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<p><span style="font-weight: 400;">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.</span></p>
<p><span style="font-weight: 400;">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.</span></p>
<p><span style="font-weight: 400;">Central to this success is a commitment to </span><a href="https://datatonic.com/services/training/" target="_blank" rel="noopener"><span style="font-weight: 400;">training businesses for the future of cloud data + AI</span></a><span style="font-weight: 400;">. 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.</span></p>
<p><i><span style="font-weight: 400;">“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.”</span></i></p>
<p><span style="font-weight: 400;">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.</span></p>
<p><i><span style="font-weight: 400;">“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</span></i></p>
<p><b>About Datatonic</b></p>
<p><span style="font-weight: 400;">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.</span></p>
<p><span style="font-weight: 400;">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.</span></p>
<p><span style="font-weight: 400;">For more information, visit</span> <a href="https://datatonic.com/"><span style="font-weight: 400;">https://datatonic.com/</span></a><span style="font-weight: 400;">.</span></p>


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


Datatonic Wins Two 2026 Google Cloud Partner of the Year Awards

<p><span style="font-weight: 400;">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.</span></p>
<p>&nbsp;</p>
<ol>
<li><b> Agentic Media Search + Discovery</b></li>
</ol>
<p><span style="font-weight: 400;">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.</span></p>
<p><span style="font-weight: 400;">This shift is already being proven by real-world applications. For example, Datatonic collaborated with </span><a href="https://datatonic.com/case-studies/cineverse-generative-ai/"><span style="font-weight: 400;">Cineverse</span></a><span style="font-weight: 400;"> 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;).</span></p>
<p><span style="font-weight: 400;">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.</span></p>
<p>&nbsp;</p>
<ol start="2">
<li><b> AI-Enriched Media Asset Management</b></li>
</ol>
<p><span style="font-weight: 400;">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. </span></p>
<p><span style="font-weight: 400;">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. </span></p>
<p>&nbsp;</p>
<ol start="3">
<li><b> Creative Transformation to Accelerate Content</b></li>
</ol>
<p><span style="font-weight: 400;">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. </span></p>
<p><span style="font-weight: 400;">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.</span></p>
<p><span style="font-weight: 400;">This can also increase conversion rates for marketing materials due to better personalisation to customers. </span></p>
<p>&nbsp;</p>
<ol start="4">
<li><b> Hyper-Personalization + 24/7 Customer Engagement</b></li>
</ol>
<p><span style="font-weight: 400;">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. </span></p>
<p><span style="font-weight: 400;">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.</span></p>
<p>&nbsp;</p>
<ol start="5">
<li><b> Intelligent Content Operations + Localisation</b></li>
</ol>
<p><span style="font-weight: 400;">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.</span></p>
<p><span style="font-weight: 400;">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.</span></p>
<p><span style="font-weight: 400;">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.</span></p>
<p>&nbsp;</p>
<p><b>Moving Forward</b></p>
<p><span style="font-weight: 400;">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.</span></p>
<p><a href="https://datatonic.com/contact/"><span style="font-weight: 400;">Get in touch to learn more</span></a><span style="font-weight: 400;">.</span></p>


Emerging Trends in Media + Entertainment for 2026 + Beyond

<p><span style="font-weight: 400;">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.</span></p>
<p><span style="font-weight: 400;">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.</span></p>
<p><span style="font-weight: 400;">While </span><a href="https://www.decisionmarketing.co.uk/news/agencies-firmly-in-the-firing-line-as-cmos-plot-cutbacks"><span style="font-weight: 400;">trends indicate a slight decrease in agency spending</span></a><span style="font-weight: 400;">, successful agencies are adapting to adopt the latest technologies to support their clients, achieving record revenue and profits with AI-driven growth. </span></p>
<p>&nbsp;</p>
<ol>
<li><b> AI Agents for Media Buying</b></li>
</ol>
<p><span style="font-weight: 400;">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.</span></p>
<p><span style="font-weight: 400;">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. </span></p>
<p><span style="font-weight: 400;">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.</span></p>
<p>&nbsp;</p>
<ol start="2">
<li><b> Hyper-Personalization at Scale </b></li>
</ol>
<p><span style="font-weight: 400;">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. </span><a href="https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/the-value-of-getting-personalization-right-or-wrong-is-multiplying"><span style="font-weight: 400;">McKinsey research</span></a><span style="font-weight: 400;"> shows that 71% of consumers expect personalized interactions. Generative AI is rapidly closing this gap, allowing agencies to scale personalization to an unprecedented degree.</span></p>
<p><span style="font-weight: 400;">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.</span></p>
<p><span style="font-weight: 400;">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.</span></p>
<p>&nbsp;</p>
<ol start="3">
<li><b> The Creator Economy as a Data Science Channel</b></li>
</ol>
<p><span style="font-weight: 400;">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.</span></p>
<p><span style="font-weight: 400;">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.</span></p>
<p><span style="font-weight: 400;">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. </span></p>
<p><span style="font-weight: 400;">Concurrently, these systems authenticate performance by employing measures to distinguish genuine audience engagement from fraudulent activities, thereby guaranteeing transparent and accurate reporting for clients.</span></p>
<p>&nbsp;</p>
<ol start="4">
<li><b> Measuring Attention, Not Impressions</b></li>
</ol>
<p><span style="font-weight: 400;">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.</span></p>
<p><span style="font-weight: 400;">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.</span></p>
<p><span style="font-weight: 400;">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.</span></p>
<p>&nbsp;</p>
<ol start="5">
<li><b> Privacy-First Data Strategy</b></li>
</ol>
<p><span style="font-weight: 400;">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.</span></p>
<p><span style="font-weight: 400;">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.</span></p>
<p><span style="font-weight: 400;">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.</span></p>
<p>&nbsp;</p>
<p><b>Moving Forward</b></p>
<p><span style="font-weight: 400;">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.</span></p>
<p>&nbsp;</p>
<p><a href="https://datatonic.com/contact/"><span style="font-weight: 400;">Get in touch to learn more</span></a><span style="font-weight: 400;">.</span></p>


Emerging Trends in Agency for 2026 + Beyond

<p><span style="font-weight: 400;">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.</span></p>
<p><a href="https://www.gartner.com/en/finance/insights/future-of-finance-2030"><span style="font-weight: 400;">Gartner</span></a><span style="font-weight: 400;"> 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.</span></p>
<p>&nbsp;</p>
<ol>
<li><b> The Rise of Agentic AI in Fraud Detection</b></li>
</ol>
<p><span style="font-weight: 400;">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.</span></p>
<p><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" /></p>
<p>&nbsp;</p>
<p>&nbsp;</p>
<p><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.</p>
<p>&nbsp;</p>
<p><span style="font-weight: 400;">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:</span></p>
<ul>
<li style="font-weight: 400;" aria-level="1"><span style="font-weight: 400;">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).</span></li>
<li style="font-weight: 400;" aria-level="1"><span style="font-weight: 400;">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.</span></li>
</ul>
<p>&nbsp;</p>
<ol start="2">
<li><b> Predictive Maintenance with AI for Insurance Adjustments</b></li>
</ol>
<p><span style="font-weight: 400;">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. </span></p>
<p><span style="font-weight: 400;">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.</span></p>
<p><span style="font-weight: 400;">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.</span></p>
<p><span style="font-weight: 400;">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. </span></p>
<p>&nbsp;</p>
<ol start="3">
<li><b> Transforming Complaints Handling</b></li>
</ol>
<p><span style="font-weight: 400;">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:</span></p>
<p><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" /></p>
<p>&nbsp;</p>
<p>&nbsp;</p>
<p><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.</p>
<p>&nbsp;</p>
<ul>
<li style="font-weight: 400;" aria-level="1"><span style="font-weight: 400;">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.</span></li>
<li style="font-weight: 400;" aria-level="1"><span style="font-weight: 400;">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.</span></li>
<li style="font-weight: 400;" aria-level="1"><span style="font-weight: 400;">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.</span></li>
</ul>
<p>&nbsp;</p>
<ol start="4">
<li><b> Automating Underwriting with AI Agents</b></li>
</ol>
<p><span style="font-weight: 400;">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. </span></p>
<p><span style="font-weight: 400;">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.</span></p>
<p>&nbsp;</p>
<ol start="5">
<li><b> The AIOps Foundation</b></li>
</ol>
<p><span style="font-weight: 400;">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.</span></p>
<p><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" /></p>
<p>&nbsp;</p>
<p>&nbsp;</p>
<p><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.</p>
<p>&nbsp;</p>
<p><span style="font-weight: 400;">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.</span></p>
<p>&nbsp;</p>
<p><b>Moving Forward</b></p>
<p><span style="font-weight: 400;">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.</span></p>
<p><a href="https://datatonic.com/contact/"><span style="font-weight: 400;">Get in touch to learn more</span></a><span style="font-weight: 400;">. </span></p>


Emerging Trends in Insurance for 2026 + Beyond

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<h3>What is data governance?</h3>
<p>“<em>The exercise of authority, control, and shared decision making (planning, monitoring and enforcement) over the management of data assets.</em>” 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:</p>
<p><strong>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.</strong></p>
<p>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)</p>
<p>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.</p>
<h3>What does data governance cover?</h3>
<p>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 <strong>organizational </strong>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.</p>
</div>
</div>
<div class="et_pb_module et_pb_image et_pb_image_0 et-animated et_had_animation"><a class="et_pb_lightbox_image" title="" href="https://www.syntio.net/wp-content/uploads/sites/9/2022/08/16161436691614699619img1_v_1616143669.jpg.jpeg"><span class="et_pb_image_wrap "><img loading="lazy" decoding="async" class="wp-image-614" title="16161436691614699619img1_v_1616143669.jpg" src="https://www.syntio.net/wp-content/uploads/sites/9/2022/08/16161436691614699619img1_v_1616143669.jpg.jpeg" sizes="auto, (min-width: 0px) and (max-width: 480px) 480px, (min-width: 481px) 826px, 100vw" srcset="https://www.syntio.net/wp-content/uploads/sites/9/2022/08/16161436691614699619img1_v_1616143669.jpg.jpeg 826w, https://www.syntio.net/wp-content/uploads/sites/9/2022/08/16161436691614699619img1_v_1616143669.jpg-480x417.jpeg 480w" alt="" width="537" height="467" /></span></a></div>
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<h3>What is not data governance?</h3>
<p>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.</p>
<ul>
<li><strong>Data governance is </strong><strong>not an exact process.</strong>
<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><strong>Data governance is not only about data privacy.</strong>
<ul>
<li>It is about <strong>implementing the process </strong>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><strong>Storing data in a central repository or a data lake is not governance.</strong>
<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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<p>To conclude, data governance is <em>not</em> a function performed by those who manage information, meaning there must be a separation of duties between those who manage and those who govern.</p>
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<h3>Data Governance Goals</h3>
<p>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.</p>
<p>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.</p>
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<p>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.</p>
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<h4>Where to start?</h4>
<p>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?</p>
<p>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.</p>
<p>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.</p>
<p>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.</p>
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Introduction to Data Governance

<p>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.</p>
<h3>What is Federated Data Governance?</h3>
<p>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:</p>
<ul>
<li>
<ul>
<li><strong>Decentralized ownership:</strong> Data management is distributed, allowing teams to manage their data according to their specific needs and context.</li>
<li><strong>Shared principles and guidelines:</strong> A common set of governance principles and standards is established, ensuring interoperability across the organization.</li>
<li><strong>Coordination and collaboration: </strong>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>
</ul>
</li>
</ul>
<h3>Supporting Data Mesh with Data-as-a-Product</h3>
<p>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.</p>
<p>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.</p>
<p>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.</p>
<h3>Enhancing Business Agility with Federated Data Governance</h3>
<p>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:</p>
<ul>
<li>
<ul>
<li><strong>Faster decision-making:</strong> 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><strong>Improved collaboration</strong>: 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><strong>Scalability and flexibility:</strong> 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>
</ul>
</li>
</ul>
<p>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.</p>
<h3>The Important Question: What Next?</h3>
<p>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:</p>
<ul>
<li>
<ul>
<li><strong>Assess the current data governance landscape:</strong> 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><strong>Define a vision and objectives:</strong> 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><strong>Establish guiding principles:</strong> 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><strong>Identify data domains and stakeholders:</strong> 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><strong>Foster a culture of collaboration and communication:</strong> 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><strong>Develop a governance framework:</strong> 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><strong>Iterate and improve: </strong>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>
</ul>
<p>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.</p>


Federated Data Governance

<p>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.</p>
<h3>1. What are your biggest data related challenges?</h3>
<p>This question should help you to determine your priorities and therefore, the next steps when it comes to data governance.</p>
<p>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.</p>
<p>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.</p>
<p>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”).</p>
<p>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.</p>
<h3>2. What are the drivers for this initiative?</h3>
<p>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.</p>
<p>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.</p>
<p>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.</p>
<p>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.</p>
<p>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.</p>
<p>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.</p>
<h3>3. What do you see as a result?</h3>
<p>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.</p>
<p>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.</p>
<h3>Conclusion</h3>
<p>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.</p>


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>
<p>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.<br />
The 4 pillars would be:</p>
<ol>
<li><strong>Domain-oriented</strong>, <strong>decentralized</strong> data ownership and architecture</li>
<li>Creating data products</li>
<li><strong>A self-service</strong> data infrastructure platform</li>
<li><strong>Federated</strong> governance</li>
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<p>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.</p>
<p>The second pillar states that there <strong>must be</strong> 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.</p>
<p><strong>A self-service</strong> 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.</p>
<h3>Data Mesh Concept</h3>
<p>Anyone who has ever expressed any interest in data mesh has probably come across the following diagram.</p>
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<p>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 <strong>e</strong><strong>nterprise organization</strong> which has an <strong>organizational alignment </strong>around creating data products which are <strong>built on actual consumption</strong> by analysts and data scientists, and are providing an <strong>alignment</strong> between data <strong>engineering</strong> and data <strong>analysis.</strong></p>
<h3>Why Data Mesh?</h3>
<p>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.</p>
<p>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.</p>
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<p>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?</p>
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<p>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?</p>
<p>With all that being said, a shift to modern distributed architecture sounds quite good now doesn’t it?</p>
<p>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.</p>
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<p>Shifting involves shifting to a data mesh architecture.</p>
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<h3>Data Mesh Implementation</h3>
<p>To mesh or not to mesh: that is the question indeed. We are not saying everyone should switch but who should?</p>
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<p>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.</p>
<h3 class="master-article__text">Conclusion</h3>
<p>First, rate your company by the previously described standards. Then ask yourself:<br />
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?</p>
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<p>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.</p>
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Introduction to Data Mesh

<p>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.</p>
<h3>The Goal of Data Mesh</h3>
<p>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.</p>
<p>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?</p>
<h3>Setting up Data Mesh</h3>
<p>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.</p>
<p>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.</p>
<h3>How to Make an Impact</h3>
<p>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.</p>
<p>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.</p>
<p>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.</p>
<h3>The Future</h3>
<p>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.</p>


Data Mesh 101: Data-Driven Transformation

<p>On the surface, debt looks to be simple: You borrow, and you pay it back. Organizations may borrow more or borrow less to fulfill obligations to customers, to take opportunities for growth or to help the company through a lean time. Financial debt is reasonably easy to understand as a concept. We may use complex mechanisms for borrowing, but calculating the cost is straightforward: how much will be borrowed, how long will it be borrowed and how much interest will be charged?</p>
<p>There are, however, many other kinds of debt an organization takes on apart from financial debt. Your company may be cash- or asset-rich but may suffer greatly from hidden debt. Debt can hide in your company, and while you may have an idea of what and where it is, calculating its impact can be very hard.</p>
<p>If we look at debt types other than financial debt, we can see how that debt impacts an organization. In this digital world in which we live, we often hear of technical debt, but knowledge debt and organizational debt are discussed much less often.</p>
<h3>Technical Debt</h3>
<p>Technical debt is defined by the technology system used, its age, its complexity and its fitness for purpose. All these points and more can introduce costs to your organization. Older systems typically need more care and looking after, adding cost. Complex systems are harder to change and can introduce problems when changed.</p>
<p>Typically, over time, with the changing needs of an organization and customers, systems become less fit for purpose. When you change these systems, you introduce complexity, making them even more expensive to maintain over time. This expense is the interest payment on the debt in terms of time and money.</p>
<p>Technical debt, while difficult to quantify, is reasonably easy for us to understand. The others, unfortunately, may be harder to quantify.</p>
<h3>Knowledge Debt</h3>
<p>Knowledge debt can be defined by looking at how your organization learns and improves over time. Is your staff up to date with the latest ways of working within their field? Are you allowing them time to grow and offering training? How open are you to new ideas? How easy is it to implement change in your organization? This may seem counter-intuitive, but there is a cost to your organization for training. Big change also comes with an associated and quantifiable cost, and this may be the reason why we only make big changes when we must.</p>
<p>When it sets in, knowledge debt can be very difficult to remove if people are set in their ways. It can be very difficult to enable them to change, and the inertia to remain the same can be very strong. Standardization in an organization is what enables scale, and the ability to serve more customers using fewer resources is something we should strive for, but only to a certain point, as we still need to be agile.</p>
<h3>Organizational Debt</h3>
<p>Possibly the worst type of debt is organizational, as it’s the best hidden and hardest to quantify. When you look at an organization, whether it is a company, a government or even the smallest part of a system, there is organizational debt. This is the organization’s processes and procedures.</p>
<p>Typically, processes are not removed from an organization; more just continue to be added over time. When something goes wrong, an organization might add a process to ensure it doesn’t happen again. Most of these problems are likely one-off issues and unlikely to reoccur, but the time taken to follow that process though is there forever, and all organizations do is add more and more. Companies rationalize this as control and risk reduction.</p>
<p>Businesses need the ability to scale. This is provided by following a process. Process for process’ sake, however, kills agility in an organization. Bureaucracy seems to invade everything we do. It’s everywhere and seems to grow daily, stifling our ability to move forward. This is the debt that is normally the most expensive and most difficult to spot.</p>
<h3>Embracing Change And Daily Transformation</h3>
<p>In any organization, customers should be met in the most efficient manner possible to give them the products and services they need while minimizing the cost of doing that business. The processes your company creates allow that to happen, and employee training needs to be what supports that process. However, this approach doesn’t take into account how competitors may change the way they do business or how customers’ needs change over time. Process is the opposite side of the same coin as innovation, and innovation is stifled by process and the inability to change.</p>
<p>Change is a wonderful thing and should be encouraged, though it should be in the right way, as you still need to scale. The ability to scale and reduce costs versus the agility needed to meet customers’ changing needs is a fine line to walk for any company. One way to address this is by introducing a policy of continuous change, in a way that encourages improvement, with effective measurement of the impact. If you need one example of why you should consider adopting a continuous improvement approach to your organization, just take a look at the size of the digital transformation market. It’s worth trillions of dollars.</p>
<p>Continually assess how your company is performing. Look at the processes your organization follows. Are they still fit for purpose? Are you assessing risk with reality or fear behind your decisions? Encourage your employees to learn and adapt to changing needs to help them to be happier, more dynamic and more engaged.</p>
<p>Once you look at all of these, you might start to see a huge change in your ability to meet business demands. And if you are continuously improving and removing various kinds of debt from your organization, you won’t need to transform with a big bang, as your company will be transforming every day.</p>


Exploring Types of Debt in Technology

<p>Digital Transformation can be categorised in many ways and mean different things to different companies. At its core, it is a fundamental change to business processes, either by computerising existing manual processes, the modernisation of existing systems that are no longer fit for purpose, or the creation of new systems to support new business channels. In nearly all cases it breaks down into three distinct categories: foundation, core business, or new business.</p>
<p>In order to meet customers’ business needs, businesses have had to accelerate that journey not just to maintain pace with digital natives, but because the customer has changed their behaviour. Customers were already looking at the world differently and many established businesses have been disrupted. The pandemic has accelerated that, massively and globally. We also see huge disruption in global supply chains and with logistics, as countries and businesses open up again, exacerbating the challenges faced by businesses even further.</p>
<h3>What does this mean for business?</h3>
<p>Our understanding of traditional IT needs to change. Manual processes have always been the standard way of working, but in today’s world Digital must become the way we do business. Digital Transformation is not something that we order and expect to be delivered, sometimes poorly, in 2-5 years. It is now something that should underpin the whole business. It needs to be adaptable and agile.</p>
<p>Businesses that struggle to learn how to make this work for them will see an ever-shrinking market share. Even if you have unique products and almost complete market share, you will see disruption. The barriers to entry in most businesses are reducing, as you can market and sell a product without ever needing to make or store it. As long as you meet the customer how they want, you will win their custom over the long term, if not the short.</p>
<p>Businesses need to address how they meet the customer, they need to modernise their systems and become data driven. In some cases, they will need to create new capabilities, where existing systems are manual or don’t exist, as with new business processes. Automation and speed are key. This means reducing the long-lived platforms of the 90s in favour of slim, customer focused products with flexible backend systems.</p>
<h3>How do we achieve this?</h3>
<p>As mentioned above, we need to look at our business differently, by merging our technology and business capabilities together. We must look at technology as the way in which we do business, as well as an enabler and method of reducing cost. Scalability can come from repeatable, well created automation in every aspect of what we do. We need to give the business access to data to allow for decision making at speed, based on real data points, not gut feelings. The only way to really succeed is by being agile, not just in our approach to developing software systems, but in the sense of business agility. Only then will we be able to meet customers the way they want and be able to pivot easily if needed.</p>
<p>When looking at the change we must be aware of biting off more than we can chew. We can’t change everything all at once, we need to ensure that we can measure the success of the changes we make and approach them in the right way. The ability to do this with haste is what we mean by business agility, however if we don’t have a technology architecture that will support our growing needs, we will spend more resources in terms of time and money to reach our goals. Another consideration is where to start. When approaching transformation, the best piece of advice would be to concentrate on the areas that your business needs to be unique.</p>
<p>For example, there are no benefits to spending all of your efforts implementing a new way of storing internal documents, if the customer can’t find what they want to purchase on your website. That doesn’t mean that the new document system isn’t needed, just that it can wait or can be bought off the shelf. Where companies have a differentiator, they should invest in systems that will highlight that difference. Where they have a system that runs the business, for example: accounting and email systems, they should look to purchase what they need. They should keep in mind that if any system tries to do too much, it will likely be a bottle neck in the future. Cloud services are an ideal way to test future systems, as the speed of development and procurement of new infrastructure is an ideal way to understand what will work and what won’t, even if the end system will not run in cloud, well at least not public cloud.</p>
<h3>Conclusion</h3>
<p>The plus when working in this way, is that it is far cheaper to work like this, than in a slow to change, siloed and monolithic traditional way. Changing our culture is hard, but it is by far the most effective way of accelerating change within an organisation. It is the foundation that underpins the journey of transformation. Embracing innovation and continuous improvement will ensure that we are always ready to be disrupted or better yet, to be the disrupter.</p>


Introduction to Digital Transformation

<p><span style="font-weight: 400;">In the digital world we live in, data is the new gold, and unlike other resources, the amount of existing data is increasing day by day. </span></p>
<p><span style="font-weight: 400;">While the industrial and technological revolutions powered by fossil fuels and electricity were changing the world at a steady pace, the all-out digital revolution fueled by data is causing especially rapid changes. Many people, companies and even governments, don’t have well-established processes for securing the data that they create, process and store, which makes them particularly vulnerable. </span></p>
<p><span style="font-weight: 400;">With </span><a href="https://datatonic.com/insights/the-importance-of-data-foundations-for-ai-agents/"><span style="font-weight: 400;">strong data foundations becoming especially relevant for AI use cases</span></a><span style="font-weight: 400;">, this article will explore the main features of data security, explain some principles of security implementation, and highlight the importance of data security when building your data + AI platform.</span></p>
<h3>Let’s Demystify Data Security</h3>
<p><span style="font-weight: 400;">To implement data security principles in our businesses and habits, we need to understand the concept of it, and know which methods we have at our disposal to prevent data breaches.</span></p>
<p><span style="font-weight: 400;">Data security is a subfield of cyber security that studies and implements sets of methods and processes, that protect digital data from corruption, confidential information from unauthorised access, and prevent system breaches.</span></p>
<p><span style="font-weight: 400;">Some means of implementing security to the system include designing the data encryption procedures, defining data privacy policies throughout the environment, and establishing data protection methods.</span></p>
<h3>Modern Best Practices</h3>
<p><span style="font-weight: 400;">Analyzing your system’s vulnerabilities is the first step toward a secure environment. Instead of &#8220;reinventing the wheel,&#8221; leverage established services that stay compliant with evolving regulations. Key habits include:</span></p>
<p>&nbsp;</p>
<p><span style="font-weight: 400;"><strong>1. Follow the Principle of Least Privilege:</strong> Users, applications, and systems should only have the absolute minimum permissions and access rights needed to perform their specific job functions or tasks, nothing more.</span></p>
<p><img loading="lazy" decoding="async" class="alignnone wp-image-25081" src="https://cms.datatonic.com/app/uploads/2026/01/Principle-of-Least-Privelige-300x113.png" alt="" width="499" height="188" srcset="https://cms.datatonic.com/app/uploads/2026/01/Principle-of-Least-Privelige-300x113.png 300w, https://cms.datatonic.com/app/uploads/2026/01/Principle-of-Least-Privelige-1024x387.png 1024w, https://cms.datatonic.com/app/uploads/2026/01/Principle-of-Least-Privelige-768x291.png 768w, https://cms.datatonic.com/app/uploads/2026/01/Principle-of-Least-Privelige.png 1208w" sizes="auto, (max-width: 499px) 100vw, 499px" /></p>
<p><span style="font-weight: 400;"><strong>2. Advanced Encryption:</strong> Use Symmetric (one key) or Asymmetric (public/private keys) algorithms to ensure intercepted data remains useless to hackers.</span></p>
<p><span style="font-weight: 400;"><strong>3. Multi-Factor Authentication (MFA):</strong> Move beyond 2FA toward biometric and hardware-key-based logins where possible.</span></p>
<p>&nbsp;</p>
<h3>The Human Element</h3>
<p><span style="font-weight: 400;">Security is only as strong as the people using it. </span><a href="https://jamesclear.com/new-habit"><span style="font-weight: 400;">Research</span></a><span style="font-weight: 400;"> suggests it takes around 66 days to turn a new change into a routine habit. To succeed, organizations must move beyond </span><i><span style="font-weight: 400;">defining</span></i><span style="font-weight: 400;"> policies and focus on </span><i><span style="font-weight: 400;">implementing</span></i><span style="font-weight: 400;"> them through clear communication and user-friendly tools, such as password managers.</span></p>
<h3>The True Cost of a Breach</h3>
<p><span style="font-weight: 400;">Many individuals claim they have &#8220;nothing to hide,&#8221; but in the business world, the stakes are quantifiable. Data typically falls into two categories: Metadata (logs) and Confidential Information (PII, financial data, and trade secrets).</span></p>
<p><span style="font-weight: 400;">Leaking confidential data damages a brand, as well as triggering massive legal penalties under laws like GDPR, CCPA, and HIPPA.</span></p>
<ul>
<li style="font-weight: 400;" aria-level="1"><span style="font-weight: 400;">The Price Tag: Recent data shows the average cost of a breach has climbed toward $5 million.</span></li>
<li style="font-weight: 400;" aria-level="1"><span style="font-weight: 400;">PII Value: Personally Identifiable Information is a top target, often sold on the dark web for high premiums.</span></li>
<li style="font-weight: 400;" aria-level="1"><span style="font-weight: 400;">Remote Work Risk: Breaches in remote environments can cost significantly more due to the lack of controlled infrastructure.</span></li>
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<h3>Mitigating Risk</h3>
<p><span style="font-weight: 400;">To fully leverage the power of AI while mitigating risk, organizations must implement robust guardrails, especially when feeding confidential data into large language models (LLMs) and agents. These guardrails act as a critical layer of control, preventing unauthorized data leakage or exposure to external users. For instance, using techniques like data masking, tokenization, or secure prompt engineering can ensure that while the AI agents have the necessary context from proprietary or confidential information to generate accurate outputs, the sensitive underlying data itself is never directly shared, displayed, or embedded in customer-facing responses, thereby maintaining strict regulatory compliance and data privacy.</span></p>
<h3>Conclusion</h3>
<p><span style="font-weight: 400;">Data security is no longer &#8220;optional&#8221;; it is a prerequisite for doing business. It is far more cost-effective to invest in prevention today than to manage damage control tomorrow. Innovative AI use cases thrive on massive datasets; however, if the underlying data is corrupted, biased, or leaked, the AI models themselves become liabilities rather than assets. </span></p>
<p><span style="font-weight: 400;">Secure data pipelines are the only way to ensure that AI innovation remains ethical, reliable, and competitive. Analyze your systems, implement strong policies, and treat data with the responsibility it deserves.</span></p>


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