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MLOps encompasses the set of practices, technologies, and culture that enable Machine Learning to deliver real business value in an organisation. Borrowing concepts from DevOps and applying them to Machine Learning, MLOps enables teams to move rapidly from offline experimentation to a deployed production system and beyond, unlocking business potential from Data Science.
In this webinar, Datatonic’s Machine Learning Engineering Lead, Felix Schaumann and Google Cloud&#8217;s Customer Engineer, Ivan Nardini, show you how to use <a href="https://datatonic.com/insights/datatonic-updated-turbo-templates-mlops-adoption-2024/" target="_blank" rel="noopener">Datatonic&#8217;s new Turbo Templates</a> to kickstart your Machine Learning development. 
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<iframe loading="lazy" title="MLOps in action: Operationalize your ML workflow using pipeline templates" width="500" height="281" src="https://www.youtube.com/embed/SdR5P2BDGow?feature=oembed" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
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Looking to get started with MLOps but need help finding where to start? Datatonic is Google Cloud&#8217;s 5x Partner of the Year and 2x Machine Learning Partner of the Year. <a href="https://datatonic.com/contact/" target="_blank" rel="noopener">Get in touch</a> to start planning your next data + AI project.

<h1>Download 
the guide</h1>
A guide by data leaders, for data leaders, looking to turn Data Mesh principles into practice on Google Cloud.

MLOps in Action: Operationalize Your ML Workflow Using Pipeline Templates

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.
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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. 
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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. 
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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.
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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. 
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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>.
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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”.
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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. 
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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.
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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.
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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.
&nbsp;
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. 
&nbsp;
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>
&nbsp;
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>.

Datatonic Selected as Launch Partner for Google Agentspace to Revolutionize Internal Productivity with AI Agents

Author: Dino Chioda, Global Head of Analytics
Business Intelligence (BI) tools like Looker help businesses shape raw data into actionable insights, empowering employees to make data-driven decisions. With such tools, we can build beautiful dashboards, explore data with ease, and finally feel like we&#8217;re harnessing the true potential of our data. 
But is creating a report truly the goal, or is there some greater value that can be generated from your efforts? Too often, the journey ends at the dashboard, leaving a gap between understanding and action. Defining data workflows that extend beyond your BI tool could be the next frontier of value creation.
While a report reveals the &#8220;what&#8221; and perhaps even the &#8220;why,&#8221; it rarely dictates the &#8220;how&#8221; or the &#8220;next steps.&#8221; True data-driven organizations bridge this gap by integrating their BI tools into broader workflows that automate actions and drive tangible outcomes.
<h3>Defining data-driven workflows is essential</h3>
They drive action: A dashboard showing a dip in sales is valuable, but a workflow that automatically alerts the sales team and triggers a targeted marketing campaign is transformative. Extending your workflow allows you to move seamlessly from insight to action, closing the loop and maximizing the impact of your data. 
For example, If a customer abandons their online shopping cart, Looker can trigger an action to send a special discount or offer to the customer who abandoned their online shopping cart, encouraging them to complete the purchase.
They improve efficiency: Manual processes are slow, error-prone, and don&#8217;t scale. By integrating your BI tool with other systems, you can automate repetitive tasks, freeing up your team to focus on strategic initiatives. Imagine automatically generating reports, updating CRM systems, or even triggering product recommendations based on real-time data. 
For example, a Looker dashboard might identify high-potential leads based on website activity, demographics, or product engagement. An action could automatically push these leads to a CRM (like Salesforce or HubSpot) and assign them to specific sales reps based on territory or expertise. This automates lead qualification and distribution, improving sales efficiency.
They foster collaboration: Workflows that extend beyond the BI tool ensure the right information reaches the right people at the right time. This could involve automatically sharing dashboards with relevant stakeholders, assigning tasks based on data triggers, or even integrating with project management tools. For example, data triggers in Looker could automatically create tasks in project management tools (like Jira or Asana) for specific teams to address.
They speed up response time: In today&#8217;s fast-paced world, reacting to data in real-time is crucial. Data-driven workflows enable businesses to set up alerts and triggers based on specific data points, allowing users to respond to changes in the market, customer behaviour, or operational performance with agility. 
For example, Looker can analyze sensor data from equipment to predict potential failures. An action could automatically schedule maintenance or alert technicians before a breakdown occurs, minimizing downtime.
They leverage governed data: By defining clear workflows, you can improve data governance and ensure data consistency across your organization. This includes defining clear roles and responsibilities, automating data validation processes, and ensuring compliance with relevant regulations.
<h3>Best practices for implementing data-driven workflows</h3>
Implementing impactful data-driven workflows isn’t rocket science but there are a few moving pieces to consider:
<ol>
<li style="font-weight: 400;" aria-level="1">Focus on Concrete Use Cases: Start by identifying specific business challenges that can be addressed by extending your data workflows. Focus on areas where automation and real-time responsiveness can make the biggest impact. And don’t forget to define the right KPIs to measure impact and success.</li>
<li style="font-weight: 400;" aria-level="1">Integrate with Other Systems: Connect your BI tool with other relevant systems, such as your CRM, marketing automation platform, project management tools, and even your operational systems.</li>
<li style="font-weight: 400;" aria-level="1">Leverage APIs and Webhooks: Most of these integrations rely on APIs (Application Programming Interfaces) or webhooks provided by the upstream systems. The ability of a BI tool like Looker to connect to these APIs is crucial.</li>
<li style="font-weight: 400;" aria-level="1">Define Clear Triggers and Actions: Establish clear triggers based on specific data points and define the corresponding actions that should be taken.</li>
<li style="font-weight: 400;" aria-level="1">Monitor and Optimize: Implement robust error handling to ensure that actions are executed reliably and that any errors are logged and addressed. Continuously monitor your workflows to ensure they are performing as expected and make adjustments as needed.</li>
</ol>
&nbsp;
<h3>Conclusion</h3>
Beyond the pretty dashboards and insightful reports, lies the true power of data: the ability to drive action. By extending your data workflows beyond your BI tool, you can unlock this potential and transform your organization into a truly data-driven powerhouse.
Want to learn more? <a href="https://datatonic.com/events/looker-genai-webinar/" target="_blank" rel="noopener">Watch our recent webinar</a> where we discuss the value of combining Looker + GenAI. 
As an award-winning Google Partner with extensive experience with Looker, Datatonic can help your organization define and implement secure, effective data-driven workflows. <a href="https://datatonic.com/contact/?utm_source=website&amp;utm_medium=organic&amp;utm_campaign=LookerBlog_202502&amp;utm_term=" target="_blank" rel="noopener">Get in touch</a> to start your next project.

Beyond the Dashboard: Drive Action through BI

<a href="https://www.linkedin.com/feed/update/urn:li:activity:7292566692935647232" target="_blank" rel="noopener">Our recent podcast</a> discussed the impact of DeepSeek, a startup that has quickly made waves in the AI industry with its innovative models. With just over two hundred employees, DeepSeek has introduced models that are not only high-performing but also significantly more cost-effective than many existing alternatives. Let&#8217;s dive into the details of their breakthrough and what it means for the future of AI.
<h3>DeepSeek&#8217;s Groundbreaking Models</h3>
Over the last two months, DeepSeek has released two significant models:
<ul>
<li style="font-weight: 400;" aria-level="1">V3: This chat model is notable for its significantly lower training costs. DeepSeek reported it cost around $5 million to train, which is substantially less than what other AI labs have reported (<a href="https://www.techradar.com/pro/openai-spent-usd80m-to-usd100m-training-gpt-4-chinese-firm-claims-it-trained-its-rival-ai-model-for-usd3-million-using-just-2-000-gpus" target="_blank" rel="noopener">$80m-100m for OpenAI</a>). While this figure excludes auxiliary costs like hardware, data labeling, and human capital, the training is still likely much cheaper than other models.</li>
<li style="font-weight: 400;" aria-level="1">R1: This is an open-weights reasoning model that was fine-tuned from V3 using mainly reinforcement learning, similar to OpenAI&#8217;s models, and it stands out as a cost and performance-competitive open-source reasoning model. </li>
</ul>
&nbsp;
<h3>Lower Costs, Higher Competition</h3>
The reduction in cost of training and running an AI model of this performance that is open-weights is a key factor in DeepSeek&#8217;s disruption. One million output tokens using DeepSeek R1 on Together AI costs ~$6 (and only $0.55 on Deepseek’s own API currently), compared to ~$60 for OpenAI’s o1, demonstrating a significant cost reduction for inference. This cost efficiency has major implications for businesses looking to implement AI:
<ul>
<li style="font-weight: 400;" aria-level="1">Reduced operational costs: Lower inference costs make it more affordable for companies to embed LLMs into their products, leading to a greater return on investment.</li>
<li style="font-weight: 400;" aria-level="1">Increased adoption: Cheaper AI models could accelerate the adoption of AI technologies across various industries.</li>
<li style="font-weight: 400;" aria-level="1">Stimulated competition: DeepSeek’s innovations are pushing down prices across the industry, with other players like Google adjusting their prices to stay competitive.</li>
</ul>
At this point it is worth noting that the trend in cost reduction has since been continued by Google’s release of the top-performing Gemini 2.0 Flash with a cost of $0.4 per one million output tokens.
<h3>Reasoning Models vs. Chatbot Models</h3>
There are also some key differences between reasoning and chatbot models.
<ul>
<li style="font-weight: 400;" aria-level="1">Chat models work by predicting the next word in a sequence and are good at tasks like grammar, syntax, world knowledge, and translation. However, they can struggle with complex problems that require multi-step solutions.</li>
<li style="font-weight: 400;" aria-level="1">Reasoning models are based on chat models, but fine-tuned to produce and evaluate intermediate steps of reasoning to reach a conclusion. They use a reinforcement learning approach to favor a good path or intermediate step.</li>
</ul>
Reasoning models, such as DeepSeek’s R1 are particularly useful when dealing with complex, multi-step approach and planning problems. They are actively used in coding and research agents. These models will thrive in industries where information is vast and complex such as law, academia, finance and medicine.
<h3>Open Source Considerations</h3>
DeepSeek&#8217;s R1 model is an open-weights model. This means the model architecture, training code, and pre-trained weights are publicly available. Lately, there has been a rise in the adoption of open-weight language models by enterprises. However, there are things to consider:
<ul>
<li style="font-weight: 400;" aria-level="1">Hosting costs: While the model weights are free, hosting it yourself can be expensive, potentially costing more than using a cloud provider’s pay-as-you-go service.</li>
<li style="font-weight: 400;" aria-level="1">Data privacy: Hosting your own model can be beneficial for data privacy, as your data doesn&#8217;t have to be sent to a third party.</li>
<li style="font-weight: 400;" aria-level="1">Security: Open-weights models might have security vulnerabilities, with some models failing cybersecurity tests. However, for some internal applications, like document classification, these vulnerabilities may not be as relevant.</li>
<li style="font-weight: 400;" aria-level="1">Interoperability: Businesses should consider interoperability and how easily models can be swapped in and out of their systems, and may want to use smaller models for specific tasks.</li>
</ul>
&nbsp;
<h3>Potential Use Cases for DeepSeek Models</h3>
With this in mind, there are various potential use cases for DeepSeek models:
<ul>
<li style="font-weight: 400;" aria-level="1">Automation and efficiency: DeepSeek’s cost-effective models can significantly reduce the cost of high-throughput, inference-serving use cases.</li>
<li style="font-weight: 400;" aria-level="1">Personalization: As chatbots become more common, these models can power user-facing applications at competitive prices.</li>
<li style="font-weight: 400;" aria-level="1">Agentic systems: Reasoning models are well-suited for building agentic systems where complex decisions are required.</li>
<li style="font-weight: 400;" aria-level="1">Research and productivity: Reasoning models can assist in research and boost internal productivity.</li>
<li style="font-weight: 400;" aria-level="1">Marketing: Reasoning models can help create effective marketing campaigns by analyzing large amounts of data and past performance.</li>
</ul>
&nbsp;
<h3>Risks + Comparisons to other models</h3>
DeepSeek&#8217;s models offer significant advantages in terms of cost and performance, but there are potential risks and downsides to consider, particularly when compared to more established models like Google Cloud&#8217;s. 
While DeepSeek offers open-weights models, the company is based in China, which raises data privacy concerns for some users, especially when using the DeepSeek API, as data may be subject to Chinese government directives. Many users have found questions or topics that DeepSeek refuses to discuss. This is not a risk with Google&#8217;s models. 
Furthermore, with such low costs, DeepSeek might not have put as much effort into security as it has into performance. Although DeepSeek&#8217;s models are performant, the potential trade-off in security is something businesses should carefully consider, especially if the use case involves sensitive data or public-facing applications.
<h3>Conclusion</h3>
DeepSeek&#8217;s advancements in AI model development have significantly disrupted the GenAI market. Their open-weights reasoning model and lower training costs have made advanced AI more accessible and affordable for businesses. 
While questions surrounding open-weights models, hosting, and security still need to be addressed, the potential for innovation and wider adoption is undeniable. As the industry continues to evolve, DeepSeek&#8217;s impact will likely encourage further advancements and lower prices, making AI tools more accessible to everyone.
&nbsp;

DeepSeek’s Disruptive Entrance into the GenAI Market

With experience of training 10,000+ learners in 2024 for clients such as <a href="https://datatonic.com/case-studies/training-telia-upskilling-in-ai/">Telia</a> and Vodafone, Datatonic is upskilling leading businesses to prepare for what’s next in data + AI. These courses will join Datatonic’s existing offerings, which include Google Cloud fundamentals, GenAI, and Looker training.
FinOps, or financial operations, is a discipline that helps organizations understand and manage their cloud costs. By understanding how cloud resources are used and how they are billed, organizations can make informed decisions about how to optimize their cloud spending.
Following experience delivering large-scale FinOps platforms to save millions annually for global businesses, Datatonic’s FinOps courses, Getting Started with FinOps and Technical Foundations of FinOps on Google Cloud, are designed to help organizations of all sizes implement and manage a successful FinOps program. The courses cover a wide range of topics, enabling learners to:
<ul>
<li style="font-weight: 400;" aria-level="1">Understand the core principles of FinOps and how it bridges the gap between technology and finance in the cloud.</li>
<li style="font-weight: 400;" aria-level="1">Explore proven techniques to identify cost inefficiencies and optimize resource utilization.</li>
<li style="font-weight: 400;" aria-level="1">Utilize advanced monitoring tools and data-driven insights to track spending patterns, forecast costs, and make informed decisions.</li>
</ul>
&nbsp;
“We are excited to add FinOps courses to our training roster,” said Natasha Jones, Academy Lead. “FinOps is an essential discipline for any organization that wants to get the most out of their cloud investment. These courses will help organizations understand and manage their cloud costs, and make informed decisions about how to optimize their cloud spending.”
In addition to FinOps training, Datatonic continues to offer a comprehensive suite of courses designed to upskill individuals and organizations on the power of data and AI within Google Cloud. 
Datatonic’s FinOps courses are available online and in-person. For more information, please <a href="http://datatonic.com/contact">contact us</a>.

London, 30th January 2025 – Datatonic today announced the addition of FinOps courses to its training offering.

Generative AI

Retail Assistant

Gameplay Assistant

Fan Engagement

Media Search + Discovery

Creative Assistant

Agentspace

Agentspace for Retail

Agentspace for FSI

Agentspace for Media

Cloud Data Migration

Managed Services

Marketing Analytics

Looker

Retail

Telecommunications

Gaming

Media

Financial Services

Events

Whitepapers

Insights

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