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<h2 data-path-to-node="1">Discover how to scale your AI deployments with ease.</h2>
The era of GenAI experimentation is over. While building a single AI agent is easy, scaling to 50+ use cases across a global enterprise is where most organizations stall. Between security bottlenecks, manual governance, and inconsistent performance, the DIY approach simply can&#8217;t keep up with the speed of the market.
It’s time to move from bespoke assembly to a high-throughput AIOps production line.
Our latest guide, From Flat-Pack to Factory: Scaling Enterprise Agents with Platform-based AIOps, outlines the architectural blueprint and mindset shift required to turn agentic AI into a measurable value driver.
Key Insights Include:
<ul data-path-to-node="8">
<li>
Building the Right Architecture: How to decouple shared enterprise foundations from unique agent logic to accelerate deployment by up to 80%.
</li>
<li>
Agentic AI Templates: The impact of using pre-approved templates and MCP servers to standardize tools, skills, and data access.
</li>
<li>
Real-World Impact: Case studies from leaders, demonstrating how to reduce deployment time from three months to less than two weeks.
</li>
</ul>

Download Guide

<h1>Download 
the guide</h1>
A guide by AI leaders, for AI leaders, looking to build a high-performance AIOps platform to deploy AI at scale.

From Flat-Pack to Factory: Scaling Enterprise Agents with Platform-based AIOps

The telecommunications landscape is undergoing a fundamental shift, moving from simple connectivity to needing to become AI-native. Successful telcos are adopting AI-native telco operations, transforming legacy infrastructure into automated engines that optimize network performance and fuel hyper-personalized growth. By moving away from reactive maintenance and generic outreach, operators can unlock the latent value within their massive datasets.
&nbsp;
<ol>
<li> Adopt Dynamic Network Optimization with Reinforcement Learning</li>
</ol>
The first major trend involves a move toward self-healing and self-optimizing infrastructure. Modern telcos are increasingly leveraging Reinforcement Learning (RL) to optimize active network resources in real-time. Unlike traditional static models, RL agents learn from the environment to adjust parameters dynamically, ensuring maximum throughput and minimal latency even during peak usage. 
Beyond performance, this intelligence extends to physical infrastructure management. Automated site ranking and AI decommissioning tools are now being used to identify underperforming or redundant hardware. By intelligently consolidating resources, operators are slashing energy costs and overheads, aligning operational efficiency with critical sustainability goals.
&nbsp;
<ol start="2">
<li> Revamp Customer Experience with Autonomous Customer Service Agents</li>
</ol>
Customer expectations have reached an all-time high, and traditional support models are struggling to keep pace. The telco industry is responding by pivoting toward autonomous agent support to accelerate resolution times and boost Net Promoter Scores (NPS). These aren&#8217;t the basic chatbots of the past; they are sophisticated, AI-driven systems capable of handling complex queries from end to end. AI can also be used to summarise previous calls, providing a clear and consistent customer journey to all call centre staff involved.
The impact is measurable and immediate. For one major European Telecom, Datatonic helped implement solutions that boosted First Call Resolution (FCR) by 30% and cut Average Call Time (ACT) by 20%. This shift allows human agents to focus on high-value interactions while the AI ensures that routine issues are resolved instantly and accurately.
&nbsp;
<ol start="3">
<li> Gain Market Share with Hyper-Personalization in Customer Value Management</li>
</ol>
In a saturated market, growth is no longer about mass marketing but about precision. Hyper-personalized experiences are the gold standard for growing a customer base without scaling costs. By adopting advanced machine learning models, telcos can now predict individual needs and deliver bespoke offers at the exact moment they are most relevant. 
This speed to market is a game-changer. For example, organizations can transform their content workflows, moving from a process where personalized article generation takes weeks to one where it is completed in mere minutes. This agility allows marketing teams to react to trends and customer behaviors in real-time, significantly increasing conversion and retention.
&nbsp;
<ol start="4">
<li> Reduce Downtime with Predictive Maintenance and Zero-Touch Operations</li>
</ol>
AI-native telcos are deploying sophisticated fault-prediction models that analyze patterns in network telemetry to identify potential outages before they affect the end-user. By integrating these insights into automated workflows, operators are achieving zero-touch operations where the system can reroute traffic or trigger a maintenance request autonomously. 
This reduces the need for manual intervention and prevents the costly downtime that erodes customer trust. As networks become more complex with the continued rollout of 5G and IoT, these predictive capabilities are a prerequisite for operational stability.
&nbsp;
<ol start="5">
<li> Ethical AI and Data Sovereignty</li>
</ol>
As AI becomes deeply embedded in telco operations, the fifth trend centers on the ethical and secure management of data. Telcos sit on some of the world’s most sensitive consumer data, making privacy and AI governance a top priority. In 2026, we are seeing a massive shift toward Privacy-Enhancing Technologies (PETs) and decentralized data architectures. 
Leading operators are building transparent AI frameworks to ensure that automated decisions—whether in credit scoring for a new contract or network management for a corporate client—are fair, explainable, and compliant with evolving global regulations. This focus on ethics is becoming a competitive advantage, as consumers increasingly flock to brands they can trust with their digital lives.
&nbsp;
Moving Forward
The telecommunications industry is rapidly moving towards an AI-native future. By prioritizing autonomous customer service and hyper-personalization, operators can unlock unprecedented growth and efficiency. As networks evolve through intelligent optimization and predictive maintenance, maintaining a strong foundation of ethical AI and data sovereignty will be essential for securing long-term consumer trust and competitive advantage.
<a href="https://datatonic.com/contact/">Get in touch to learn more</a>.

Emerging Trends in Telco for 2026 + Beyond

The gaming industry has always been at the cutting edge of technology, but in 2026, we’ve moved past the experimental phase. For any global gaming company, data and AI are no longer just buzzwords; they are the structural backbone of development, community management, and long-term viability. By shifting focus from manual labor to intelligent systems, studios are creating more immersive worlds than ever before.
&nbsp;
<ol>
<li> Faster Game Design and IP Focus</li>
</ol>
In the modern development cycle, game design has undergone a radical transformation. By utilizing AI to scale environments instantly, developers are no longer bogged down by the repetitive aspects of world-building. 
This allows designers to step back from the manual placement of assets and instead dedicate their creative energy to high-level IP development and complex storytelling. When the heavy lifting of environment generation is automated, the human element can focus on what truly matters: the emotional resonance and narrative depth of the game. Furthermore, this transition reduces the time-to-market for major titles, enabling studios to iterate on player feedback and release content updates with unprecedented speed and precision.
&nbsp;
<ol start="2">
<li> Dynamic Retention Through Richer Worlds</li>
</ol>
Because AI can populate worlds with diverse, non-repetitive content, players remain engaged for longer periods. These environments feel less like static backdrops and more like living ecosystems, keeping the exploration phase fresh and preventing the early-stage churn that often plagues massive open-world titles. 
By dynamically generating unique quests, reactive NPC behaviors, and evolving landscapes based on player choices, AI ensures that no two playthroughs are identical, significantly deepening the player&#8217;s sense of agency and immersion within the game world. 
This technology allows developers to move beyond handcrafted, linear paths, creating emergent gameplay where the world truly breathes and responds to every action. Consequently, studios can provide hundreds of hours of high-quality, non-repetitive content that keeps players returning to explore new possibilities and personal narratives.
&nbsp;
<ol start="3">
<li> Scaling Communities with Lore-Accurate Guidance</li>
</ol>
Community building in 2026 is about more than just forums and message boards, and much more focused on integration. One rising trend is the use of instant, lore-accurate AI guidance to help players navigate complex game systems. 
This technology allows a studio to scale its player base rapidly without a proportional increase in human support overhead. By providing players with immediate, in-character answers to their questions, companies can maintain higher satisfaction levels and deep immersion while keeping operational costs manageable. 
Furthermore, by integrating these assistants directly into the game&#8217;s UI, developers can guide players through complex mechanics or narrative beats without breaking the fourth wall, ensuring that the community grows around a shared, deeply understood lore.
&nbsp;
<ol start="4">
<li> Boost Developer Productivity with Context-Aware Code</li>
</ol>
AI is now being deployed to accelerate software programming by automating routine developer tasks like code completion and generation. These AI pair programmers proactively suggest code based on the current context as a developer types, which significantly improves active coding time, change list (CL) throughput, and code quality. 
By automating the repetitive aspects of development, studios can remove the &#8220;tax&#8221; on their creative teams and allow human developers to focus on high-level design and innovation. This foundational shift ensures a scalable, collaborative environment that accelerates the entire development workflow.
&nbsp;
<ol start="5">
<li> Autonomous QA and Lifecycle Automation</li>
</ol>
One of the most significant operational shifts is occurring in quality assurance, where studios are replacing manual bottlenecks with autonomous agents that can test games 24/7. These agents can simulate thousands of hours of gameplay in a fraction of the time of traditional testing, catching edge-case bugs and performance dips instantly. 
This new agentic testing is enabling significantly more stable launches and allowing human developers to spend more time on more creative and subjective elements like &#8220;fun factor&#8221; and game balance rather than just searching for issues.
&nbsp;
Conclusion
As we look at the trajectory of the industry, it is clear that the integration of data and AI is the primary driver of growth for the modern gaming powerhouse. By automating the QA lifecycle and boosting player retention, studios are proving that efficiency and creativity are not mutually exclusive. For global gaming companies to thrive in this era, they must embrace these tools to build worlds that are not only larger and more complex but also more stable and welcoming to players of all skill levels.
<a href="https://datatonic.com/contact/">Get in touch to learn more</a>.

Emerging Trends in Gaming for 2026 + Beyond

London, 21st April 2026 — Datatonic, a global leader in cloud data + AI, announced today that it has won two 2026 Google Cloud Partner of the Year awards, in the Country: EMEA &#8211; UK/IE and Training: EMEA categories, bringing its total to 12 awards across various regions and industries.
In the UK, Datatonic has been driving strategic and scaled adoption of enterprise data and AI platforms across the region’s largest corporations. By modernizing data infrastructure and delivering measurable business outcomes for strategic clients, Datatonic is cemented as a leading partner for UK businesses navigating the evolving data landscape.
Central to this success is a commitment to <a href="https://datatonic.com/services/training/" target="_blank" rel="noopener">training businesses for the future of cloud data + AI</a>. Through Datatonic Academy, the company has upskilled over 10,000 learners in 2026. These programs, ranging from Google Cloud fundamentals to advanced GenAI and FinOps courses, ensure that business leaders and technical teams are equipped with the expertise to maximize their cloud investments and drive future innovation.
“The Google Cloud Partner Awards honor the strategic innovation and measurable value our partners bring to customers,” said Kevin Ichhpurani, President, Global Partner Ecosystem and Channels, Google Cloud. “We are proud to name Datatonic a 2026 Google Cloud Partner Award winner, celebrating their role in driving customer success over the last year.”
Throughout the year, Datatonic has worked with industry-leading clients to help them remain competitive in the rapidly evolving data + AI landscape, scale their operations, and bring better services to customers through data-driven decision-making and continuous innovation.
“We are incredibly honored and grateful to be recognized with two Google Cloud Partner of the Year awards. The UK/IE award validates our dedication to driving strategic data and AI adoption across the region’s largest corporations and supporting UK businesses, and the Training &#8211; EMEA award is a profound testament to our commitment to upskilling thousands through our Datatonic Academy. We look forward to continuing this strong partnership with Google Cloud.” – Scott Eivers, CEO, Datatonic
About Datatonic
Datatonic, 12 X Google Cloud Partner of the Year, is the leading cloud data and AI transformation partner 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 Wins Two 2026 Google Cloud Partner of the Year Awards

In an industry where attention is the primary currency, the ability to stand out and connect with audiences has never been more critical. There has been a fundamental shift in how media and entertainment leaders approach innovation, moving beyond simple automation to deep creative transformation, the strategic fusion of human creativity with the power of data and AI. Here are five key trends and use cases where AI is currently redefining the media and entertainment landscape.
&nbsp;
<ol>
<li> Agentic Media Search + Discovery</li>
</ol>
Gone are the days of endless scrolling through generic categories. The new standard is Agentic Search, where users, both internally and customers, interact with content libraries through natural language.
This shift is already being proven by real-world applications. For example, Datatonic collaborated with <a href="https://datatonic.com/case-studies/cineverse-generative-ai/">Cineverse</a> to develop &#8220;Ava,&#8221; a fully interactive Generative AI chatbot. Ava doesn&#8217;t just recommend movies based on fixed genres; it uses Agentic Search principles to interpret nuanced requests, like a user’s current mood (&#8220;I need a feel-good movie from the 90s&#8221;) or specific situations (&#8220;A film to watch with my family that isn&#8217;t animated&#8221;).
This is highly relevant to various types of media businesses, including video streaming platforms, digital music services, online news aggregators, podcast providers, and even e-commerce sites that feature media content. Any platform where content discovery is a core part of the user experience stands to gain a competitive edge from implementing Agentic Search.
&nbsp;
<ol start="2">
<li> AI-Enriched Media Asset Management</li>
</ol>
AI is being applied in a similar way for media asset management. Managing vast archives that span decades and thousands of hours of footage is a massive task. Traditional indexing is being replaced by AI-enriched indexing, making every asset searchable by complex variables like emotion, brand safety, and time. 
This technology is highly relevant to editing teams within the media + entertainment industry, allowing teams to see significantly faster video asset discovery, reducing manual effort, and reduces the high costs associated with re-shooting scenes when existing footage cannot be found. This can also be used by marketing teams to accelerate finding clips for social media and campaign development. 
&nbsp;
<ol start="3">
<li> Creative Transformation to Accelerate Content</li>
</ol>
AI is no longer just a tool for analysts; it has entered the creative studio as a co-pilot. By moving from fragmented ideation to AI-powered workflows, studios are unlocking new creative frontiers without traditional resource constraints. 
Media and entertainment brands are leveraging Google Cloud to deliver personalized marketing content at scale, creating significantly more personalized content in a fraction of the time. This transformation enables significantly faster creative idea generation and increases in content output through centralized AI platforms.
This can also increase conversion rates for marketing materials due to better personalisation to customers. 
&nbsp;
<ol start="4">
<li> Hyper-Personalization + 24/7 Customer Engagement</li>
</ol>
Engagement is moving from one-to-many broadcasting to a one-to-one conversational model. Brands are seeing an increasing demand for personalized customer experiences that provide 24/7 access to information and interactivity. 
For example, major sports brands, such as the NBA and NFL, are creating conversational AI experiences. These in-app agentic experiences provide instant responses to fan queries about players and stats, while dynamically retrieving video highlights. Such personalized experiences can drive higher repeat engagement and significant growth in new fan acquisition.
&nbsp;
<ol start="5">
<li> Intelligent Content Operations + Localisation</li>
</ol>
Scaling global operations requires moving past manual editing and translation. AI-driven operations allow for localisation, ad placement, and editing at scale while maintaining brand voice and safety. By implementing autonomous workflows, organizations can ensure that their messaging remains culturally resonant and compliant across diverse markets without the bottleneck of human-only review cycles.
Broadcasters are using Machine Learning to improve ad-targeting efficiency by more precisely matching viewer demographics, reducing advertising waste and increasing the value of every impression. Meanwhile, global brands are using AI content agents to translate and localize marketing materials in minutes rather than weeks. These agents are trained on specific brand guidelines to ensure that tone of voice and technical terminology remain consistent, regardless of the target language or region.
For large enterprises, this can result in millions saved in translation costs alone each year while drastically accelerating time-to-market for global campaigns. Beyond simple cost savings, the speed of AI-driven localization allows brands to react to real-time cultural trends and news cycles globally, providing a significant competitive advantage in the fast-moving media and entertainment landscape.
&nbsp;
Moving Forward
The key to standing out in the media and entertainment landscape is prioritising the broader concept of creative transformation. Companies that prioritize agentic search and generative content production will redefine content discovery and scale personalization to an unprecedented degree. By embracing these autonomous operations, leading organizations will secure audience attention and maintain a significant competitive edge in the fast-moving global market.
<a href="https://datatonic.com/contact/">Get in touch to learn more</a>.

Emerging Trends in Media + Entertainment for 2026 + Beyond

The advertising and marketing agency landscape is on the cusp of its most profound transformation in decades. The conversation has moved far beyond simple automation; the new frontier is autonomy. Clients demand demonstrable ROI, transparent metrics, and the agility to respond to a fragmented, privacy-conscious consumer base. As we navigate 2026 and look further out, the tools and operating models that once defined the industry are giving way to a new era powered by intelligent systems.
The core challenge for agencies is scaling creativity and personalization while managing unprecedented data complexity and professional quality. The following trends illustrate how leading agencies are redefining their value proposition, shifting from service providers to integrated intelligence partners.
While <a href="https://www.decisionmarketing.co.uk/news/agencies-firmly-in-the-firing-line-as-cmos-plot-cutbacks">trends indicate a slight decrease in agency spending</a>, successful agencies are adapting to adopt the latest technologies to support their clients, achieving record revenue and profits with AI-driven growth. 
&nbsp;
<ol>
<li> AI Agents for Media Buying</li>
</ol>
Media buying has long been a labor-intensive process requiring human planners and traders to constantly monitor and adjust bids across numerous platforms. The emerging trend is the deployment of AI Agents that operate with goal-oriented autonomy, moving beyond simple programmed rules.
With autonomous budget allocation, AI agents can be given a campaign goal and independently redistribute budgets across channels (Programmatic, Social, Search) in real-time based on predictive performance modeling, often reacting to micro-shifts in consumer behavior faster than any human team. This significantly reduces repetitive manual tasks for creative teams, enabling them to focus on creating content and campaigns that will land well with customers. 
This is a small part of real-time campaign optimization: agents can manage bid adjustments, creative rotation, and pacing, freeing human media teams to focus entirely on high-level strategy, partnership negotiation, and client relationship management.
&nbsp;
<ol start="2">
<li> Hyper-Personalization at Scale </li>
</ol>
The dream of &#8220;the right message to the right person at the right time&#8221; has historically been limited by the cost and time required to manually produce hundreds of creative variations. <a href="https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/the-value-of-getting-personalization-right-or-wrong-is-multiplying">McKinsey research</a> shows that 71% of consumers expect personalized interactions. Generative AI is rapidly closing this gap, allowing agencies to scale personalization to an unprecedented degree.
Generative AI tools are being integrated into the creative workflow to produce thousands of variations of copy, image assets, and video snippets tailored for micro-segments identified using the vast amounts of data available.
This approach moves the industry past traditional A/B testing (comparing two versions) to comparing potentially thousands of versions, ensuring every consumer touchpoint feels bespoke and relevant, dramatically boosting conversion rates.
&nbsp;
<ol start="3">
<li> The Creator Economy as a Data Science Channel</li>
</ol>
The creator economy has matured from a tactical PR play into an essential, high-ROI marketing channel. For agencies, the challenge is managing the sheer volume and variability of creators and ensuring performance is authentic and compliant.
Agencies are increasingly using advanced data science models to predict the Return on Investment (ROI) of a creator before any contract is finalized. These proprietary models meticulously analyze several data points, including creator audience demographics, historical engagement rates, and content affinity. This predictive capability allows agencies to make data-driven decisions and select creators who offer the highest likelihood of a successful and profitable campaign for their clients.
Furthermore, sophisticated AI systems are now integrating compliance and authentication into the creator management process. These agents can automate crucial administrative tasks such as managing contracts and payment schedules. More importantly, they ensure adherence to regulatory standards, particularly FTC compliance, by automatically monitoring and verifying necessary content disclosures. 
Concurrently, these systems authenticate performance by employing measures to distinguish genuine audience engagement from fraudulent activities, thereby guaranteeing transparent and accurate reporting for clients.
&nbsp;
<ol start="4">
<li> Measuring Attention, Not Impressions</li>
</ol>
The traditional metric of &#8220;impression&#8221; or &#8220;viewability&#8221; is increasingly viewed as a weak proxy for true engagement. Consumers are experts at tuning out irrelevant ads, and now the industry is shifting to measuring genuine consumer attention.
Agencies are now integrating new technologies to go beyond simple impressions and quantify whether a consumer truly noticed an ad, using advanced attention metrics such scroll depth analysis, and time-on-screen metrics. The ultimate focus has shifted to connecting these high attention scores directly to measurable sales lifts or brand lift, which establishes a clear, data-driven link between creative quality, media placement, and tangible business results, ultimately making media spend demonstrably more efficient.
While this applies to all industries, Agencies are in a unique position with their IP to understand the most successful engagements by combining 1st and 3rd party data.
&nbsp;
<ol start="5">
<li> Privacy-First Data Strategy</li>
</ol>
With increasingly strict global privacy regulations, including GDPR and more regionally-specific CCPA, the old models of targeting and measurement based on borrowed data are obsolete. Agencies are transitioning to a privacy-first data strategy.
The rise of data clean rooms is a significant emerging trend. Agencies are increasingly taking on the role of architects for these secure, neutral environments. The core function of a data clean room is to allow clients&#8217; first-party data to be matched with media partner data, such as from Google or Amazon. Crucially, this is achieved without ever exposing or sharing the raw, personally identifiable information, ensuring privacy is maintained.
Another key area is first-party data consulting. Agencies are now proactively investing in building robust and ethical first-party data assets. This reduces their dependence on often opaque third-party systems. By establishing proprietary data assets, agencies gain a distinct competitive advantage in both targeting and measurement, making their strategy future-proof against ongoing and anticipated privacy shifts.
&nbsp;
Moving Forward
The firms that will lead the next decade of advertising will be those that move fastest from managing tasks to deploying autonomous intelligence. Human employees will always be required to develop new ideas and break the mold, but success in this new landscape requires not just buying new technology, but fundamentally restructuring operational workflows around speed, compliance, and demonstrable ROI driven by autonomous systems.
&nbsp;
<a href="https://datatonic.com/contact/">Get in touch to learn more</a>.

Emerging Trends in Agency for 2026 + Beyond

The insurance industry has reached a critical inflection point. For the last few years, the conversation was dominated by &#8220;digital transformation&#8221;, a broad term that often meant moving paper processes to a mobile app or online portal. But as we move through 2026, the baseline has shifted. More focus on customer experience, tightening regulatory scrutiny, and the scale of sophisticated fraud have forced institutions to move beyond simple automation.
<a href="https://www.gartner.com/en/finance/insights/future-of-finance-2030">Gartner</a> predicts that by 2030, at least 15% of day-to-day finance decisions will be made autonomously. Today’s leaders are seeking systems that can think, collaborate with various teams, and resolve human friction without constant oversight. To understand where the industry is heading, we can look at examples of cutting-edge tech that are currently redefining the insurance landscape.
&nbsp;
<ol>
<li> The Rise of Agentic AI in Fraud Detection</li>
</ol>
Traditionally, AI in fraud detection acted like a smoke detector; it made a noise when it sensed trouble, but it still required a human firefighter to show up and put out the flames. This created large backlogs in compliance departments, and slower resolution for legitimate transactions or claims.
<img loading="lazy" decoding="async" class="wp-image-25112 alignleft" style="font-family: Georgia, 'Times New Roman', 'Bitstream Charter', Times, serif;" src="https://cms.datatonic.com/app/uploads/2026/02/70-Insurance.png" alt="" width="200" height="179" />
&nbsp;
&nbsp;
<a href="https://www.weforum.org/publications/industries-in-the-intelligent-age-white-paper-series/financial-services/" target="_blank" rel="noopener">of financial services executives believe</a> that Al will directly tie to revenue growth in upcoming years.
&nbsp;
Agentic AI represents a shift from AI that assists to AI that acts. Unlike traditional models, these agents are given a goal rather than just a set of instructions. This includes:
<ul>
<li style="font-weight: 400;" aria-level="1">Autonomous Investigation: When a red flag is raised, an AI agent can independently navigate internal systems to verify identities and pull cross-channel data (like geolocation or device biometrics).</li>
<li style="font-weight: 400;" aria-level="1">Closing the Loop: These agents can draft comprehensive Suspicious Activity Reports (SARs) for human review, meaning that 90% of the investigative groundwork is complete before a human claims handler opens the file.</li>
</ul>
&nbsp;
<ol start="2">
<li> Predictive Maintenance with AI for Insurance Adjustments</li>
</ol>
In 2026, AI is enabling insurers to move from a reactive insurer to a proactive partner for their customers. As traditional history-based models fail to predict modern weather volatility, the industry is replacing them with forward-looking, hyper-local intelligence. 
Historically, insurers priced property risk based on broad regions (like postcodes/zip codes). In 2026, they can use digital twin technology, virtual 3D replicas of specific buildings and their environments.
This unlocks hyper-local modeling; instead of simply adding manual notes, such as &#8220;high-risk flood zone,&#8221; insurers are now able to see the exact elevation of a building’s front door and the drainage capacity of its specific street. This can be combined with real-time alerts. Insurers ingest real-time data from Earth Observation (EO) satellites and local IoT sensors. For example, if a sensor detects rising groundwater nearby to a house or warehouse, an automated risk alert can be triggered for the owner.
Lastly, predictive maintenance means that AI can analyze satellite imagery to spot property vulnerabilities before a storm hits, such as overhanging tree limbs or clogged commercial gutters, and prompts the owner to fix them. This allows insurers to maintain lower insurance prices as the damage risk becomes lower, reducing customer churn. 
&nbsp;
<ol start="3">
<li> Transforming Complaints Handling</li>
</ol>
The &#8220;complaints department&#8221; has long been viewed as a cost center, where brand loyalty becomes less important than cost and convenience. However, leading  businesses across multiple industries are now using AI to change this. In insurance, where products are complex and vary between customers, AI can be used to streamline the resolution process. An initial program of investment and upskilling in AI use cases will lead to large long-term impact:
<img loading="lazy" decoding="async" class=" wp-image-25114 alignleft" src="https://cms.datatonic.com/app/uploads/2026/02/90-Insurance.png" alt="" width="200" height="180" />
&nbsp;
&nbsp;
<a href="https://www.weforum.org/publications/industries-in-the-intelligent-age-white-paper-series/financial-services/" target="_blank" rel="noopener">of leaders believe</a> their organization needs to consider significant adjustments or total transformation of their reskilling strategy to support the future.
&nbsp;
<ul>
<li style="font-weight: 400;" aria-level="1">Predictive Triage: AI analyzes the sentiment and history of a customer interaction in real-time, identifying high-risk complaints before they escalate to a regulator or churn to another provider.</li>
<li style="font-weight: 400;" aria-level="1">Standardized Excellence: By using Generative AI to assist human agents, companies can ensure that responses are not only faster, but also consistent and fully compliant with the latest financial regulations.</li>
<li style="font-weight: 400;" aria-level="1">Frictionless Transitions: By handling the repetitive administrative tasks of logging and categorizing complaints, AI allows human specialists to focus entirely on empathy and complex problem-solving for customers.</li>
</ul>
&nbsp;
<ol start="4">
<li> Automating Underwriting with AI Agents</li>
</ol>
Using AI in Underwriting has transitioned from basic task automation to goal-oriented autonomy, where AI agents can act as independent executors rather than simple calculators. These agents can proactively navigate the entire underwriting lifecycle, independently querying third-party APIs for real-time risk signals (such as geospatial data), identifying missing documentation, and drafting personalized broker emails to bridge information gaps without human intervention. 
By handling a significant percentage of the repetitive manual work, these systems allow human underwriters to shift from data entry to high-level portfolio strategy. Most importantly, this operational efficiency is unlocking entirely new markets: sectors that were previously &#8220;uninsurable&#8221; due to high administrative costs or lack of historical data, such as micro-insurance for gig workers or coverage for climate-vulnerable small businesses, are now profitable because agentic AI brings the cost of underwriting decisions down.
&nbsp;
<ol start="5">
<li> The AIOps Foundation</li>
</ol>
While agentic fraud detection, predictive maintenance, and advanced underwriting are some of the visible applications of insurance’s digital evolution, they are only possible because of a robust, underlying operational layer: AIOps. These practices serve as the necessary engineering foundation, transitioning AI from lab-based experiments to enterprise-grade, regulated production systems.
<img loading="lazy" decoding="async" class="wp-image-25116 alignleft" src="https://cms.datatonic.com/app/uploads/2026/02/84-Insurance.png" alt="" width="200" height="178" />
&nbsp;
&nbsp;
<a href="https://www.weforum.org/publications/industries-in-the-intelligent-age-white-paper-series/financial-services/" target="_blank" rel="noopener">of financial organizations</a> are implementing or planning a framework to govern how Al will be built, trained, used and audited.
&nbsp;
AIOps ensures that the complex models driving fraud detection or complaint triage are built, deployed, and monitored reliably, preventing drift and ensuring fairness, and provides the observability and automation needed to manage the vast infrastructure supporting these models, automatically resolving system issues, managing resource allocation, and ensuring that every AI-driven decision is traceable, auditable, and compliant with data regulations. Without these operational foundations, the cutting-edge use cases described above would remain stuck in perpetual pilot projects.
&nbsp;
Moving Forward
These examples demonstrate that the future of insurance is focused on building a more resilient, collaborative, and responsive ecosystem. The companies that embrace these innovative use cases and privacy-first architectures are the ones that will define the next decade of insurance.
<a href="https://datatonic.com/contact/">Get in touch to learn more</a>.

Emerging Trends in Insurance for 2026 + Beyond

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<h3>What is data governance?</h3>
“The exercise of authority, control, and shared decision making (planning, monitoring and enforcement) over the management of data assets.” would be an official data governance definition by DMBOK.However, whilst investigating this subject, we found a variety of definitions on data governance which mostly overlap but sometimes not so much. Considering how we like things to be simple and understandable, we settled on:
Data governance is a set of principles and practices that ensure high quality throughout the complete lifecycle of your data. It is practical and actionable framework which helps stakeholders identify and meet their information needs.
To summarize, data governance should serve all stakeholders – people that need data in that organization (and even beyond, for instance if data is shared between multiple companies) and it should be applicable for any organization, no matter what its primary business is (for example: pharmacy, finance, retail, telco)
Having said that, data governance is something that you do when you need to have data that can be trusted, easily available, usable, integrated, and secure.
<h3>What does data governance cover?</h3>
Most businesses will naturally do some of the things that fall under data governance, especially master data management (MDM) which is a crucial step in any data-related business. However, data governance is here to put all those things “under one governing hat,” to systematize it better and push necessary organizational or technological changes that are needed so all this work will not be in vain. Other elements are risk management, metrics, data quality, policies, processes, and others you can see on picture below.
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<h3>What is not data governance?</h3>
Now that we have gone through the basic definition of data governance and what that covers, it would be good to explicitly state what data governance is not.
<ul>
<li>Data governance is not an exact process.
<ul>
<li>There is no algorithm or book that can tell you what the exact steps are to achieve data governance. As we said, it is a set of principals specific to your company and your data but created with global policies like GDPR, PCI DSS or others in mind.</li>
</ul>
</li>
<li>Data governance is not only about data privacy.
<ul>
<li>It is about implementing the process around data privacy or security, and inter-departmental data exchange agreements, but as we stated in the previous section, it covers much more such as MDM, organizational changes etc.</li>
</ul>
</li>
<li>Storing data in a central repository or a data lake is not governance.
<ul>
<li>However, controls around accessing and processing the critical data from that repository that make sense to your organization and bring value, are part of data governance.</li>
</ul>
</li>
</ul>
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To conclude, data governance is not a function performed by those who manage information, meaning there must be a separation of duties between those who manage and those who govern.
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<h3>Data Governance Goals</h3>
J. Ladley in his book “Data Governance How to Design, Deploy, and Sustain an Effective Data Governance Program” proclaims that the ultimate goal of data governance is for it to disappear as a stand-alone program and become a part of the business core. Similar to how financial controls and events are already seen as regular activities and not special programs.
Although this is a great end goal, we need to find our own starting point and build the processes from there. We need to find few goals that would highlight what needs to be achieved with these data governance activities that we can later incorporate into our everyday processes. Here we decided to present four as an example. Bear in mind that data governance is a tailored process and the goals, although very similar, might vary from business to business and process to process.
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The benefits of successfully implemented data governance for a company are enormous and have a compound effect. From cleaner and leaner data, to data-driven decision making, creating better business results. Positive business results play a key role in increasing your company’s reputation, leading to increasing the company’s overall market value.
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<h4>Where to start?</h4>
You might be eager to start implementing its principles as soon as possible, now that we have gone through the definitions and benefits of data governance. Two final questions to think about though are– how long does it take to implement data governance, and how much does it cost?
Well, the bad news is that there is no straight answer to the first question, and the shortest answer to the second is- “a lot.” However, when done properly, data governance is still cheaper than paying for the consequences of not having it implemented. If looking at financial loss, we can mention that cost of breaching GDPR alone can go up to€20 million or 4% of the company’s global annual turnover.
Now, CFOs might want to take out the calculator and compare the cost of fines with cost of setting up teams (the good news being that you might already have some initiatives in place) and implementing data governance, but before you do that, remember that leaking confidential or sensitive data can have far greater consequences than what is seen on the surface, such as customer turnover or loss of reputation that could bring further financial harm to your company.
If you haven’t started with data governance yet, developing a plan is a good place to begin. Start small, go one step at a time, improve based on feedback and remember that data governance is a continuous and iterative process, and not a one-time project.
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Introduction to Data Governance

In a world where data fuels innovation and growth, organizations face the challenge of effectively managing and sharing data to remain competitive. Traditional data governance approaches can be cumbersome and hinder business agility. Federated data governance offers a solution by decentralizing data management, incorporating data mesh principles, and focusing on data-as-a-product. In this blog post, we will delve into the concept of federated data governance and its potential to improve business agility. We will then follow this up with a list of steps to get you to that goal.
<h3>What is Federated Data Governance?</h3>
Federated data governance is an approach that distributes data management responsibilities across an organization. This empowers teams to manage their data autonomously while adhering to shared principles and guidelines. The core concept of federated data governance is based on three key principles:
<ul>
<li>
<ul>
<li>Decentralized ownership: Data management is distributed, allowing teams to manage their data according to their specific needs and context.</li>
<li>Shared principles and guidelines: A common set of governance principles and standards is established, ensuring interoperability across the organization.</li>
<li>Coordination and collaboration: A central coordinating body or function facilitates communication, collaboration, and alignment across different data domains. However, rather than just creating the rules, they are helping to keep best practices aligned from the decentralized teams.</li>
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</li>
</ul>
<h3>Supporting Data Mesh with Data-as-a-Product</h3>
Data Mesh, an architectural and organizational paradigm, is crucial for well federated data governance – they go hand-in-hand. Data Mesh also works best with federated data governance. Data Mesh treats data as a product, with teams acting as data product owners who are responsible for the entire lifecycle of their data. This includes data quality, discoverability, access control, and documentation.
The data-as-a-product concept ensures that data is treated as a valuable asset, with data product owners focused on delivering high-quality, easily accessible products that meet consumer’s needs. By establishing a strong connection between data producers and consumers, the data mesh enables efficient data sharing across the organization, supporting the goals of federated data governance.
These concepts will not only help with giving the team better agility and in making decisions that will facilitate their needs, while also adhering to the overall needs of the organization, they will ensure that those nearest the creation of the data – those that understand that data best- are the ones shepherding it, sharing it and ensuring it meets the quality needs of the consumers of that data.
<h3>Enhancing Business Agility with Federated Data Governance</h3>
Federated data governance and Data Mesh address the challenges of today’s complex and dynamic business environment. By distributing data management responsibilities and embracing data-as-a-product, federated data governance offers several benefits that promote business agility:
<ul>
<li>
<ul>
<li>Faster decision-making: Decentralized governance enables data product owners to better understand and respond to their consumer’s needs, leading to timely and accurate data availability for decision-making, empowering teams to make informed decisions quickly.</li>
<li>Improved collaboration: like with Data Mesh, Federated Data Governance fosters collaboration, as data product owners and consumers work together to ensure data accessibility, usability, and quality. This enhanced collaboration can break down silos and drive innovation.</li>
<li>Scalability and flexibility: Federated data governance enables organizations to scale their data operations effectively. As new data sources and use cases emerge, teams can adapt and incorporate them into their existing data management processes.</li>
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</li>
</ul>
Federated data governance, supported by Data Mesh concepts and a data-as-a-product approach, has the potential to revolutionize the way organizations manage and share data. By decentralizing ownership, encouraging collaboration, and treating data as a valuable asset, federated data governance can enhance business agility and equip organizations to thrive in the data-driven landscape of today’s competitive market.
<h3>The Important Question: What Next?</h3>
For companies looking to implement federated data governance, the following steps outline an approach to starting a project that addresses the challenges associated with this new paradigm:
<ul>
<li>
<ul>
<li>Assess the current data governance landscape: Begin by evaluating your organization’s existing data governance processes, structures, and challenges. Identify bottlenecks, inefficiencies, and areas where centralization may be impeding progress or limiting collaboration.</li>
<li>Define a vision and objectives: Clearly articulate the vision for data governance within your organization, and establish the objectives you aim to achieve, such as improved business agility, collaboration, and faster decision-making.</li>
<li>Establish guiding principles: Develop a set of guiding principles that will underpin your governance model. These principles should address key aspects such as data ownership, shared standards, and collaboration.</li>
<li>Identify data domains and stakeholders: Divide your organization’s data landscape into domains, assigning ownership to specific teams or departments. Ensure that each domain has clear and accountable data product owners and stakeholders who will be responsible for managing the data in their domain.</li>
<li>Foster a culture of collaboration and communication: Encourage cross-functional communication and collaboration between data product owners, data consumers, and other stakeholders. This can be achieved through regular meetings, workshops, training sessions, and other less structured feedback methods, as well as by generally promoting a culture of data sharing and transparency.</li>
<li>Develop a governance framework: Create a framework that outlines roles, responsibilities, and processes for managing data across the organization. This framework should also include a central coordinating function to ensure alignment and coherence across the different data domains.</li>
<li>Iterate and improve: Continuously evaluate and refine your approach based on feedback, lessons learned, and evolving business needs. Foster a culture of continuous improvement to ensure your data governance model remains agile and responsive to the organization’s needs, remembering that processes that hinder or that are no longer required should also be assessed and removed if they are no longer fit for purpose.</li>
</ul>
</li>
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By following these steps, organizations can effectively start a project to implement federated data governance, paving the way for increased business agility, more efficient data sharing, and better collaboration across the organization.

Federated Data Governance

Determine your potential next steps in data governance implementation and work prioritization. With each question, or group of questions, we will also explain why it should be asked, and how it can help.
<h3>1. What are your biggest data related challenges?</h3>
This question should help you to determine your priorities and therefore, the next steps when it comes to data governance.
Now, depending on the data governance approach that one follows, one can argue that there are clear next steps for data governance, like assessing current state and defining scope, defining and filling roles, and following with a defining data governance mission and vision statement, however this is often too abstract for most companies. The issue that many organizations have, as Lauren Maffeo, author of “Designing Data Governance from the Ground Up Six Steps to Build a Data-Driven Culture” nicely puts it, “[Data governance is] an admittedly nebulous term that can seem to mean everything and nothing”. This first question helps to “personalize” data governance to you and your organization by bringing attention to the issues you know you have, and check if there are ongoing solutions, instead of formalizing governance and building it from the ground up.
This approach brings you head on into thinking about data ownership and accountability, two important enablers of data governance.  Basically, this wall comes sooner than in traditional approaches where in the initial phases you have a feeling that you are doing something governance related (after all, you are hiring, writing perfect mission statements etc.) when in reality you are postponing the “what now” moment when it is time to turn theory into practice. Think about what your definition of governance is, and what you want to highlight and formalize.
Another reason is the agility of this approach – doing it now and fixing it later when it is centrally formalized, rather than waiting to create it once and for it to be “perfect”. In this later approach it often happens that teams just follow rules blindly instead of understanding the why behind that “perfect” approach, which again impacts accountability (“I followed what centralized governance told me, so I can’t be blamed” instead of “What is the impact to this, with my data/consumers/domain”).
Lastly, thinking about different issues in the organization allows teams to prioritize what they need, and therefore be able to have it right where they are in their governance journey. If you were to follow a centralized approach, then everyone should end at exactly same point, which for some teams is harder than for others – as some teams are more mature than the others, some have greater technical debt, and some are more impacted by specific issues compared to others (motivation). In conclusion, different teams have different struggles, and following the same approach is not going to yield the same results.
<h3>2. What are the drivers for this initiative?</h3>
This question is not necessarily explicitly asked, but finding answers to it is important for understanding the state of the organization and its maturity, as well as to some extent if there is (and if so, where) a real desire in the organization for change. It also helps to find out if there are some burning issues (i.e., legal) that prompted it but that did not come out through answering question 1.
Answering this question will also help you to better prepare for the organizational pushback that will inevitably come. For example, if the data governance initiative was a push from the top that usually means good support from upper stakeholders and faster buy-in, but may cause turmoil on the level you are operating. Interestingly enough, there are 2 different scenarios in cases when governance is a top-down initiative: The first, people in the organization embracing the change but not really being ready or knowing how to change, and secondly, those that will show you they don’t want the change on every possible step of the way.
The former ones on the other hand will often actively participate without complaining until it is time for takeover when they report to higher-ups (and whoever wants to listen to them really) how unsatisfied they are with the proposed solution. They might even create some benchmarks that this data governance initiative has not satisfied, but that were never before disclosed, and that are often not even relevant for the initiative. As an example, maybe according to this assessment, data mesh is not way to go for them, because the data catalog tool they chose can’t show lineage well for their main system.
This is where question 3 “What do you see as a result?” and the conversation around expectations comes into play – making employees talk about their expectations and bringing them onto the same page, as well as explaining concepts repeatedly and keeping everyone engaged and responsible -because they are not lacking inclusion but rather engagement. However, escalations will happen, since question of ownership always brings out fear of change, authority, and autonomy, and fear causes a fight (direct escalation), flight (avoiding responsibility), or freeze (do nothing and let initiative sink) response, with each response causing an escalation in one way or another.
In any case, be aware not to fall/run into the pitfall of having too much management. Sometimes certain people in the organization will try to micromanage the whole initiative, which on the surface may seem as if you have embraced the concepts, when the reality is that you are still where you were at the beginning because there was no real autonomy (or even time) for the teams to really participate in the way they found suitable for them.
The idea behind question 2 is to help you and everyone involved, understand your organization and stakeholders, and prepare for the social part of data governance. In the end, organizations go with major changes like data mesh for one of two reasons, number one, reaching the ceiling of the current architecture and being unable to proceed without creating bottlenecks or technical debt, or two, wanting to proactively change before reaching that ceiling. In any case, change is inevitable.
<h3>3. What do you see as a result?</h3>
The final question is about expectations and results. As aforementioned, it&#8217;s important to know what questions or issues you and everyone involved expect to solve at the end of this initiative. This question helps to get everyone onto the same page – comparing what was said vs what was heard, while simultaneously helping you to determine how deep you should go into your process or what the scope should be. In some cases, you can’t go too deep because the organization is not ready, so it is better to have a few simple principles for data governance for the whole organization, than to go into too many details. In other cases, you will go much deeper with the details but will do it for a few data products. It all depends on the information you gathered through answering questions 1 and 2.
One thing that happens surprisingly often is that the goal the organization sets up is to get data from one source. It sounds simple and not wholly related to governance, but then turns out to be an issue of someone holding data hostage. Such individuals or teams are afraid they will lose authority or relevance if they “release” the data. While in some rare cases this can be true, in most cases it is just the contrary, with governance formalizing the authority while also, within the context of data mesh, opening that data up for self-serve. In any case, be aware of that pitfall as well.
<h3>Conclusion</h3>
The bottom line is that data mesh and data governance, although very powerful, are not silver bullets for all your data and organization related issues. They are just tools you must learn to utilize to become an effective, true data-driven organization.

Ramping up your Data Governance

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<div>To quote Zhamak Dehghani who came up with the terminology: “a data mesh is a type of data platform architecture that embraces the ubiquity of data in the enterprise by leveraging a domain-oriented, self-serve design.” In our very modest wording, we can say that data mesh is a type of data architecture, that federates data ownership amongst domains of data owners who are held responsible for providing the data as products, while enabling communication between distributed data across different locations.</div>
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<h3>Data Mesh Pillars</h3>
Like every popular system, data mesh uses pillars or global variables which provide insights into the importance of data mesh placement by carrying out policies for competitiveness. 
The 4 pillars would be:
<ol>
<li>Domain-oriented, decentralized data ownership and architecture</li>
<li>Creating data products</li>
<li>A self-service data infrastructure platform</li>
<li>Federated governance</li>
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The first data mesh pillar advises us not to centralize data ownership. Meaning, in most use cases we have a data warehouse or data lake team who is responsible for data, its usage and access provisioning. Data mesh hereby accentuates that the ownership of data goes to the team who knows the data best and can package it for others in the best possible way. Shortly written, decentralization of data ownership.
The second pillar states that there must be data products. In more or less every company, data is used by data scientists, data engineers, data analysts and business analysts, which in fact a quite a lot of people, with very diverse requirements. Hence for each data usage request, it is good to provide a certain data product by which a specific team can access the data they need.
A self-service data infrastructure platform is the third data mesh pillar. Well, it just makes sense. Having multiple data products can be messy and confusing, not to mention a lot of work. A more practical solution, which data mesh propagates, is to have a unified platform where data products are created and consumed so that the overall data platform consists of one-to-many data nodes.
<h3>Data Mesh Concept</h3>
Anyone who has ever expressed any interest in data mesh has probably come across the following diagram.
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This simple, yet informative diagram shows how a data mesh works in a specific enterprise. If the four pillars are followed, one should end up with an enterprise organization which has an organizational alignment around creating data products which are built on actual consumption by analysts and data scientists, and are providing an alignment between data engineering and data analysis.
<h3>Why Data Mesh?</h3>
Although we are sure that after understanding the data mesh concept, the reader has multiple questions and answers on whether the data mesh would be a good/bad fit for their company, let’s go through why we should at least consider data mesh architecture.
There is a popular notation called the curse of the data lake monster. Heard about it? Good! Now let’s dig deeper. Having a single unified data model is practical to have. If we spent enough time building a robust data infrastructure, use-cases will present themselves later. If we build it, they will come! But will they? Often, data users such as data scientists or data analysts are different teams in a company’s hierarchy. While scientists and analysts are more aligned with business perspective, the data engineers are much closer to the infrastructure behind it. This can already indicate the possible problematic thinking and the tendency to consider a data lake purely as an infrastructure problem. Also, we can’t forget about Conway’s law.
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Technology is evolving over time, but it looks like structuring and managing data is staying the same. Whether we are talking about Warehouses, Data Lakes or the Cloud, we keep building a centralized platform to store the data and thinking that is good enough. Someone will use it if we just collect and organize the data for them, so why is that not working out quite as imagined?
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On one side, never was so much money and time invested into building data platforms which can provide data driven solutions. But where are the results? Having centralized data infrastructure often causes far more complexity than it should, and also disables the management from seeing the true value of investment. Knowledge? Well, we can also say goodbye to that. There is always a person who has the most experience and knows every single detail in the backend, and surprise, surprise… they often quit. The knowledge transfer disappears from that day forward and you are stuck with months of learning whatever is in the background. So that we don’t miss out on this, how about we rewrite it or migrate the data somewhere else?
With all that being said, a shift to modern distributed architecture sounds quite good now doesn’t it?
This would not be a data engineering blog if we did not mention word monolithic at least a few times. Well, centralized architecture is of course followed with huge monolithic structures on top of it. But building microservices for accessing the centralized data will not help you there.
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Shifting involves shifting to a data mesh architecture.
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<h3>Data Mesh Implementation</h3>
To mesh or not to mesh: that is the question indeed. We are not saying everyone should switch but who should?
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You can make your architecture full data mesh and decentralize ownership of both raw data and transformed data or you can use a hybrid approach and ‘mesh’ only one. It depends on your resources. In time, you can make changes and decentralize the part that is left.
<h3 class="master-article__text">Conclusion</h3>
First, rate your company by the previously described standards. Then ask yourself: 
Do you value serving over ingesting? Discovering and using over extracting and loading? Do you prefer publishing events as streams over centralized pipelines? And finally, do you want an ecosystem of data products over a centralized data platform?
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This will help your decide whether data mesh is the right concept for you, and you end up with a paradigm used for breaking down the big data monolith into harmonized, collaborative, and distributed ecosystems, to enable close collaboration between data scientists +  data engineers which leads into getting the results faster and more aligned with your business goals.
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Introduction to Data Mesh

In our world today, when researching Data Mesh, you might have come across phrases like “a sociotechnical approach to build a decentralized data architecture”, data domains, and Data As A Product. The concepts are simple to understand; decentralised, data as a product, and data owners are the ones that know the data best. However, understanding the actions needed to achieve these concepts is harder. By looking at the desired outcome and work backwards, we can understand those steps.
<h3>The Goal of Data Mesh</h3>
The goal of most companies is to make money and from that, a healthy profit. By providing customers with products and services that they want at the right quality and price point, we can do that. But, this involves the changing needs of the customer and disrupting competitors in a rapidly changing global economy. We can expand the goal to be – meeting the customers in a way that protects or increases our revenue, whilst being able to quickly adapt to disruption, and minimising the cost of operations.
This is much more realistic to how most companies work to fulfil their goals, they want to be agile and create value. This is where agility comes in, as after all, being able to quickly adapt is what agility really is. Having business agility means meeting the customer needs quicker, creating happier customers, but it also means less disruption by competitors. It’s also much cheaper for you as a company, as less time and effort needs to go into changing existing solutions to meet those new challenges, and so the cost of operations reduces. This is the holy grail: happy customers, business agility and cheaper operations, and who doesn’t want that?
<h3>Setting up Data Mesh</h3>
When you read about Data Mesh you see a conceptualised and very high-level description of a utopian world where all data is shared, and companies can enjoy the benefits of bringing that data together to gain insights or improve operational effectiveness. How though, do we translate that sentiment into something operational and actionable? In a perfect world we would have cross functional teams, publishing data as a product, and an updated data catalogue with information about the data, such as its quality, security and privacy, all things that need to be taken care of, and then finally, where to get the data. The trouble is that many companies will look at data mesh as a technology problem when it’s all about mindset and approach.
Our solution to this is to focus on the process, governance, and mindset from day one. Businesses then can create patterns and reusable assets to compliment how the company will create their data mesh solution. Having the technology part of the solution be out of the box and easily usable means we can focus on the parts that will make the real difference.
<h3>How to Make an Impact</h3>
Businesses create cross functional teams that include all the roles needed to capture, transform, govern, and publish the data for each data creator. Data-as-a-Product also changes the responsibility model. Source data providers become responsible for the quality of the data instead of centralized Data teams, and this is a fundamental shift that massively improves data quality. Keep in mind that a data consumer can also be a data publisher if they are creating new data from the data they are consuming.
Many companies went agile – small A – by adopting an Agile framework – large A – but they did this without the companywide mindset change required to really have the best impact. Unfortunately, this will be the same for data mesh, it will be seen as a technology problem.
The next challenge- you want to get the benefits without spending a year or more making an infrastructure to support the needed changes to ways of working with data. This again is a challenge and will become more of a challenge over time. Much like with Agile, companies will be inundated with offers for Data Mesh platforms and products, most of them will be tech solutions and centrally managed. This is really an antipattern. Teams need to be able to achieve their goals, adhere to standards and the correct governance, whilst also being able to publish their data. Ideally this needs to be self-service. The next challenge will be – unless you already have a super modern architecture- you’ll have some systems, possibly legacy, that really don’t want to easily give up their data.
<h3>The Future</h3>
In many ways, data mesh is similiar to agile. For years we have tried to decouple monolithic systems to remove dependencies between systems to reduce the management overhead. The introduction of DevOps teams – further reduces unnecessary dependency management. To us, Data Mesh is the agile transformation of data, we want to decouple data from the systems that create it, organisation’s monolithic systems are now the data platforms many have relied on for years. Data needs to go through the same revolution, it is your gold, it has intrinsic value. Going through this process and continually improving how data flows in your organisation, using DataOps teams, will mean that your operations are cheaper, time to value is vastly improved, and best of all – having business agility means you’re always ready to take on the next competitor.

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Cloud Data Migration

Gemini Enterprise

Gemini Enterprise for FSI

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Gemini Enterprise for Retail

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

From Flat-Pack to Factory: Scaling Enterprise Agents with Platform-based AIOps

Discover how to scale your AI deployments with ease.

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