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<hr /> If it feels like you’re drowning in AI, you’re not alone.ChatGPT might only be <a target="_blank" href="https://www.cnbc.com/2023/11/30/chatgpts-one-year-anniversary-how-the-viral-ai-chatbot-has-changed.html#:~:text=Nov.,human%20than%20an%20AI%20system.">a year old</a> (and acting a bit like a toddler), but the wave of innovation it spurred is dominating every conversation. To complicate matters, the ecosystem dynamics are evolving on a daily… er, hourly basis. One possible life raft on these stormy seas has emerged: data provenance. But before we drop anchor on that, let’s review the last few weeks of “AI headwinds,” shall we? And try to keep your head above water…First, we had the <a target="_blank" href="https://www.nytimes.com/2023/11/22/technology/openai-board-capitalists.html">epic drama at OpenAI</a> as Sam Altman played musical chairs with <a target="_blank" href="https://cmscritic.com/products/microsoft-sharepoint">Microsoft</a>. The market is still unpacking what it means, but the hit left more than a black eye for both brands.Synchronously, AWS opened the proverbial floodgates at <a target="_blank" href="https://reinvent.awsevents.com/">re:Invent,</a> the cloud juggernaut’s annual confab in Las Vegas. AI felt like the only show on the strip, with Amazon introducing a slew of new models, services, and its hyper-connective <a target="_blank" href="https://aws.amazon.com/blogs/aws/introducing-amazon-q-a-new-generative-ai-powered-assistant-preview/">Amazon Q assistant</a>. Finally, not to be bested by its cloud rivals, Google just unveiled its highly anticipated <a target="_blank" href="https://blog.google/technology/ai/google-gemini-ai/#sundar-note">Gemini AI</a> last week, an advanced LLM built for multimodal reasoning across text, images, video, and code.And that’s just scratching the surface. Floodgates… drowning… is there a lifejacket on this boat?  Despite the <a target="_blank" href="https://www.nytimes.com/2023/12/08/technology/eu-ai-act-regulation.html">EU’s landmark agreement</a> to limit the use of artificial intelligence, we can all agree that the genie isn’t going back in the bottle. And with businesses set to increase their AI investments by <a target="_blank" href="https://www.prnewswire.com/news-releases/north-american-businesses-plan-to-invest-67-more-in-generative-ai-in-2024-to-drive-efficiency-experience-and-growth-infosys-research-301967370.html#:~:text=The%20research%20forecasts%20that%20companies,67%25%20in%20the%20next%20year.">almost 70% next year</a>, the pros currently outweigh the cons.  Still, there are challenges aplenty. As more organizations dive into AI, big questions linger around accuracy, security, and governance. Between rampant hallucinations, unsavory biases, and other “shenanigans,” organizations are wrestling with existential issues as they lean more heavily on AI to crunch their data. It’s worth pointing out that machine learning technologies have been around for quite some time, and data has always been the lifeblood. The recent AI acceleration is due in part to the advent of new LLMs, but also the growth of compute capabilities and cloud services to manage more of the heavy lifting. Now, enterprises can rapidly build and train their own AI models and leverage more datasets for critical use cases – like <a target="_blank" href="https://aws.amazon.com/blogs/publicsector/predict-diabetic-patient-readmission-using-multi-model-training-amazon-sagemaker-pipelines/">predicting diabetes</a> based on procedure data from diagnoses and medical claims.  But what about the trustworthiness of the data they’re feeding to AI? Do they know where, when, and how it was collected? If they rely on third-party entities, how do they ensure the data they inherit is clean and complete?This is one of the questions the <a target="_blank" href="https://dataandtrustalliance.org/">Data &amp; Trust Alliance</a> is endeavoring to answer. The not-for-profit consortium is comprised of Fortune 500 businesses across multiple industries (IBM, Pfizer, Nielsen, Walmart, Meta, just to name a few) – all focused on learning, developing, and adopting responsible data and AI governance.I recently spoke with <a target="_blank" href="https://www.linkedin.com/in/kristinapodnar/">Kristina Podnar</a>, Senior Policy Director for the organization, about the Alliance’s proposed <a target="_blank" href="https://dataandtrustalliance.org/our-initiatives/data-provenance-standards">Data Provenance Standards</a>. Kristina is an industry authority and one of the sharpest, most passionate voices on the subject of Digital Policy. She walked me through the proposed standards and the elegant role of metadata in delivering this essential layer of transparency.   <h3>An alliance built for trust</h3>A bit of history for context: The Data &amp; Trust Alliance was formed in 2020 by two heavy hitters: <a target="_blank" href="https://www.linkedin.com/in/ken-chenault/">Ken Chenault</a>, General Catalyst chairman and managing director, and former American Express chairman and CEO; and <a target="_blank" href="https://en.wikipedia.org/wiki/Samuel_J._Palmisano">Sam Palmisano</a>, former CEO of IBM. In addition to helming some of the world’s largest brands, these pioneering luminaries have served on numerous boards – including Airbnb, NCAA, Berkshire Hathaway – and continue to be influential figures across industries. As Kristina recalled, both Chenault and Palmisano observed that the next era of business won’t be dependent on data and AI, but on trustworthy data and AI. To help guide the responsible use of data and intelligent systems, they formed the Data &amp; Trust Alliance – which resides within the <a target="_blank" href="https://www.thecge.net/">Center for Global Enterprise</a> (CGE), a New York-based non-profit.The Alliance is built on a core belief that every enterprise in every industry is (or will soon be) a data enterprise, and must be responsible stewards of their data, algorithms, and AI. To support this mission, the organization doesn’t lobby or advocate on public policy issues; instead, it maintains a fierce commitment to developing tools and sharing practices to enhance learning. “Unlike other coalitions that publish a lot of guiding principles, the Data &amp; Trust alliance is all about actually creating tools, which member companies can adopt,” she said. “In the coalition are folks like American Express, Deloitte, Nike, Starbucks, AARP – the list goes on. These organizations get together around a table and ask, what are the biggest challenges we're facing right now? And then let's actually create a solution and adopt it.”According to Kristina, there have been several initiatives that the Alliance has taken on since its inception. This includes a data assessment resource for mergers and acquisitions, and a toolkit for preventing or detecting bias in HR hiring practices when using AI. The latter predates <a target="_blank" href="https://www.nyc.gov/site/dca/about/automated-employment-decision-tools.page">NYC’s Automated Employment Decision Tool law</a> – the first of its kind in the country, and a potential indicator of future legislation (the bill has also <a target="_blank" href="https://www.technologyreview.com/2021/08/04/1030513/podcast-beating-the-ai-hiring-machines/">garnered criticism</a> from a range of entities).<h3>The Data Provenance Standards initiative </h3>According to the Data &amp; Trust Alliance, having clear data provenance – understanding the origin, lineage, and rights associated with data – is vital to establishing trust around the insights and decisions coming from data-enabled systems. This includes AI applications. As it stands, data scientists <a target="_blank" href="https://www.anaconda.com/resources/whitepapers/state-of-data-science-report-2022">spend</a> nearly 40% of their time on data preparation and cleansing tasks, which is a huge drag on productivity. Further, 61% of CEOs <a target="_blank" href="https://www.ibm.com/thought-leadership/institute-business-value/en-us/c-suite-study/ceo">cite</a> the lack of clarity into data lineage and provenance as the central risks preventing the adoption of AI across their businesses.All these factors led to the development of the Data Provenance Standards, which were co-created by 19 Data &amp; Trust Alliance member organizations, along with multiple third-party industry partners and ecosystem experts. The organization took a “for industry, by industry” approach when developing the proposed standards, identifying provenance pain points from 25 use cases across its diverse members. The resulting set focuses on the eight most essential, valuable, and feasible standards for small and large enterprises to implement – irrespective of industry.“The interesting thing about these data provenance standards is they’re built on top of AI use cases,” Kristina shared. “But in reality, they apply to any data, including traditional data. It's the first set of standards really targeted at trustworthiness.”Unlike other principles and industry guidelines that are voluntary in compliance, the Data &amp; Trust Alliance’s provenance standards were designed to be actionable specifically for data governance practitioners. When implemented, they will provide transparency into the origin of the datasets used for both traditional data applications and a rapidly growing number of AI applications – which will ultimately enhance AI value and trustworthiness. <h3>It’s all in the metadata</h3>The word policy might give you the shivers, but that’s where the Data Provenance Standards provide an elegant solution: metadata.“First of all, it's not policy,” Kristina explained. “They are a set of standards, and it's rich metadata that goes along with that. They extend the concept of metadata into a set of trustworthy considerations. At the root of all eight standards, there's a provenance metadata unique ID, and that unique label tracks the history and origin for each data set.”As she further pointed out, the standards apply at the dataset level – not at the element row or table level – acting as an overall “fingerprint” for divining the lineage of the dataset. The eight proposed Data Provenance Standards surface metadata and associated values for: <ol><li>Lineage: Provides the provenance metadata unique ID, along with a blockchain ledger ID or other identifier. </li><li>Source: Contains information about the user, organization, software systems, and even IP address of the originator. </li><li>Legal Rights: Includes details about attribution, copyrights and trademarks, and any laws or regulations that are applicable. </li><li>Privacy and Protection: Covers any protected data classifications or if any privacy-enhancing techniques were applied.</li><li>Generation Date: Codes the year, date, month, and timestamp for the data. </li><li>Data Type: Delineates if the data is structured or unstructured.</li><li>Generation Method: Provides info on how the data was created, via web crawling, data mining, or other methods. </li><li>Intended Use and Restrictions: Details how the data should be used and by what audiences. </li></ol>To see example values, you can download the <a target="_blank" href="https://dataandtrustalliance.org/Data-Provenance-Standards-Information-pack.pdf">Standards information pack</a> from the Data &amp; Trust Alliance website. <h3>Helping enterprises make informed choices about data </h3>Providing information about the origin and rights associated with datasets gives enterprises more confidence in the data they source and use. This can have significant benefits, like improving operational efficiency, regulatory compliance, collaboration, and value generation.“If you think about the biggest challenge that corporations or any entity face today, everybody will tell you it’s data quality,” Kristina injected. “We were originally asked to develop a set of standards around data quality, but quality is very subjective. With our Data Provenance Standards, what we can all agree on is where data originated, and the attributes that it actually has.”To illustrate the impact of the Data Provenance Standards, Kristina narrates <a target="_blank" href="https://www.loom.com/share/d94d162418b3485890efdf8b330164ee?sid=9a21dae1-ea68-4ee7-b22b-9fa93f908511">a short video</a> that outlines a hypothetical use case in the healthcare industry. The story focuses on a researcher at a fictitious company that is using predictive AI modeling to study Hepatitis C in young women. The researcher acquires data regularly from third-party sources and needs to ensure that the dataset is reliable. Using the Data Provenance metadata, the researcher is able to inform her decision-making based on a number of key variables. By examining the lineage standard, she sees that the current dataset is derived from multiple sources. The generation date also indicates how fresh the data is and how much cleaning might be involved. With the intended use and restrictions, the researcher is also able to identify blockers for sharing the data with specific entities, such as the federal government. It's a fantastic video and use case. I’ll let you watch it to hear the researcher’s verdict, but the point is clear: armed with this metadata, enterprises can rely on essential information and attributes to make a decision with greater confidence – and ultimately improve outcomes. <h3>Why it matters for DX applications</h3>It’s easy to see how the proposed Data Provenance Standards will impact the digital experience ecosystem. First and foremost: DX applications rely on data. The list is long, but for starters, think about acquiring outside datasets to power digital advertising and marketing initiatives.“It will be interesting for third-party data procurement purposes,” said Kristina. “But I think in due course, it’s going to start to impact adtech and martech. It can't help but impact it.”AI is also being woven across every layer of the digital stack, so enhancing transparency will be key to trust at every level – particularly in a decoupled, API-first world that taps lots of data sources. This includes CMS and DXP platforms that are becoming training grounds for AI and connecting with a myriad of external services driven by data.  The new standards are still in a “proposed” mode as the Data &amp; Trust Alliance continues to gather feedback from the market. You can participate in the process by <a target="_blank" href="https://dataandtrustalliance.org/our-initiatives/data-provenance-standards">reviewing the standards</a> and providing input on the metadata and values <a target="_blank" href="https://us14.list-manage.com/survey?u=f2c4b0ec47814ddee58d70038&amp;id=fb47ee5e98&amp;attribution=false">via an online survey</a>.  As a resident Digital Policy rockstar, Kristina seems bullish on the potential impact – namely because the Data &amp; Trust Alliance represents leading global brands that are committed to ensuring this vital transparency.“The reason why this matters is because around the table are the largest procurement entities for data in the world,” Kristina said emphatically. “So if they start to adopt these standards and apply them to their data in the form of metadata, what you actually have is a huge ecosystem play.”That ecosystem won't stop the AI floodgates from opening further, but it might help keep things afloat.   <hr /><figure class="image"><img src="https://cmscritic.com/ms-content/uploads/2023/11/cms-kickoff-2024-banner.jpg" /></figure><h3>See Kristina Podnar at CMS Kickoff 2024 - January 16-17</h3>CMS Critic is a proud conference partner at this second annual edition of the prestigious international Boye &amp; Co conference, dedicated to the global CMS community. This event will bring together top-notch speakers, Boye &amp; Company's renowned learning format, and engaging social events.Tired of impersonal and overwhelming gatherings? Picture this event as a unique blend of masterclasses, insightful talks, interactive discussions, impactful learning sessions, and authentic networking opportunities. Prepare for an unparalleled in-person CMS conference experience that will equip you to move forward in 2024. Along with Kristina Podnar, hear from leading voices across the industry on a wide range of topics, including:<ul><li><a target="_blank" href="https://www.boye-co.com/speakers/andrew-kumar">Andrew Kumar</a>, GVP at Uniform</li><li><a target="_blank" href="https://www.boye-co.com/speakers/becky-brown">Becky Brown</a>, Content Strategy Lead at Johnson &amp; Johnson</li><li><a target="_blank" href="https://www.boye-co.com/speakers/marli-mesibov">Marli Mesibov</a>, Content Strategy at Verily</li></ul><h4> </h4><img style="width:39.13%" src="https://cmscritic.com/ms-content/uploads/2023/11/don-cesar-hotel.jpg" /><h4>The Don CeSar: a world-class venue</h4>Is there a better location for a winter kickoff than Florida's beautiful, sugary sand beaches? CMS Kickoff 2024 will be held at the iconic Don CeSar, just steps from the Gulf of Mexico. Dubbed the “Pink Palace,” this majestic hotel and resort provides a stunning backdrop to the conference, along with access to the local food and culture of St. Petersburg Beach. <h4>Get your tickets today</h4>CMS Kickoff offers an intimate, highly focused experience. Space is limited, and only a few seats remain. Don't miss this exclusive opportunity!<a target="_blank" href="https://www.boye-co.com/conferences/cmskickoff24/tickets">Purchase tickets now &gt;</a>

Can you trust your AI data? New standards emerge to help ensure transparency

matt-garrepy

<hr /> When I spoke to the incomparable <a target="_blank" href="https://www.linkedin.com/in/kristinapodnar/">Kristina Podnar</a> last November, she was – in a word – ecstatic.And rightly so: the <a target="_blank" href="https://dataandtrustalliance.org/work/data-provenance-standards">Data Provenance Standards</a>, an initiative she helped guide into existence as Senior Policy Director at the <a target="_blank" href="https://dataandtrustalliance.org/">Data &amp; Trust Alliance (D&amp;TA)</a>, was finally emerging from the chrysalis and taking flight.The D&amp;TA has become a leading voice in the chorus for trustworthy data and AI. Founded in 2020 by market mavens <a target="_blank" href="https://www.linkedin.com/in/ken-chenault/">Ken Chenault</a> and <a target="_blank" href="https://en.wikipedia.org/wiki/Samuel_J._Palmisano">Sam Palmisano</a>, the organization has been keenly focused on guiding the responsible use of data and intelligent systems.If you’re not familiar with the Data Provenance Standards, I encourage you to read my <a target="_blank" href="https://cmscritic.com/can-you-trust-your-ai-data-new-standards-emerge-to-help-ensure-transparency">previous interview with Kristina</a>, who is an oracle on all things Digital Policy.As we discussed in our conversation, having clear data provenance – understanding the origin, lineage, and rights associated with data – is vital to establishing trust in the details and decisions coming from data-enabled systems. This includes AI applications, which are exploding in popularity and expanding across numerous industry use cases. The Data Provenance Standards were co-created by 19 member organizations of the Data &amp; Trust Alliance, along with multiple third-party industry partners and ecosystem experts. Contributing players include American Express, Humana, IBM, Mastercard, Pfizer, and Walmart (to name a few) – obviously some of the biggest and most notable brands in the world. Now, after testing and validation with more than 50 organizations both inside and outside the Data &amp; Trust Alliance, the organization <a target="_blank" href="https://dataandtrustalliance.org/work/data-provenance-standards">just released version 1.0.0</a> of its Data Provenance Standards. The announcement was detailed in a blog post by the brilliant <a target="_blank" href="https://www.linkedin.com/in/saira-jesani/">Saira Jesani</a>, Executive Director of D&amp;TA – who I’ve also spoken to previously about this ambitious initiative. <figure class="image image-style-align-center image_resized" style="width:63.94%"><img src="https://cmscritic.com/ms-content/uploads/2024/07/saira-kristina-headshots-2.jpg" alt="Headshots of Saira Jesani, Executive Director, and Kristina Podnar, Senior Policy Director" /></figure>As Saira noted in her overview, the entire journey started because the D&amp;TA members and the broader business community were yearning for, as she called it, “better rules for the road around data quality.” Exacerbated by the race to adopt AI, she went on to frame how data has become the most sustainable source of competitive advantage. This revelation ultimately led to the creation of the Data Provenance Standards, and now to the vital feedback loop that will continue to improve the framework.<h3 id="what-are-the-data-provenance-standards?">What are the Data Provenance Standards?</h3>When first proposed back in November of 2023, Kristina was quick to point out that the new Data Provenance Standards were not a form of policy, but benchmarks accompanied by rich metadata. Functionally, this metadata would serve as a set of trustworthy considerations when validating the integrity of datasets. At launch, eight proposed standards were included, each with a unique provenance metadata ID and a label for tracking the history and origin of each data set. It’s worth noting that the standards apply at the dataset level, acting as an overall “fingerprint” for divining details like the data’s lineage, source, legal rights, and more.<hr /><h4 style="text-align:center" id="-"> </h4><h4 style="text-align:center" id="“these-practical-standards,-co-created-by-senior-practitioners-across-the-industry,-are-designed-to-help-evaluate-whether-ai-workflows-align-with-ever-changing-regulations-while-also-helping-generate-increased-business-value.”">“These practical standards, co-created by senior practitioners across the industry, are designed to help evaluate whether AI workflows align with ever-changing regulations while also helping generate increased business value.”</h4><h4 style="text-align:center" id="-"> </h4><h4 style="text-align:center" id="rob-thomas">ROB THOMAS</h4><h4 style="text-align:center" id="senior-vice-president,-ibm-software-and-chief-commercial-officer">Senior Vice President, IBM Software and Chief Commercial Officer</h4> <hr /> According to Saira, the standards were designed to facilitate business adoption via two key focus areas. The first is to demonstrate tangible business value by helping organizations assess which datasets are of higher quality. This includes delivering the transparency required for efficient, accurate, and reliable use of data – as well as mitigating the risks from legal, copyright, and regulatory compliance issues.  The second is making the implementation of the standards more streamlined and attainable, thereby encouraging adoption. This is achieved by homing in on the most essential metadata needed to reveal and understand a dataset’s origin, method of creation, and whether it can be legally used.Since last November, we’ve seen significant changes across the regulatory landscape regarding data and AI, chiefly marked by <a target="_blank" href="https://www.europarl.europa.eu/topics/en/article/20230601STO93804/eu-ai-act-first-regulation-on-artificial-intelligence">Europe’s sweeping EU AI Act</a>. As Saira noted, while this is happening at a macro level, we still don’t have standard definitions for its critical elements. To call this a conundrum would be selling it short, to say nothing of the challenges surrounding the quality and integrity of datasets – which are essential for AI model training to deliver accurate and trustworthy results.   “Model evaluation is difficult, with little transparency around the data that trains and feeds those models,” she stated. “The consequences – from copyright infringement to privacy to authenticity – could affect both the technology’s business value and its acceptance by society, limiting organizations’ ability to determine what is to be trusted.”There’s a lot at stake, and that’s why the creation of the Data Provenance Standards is an evolutionary process – one in which the D&amp;TA is steadfast in its commitment to engaging with stakeholders, reviewing feedback, and modifying its approach to improve outcomes and further encourage adoption. <h3 id="what’s-new-in-v1.0.0?">What’s new in v1.0.0?</h3>Since the Data Provenance Standards were first proposed, Saira and Kristina have been working diligently with the D&amp;TA’s ecosystem of data, AI, ethics, and legal experts to enhance business adoption, gauge feedback, and continuously test and validate the standards. This has led to version 1.0.0, which includes multiple refinements.First and foremost, the original eight categories have been simplified down to three standards, streamlining the adoption and facilitation. The accompanying metadata has also been revised, focusing on detail that “shows, not tells” – and giving data, AI, and legal teams stronger evidence to help inform critical decisions.There’s also been a concerted effort to make D&amp;TA queries more specific about <a target="_blank" href="https://www.ftc.gov/policy/advocacy-research/tech-at-ftc/2024/02/keeping-your-privacy-enhancing-technology-pet-promises">Privacy Enhancing Technologies (PETs)</a> to attain more accurate information. PETs are a broad set of tools and practices that enable organizations to build products and functionality while protecting the privacy of users’ data. Demonstrating how PETs fit into the equation further acknowledges D&amp;TA’s commitment to transparency. Along those same lines, v1.0.0 also sports refinements around surfacing consent language – in particular, the content shown to a user when collecting their personal data to assess risks associated with consumer data usage.Here’s one of the biggest outcomes thus far: Thanks to persistent testing and validation over several months, D&amp;TA has assembled a number of case studies, including <a target="_blank" href="https://www.ibm.com/downloads/cas/KY5VLRP9">one that has been released by IBM</a>. This example illustrates how the Data Provenance Standards increased overall data quality and reduced clearance review time for datasets used to train AI models. <h3 id="what’s-next-–-and-why-it-matters">What’s next – and why it matters</h3>The Data &amp; Trust Alliance is shepherding a flock of forward-thinking organizations that recognize the challenges – and opportunities – associated with data quality and integrity. Market-leading brands represent a cabal of early adopters around the Data Provenance Standards, but their participation is also an incentive for their broad ecosystems to join the fold, if for no other reason than to do business with the Pfizers and Walmarts of the world.Leading by example is perhaps an obvious force multiplier, but as Saira observed in her post, the D&amp;TA’s goal is to increase transparency across the broader business scope – and encourage all organizations to take advantage of this free tool and realize the benefits of adoption.Accelerating and expanding that adoption is next on the docket. While numerous data suppliers and producers of all sizes participated in the feedback cycle of the Data Provenance Standards, the organization is focused on fully enlisting them as partners in adoption and easing the process with toolset providers. The D&amp;TA’s innovative work is manifesting quantifiable benefits. Since the launch of the proposed Data Provenance Standards, the business value has become more palpable – particularly around the impact to productivity. As it stands, data scientists <a target="_blank" href="https://www.anaconda.com/resources/whitepapers/state-of-data-science-report-2022">spend</a> nearly 40% of their time on data preparation and cleansing tasks, which is a huge drag on productivity. On top of that, 61% of CEOs <a target="_blank" href="https://www.ibm.com/thought-leadership/institute-business-value/en-us/c-suite-study/ceo">cite</a> the lack of clarity into data lineage and provenance as the central risks preventing adoption of AI across their businesses.Refinement is certainly a predictable expectation when launching a new concept like this. But already, the proof is in the provenance pudding – and the IBM case study is a poignant example of how these standards translate into real business value. Reducing friction and minimizing the barriers to testing AI models can accelerate time to market, all resulting from greater trust and confidence around datasets that have leveraged these standards.In my previous conversations with Kristina (including a chat at the <a target="_blank" href="https://cmscritic.com/a-cms-consultants-takeaways-from-cms-kickoff-2024">Boye &amp; Company CMS Kickoff</a> event in January), we discussed the potential impact of the standards on the content, ad tech, and marketing industries. All of these layers rely heavily on data, but AI has also infected every CMS and martech platform on the planet, both directly and indirectly. This cross-section is pivotal, as brands will increasingly rely on more trustworthy data to support their AI initiatives. The <a target="_blank" href="https://cmscritic.com/what-is-a-content-management-system-cms">content management ecosystem</a> is at the foundation of how these technologies and campaigns will be managed. As such, the impact of the Data Provenance Standards could bring greater control and confidence to this sector of the market landscape. In an economy where data is the most valuable currency – and every business across industries is now (or soon will be) a data enterprise – trust is paramount. As organizations race to leverage the potential of AI, good data is absolutely essential. This takes on a heightened imperative when we consider the kinds of initiatives that AI is supporting, like cancer or diabetes research. This is precisely why the Data Provenance Standards are emerging as a key foundation for ensuring the future of data, AI, and – most importantly – people.   <hr /><h3 id="upcoming-conferences">Upcoming conferences</h3> <img style="width:29.67%" src="https://cmscritic.com/ms-content/uploads/2024/01/cms-connect-2024-logo.png" alt="CMS Connect 24 logo" /><h4 id="cms-connect-24"><a target="_blank" href="https://www.boye-co.com/conferences/cmsconnect24">CMS Connect 24</a></h4>August 6-7, 2024 – Montreal, CanadaWe are delighted to present our first annual summer edition of our prestigious international conference dedicated to the global content management community. Join us this August in Montreal, Canada, for a vendor-neutral conference focused on CMS. Tired of impersonal and overwhelming gatherings? Picture this event as a unique blend of masterclasses, insightful talks, interactive discussions, impactful learning sessions, and authentic networking opportunities. <img style="width:29.58%" src="https://cmscritic.com/ms-content/uploads/2024/01/cms-kickoff-2025-logo.png" /><h4 id="cms-kickoff-25"><a target="_blank" href="https://www.boye-co.com/conferences/cmskickoff25/tickets">CMS Kickoff 25</a></h4>January 14-15, 2025 – Tampa Bay Area, FloridaJoin us next January in the Tampa Bay area of Florida for the third annual CMS Kickoff – the industry's premier global event. Similar to a traditional kickoff, we reflect on recent trends and share stories from the frontlines. Additionally, we will delve into the current happenings and shed light on the future. Prepare for an unparalleled in-person CMS conference experience that will equip you to move things forward. This is an exclusive event – space is limited, so secure your tickets today.

Data & Trust Alliance Releases v1.0.0 of Data Provenance Standards, Enhancing Data Quality for AI

<hr /> As 2024 begins to take shape, digital optimization is becoming paramount for organizations as they focus on the ever-elusive goal of growth. With increasing competition across digital properties, brands seek new ways to understand customer behavior and create personalized experiences that allow them to attract, engage, and retain customers.The tools are getting better – and <a target="_blank" href="https://cmscritic.com/amplitude-introduces-snowflake-native-amplitude-app-with-new-snowpark-container-services">Amplitude</a> continues to lead with innovation. The company recently announced the expansion of its Digital Analytics Platform to include Session Replay, new experimentation capabilities, and simplified event tagging. With Session Replay, Amplitude now combines qualitative and quantitative data to help companies understand the “why” behind user behavior. The platform will also offer new Experiment capabilities to help teams build personalized websites and campaigns. Additionally, a visual tag editor will effectively lower the barrier to entry for companies getting started with data collection and instrumentation. “Understanding customer behavior is the key to building successful products and enabling businesses to thrive," said <a target="_blank" href="https://www.linkedin.com/in/francoisajenstat/">Francois Ajenstat</a>, Amplitude’s Chief Product Officer. "With these new capabilities, we’re bringing the next generation of digital analytics to market, combining qualitative and quantitative insights and simplifying how companies get started with Amplitude. With a single platform that is easy to use and easy to scale, every company can deliver better customer experiences that drive growth.”Alongside redesigned pricing options to meet the needs of organizations of all sizes, these new capabilities will help companies see everything their customers do, understand what drives growth, and build better products – all in one place.<h3 id="multiple-platform-enhancements">Multiple platform enhancements</h3>The new features of Amplitude’s platform include:<h4 id="session-replay">Session Replay</h4>With Session Replay, companies can visualize the user journey through the product, showing what customers like and where they get stuck. Integrated with Amplitude Analytics, organizations can access qualitative and quantitative insights to fix issues, drive greater conversion, and, ultimately, improve the customer experience. Session Replay is generally available today.<h4 id="experiment-for-web">Experiment for web</h4>Deliver personalized experiences with Amplitude's new web experimentation capabilities. Easily test multiple landing pages to optimize web and campaign performance. Paired with Amplitude Analytics, teams can gain a holistic understanding of what impacts customer behavior and how to deliver experiences that drive growth. Experiment for web will be generally available in the first half of 2024.<h4 id="visual-tag-editor">Visual tag editor</h4>Visual tag editor automates the data collection and instrumentation process to help teams intuitively track website or app activity. Visual tag editor will be generally available in the first half of 2024. With plans for organizations of all sizes, companies can get all the tools they need at once or start small and scale as their business grows. Plans include Free, Plus, Growth, and Enterprise, and can be explored on the Amplitude website. <hr /><h3 id="upcoming-conferences">Upcoming conferences</h3> <img style="width:29.67%" src="https://cmscritic.com/ms-content/uploads/2024/01/cms-connect-2024-logo.png" alt="CMS Connect 24 logo" /><h4 id="cms-connect-24"><a target="_blank" href="https://www.boye-co.com/conferences/cmsconnect24">CMS Connect 24</a></h4>August 6-7, 2024 – Montreal, CanadaWe are delighted to present our first annual summer edition of our prestigious international conference dedicated to the global content management community. Join us this August in Montreal, Canada, for a vendor-neutral conference focused on CMS. Tired of impersonal and overwhelming gatherings? Picture this event as a unique blend of masterclasses, insightful talks, interactive discussions, impactful learning sessions, and authentic networking opportunities. <img style="width:29.58%" src="https://cmscritic.com/ms-content/uploads/2024/01/cms-kickoff-2025-logo.png" /><h4 id="cms-kickoff-25"><a target="_blank" href="https://www.boye-co.com/conferences/cmskickoff25/tickets">CMS Kickoff 25</a></h4>January 14-15, 2025 – Tampa Bay Area, FloridaJoin us next January in the Tampa Bay area of Florida for the third annual CMS Kickoff – the industry's premier global event. Similar to a traditional kickoff, we reflect on recent trends and share stories from the frontlines. Additionally, we will delve into the current happenings and shed light on the future. Prepare for an unparalleled in-person CMS conference experience that will equip you to move things forward. This is an exclusive event – space is limited, so secure your tickets today.

Amplitude Expands Platform Capabilities with Session Replay and Simplified Pricing

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"Toto, I've a feeling we're not in Kansas anymore..."
Those words uttered in the 1939 film <a href="https://en.wikipedia.org/wiki/The_Wizard_of_Oz_(1939_film)" target="_blank">The Wizard of Oz</a> have come to mean a lot of things, but chief among them is “we’ve entered unfamiliar territory.” And while we’re not in a strange new land somewhere over the rainbow, there are certainly huge changes coming to the largest data analytics platform in the world – and that might leave a lot of people clicking their heels and wishing for home.
The story begins last year when Google announced it would sunset <a href="https://www.rootandbranchgroup.com/google-analytics-4-vs-universal-analytics/" target="_blank">Universal Analytics (UA)</a> and require users to migrate to <a href="https://support.google.com/analytics/answer/10089681?hl=en" target="_blank">Google Analytics 4 (GA4)</a>. Now, as we approach the zero-day reality of the mandated change, holdouts are being told that if they don't make the move, Google will do it for them. 
<h2>Follow the Yellow Brick Road... or else</h2>
Back in the halcyon days of 2014, <a href="https://www.eonline.com/news/1071481/remember-when-apple-forced-a-u2-album-on-us-all" target="_blank">Apple and U2</a> shipped a copy of their album Songs of Innocence to every iTunes account on the planet. They saw it as a gift, but it became a touchpoint about data privacy and corporate overreach. 
In some ways, the recent announcement by Google to forcibly move a customer's account evokes the same sentiment. Sure, users and businesses have the option to drop Google Analytics and turn to different platforms like <a href="https://www.semrush.com/" target="_blank">Semrush</a> or <a href="https://www.cmscritic.com/unlocking-the-power-of-products-and-people-a-conversation-with-amplitudes-new-cmo-tifenn-dano-kwon/">Amplitude</a>. But how practical is that when you've been tracking everything on Google for years? 
If you're not familiar with the nitty gritty: in March 2022, Google first announced its flagship UA would be <a href="https://support.google.com/analytics/answer/11583528?hl=en#:~:text=On%20July%201%2C%202023%2C%20standard,Google%20Analytics%204%20going%20forward." target="_blank">“going away,”</a> leaving many people in the dark about what to expect. Shortly after, it introduced GA4 – which offered some intriguing new features and capabilities.
Google also informed customers they could continue using and collecting data in UA until July 1, 2023. At that point, they would have an additional six months to export their legacy data, which can't be migrated into GA4. 
While Google has been proactively communicating with its users, many have delayed or resisted the call for change. As the end date approaches, these users are in danger of losing their legacy data and missing out on collecting sufficient data in the new GA4. 
Google's solution? We'll take care of it for you – whether you like it or not.
The search giant announced its intentions in <a href="https://searchengineland.com/make-the-switch-to-ga4-or-google-will-do-it-for-you-392612" target="_blank">an email</a> informing users that any accounts without an established GA4 property by March 2023 would have one configured for them by reusing existing site tags.
GA4 might be the Emerald City, but forcing users down the Yellow Brick Road feels more like the work of the Wicked Witch. It's true that many customers could stand to lose a lot of valuable data if they don’t make the switch before July – and the new GA4 has certainly received positive praise from some users (a whole lot of negative, too). But even Steve Jobs and Bono weren't considering the sacred trust that exists between customers and brands when it comes to the power of choice. 
<h2>Pay no attention to the man behind the curtain</h2>
In the film adaptation of The Wizard of Oz, Dorothy and her newfound friends journey to see a wizard that will grant them their greatest wishes. Upon closer inspection, they discover the deity they sought was just an ordinary man complicit in deceiving people.
Not to drag out the metaphor, but it might be worth taking a closer look at what’s behind the GA4 curtain as well, and that's the Google Cloud.
Unlike data storage with UA, GA4 only allows access to user and event data <a href="https://support.google.com/analytics/answer/7667196?hl=en" target="_blank">for up to 14 months</a> – then it's deleted on a rolling monthly basis. Once the retention period has expired, users will have limited options for accessing their data unless they buy storage in a Google data warehouse.
Since UA users have always had the option to access their legacy data indefinitely, this represents a huge change. In effect, it forces users to assimilate to the Google Cloud and pay a lot more to access data they've always had for free.
<h2>Privacy matters</h2>
One of the primary reasons Google introduced GA4 was to comply with austere data privacy measures impacting Europe. Because UA did not meet regulations such as <a href="https://gdpr.eu/" target="_blank">GDPR</a>, the new GA4 was deployed to help meet those requirements. But lingering questions remain about legacy data being forced onto Google Cloud products without user consent.
To access large chunks of this data, users will need to use <a href="https://cloud.google.com/bigquery-ml/docs" target="_blank">BigQuery</a> – a serverless, ML-powered data warehouse platform in the Google Cloud. It comes at a hefty price for small businesses or individual users who have not had to pay until the forced GA4 migration. This added cost could have a chilling effect on Google's customer base, and perhaps have them seeking alternatives. 
Regarding complexity, many users who have switched to GA4 say it requires more developer-focused knowledge of analytics, as it’s a completely new way of gathering data. This could mean even more costs associated with migration, especially if your team isn't prepared to handle the changes.
In the epic "Battle of the Clouds," things are heating up for Google, which currently trails it two largest rivals. According to <a href="https://www.statista.com/chart/18819/worldwide-market-share-of-leading-cloud-infrastructure-service-providers/" target="_blank">Statista</a>, Amazon Web Services (AWS) dominates the market share at 34 percent, with Azure well behind at 21 percent. By further integrating its analytics with its cloud property, Google could strengthen its relative position and drive more adoption by default.
The search king has always reigned over the analytics market, and if GA4 users want to expand their data resources, it seems like Google is shouting from the rooftops, “there’s no place like home.”

Who’s Behind the Curtain? GA4’s Takeover of Universal Analytics

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