Adamma Morrison

We're facing more and more questions about the use of synthetic data in market research. I'd recommend this blog post, which really resonated with me. So much of the conversation is "how closely can this replicate human data?" But there are so many other considerations researchers should be thinking about. https://lnkd.in/eSQfSTPj

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There are very few things that excite me more than applying my experience in new ways. The Master of Analytics program in NC State University allows me to do just that, applying lessons I've learned in intelligence research to data science. While there are endless topics in #machine_learning I am eager to write about, one topic that felt especially close to heart was how language is shaping our decision-making. In "Why Lingo Matters in Analytics", I chose to tackle one specific aspect of this: how our framing of the data must be as accurate and formal as our actual data. Our framing must be accurate and formal, because all throughout history people were hearing and reading ideas via *words*, not numbers; and thus we evolved to make decisions based on *words* and not based on numbers. When a data analyst/scientist makes a recommendation to a stakeholder, the actionable part is always in the words, never in the numbers. The numbers are there only to show rigor and make our claims verifiable. To quantify. To answer the "how much", but never the "what" or "why". The true benefit of AI is allowing us to reach conclusions much faster (and make funny animal videos faster too, I guess). But to reach these conclusions safely, and make the right decisions, we need the right framing to judge the numbers by. Have a fun read!

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Real transformation happens when we shift from building dashboards to solving real problems for real roles. From requests → to outcomes. From data delivery → to decision support. The future of data teams isn’t being a report factory. It’s becoming partners who deeply understand business personas, workflows, and moments that matter — and design solutions around them. This is the evolution data teams need: Less “dashboard concierge,” more “people-first problem solving.”

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A plea for data literacy education! 🧑‍🏫 In this interview, Dr. Joe Perez explains why companies can no longer avoid investing in their employees’ data skills and outlines how to approach this effectively. 👉 https://lnkd.in/eEAxPpJM #tgoa #thegroupofanalysts #knowledgeworkers #dataliteracy

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One more for today - just because it’s too insightful and valuable to miss for anyone wanting to step up their agentic analytics game. Thanks to @Pankaj this repository teaches you everything from A-Z on ontologies, Enterprise AI Architectures, approaches and concepts - from high level to deep down the rabbit hole. https://lnkd.in/d3uxNfcR

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In a world where the line between B2C and B2B is increasingly blurry, are your consumer insights actually driving meaningful innovation? Or are you drowning in data without actionable direction? What I'm seeing across industries: • Teams spending WEEKS on ideation that could be compressed into HOURS • Insights professionals struggling to translate trends into tangible product concepts • Innovation leaders fighting to get buy-in from skeptical executives • The gap between spotting a trend and implementing it is growing wider One innovation leader I spoke with recently confessed: "We're collecting more consumer data than ever, but somehow generating fewer breakthrough ideas." The most successful teams I've observed are doing three things differently: Using AI-augmented research to accelerate the discovery process Breaking out of industry echo chambers to find adjacent opportunities Implementing rapid prototyping frameworks that compress the innovation timeline Question for my network: What's your biggest challenge in translating consumer insights into actual innovation? Is it finding the right trends, getting organizational buy-in, or something else entirely? #ConsumerInsights #Innovation #TrendForecasting #ProductDevelopment #FutureOfBusiness

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"We can just plug in the historical data right?" My head: "No, because it's captured differently with different segmentation, at a detail level we don't support through two different systems with inconsistent naming conventions and saved in CSVs on someones hard drive but we don't remember who it is, except for the weeks they went on PTO which are just missing and can't be replaced." My mouth: "We'll look into it."

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Last week, I spoke at the Data Behind the Scenes conference #DataBS It's a conference run by Randy Au for data practitioners who are tired of polished case studies that skip over the actual work. Instead you get honest takes on project realities: the messy bits, the trial and error, the things that go wrong. I spoke about the 10 red flags that your multi-market research data went sideways. We've all had that moment, staring at survey results that look technically correct but feel fundamentally wrong, then having to explain why three countries' worth of data is unusable. Links to my talk, as well as the full playlist, are in the comments

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Dr. Ramla Jarrar, an MMM expert, shares an important perspective every marketer using MMM should read. The customer journey is complex, and while MMM is a powerful tool for understanding how spend impacts that journey, its limitations are often overlooked and nuances brushed over once results are packaged in polished decks. The tension I often see: marketers need one strong KPI to track large marketing investments, but the complexity of measurement means no single framework provides that holy grail metric. Measurement methodologies are sophisticated and technically robust, yet each has constraints. Triangulating across multiple frameworks—MMM, incrementality testing, attribution, brand tracking—provides better insights, though interpretation remains as much art as science. This requires partnership. Marketers need to understand what each methodology can and cannot deliver. Analytics partners, whether internal teams or external consultancies, have the responsibility to explain assumptions, acknowledge limitations, and help interpret results responsibly. When both sides engage with this complexity honestly, we make better decisions. A question for marketers: where do you find the biggest gaps between marketing performance measurement and the questions you need answered? #MarketingMixModeling #MMM #MarketingAnalytics #MarketingMeasurement #MarketingROI #DataDrivenMarketing #MarketingStrategy

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On June 1, 2025, #Snowflake announced its $250M acquisition of Crunchy Data, a leading open-source PostgreSQL provider—marking a bold move to strengthen its position in the enterprise AI ecosystem. This strategic acquisition aims to: 🔹 Enhance Snowflake’s support for open-source, developer-friendly environments 🔹 Expand AI/ML capabilities with real-time, scalable data infrastructure 🔹 Fuel the growth of next-gen AI applications across industries In our latest blog, we explore what this means for the future of enterprise data strategy and how this acquisition reshapes the cloud and AI landscape. Read on: https://okt.to/3Exbve Get in touch: Mansi Gupta Ishi Thakur #EnterpriseAI #Snowflake #CrunchyData #AIInfrastructure #CloudDataPlatforms #EverestGroup

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