The Role of Data in Business

🚨 Standards in Travel Distribution – Real Fix or More Noise? 🚨 Overture Maps Foundation and the OpenTravel Alliance just announced a collaboration to tackle one of our industry’s oldest headaches: data interoperability for hotels and travel locations. Their proposal: - GERS IDs → one persistent, universal ID for every physical place (a hotel, rental station, POI). - Bridge Files → link your existing system IDs (Amadeus, Booking.com, Expedia, GDSs, etc.) to that universal one. - Open data foundation → an open, global dataset (60M+ POIs), backed by Amazon Web Services (AWS), Meta, Microsoft, TomTom, Esri. 👉 On paper, this could finally solve the chaos of duplicate IDs, mis-mapped hotels, and costly reconciliation across suppliers, OTAs, GDSs, and OBTs. But here’s the reality check: - We’ve seen similar efforts before: HTNG IDs, GIATA GmbH MultiCodes, Pegasus standards, Gimmonix, Hospitality ID — all useful in their ways, but none reached full ubiquity. - Adoption is everything. Unless Marriott International, Hilton, Accor, Hyatt, Wyndham Hotels & Resorts, Airbnb, Booking Holdings (NASDAQ: BKNG), Expedia Group, Sabre Corporation, BCD Travel, American Express Global Business Travel and others embrace it, this stays niche. - The real win could be in future AI use cases: imagine SAP Concur’s AI agent seamlessly asking Hilton’s AI agent about availability, anchored on the same ID. That vision needs a common “language,” and GERS could be it. 💡 My take: this is not just marketing fluff — it does address a real structural problem — but it’s only as strong as the industry’s willingness to adopt it. Right now, it feels like a solid foundation with potential, not a game-changer… yet. Curious to hear from hoteliers, GDSs, and tech providers: 👉 Do you see this as the missing link for corporate hotel distribution and AI innovation? Or just another standard that looks good on paper but struggles to scale?

So, you’ve embraced data democratization 🥇 But do you have the governance to match? In today’s world, data democratization often means turning business teams from being just consumers into active creators and owners. Today’s modern data stack pushes you to choose two paths for data democratization: 1️⃣ Centralized data teams own “golden datasets” and “golden metrics.” 2️⃣ Let analysts define their own dashboards. However, without proper guardrails, democratization can lead to chaos, conflicting metrics, compromised #dataquality, and mistrusted metrics. This lack of guardrails results in confusion, mistrust, and flawed decision-making. Now, imagine a world where every business teams can own their own "golden metrics", even if they are not #SQL savvy. A world where they share definitions without duplication, enabling departments to build upon each other's work, and where dashboards simply visualize metrics rather than containing their business logic. The key to achieving this harmonious ecosystem lies in using a #semanticlayer with a strong metric layer. This layer empowers business teams to rapidly iterate and adapt their "golden metrics" to keep pace with the ever-changing market landscape, while ensuring consistency and governance. Executives and managers benefit from a centralized, transparent, and trusted source of truth for strategic decision-making. Data teams see reduced ad-hoc requests and maintained #datagovernance and quality standards. Business teams gain the agility to create and own their metrics, fostering self-service and collaboration across the organization. In this new era, true #data democratization is possible and can be controlled and managed, enabling organizations to adapt swiftly to market changes, drive efficiency through standardized metrics, and accelerate time-to-market.

Drawing insights from #Data that can create positive change or improved performance will have limited use if a company cannot motivate and empower a broad range of its people – beyond the data experts – to understand, access, use and share it. We call this process data democratization and, as this blog from our financial services data expert Sharat Bangera explains, it helps employees make more informed decisions, anticipate challenges, and identify growth opportunities – vital for establishing and maintaining a competitive edge. Sharat describes how organizations can begin to reap the rewards from democratized data: https://bit.ly/3ZC1RA9 #GetTheFutureYouWant

🚨𝐀𝐧𝐨𝐭𝐡𝐞𝐫 𝐰𝐚𝐤𝐞-𝐮𝐩 𝐜𝐚𝐥𝐥 𝐟𝐨𝐫 𝐄𝐮𝐫𝐨𝐩𝐞'𝐬 𝐈𝐧𝐝𝐮𝐬𝐭𝐫𝐲: 𝐆𝐞𝐭 𝐭𝐡𝐞 𝐃𝐚𝐭𝐚 𝐑𝐢𝐠𝐡𝐭! 𝐋𝐞𝐭’𝐬 𝐛𝐞 𝐡𝐨𝐧𝐞𝐬𝐭: 👉 𝐃𝐚𝐭𝐚 𝐝𝐨𝐞𝐬𝐧’𝐭 𝐬𝐨𝐮𝐧𝐝 𝐬𝐞𝐱𝐲. 👉 𝐁𝐮𝐭 𝐰𝐡𝐚𝐭 𝐞𝐥𝐞𝐜𝐭𝐫𝐢𝐜𝐢𝐭𝐲 𝐰𝐚𝐬 𝐭𝐨 𝐭𝐡𝐞 𝐬𝐞𝐜𝐨𝐧𝐝 𝐢𝐧𝐝𝐮𝐬𝐭𝐫𝐢𝐚𝐥 𝐫𝐞𝐯𝐨𝐥𝐮𝐭𝐢𝐨𝐧, 𝐝𝐚𝐭𝐚 𝐢𝐬 𝐭𝐨 𝐭𝐡𝐞 𝐟𝐨𝐮𝐫𝐭𝐡. Data is the foundation of all the fancy new stuff you can do with AI – from autonomous #smartfactories to real-time optimization and self-learning production systems. Sounds like science-fiction? Maybe. But it’s closer than we think. Companies in China and the U.S. like Xiaomi or Tesla are building digital hyper-automated factories and setting the pace. Meanwhile – as our recent study on #Industry40 with MHP – A Porsche Company shows – many companies in Europe are still holding on to inflexible, analog-era approaches in a world where "𝐠𝐞𝐭𝐭𝐢𝐧𝐠 𝐭𝐡𝐞 𝐫𝐢𝐠𝐡𝐭 𝐝𝐚𝐭𝐚 𝐫𝐢𝐠𝐡𝐭" is becoming essential to future competitiveness. In a recent conversation with the Frankfurter Allgemeine Zeitung, I shared my perspective on the findings: "AI needs real-time, high-quality data to become the brain of production systems, steering machines autonomously and dynamically. That's why digital competence in management is so crucial and why the lack of it holds Germany back. A key issue is that responsibility for advancing AI in companies is often given to long-serving managers who lack sufficient expertise and experience with data, AI, and software. This needs to fail simply because the approaches to hardware-centric vis-à-vis digital projects differ fundamentally.” 🔑 𝐊𝐞𝐲 𝐏𝐫𝐨𝐛𝐥𝐞𝐦𝐬 𝐰𝐢𝐭𝐡 𝐃𝐚𝐭𝐚 1. Focus on avoiding risks with defensive data strategies 2. Lack of IT leadership in top management 3. Data locked in silos and legacy systems 4. Lack of digital literacy and data analytics competencies 5. Missed opportunities of monetizing data and data-driven innovation 6. Overly centralized data control 7. No real-time data integration across value chains 8. Data management is neglected 9. Bureaucracy and overly restrictive interpretation of GDPR 10. Limited training and upskilling 🔍 𝐖𝐡𝐚𝐭 𝐧𝐞𝐞𝐝𝐬 𝐭𝐨 𝐡𝐚𝐩𝐩𝐞𝐧? 1. Change the mindset at the top: Treat data as a strategic asset 2. Shift towards offensive data strategy 3. Integrate data in real-time on a company-wide platform 4. Democratize data access 5. Empower CIOs: Establish leadership and competence at the top 6. Clarify data responsibilities and establish dedicated roles (Chief Data Officer, Data Steward) 7. Decentralize data control to encourage fast and flexible decision-making 8. Invest in future skills of employees 9. Shift from compliance-only to innovation-friendly governance 👉 Access the full study here: https://lnkd.in/dK6WegYy What’s your view – where do you see the biggest roadblocks?

As we prepare to launch Water & Music's "State of Data in the Music Industry 2024" report this week, I wanted to share why I'm particularly excited about this research and its implications for the future of the music business. Historically, Water & Music has focused on covering emerging music-tech trends like AI, Web3, metaverse, and gaming. Our upcoming report takes a different approach, diving deeper into the fundamental issue underlying all of these innovations: Data strategy. Below are just a handful of examples of the second-order effects that a good (or bad) data strategy can have on music tech innovation: 🤖 Music & AI: The effectiveness of your AI strategy hinges on the quality of your underlying data. This isn't just about having more data — it's about having the *right* data, properly structured and transparently sourced. The importance of data quality and valuation is at the heart of the ongoing lawsuits between rights holders and AI companies (e.g. major labels vs. Suno and Udio). 💲 Music & Web3: By definition, blockchain strategy IS data strategy, especially when it comes tracking music rights and fan behavior on-chain. A blockchain won't magically improve poor-quality data; it's a garbage-in, garbage-out system that will only make poor-quality data more immutable and harder to correct. 🏟 Music & Superfans: From streaming and social media to e-commerce and live events, the current fan data landscape is incredibly fragmented, with each channel offering a unique perspective on fan behavior. Success with digital fandom depends on your ability to integrate these diverse data sources into a cohesive fan strategy and journey, while respecting platform limitations and privacy concerns. 🎵 Music & Catalogs: While not often framed this way, the uncertain future of catalog acquisitions is, at its core, a data issue. This goes beyond just projecting catalog performance based on consumer behavior. It extends to managing the intricate web of music copyright itself — ensuring proper royalty collection across various rights types, and accurately accounting for ownership changes across royalty systems. These unglamorous but critical aspects of catalog management all hinge on robust, reliable data infrastructure. All to say... Our State of Data report isn't just a snapshot of where we are — it's a roadmap for where we're heading. Understanding the current state of music industry data can help us better prepare for the technological shifts on the horizon. I can't wait to have you all dive in. Sign up for our free newsletter to be the first to receive the report's executive summary: https://lnkd.in/ejzkJ8WF Water & Members will get full access to the entire report, including chart visualizations, detailed tool rankings, and other behavioral insights. Peep the comments to learn more! 🤓 #musicindustry #datastrategy #musictech #musicai #musicbusiness #musicbiz

After training over 200 stakeholders to “do research,” here’s what I’ve learned about democratization: 1. Most teams confuse access with ability ↳ Giving someone the tools doesn’t mean they know how to use them ↳ Training ≠ mastery. Observation ≠ synthesis Create role-specific research access levels (Observer, Assistant, Lead) with clear expectations 2. No other discipline trains people to take over their role in a 1-day workshop ↳ You wouldn’t ask your engineer to teach “basic backend” to the whole team ↳ You wouldn’t expect your PM to lead a sprint after one Agile class Position research enablement as awareness training, not role transfer. Align with L&D or HR to set expectations 3. Democratization only works with boundaries ↳ Usability testing = yes. Strategic discovery = no ↳ “Try it out” = with shadowing and structured feedback Maintain a “research runway” doc, a living list of studies that can safely be democratized with criteria like risk, audience, and business impact 4. The more research you’re trying to democratize, the more research help you probably need ↳ If your enablement plan is replacing headcount, something’s broken Use enablement metrics (how many non-researchers are running studies?) to make the case for hiring If you want democratization to work: - Set guardrails. Define what types of research can be shared—and what shouldn’t - Design tiered training. Beginner > Intermediate > Partner. Make it real, not one-off - Pair every new researcher with a mentor. No solo flights - Track outcomes, not just activity. Empowerment means accountability Democratization isn’t a bad idea but when it lacks boundaries, it turns research into theater and teams stop taking it seriously. Swipe through for my complete democratization checklist based on lessons from training 200+ stakeholders across product, design, and ops. Drop a 💬 if you’ve lived through “research chaos” disguised as democratization

LinkedIn helped me set up and scale my businesses— ‬ here’s what I have to share.‬ ‭ When most businesses think of LinkedIn, ‭ They think of a place to post jobs or search for candidates.‬ ‭But if you're still using LinkedIn just for hiring, you're missing out on its true power.‬ ‭LinkedIn is a goldmine for inbound leads and an unlimited resource for growing your business.‬ ‭Here’s how I used LinkedIn to build and scale two businesses:‬ ‭ 1. 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐞𝐝 𝐏𝐫𝐨𝐟𝐢𝐥𝐞 = 𝐓𝐫𝐮𝐬𝐭‬ ‭Your LinkedIn profile is not just a resume—it’s your first impression.‬ ‭ I transformed my profile into a landing page that clearly communicated‬ my expertise and business value.‬ ‭ ACTION:‬ ‭ Shift the focus from just listing your achievements to explaining how you solve problems for your‬ clients.‬ ‭ 2. 𝐂𝐨𝐧𝐬𝐢𝐬𝐭𝐞𝐧𝐭, 𝐕𝐚𝐥𝐮𝐞-𝐃𝐫𝐢𝐯𝐞𝐧 𝐂𝐨𝐧𝐭𝐞𝐧𝐭 = 𝐋𝐞𝐚𝐝𝐬 ‭People come to LinkedIn for insights, not ads.‬ I regularly posted valuable content—industry insights,‬ ‭ personal experiences, and tips that my audience found useful.‬ ‭ ACTION:‬ ‭ Share content that adds value to your audience's journey and solves their problems. This is‬ what creates consistent inbound leads.‬ ‭ 3. 𝐆𝐞𝐧𝐮𝐢𝐧𝐞 𝐄𝐧𝐠𝐚𝐠𝐞𝐦𝐞𝐧𝐭 = 𝐑𝐞𝐚𝐥 𝐂𝐨𝐧𝐧𝐞𝐜𝐭𝐢𝐨𝐧𝐬‬ ‭It’s not just about posting. I built genuine relationships by engaging with comments,‬ joining discussions, and offering advice.‬ This wasn't just about visibility, it was about building trust.‬ ‭ACTION:‬ ‭ Take the time to build real connections by being genuinely helpful and interactive with your‬ network.‬ ‭4. 𝐋𝐞𝐯𝐞𝐫𝐚𝐠𝐞 𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧 𝐆𝐫𝐨𝐮𝐩𝐬 = 𝐍𝐢𝐜𝐡𝐞 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐢𝐞𝐬‬ LinkedIn Groups helped me reach niche communities that were actively seeking solutions‬ I could offer. I focused on providing real value in these groups,‬ which turned into meaningful business leads.‬ ‭ ACTION:‬ ‭ Join groups that align with your industry and actively contribute to discussions.‬ ‭This positions you as an expert and helps you reach a targeted audience.‬ ‭ 5. 𝐏𝐫𝐢𝐯𝐚𝐭𝐞 𝐎𝐮𝐭𝐫𝐞𝐚𝐜𝐡 = 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐜 𝐆𝐫𝐨𝐰𝐭𝐡‬ I took LinkedIn's messaging feature seriously.‬ Instead of sending random connection requests,‬ I sent personalized, strategic messages that showed‬ the value I could bring to them.‬ ‭ ACTION:‬ ‭ Use LinkedIn messages to build genuine relationships and offer solutions tailored to their needs.‬ LinkedIn isn’t just another platform—it’s a business growth engine.‬ An optimized profile, valuable content, real engagement, and meaningful relationships can‬ transform your LinkedIn presence into a constant source of growth.‬ ‭Ready to unlock LinkedIn’s full potential?‬ Let’s connect and I’ll show you how to transform‬! ‭

Is “good enough” data really good enough? For 88% of MOps pros, the answer is a resounding no. Why? Because data hygiene is more than just a technical checkbox. It’s a trust issue. When your data is stale or inconsistent, it doesn’t just hurt campaigns; it erodes confidence across the org. Sales stops trusting leads. Marketing stops trusting segmentation. Leadership stops trusting analytics. And once trust is gone, so is the ability to make bold, data-driven decisions. Research tells that data quality is the #1 challenge holding teams back from prioritizing the initiatives that actually move the needle. Think of it like a junk drawer: If you can’t find what you need (or worse, if what you find is wrong), you don’t just waste time, you stop looking altogether. So what do high-performing teams do differently? → They schedule routine maintenance. → They establish ownership - someone is accountable for data processes. → They invest in validation tools - automation reduces the manual grind. → They set governance policies - because clean data only stays clean if everyone protects it. Build a culture where everyone values accuracy, not just the Ops team. Because clean data leads to clearer decisions and a business that can finally operate with confidence.

Google's AI Overviews now control 40% of local search results (source: LocalFalcon), but most businesses have no strategy for it. While competitors focused on traditional local SEO, we positioned this shopping mall to dominate Google's AI Overviews for competitive keywords. The result? A +370% increase in AI Search traffic, +82% increase in organic traffic. Here’s the exact strategy we used (copy this): 1/ Optimized Their Google Business Profile for AI Systems Google's official guidance explicitly states that keeping your GBP updated is critical for AI Overview visibility. We completed every field: • Business description with relevant keywords in first 250 characters • Consistent NAP across all platforms   • High-quality photos that AI could reference • Active review management The result? Google started pulling their business info directly into AI answers. 2/ Built Presence on Third-Party Platforms AI Systems Trust Google's Multitask Unified Model (MUM) system seeks consensus from multiple high-quality sources. We got them listed and reviewed on: • Yelp • TripAdvisor   • Facebook Business • Bing Places • Industry-specific directories Each listing reinforced their credibility signals for AI models. 3/ Targeted Informational Queries That Drive Clicks Here's where most businesses fail - they ignore informational searches. Studies show AI Overviews appear most for informational queries, but simple definitions get fully answered without clicks. The goldmine? Questions that spark curiosity or guide decisions. • "Best [service] for [specific need]" • "[Option A] vs [Option B] for [problem]"   • "How often should [audience] [activity] to see results" These trigger AI Overviews but create curiosity gaps that drive clicks. To find these queries, use Ahrefs Keywords Explorer with seed terms related to your business. Focus on the Questions section showing comparison and benefit-focused searches rather than simple definitions. Look for those with low search volume but high intent. 4/ Content Structure That Works: • Lead with the answer - First 1-2 sentences give a concise answer AI can extract • Then go deeper - Explain why, how, and which option is best • Use clear formatting - Subheadings, bullet points, numbered lists • Add unique value - Include data, examples, or practical tips competitors miss The Results: ✅ Organic traffic: +82% (140k to 256k monthly sessions) ✅ AI referral traffic: +370%  ✅ Appearing for 155 keywords in UK AI Overviews ✅ 9.6k+ keywords in top positions Key Insight: 85% of AI search sessions landed on informational subpages, not the homepage. This proves AI search drives qualified clicks when your content answers the next layer of intent beyond the basic AI answer. The Bottom Line: AI Overviews aren't killing local search - they're creating new opportunities for businesses smart enough to optimize for them. While competitors wait, early movers are locking in the advantage.

Customer service conversations are the heartbeat of your business. They are a treasure trove of data about your operation and product flows, your agents and how they treat your customers, and your customers' preferences and needs. Yet, most contact centers analyze only a fraction of these interactions, using dated technology, leaving valuable insights untapped and decisions driven by incomplete data. At Replicant, we believe it’s time to bring every conversation to light. That’s why Conversation Intelligence is transforming customer service conversations into actionable insights. By analyzing 100% of calls with the latest audio AI, leaders can identify operational issues that lead to unnecessary calls, optimize agent performance, and pinpoint automation opportunities—turning their contact centers into strategic assets. For example, a large e-commerce provider used Conversation Intelligence to uncover an issue impacting 5% of their calls. Within one week, they implemented a fix that redefined their customer service strategy, eliminating inefficiencies and elevating their customer experience. This isn’t just about solving problems; it’s about leading with clarity. When every customer conversation becomes a data point for innovation, and AI summarizes it into actions for you, your contact center becomes a competitive advantage. The future belongs to leaders who anticipate, innovate, and act boldly. Are you ready to lead the way?