Advantages of Integrated Data Solutions

Merging high-value data sources is one of the most impactful steps a data leader can take. Remember, 1+1=3. Consider this: you have product data (like type, quantity purchased, and price) in one database, and customer details (such as industry, customer size, and location) in another. Each dataset on its own offers great value to the org but when combined, their value is even greater. Combined, you can get insights not possible otherwise, think segmentation, whitespace, and basket analysis. The problem? Many crucial data sources within a company are often isolated. This includes: Billing data -> What are we billing our customers? Product Usage data -> How does revenue align with features of our product? Engagement data -> Are we in touch with our customers via email, webinars, conferences? Are they in touch with us? Support ticket data -> How helpful are we to our customer; how buggy are our products; what are our SLAs? Customer infographic data from a 3rd party like D&B, Zoominfo -> What's the profile of our customers; which industry are they part of; what's their spending capability; do they have company news? Integrating data sources LITERALLY expands the scope of analysis exponentially, allowing your analysts and data scientists to get much deeper insights. The world is complex, your data needs to mirror that. Be the data Thanos. Combine all your data stones to become the most influential data leader there is. 1+1=3. ———— I'm Erik 👋. If you like this content, leave a comment and follow me on #LinkedIn ("Follow") and Youtube (@datafool) to get more content like this.

𝗜𝗻 𝘁𝗵𝗲 𝗔𝗜 𝗲𝗿𝗮, 𝗱𝗮𝘁𝗮 𝗶𝘀 𝘆𝗼𝘂𝗿 𝗰𝗼𝗺𝗽𝗮𝗻𝘆'𝘀 𝗺𝗼𝘀𝘁 𝗶𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝘁 (𝗮𝗻𝗱 𝗺𝗼𝘀𝘁 𝗲𝘅𝗽𝗲𝗻𝘀𝗶𝘃𝗲) 𝗮𝘀𝘀𝗲𝘁. 𝗧𝗿𝗲𝗮𝘁 𝗶𝘁 𝗮𝘀 𝘀𝘂𝗰𝗵. Data issues prevent revenue teams from adopting AI, which improves pipeline efficiency. The convergence of data from marketing, sales, and customer experience allows AI to streamline information and fast-process everyday tasks, empowering sales teams to focus on customer relations. AI revenue enablement initiatives must be implemented within the framework to show results and quick wins. Thus, leadership must prepare revenue teams for #AI. 𝐂𝐫𝐞𝐚𝐭𝐞 𝐚𝐧 𝐢𝐧𝐭𝐞𝐫𝐢𝐦-𝐥𝐞𝐝 𝐝𝐚𝐭𝐚-𝐫𝐞𝐯𝐞𝐧𝐮𝐞 𝐀𝐈 𝐭𝐚𝐬𝐤𝐟𝐨𝐫𝐜𝐞. Form a marketing, sales, and customer experience team to collaboratively document all siloed and cross-functional data and processes. Ledro et al. (2023) advocated this inclusive strategy as crucial to assisting employees in adjusting to AI systems and data integration. The team will help identify AI-enabled practices, data governance, and future-ready opportunities. For example, start with marketing lead generation, top 75% funnel effectiveness, and customer onboarding. Track results and improve for future use. 𝐂𝐫𝐞𝐚𝐭𝐞 𝐀𝐈 𝐩𝐢𝐥𝐨𝐭𝐬 𝐟𝐨𝐫 𝐩𝐫𝐢𝐦𝐚𝐫𝐲 𝐜𝐨𝐦𝐦𝐞𝐫𝐜𝐢𝐚𝐥 𝐝𝐚𝐭𝐚 𝐩𝐫𝐨𝐝𝐮𝐜𝐭𝐬 𝐚𝐧𝐝 𝐈𝐂𝐏 𝐝𝐢𝐠𝐢𝐭𝐚𝐥 𝐭𝐰𝐢𝐧𝐬. Standardizing data products for crucial business entities is the task. Each data product provides a 360-degree view of the entity based on customer patterns, creating security, governance, and metadata standards for reliable data. Information management should focus on data collection, governance, and using processes and systems (Janssen et al., 2020). For more accurate forecasts and informed business decisions, team specialists can curate and select training set data points. 𝐅𝐢𝐧𝐝 𝐰𝐚𝐲𝐬 𝐀𝐈 𝐜𝐨𝐮𝐥𝐝 𝐬𝐢𝐠𝐧𝐢𝐟𝐢𝐜𝐚𝐧𝐭𝐥𝐲 𝐢𝐦𝐩𝐫𝐨𝐯𝐞 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐨𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬. Data management and integration should have a shared strategy for AI implementation that supports business goals. Ledro et al. (2023) suggest involving end-users like marketing professionals to create agile, user-friendly, and business-adaptable systems. AI-generated hyper-personalized content can significantly improve outreach and lead generation in high-impact, low-cost, low-risk use cases to support customers and reduce risk. 𝐈𝐧𝐭𝐞𝐫𝐬𝐞𝐜𝐭 𝐀𝐈 𝐰𝐢𝐭𝐡 𝐩𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐚𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬. It can improve sales projections, lead generation, and customer interactions. To improve sales efficiency and productivity, integrate and curate customer-facing data and treat it as your most valuable product to align AI-powered PE with sales and Cx. With the right tools, data, and inputs, AI can crunch numbers instantly and provide valuable sales cycle insight. It can find patterns in this data and identify sales process gaps. The more integrated your sales team is, the better they can target high-value leads.

Master data management (MDM) and data governance programs are often seen as a tough nut to crack, but the real challenge lies not in the technology but in aligning all functions and stakeholders. MDM has been viewed narrowly for too long, focusing only on match and merge capabilities. However, MDM is much more. It connects systems, catalogs data, manages hierarchies and relationships, creates high-quality, trusted insights, and drives ongoing governance and data stewardship. There are many MDM vendors in the market. While point solutions may excel in specific areas (e.g., match-merge), they often result in a fragmented landscape, requiring extensive integration efforts and a higher cost of ownership. In contrast, a holistic data management approach delivered through a single cloud platform offers the following advantages. 🔷 Standardize Processes: A unified platform ensures consistent data management practices across the organization, reducing variability and improving data quality. 🔷 Future-Proofs Investments: As business needs evolve, a comprehensive platform can adapt and scale, avoiding the need for multiple upgrades or replacements of disparate tools. 🔷 Maximizes Resource Efficiency: A modern data management platform reduces users' learning curves, streamlines support and maintenance, and minimizes the overhead of managing multiple vendors. 🔷 Ensures Comprehensive Data Governance: With all data management capabilities under one roof, it becomes easier to implement and enforce governance policies, ensuring data integrity and compliance. 🔷 Enables End-to-End Data Flow: A platform approach facilitates seamless data movement between systems, enhancing the ability to make informed decisions grounded on trust. Don’t silo your MDM and data governance efforts. Consider all data management capabilities needed for better data usage and trusted insights. While point solutions may look like a quick and dirty way to address the data issues, when you bring all the capabilities together, you create trusted data for today’s needs and future-proof your investment to address the constantly changing demands of the business. It's time to rethink what a modern MDM looks like. What are your thoughts? #MDM #SaaS #Cloud #DataGovernance #Data #Platform #TechnicalDebt #DataManagement #AI #ModernArchitecture #DataDriven #360View

"We spent more time re-formatting data than analyzing it." This was the frustrated admission from a senior scientist at a leading biotech last week. His team had just realized they'd spent 3 days trying to combine results from different assays for a crucial go/no-go decision. It's a pattern I see repeatedly: Brilliant scientists reduced to data janitors, manually copying numbers between spreadsheets and reconstructing analyses from PowerPoint slides. The real cost isn't just time - it's trust. When data lives in silos, teams start questioning each other's results. Bench scientists feel undermined when computational teams redo their analysis. Digital teams get blamed for decision delays. But there's a better way. We've found that 90% of data ingestion and routine assay analysis can be standardized and automated. When teams align on templates and workflows upfront: • Results are immediately ready for integration • Analysis that took hours happens in minutes • Scientists can focus on deeper insights • Trust builds between teams The most successful biotechs we work with have realized that data integration isn't just an IT problem - it's a competitive advantage.

Here’s an easy way to advance your business almost immediately: Eliminate your data silos. I’ve been hearing people complain about data silos for nearly 40 years now. They’re the unpleasant side effects of decisions that otherwise make good business sense, such as moving data to an application to drive a business process. Now, with technological improvements like the cloud and a constant stream of third-party data, those silos are mushrooming, and everything is getting out of sync. Consider your customer data: Is it spread across your CRM, marketing, and sales platforms? When one of those platforms updates data, is the change reflected across your other applications? Most likely, the answer is “no.” For a long time, data silos were hard to fix. But today, there’s a solution: Integrate your data. InterSystems IRIS allows you to bring applications to your data, rather than sending your data out to them. The result not only makes your system more secure, stable, and efficient, but when you see all of your data together, you can also make better business decisions, collaborate more easily, and see your entire enterprise in a single view. Think of it as the anti-silo.