in Generative AI, Datatonic provides high-impact, ROI-driven solutions that enhance decision-making, optimize operations, and revolutionize customer experiences. Our expertise extends beyond AI, offering comprehensive cloud data migration and marketing analytics solutions, training and managed services that empower businesses to unlock the full potential of their data.
For more information, visit <a href="https://datatonic.com/">https://datatonic.com/</a>.

Agentspace

Agentspace for FSI

Agentspace for Media

Agentspace for Retail

AI Workbench

Cloud Data Migration

Generative AI

Creative Assistant

Fan Engagement

Gameplay Assistant

Media Search + Discovery

Retail Assistant

Looker

Managed Services

Marketing Analytics

Financial Services

Gaming

Media

Retail

Telecommunications

Events

Insights

Whitepapers

Enterprise MDS Deployment at Scale: dbt + DevOps

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