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Data is your company’s most valuable asset. However, it only holds value if you can find it, trust it, and use it effectively. To take control of your data, follow a straightforward, step-by-step approach: assess what you have, lock it down, automate the heavy lifting, and govern continuously. In this article, I will share five practical steps you can start today, based on our decades of experience developing and helping customers implement data management best practices. Why Data Management and Handling Best Practices Matter When data is messy or inaccessible, it becomes a source of risk and cost. Good practices increase accuracy, protect sensitive information, make data discoverable, and ensure regulatory compliance. All of this saves time and money. The Importance of Data Quality Quality data is essential for informed decision-making. When data is accurate and reliable, it enhances trust across the organization. This trust leads to better collaboration and more effective strategies. Defining the Business Reason for Moving Data Comes First Many data engineering teams are told to “replicate everything.” The business expects all data to be available everywhere. However, without understanding why the data is being moved—what decisions it supports, who needs it, and how often—companies end up replicating far more than they actually use. This creates more pipelines to maintain, higher storage and compute costs, and future integrations that are harder to scale. A clear business purpose helps teams choose the right data to move, not all of it. This clarity allows them to design efficient, durable systems from the start. This upfront clarity is where tools like Sesame Software make a difference. Our platform lets teams move only the data they need, at the frequency they need it, without building excess pipelines or custom code. 5 Steps Toward Data Management Best Practices 1. Assess your data landscape. Map your sources, flows, and owners. Identifying where data lives and who uses it reveals gaps, redundancies, and risks. 2. Define clear policies. Standardize naming, retention, access, and deletion rules. This ensures everyone follows the same playbook. 3. Secure proactively. Use encryption, role-based access, and audit logging. Test your protection and access processes regularly. 4. Automate routine tasks. Automate replication, synchronization, and backups. This reduces human error and improves consistency and reliability. 5. Govern continuously. Monitor data quality, measure compliance, and refine policies with stakeholder input as systems evolve. Practical Tips That Make a Difference Start small: Pick one critical dataset (CRM, finance) and optimize it first. Use consistent naming conventions and metadata. Archive or tier older data to keep production systems fast. Set up alerts for data quality drift or failed automations. Choose tools your team will actually use, because adoption matters. How Our Solution Helps Sesame Software helps teams take control of their data by centralizing access, automating replication and synchronization, and providing secure, governed endpoints for analytics and integration. Our platform supports high-frequency data movement, granular access control, and bring-your-own storage for cost efficiency. This way, your team spends less time managing data and more time using it effectively. Next Steps for Implementing Data Management Best Practices for Your Organization [Talk to a data expert](https://go.sesamesoftware.com/demo?_gl= 13kaa10 _gcl_au MjQxNjg4MDguMTc2MjQ0MTE5MC4yMTE3NDk5NzY0LjE3NjMzOTk0ODYuMTc2MzM5OTQ4NQ..) about your specific needs. Explore our platform capabilities — from backup and replication to pipelines and migration . See how customers use Sesame Software to regain control of their data. Effective Data Management FAQ How often should I back up critical data? That depends on business impact — for mission-critical applications, aim for high-frequency or near real-time replication; for most others, daily or scheduled backups with incremental captures are common. Can automation replace manual data cleanup? Sesame Software continuously replicates data between Salesforce and NetSuite. Automation reduces manual work by handling replication, deduplication, and monitoring — but governance and human oversight remain essential. This approach keeps customer, billing, and revenue records synchronized in near real time, eliminating version mismatches and reducing the need for manual updates or reconciliation. What’s the difference between archiving and backing up? Backups are for operational protection and continuity; archives are for long-term retention and historical access. Both are important but serve different business needs. Found this post helpful? Share it with your network using the links below.

AI is not the objective. Understanding your business is. In my experience, that understanding is what true AI readiness actually looks like. Recently, I received an email from one of our vendors: “I meet with customers like yourself every day, and the most common buzzword I hear is AI. Does your business have any AI initiatives for this year and beyond? I'd love to connect with you to discuss how we've incorporated AI into our platform to help customers maximize their ROI.” The message assumes that adding AI to a product automatically creates value. I haven’t responded—not because AI isn’t useful, but because I don’t view AI as an objective in itself. My objective is to build a sustainable, profitable company that values its customers, employees, and partners. If I encounter a problem where machine learning or artificial intelligence can genuinely help, I’m happy to explore it. But adopting AI for its own sake rarely produces meaningful outcomes. A shiny, brand-name toolbox with a thousand tools might look impressive in the garage, but most people will only ever use a small fraction of them. Many AI platforms are sold the same way: high tool density, impressive feature lists, and very little alignment to a specific business outcome. Most organizations don’t need more tools. They need clearer objectives and fewer assumptions. Tools don’t create value on their own. Clarity does. When AI Ambition Outpaces Accountability Much of the AI conversation today is shaped by the pursuit of Artificial General Intelligence (AGI), the idea that machines will think like humans or outperform them. While this makes for compelling headlines, it often introduces a quiet but real cost inside organizations. Very few businesses want autonomous decision-making without human accountability. Executives are ultimately responsible for outcomes, risk, compliance, and customer trust. Systems that obscure how decisions are made—or remove clear ownership—create governance challenges long before they create value. When AGI-driven narratives dominate strategy discussions, budgets and attention can drift away from more immediate, solvable problems. The risk isn’t that organizations adopt AI too slowly, but that they allocate resources toward ambition before readiness, and spectacle before substance. AI delivers the most value when it supports human judgment, not when it attempts to replace it. AI Readiness Begins with Data That Reflects Reality Once a team defines what it’s trying to accomplish and why, a foundational question appears almost immediately: Where will the data come from, and does it accurately represent how the business actually works? In many organizations, the honest answer is no. Geographic data offers a simple example. When state and country fields are stored as free text, dozens of variations emerge for the same value: United States, USA, U.S.A., US, U.S., United States of America. This isn’t an AI problem. It’s a data governance problem. AI systems can tolerate noise, but they cannot correct systemic semantic errors, missing ground truth, or contradictory business rules. Models inherit the assumptions and structure embedded in the data they consume. After more than 30 years of building corporate data warehouses, I’ve never worked on a project that didn’t surface surprises in the data. In one case, a client migrating from a legacy financial system to Oracle discovered their data couldn’t be corrected programmatically. Business rules had changed repeatedly over time, documentation was incomplete, and there was no reliable source of truth. The only viable option was manual review and re-entry. AI can assist with classification, clustering, and anomaly detection. But when historical data reflects inconsistent or undocumented business logic, human judgment is still required to determine what is correct and what should change. Data Problems Often Reveal Process Problems In another project, a client discovered that service calls were being scheduled before customers had even signed up. This wasn’t a data quality issue caused by errors or omissions. It was a workaround created because the system couldn’t properly prioritize requests. The data wasn’t wrong—it was faithfully representing a broken process. This distinction matters. Sometimes data is messy because people make mistakes. Other times, it is messy because the business has adapted around system limitations. AI doesn’t resolve either problem on its own. In fact, it often exposes them. That exposure is not a failure. It’s a signal. Why Discovery Creates Value Before AI Ever Does In the 1990s, business process reengineering became common as organizations adopted off-the-shelf enterprise software. Companies stopped building everything from scratch and benefited from the discipline embedded in standardized systems. Today, the discovery phase of AI and machine learning initiatives offers a similar opportunity. You don’t need a trained model to generate value. In many cases, the greatest return comes from examining data quality, lineage, and usage before automation begins. That work surfaces inefficiencies, workarounds, and outdated practices that quietly undermine reporting, operations, and decision-making. Discovery does not slow innovation. It reduces risk, prevents misallocated investment, and avoids scaling the wrong solution. Organizations that skip this phase often find themselves with expensive pilots, abandoned models, and growing skepticism about AI’s value. Practical Ways Organizations Build AI Readiness Most teams improve data readiness through a combination of approaches: Clean data, clear objectives, and accountable processes create the foundation for meaningful outcomes. Standardizing data after ingestion  Cleaning and harmonizing data once it reaches a central repository can be cost-effective and minimizes disruption to downstream systems. Applying transformations during data movement  Transforming data as it is replicated between systems enforces documentation, improves consistency, and allows teams to address known issues incrementally. Fixing the underlying business processes  This approach delivers the greatest long-term impact and requires the most effort. It involves documenting current practices, defining intended behavior, and reinforcing it over time. Without this step, data issues tend to resurface, regardless of tooling. Most organizations use a blend of all three, balancing speed, cost, and durability. The Takeaway AI is not the destination. AI readiness begins with a clear understanding of the business, supported by data that accurately reflects reality and processes that are intentionally designed. Modern AI can mask data issues, but it cannot resolve their root causes. Those problems tend to reappear later as trust gaps, compliance risks, or explainability failures. Adopting AI before fixing data and processes doesn’t create advantage—it accelerates inefficiency at scale. This is not an argument against AI. It is an argument for earning the right to use it. Teams that invest in flexible, well-governed data foundations are better positioned to adopt AI responsibly, allocate budgets effectively, and deliver outcomes that stand up to scrutiny. Whether or not an AI model is ever deployed, that work creates value on its own. Evaluate readiness for composable data pipelines with a short checklist designed to highlight quick wins, compliance requirements, and integration touchpoints for an initial pilot. TL;DR AI initiatives succeed or fail long before models are deployed. The discovery phase—examining data quality, structure, and business processes—often delivers the greatest return. While modern AI can tolerate noise, it inherits the assumptions and flaws embedded in the data that feeds it. Clean data, clear objectives, and accountable processes create the foundation for meaningful outcomes. AI works best when organizations earn the right to use it. Written by Rick Banister, CEO of Sesame Software Sesame Software develops data capture and replication tools that ingest data from SaaS applications and databases into relational databases and data lakes, helping teams build reliable foundations for analytics, reporting, and future initiatives. Found this post helpful? 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Sesame Software’s latest update speeds up your time to insights with replication, integration, and compliant backups for your Salesforce Cloud data. Salesforce champions, rejoice! Sesame Software just released a game-changing update designed to revolutionize your data management experience. Get ready for lightning-fast transfers, streamlined workflows, and a deeper connection with improved Salesforce backup and recovery. Buckle up, and let’s dive into the details on protecting your data in the world’s #1 CRM. Effortless Deployment, Anywhere You Need It Sesame Software’s update offers flexible deployment options, fitting seamlessly into your existing infrastructure. Deploy on-premise, in the cloud, or even within a Docker container – the choice is yours! Plus, full compatibility with Java 17 ensures optimal performance and efficiency. Automation is Your Data Management BFF Tired of the manual grind? Sesame Software’s built-in scheduling tool takes the reins. Set your data tasks on autopilot, freeing you up to focus on strategic initiatives that drive revenue and growth. Remember, frequent backups are crucial – experts suggest enterprise businesses aim for a maximum recovery window of 15 minutes. Fort Knox-Level Data Protection for your Salesforce Backup and Recovery Data security is our top priority. Breathe easy with Sesame Software’s robust features: Record Restoration: Accidentally deleted data? No sweat! Restore it with ease. Automatic Schema Management: Keep your databases in perfect sync, automatically. Data Masking: Navigate GDPR compliance with confidence. SAML Authentication: Rest assured, only authorized users get access. Keep Your Salesforce Setup Healthy This update goes beyond data movement and security for Salesforce backup and recovery. It also streamlines metadata management, allowing for effortless export, comparison, and restoration. Easily track changes and selectively restore specific components if needed. This granular control empowers you to maintain a stable and optimized Salesforce environment. We Speak Salesforce Fluently Sesame Software understands your needs, especially for backup and recovery. This update is in direct response to our Salesforce users! Our next stop on the Sesame Software roadmap is sandbox seeding. Seeding your sandbox with test data will soon be a breeze, with a complete history of all your data changes readily available. Plus, upcoming features will offer customizable data access controls, ensuring complete confidentiality. Important Note: While full copy sandboxes are excellent for testing purposes, they aren’t designed for full Salesforce backup and recovery or data backups. Take Full Control of your Salesforce Backup and Recovery With our added features and ongoing commitment to ease-of-use, we aim to give you even more control over your data. Define custom keys, track changes with custom date columns, and as previously mentioned, we’re now able to back up your precious metadata. The Takeaway Sesame Software’s update unlocks the full potential of your Salesforce data. Experience faster transfers, smoother workflows, and deeper integration. And remember, even the best Salesforce heroes make mistakes. Sesame Software’s backup and recovery features ensure you can recover quickly and get back on track with minimal downtime. Still Trying To Figure Out Where To Start? We can help. Schedule a demo of Sesame Software today to discuss how we can help create a unified view of your data by bringing it all to one place with instant connections to on-premise or cloud enterprise applications or databases.

To a Hammer, Everything Looks Like a Nail To a digital hammer, everything looks like a data nail. AI is now part of every conversation. CIOs are being asked where AI fits, how fast it can be deployed, and what needs to happen first. But as with any new technology wave, not every problem is an AI problem. The organizations making the smartest progress aren’t forcing AI into everything—they’re identifying the places where it can genuinely improve operations, decision-making, or customer experience. In reality, most enterprises are still building the foundation: connecting their data, cleaning their systems, and evaluating the practical value of each AI project. That’s healthy. Good engineering starts with clarity, not speed. Where AI Makes a Real Impact AI is at its best when it helps people make better decisions or automates the work no one wants to do manually. Decision Support and Insight AI thrives when it has a wide range of connected data—on-prem, SaaS, cloud storage, warehouses, analytics platforms. With the right visibility, AI can help teams see both the big picture and the small details that matter. Predictive and Statistical Analysis Machine Learning (ML) models can detect patterns, highlight risks, forecast trends, and support preventative maintenance. These techniques have been reliable workhorses far longer than today’s Large Language Models (LLMs). Large-Scale Automation Reviewing thousands of customer records, analyzing transactions, or scanning for anomalies is not a good use of human hours. AI excels at that kind of scale. Fraud Detection AI is a powerful pattern-recognition tool. When implemented responsibly, it strengthens security and helps organizations react faster. Improving Product Experiences Subtle, smart features—like automated suggestions or guided fixes—often deliver more measurable value than flashy AI marketing language. LLMs vs. Algorithms LLMs generate text. They’re probabilistic systems, and they shine in areas like drafting, summarizing, and research. But consistency is not their strength. Behind every AI system is the infrastructure where algorithms and models run, each built for a different kind of precision. Deterministic algorithms, on the other hand, are predictable and repeatable. Loan decisions, pricing engines, logistics calculations—these still belong to rule-based systems built for precision. The real opportunity is knowing which tool fits the job, and combining them appropriately. LLMs vs. Machine Learning LLMs are for unstructured text. Machine Learning (ML) is for structured data and numerical analysis. If you need forecasts, patterns, or clean math, you rely on ML. If you need explanations, summaries, and natural-language interaction, you reach for LLMs. Most modern AI strategies require both. Start With the Need, Not the Tool Successful AI projects don’t begin with “We need AI.” They begin with questions like: What slows us down? Where are the bottlenecks? What decisions take too long? Where would automation free up meaningful time? Where would better data visibility improve outcomes? Modern enterprises succeed with AI when they choose projects grounded in real business problems, not hype. AI adds value when it solves a real operational problem. It stalls when it’s put in place for novelty. Personally, one of my favorite uses of AI is research. When a conversation hits a knowledge gap, I’ll ask ChatGPT for a high-level summary and source links. It gives me fast context. The difference is I verify what I read and use it as input—not as a replacement for judgment. Looking Ahead: Event-Driven Data, Edge Processing, and What’s Next for AI Readiness The future of AI will depend less on model size and more on how quickly and reliably data can move. Event-Driven Data Movement Systems are shifting from scheduled jobs to real-time triggers. When something happens—a customer update, a transaction, an alert—applications need to respond immediately. This is essential for real-time analytics and AI-assisted decision-making. Edge Processing With more data generated at the edge, not all of it needs to be shipped to a data center. Processing closer to the source improves performance, reduces cost, and increases resilience. Unified, AI-Ready Data Pipelines AI only works when the underlying pipelines work. Organizations need: Hybrid connectivity across all their systems Reliable replication and synchronization Predictable pricing (not per-GB surprises) Flexible storage options Automated governance and recovery This is where forward-looking data platforms earn their value: making sure data moves seamlessly, stays accurate, and is available when AI needs it. Setting Realistic Expectations AI is powerful, but it’s still a tool. The companies that win with it will be the ones that: Start with real needs Connect and trust their data Match the right technologies to the right problems Build systems that scale as their data grows We’ll continue sharing what we’re learning as AI and AI readiness best practices evolve and as organizations refine the systems and data management solutions that make it practical. Written by Rick Banister, CEO of Sesame Software, with Barry Polley, Data Scientist at Datafall Found this post helpful? Share it with your network using the links below.

In today’s digital world, managing data securely is not just a technical necessity — it’s a strategic priority. Every organization that handles sensitive or regulated information must navigate a complex landscape of laws, standards, and frameworks that govern how data is stored, accessed, and protected. These rules exist to build trust, reduce risk, and ensure accountability. But what does it truly mean to meet data security compliance? How do you take control of your data while satisfying regulatory requirements? In this article, we break down the essentials of data security compliance, provide actionable steps, and show how a modern data platform can help you stay ahead. Why Data Security Compliance Matters Compliance in data protection isn’t just a checkbox—it’s foundational to how an organization operates. Frameworks like GDPR, HIPAA, CCPA, and SOC 2 define rules for handling personal and sensitive data. Failing to comply can lead to financial penalties, legal liabilities, and reputational harm. When done right, pursuing data security compliance offers real advantages: It strengthens customer trust by demonstrating that privacy and protection are taken seriously. It helps prevent breaches through disciplined security measures. It enforces consistent data governance and clearer policies. It differentiates your organization in the market as a trusted steward of data. Taking control of your data means embedding compliance into your systems—not treating it as an afterthought. Key Elements of Data Security Compliance To build a robust program, focus on these core areas: Data Inventory & Classification Know what data you hold, where it lives, and how sensitive it is. Classify data into levels (e.g., public, internal, confidential, restricted) to guide protections. Access Controls & Authentication Use role-based access (RBAC) to limit permissions. Enforce multi-factor authentication (MFA) for additional security. Encryption In Transit & At Rest Encrypt datasets both during transmission and while stored—a vital layer of defense against unauthorized access. Auditing & Monitoring Perform regular security audits, monitor systems continuously, and log activity for traceability. Incident Response Planning Prepare a detailed breach response strategy that includes detection, containment, notification, and recovery steps. Training & Awareness Educate employees on best practices, phishing risks, and data handling policies—human error is often a weak link. Vendor & Ecosystem Compliance Ensure partners, vendors, and connected systems abide by your data security compliance standards. Include requirements in contracts and perform periodic assessments. By covering these areas, you build a compliance framework that supports both security and business objectives. Practical Steps to Make Data Security Compliance Work Here’s how to translate principles into action: Conduct a Data Protection Impact Assessment (DPIA) to identify risk areas and plan mitigations. Draft clear, understandable data protection policies that everyone can follow. Automate compliance tasks (classification, monitoring, reporting) using a capable platform. Regularly update security controls to counter evolving threats. Secure leadership support to allocate resources and drive cultural change. Document all compliance activities, audits, and decisions—this record is vital for audits. Communicate transparently with employees, customers, and stakeholders about your compliance efforts. Why Simplicity Matters in Data Security Compliance Managing data security compliance can feel complex—but your tools shouldn’t add friction. You need platforms that are powerful, but also straightforward enough for daily use. At Sesame Software , our goal is to simplify compliance by offering: Centralized replication and backup across systems. Automated workflows for retention policies, auditing, and reporting. Real-time monitoring and alerting for anomalous activity. Intuitive interfaces that empower both IT and compliance teams. Flexibility to support different deployment models and data residency requirements. With this balance of capability and usability, compliance becomes part of your data management foundation—not a burdensome overlay. Moving Forward with Confidence Achieving data security compliance is an ongoing journey, not a one-time fix. Regulations evolve, threats adapt, and technology shifts. But with the right mindset, processes, and platform, you can stay ahead. Remember: compliance is more than avoiding fines. It’s about building trust, ensuring resiliency, and enabling confident growth. Start now by assessing your current posture, identifying gaps, and putting in place those key elements. Over time, a mature approach to data security compliance will become a competitive advantage. Protect your most valuable asset—your data. Manage it smartly. Comply with confidence. Next Steps to Keeping Control of Your Data Take the next step toward stronger data security compliance with Sesame Software: Explore our platform: See how our replication and backup capabilities simplify compliance and data protection. Match your architecture: Learn which connectors best support your existing systems and scale requirements. Book a demo: Validate your architecture with a live walkthrough from our data experts. Download our compliance checklist: Quickly evaluate your organization’s readiness and identify areas for improvement. Data Security Compliance and Sesame Software FAQ How does Sesame Software support data security compliance? Our platform automates replication, backup, and monitoring processes to ensure your data remains consistent, auditable, and protected. These capabilities support compliance with frameworks such as GDPR, HIPAA, CCPA, and SOC 2. Can Sesame Software help with audit readiness? Yes. By maintaining synchronized, timestamped copies of your data, Sesame Software provides the visibility and traceability auditors require—reducing the effort needed to demonstrate compliance. What makes Sesame Software different from other data management tools? Sesame Software combines simplicity with enterprise-grade power. We deliver near real-time replication, flexible storage options, and built-in reliability so your team can focus on strategy, not manual compliance tasks. Found this post helpful? Share it with your network using the links below.

If your team is exploring how to bring AI into your enterprise workflows, you’ve probably hit a familiar challenge: the data isn’t ready. It’s trapped in siloed systems, inconsistent across platforms, or missing altogether. And while plenty of vendors will offer to “start fresh,” building a new data foundation from scratch is time-consuming, expensive, and often unnecessary. Here’s the good news: you may already have what you need if you can access, move, and prepare your data properly. Why Data Readiness Is the First Step in AI Success No matter how advanced the model, AI is only as powerful as the data you feed it. That means: Incomplete records = incomplete predictions Dirty data = misleading insights Inaccessible systems = missed opportunities Before building models or integrating with AI platforms, organizations need a reliable way to centralize and structure their data without months of rework or risky migrations. The AI Problem You Can’t Solve With a Model Many teams try to push forward with AI while hoping their fragmented systems “catch up.” But without a scalable way to move and sync data between systems, even the best AI projects fall short. Common issues include: Disconnected cloud and on-prem systems Manual data exports and inconsistent file formats Delays in syncing real-time data Redundant or incomplete datasets feeding downstream tools And perhaps most frustrating: AI tools only work if they can actually access the data. How Sesame Software Helps You Get AI-Ready At Sesame Software, we help you unlock your existing data so you can put it to work faster. Instead of starting from scratch, our platform helps you: ✅ Replicate and sync data across cloud and on-prem platforms ✅ Prepare clean, structured datasets for tools like IBM watsonx and other AI/ML frameworks ✅ Automate data pipelines so your models are powered by fresh, reliable inputs ✅ Avoid manual exports and brittle connections that slow down progress With no-code setup, support for Salesforce, NetSuite, MySQL, and more, and flexible deployment options, we make it easy to feed your AI tools without rebuilding your architecture. Real Results, Not Just Hype We’ve helped enterprise teams accelerate their AI readiness by months just by improving how they access and move the data they already had. That means less time wrangling spreadsheets and more time training, testing, and generating results. The Bottom Line If AI is on your roadmap, your data strategy has to come first. But that doesn’t mean ripping out systems or building new infrastructure. With the right tools, you can prep and power your AI initiatives using the systems you already trust. Ready to make your data AI-ready – without starting from scratch? Book a demo today and see how Sesame Software gives your teams full control of your data for wherever the future is moving you.

It’s been three years since ChatGPT’s public debut. This milestone seemed to bring the Turing Test within reach. Yet, rather than fearing a Skynet-style future, many of us are grappling with a more mundane problem: what exactly to do with this new toolset. Ironically, it’s often easier to tell whether you’re talking to a robot than to know if the person (or bot) on the other end is human. Personally, if the response is written in flawless Harvard-level English, I suspect it isn’t. Chat interfaces, however, are only one manifestation of AI. In a corporate context, unless they’re connected to a company’s data, their advice is often limited. As for website chatbots? I want to drive a stake through every one I’ve ever encountered. Human support staff can be disappointing at times, but chatbots manage to drive me nuts all the time. The first wave of AI projects has produced both winners and losers, as is typical with any new technology. The real challenge is that most companies still don’t know why they should use AI in the first place. End users are looking for ROI that still feels elusive, while AI vendors are wondering where the paying customers are. We see staggering valuations for AI companies. However, often the value is driven by vendor-to-vendor activity rather than actual customer demand. OpenAI’s use of Oracle Cloud Infrastructure, for example, boosted Oracle’s stock price. Other firms build on OpenAI or Grok. At some point, these giants will need more than partnerships and infrastructure—they’ll need real, paying customers. AI Adoption Challenges: From Hype to Practical ROI I started attending AI-themed trade shows in 2023. The vendors were often impressively prepared, with slick booths and bold promises to “solve all your AI problems.” But the potential customers? Many came without a clear agenda. Few had specific projects or problems in mind. They were curious to see what AI could do for them. Some came to sell consulting services. Others hoped to find ways to bring AI into their companies—often without any defined requirements. The idea of “using AI everywhere” became fashionable in some circles. However, it was both brave and, frankly, foolish. Companies were being encouraged to spend on AI “just in case” they might find a problem worth solving later. A Solution Looking for Problems If you start with a shiny new tool and then go hunting for problems to solve, you’re likely to be searching for a long time. Companies have real challenges—doing things smarter, faster, or cheaper. AI does have strong use cases: drafting customer communications, predicting revenue, automating repetitive tasks, and even performing research that would otherwise take days. But as long as organizations approach AI as a hammer looking for nails, they’ll face resistance. After all, many jobs depend on not automating certain tasks out of existence. One of my personal favorite uses of AI is research. When a conversation hits a dead end because neither person knows enough about a topic, I ask ChatGPT. In seconds, I get a solid, college-level overview—often with links to original sources. I trust it because I understand its limitations and verify the references. That’s far better than asking someone to speculate from a position of ignorance—something both humans and AI tend to do when cornered. Setting Realistic Expectations for AI Projects The real question is how to set expectations and focus on AI projects that deliver tangible results. That’s the subject of my next article: “ How to Identify AI Project Candidates in the Modern Enterprise. ” Future pieces in this series will focus on practical corporate needs—specifically, how AI can be applied to improve decision-making and deliver measurable ROI. Next Steps to Take Control of Your Data Sesame Software's data pipelines ensure information flows cleanly and consistently across systems. This provides the quality, structure, and governance AI tools need to deliver trustworthy insights. Talk to a Data Expert about building the data foundation that makes AI practical, measurable, and ROI-driven. FAQ: Data Readiness for AI Why is data readiness the first step toward effective AI? AI can only be as accurate as the data it’s built on. If your data is incomplete, inconsistent, or siloed, AI results will be unreliable. Sesame Software’s data pipelines ensure data is clean, connected, and governed—so AI tools can operate on trusted information instead of guesswork. How does Sesame Software help companies prepare for AI? Our platform streamlines data replication and integration across every environment—on-prem or cloud—creating a unified source of truth. This consistency gives organizations the reliable foundation needed for analytics, automation, and AI-driven insights. What’s the biggest barrier to AI success in most enterprises? Most AI projects stall not because of the models, but because the underlying data isn’t ready. Disconnected systems, manual data movement, and lack of governance make it impossible to scale AI with confidence. Sesame Software eliminates these barriers by keeping data synchronized and audit-ready. How can I tell if my organization is AI-ready? You’re ready when your data is unified, up to date, and governed—when insights can be trusted without constant manual cleanup. If achieving that feels out of reach, data readiness through automated pipelines is the right place to start. Found this post helpful? Share it with your network using the links below.

SANTA CLARA, CA — [Nov 3, 2025] — As data protection costs continue to rise across the enterprise software landscape, Sesame Software stands apart with a commitment to stable, predictable pricing for its Salesforce Backup and Recovery solution. While many vendors have increased rates and introduced tier-based limits, Sesame Software continues to provide enterprise-grade protection with flat annual pricing and unlimited data movement, ensuring customers can scale without surprises. “Data protection should deliver peace of mind, not budget uncertainty,” said Rick Banister, CEO of Sesame Software. “Our customers deserve a reliable solution that won’t penalize them for growth. That’s why we’ve maintained the same straightforward pricing model — no per-GB charges, no hidden fees, and no sudden increases.” Protect your Salesforce data with near real-time replication and predictable, flat-rate pricing. Sesame Software’s Backup and Recovery for Salesforce empowers organizations to safeguard their mission-critical data with near real-time replication, granular restore options, and SOC 2 Type II–certified security. Its hybrid deployment model allows enterprises to choose where data resides — on-premises, in their private cloud, or a hybrid combination — giving IT teams full control over compliance and recovery processes. In an era where most vendors are locking key functionality behind premium tiers, Sesame Software continues to prioritize accessibility and performance for all customers. The platform’s no-code interface, automated replication, and comprehensive restore capabilities make it both easy to use and powerful enough for large-scale environments. “Our goal has always been to make enterprise data management simple, scalable, and affordable,” added Banister. “We’re proud to deliver a platform that helps organizations maintain control — both of their data and their costs.” Looking ahead, Sesame Software will soon extend its trusted technology to a SaaS-based offering, providing the same reliable features and transparent pricing through a fully managed, cloud-native platform. About Sesame Software Sesame Software provides enterprise data replication and export solutions that help organizations take control of their data. View this release on PR Newswire. About Sesame Software's Salesforce Backup and Recovery Sesame Software’s Salesforce Backup and Recovery solution gives enterprises complete control over their Salesforce data — without the hidden costs or complexity common in other platforms. With near real-time replication, granular restore capabilities, and flat annual pricing, organizations can safeguard mission-critical Salesforce data while maintaining full visibility and compliance across their environment. Built for scale, performance, and simplicity, the solution ensures fast, reliable recovery from any incident — whether it’s accidental deletion, integration errors, or corruption. Engineered with SOC 2 Type II–certified security and flexible deployment options, Sesame Software lets customers decide where their data resides — on-premises, in their private cloud, or a hybrid configuration — ensuring compliance and confidence at every step. Soon, this trusted protection will also be available as a fully managed SaaS offering, bringing the same proven features, transparent pricing, and control to a streamlined, cloud-native experience.

Subscription Businesses Run on Data The subscription economy continues to outpace traditional business models, reshaping industries from software to consumer goods. Unlike one-time sales, recurring revenue depends on constant customer engagement, retention, and accurate forecasting. For companies built on subscriptions, data is no longer a back-office function, it is the fuel for survival and growth. But managing this data isn’t simple. Customer interactions often live in Salesforce, while billing, revenue recognition, and compliance reporting are handled in NetSuite. Without tight integration between the two, businesses risk creating silos that slow down insights, delay financial reporting, and leave churn risks hidden until it’s too late. The Churn Challenge Churn is the subscription economy’s biggest enemy. Even small percentages of customer loss compound quickly when revenue is recurring. The ability to spot early warning signs — declining engagement, missed payments, support escalations — can make the difference between steady growth and stagnant MRR. That requires unified, reliable data. If Salesforce and NetSuite operate in isolation, sales teams may not see financial red flags, while finance teams may lack visibility into customer sentiment. The result is an incomplete picture of customer health, and missed opportunities to intervene. Why Integration Matters Power growth with unified Salesforce + NetSuite data. Seamless integration between Salesforce and NetSuite closes this gap. When data flows in near real time between the two platforms, subscription businesses gain a single view of the customer that spans the entire lifecycle, from lead to renewal. Sales sees subscription history and payment trends without leaving Salesforce. Finance gains accurate forecasting powered by live pipeline data. Leadership has cross-system visibility into churn risk, customer lifetime value, and recurring revenue trends. This is where modern data pipelines stand apart from brittle point-to-point integrations. By replicating and synchronizing data across systems, companies get not only connectivity but also reliability, flexibility, and the ability to scale as data volumes grow. The Sesame Software Advantage Sesame Software was built to solve exactly this challenge. Our platform provides near real-time replication between Salesforce, NetSuite, and the destinations of your choice , whether that’s a cloud data warehouse, on-prem database, or data lake. For subscription businesses, this means: No blind spots between CRM and ERP Cleaner, normalized data ready for analytics and AI models Audit-ready backups for compliance and revenue recognition Flexibility to expand pipelines as new data sources come online Because Sesame Software isn’t locked into a single storage ecosystem, you also avoid the vendor lock-in that often drives up costs over time. Data can be stored where it makes the most sense — in the cloud, on-prem, or a hybrid approach. Future-Proofing the Subscription Economy As the subscription economy matures, data strategies must keep pace. Companies that succeed will be the ones that treat data as a first-class asset, not an afterthought. Seamless Salesforce + NetSuite integration is no longer optional, it’s the foundation for reducing churn, improving customer experiences, and growing recurring revenue streams. The future of data management in the subscription economy is clear: flexibility, visibility, and speed.  Businesses that invest in these capabilities today will be best positioned to thrive tomorrow. Next Steps See our full range of NetSuite and Salesforce solutions to unlock real-time insights. See how Sesame Software powers near real-time data replication  to keep your CRM, ERP, and analytics perfectly in sync. Talk to a Data Expert  about building a unified data strategy that drives renewals, visibility, and growth. Salesforce + NetSuite Integration FAQ What makes replication more reliable than traditional integration methods? Traditional integrations often move data only when triggered by specific events or schedules, creating gaps and potential data loss. Sesame Software uses automated replication that maintains a dedicated, continuously updated copy of your data. This ensures consistent performance at scale, even as data volumes grow or systems evolve — providing both reliability and flexibility for analytics and reporting. How does Sesame Software ensure Salesforce and NetSuite stay aligned? Sesame Software continuously replicates data between Salesforce and NetSuite instead of relying on point-to-point integrations. This approach keeps customer, billing, and revenue records synchronized in near real time, eliminating version mismatches and reducing the need for manual updates or reconciliation. Found this post helpful? Share it with your network using the links below.

Enterprises now manage more data than ever. This data is scattered across various apps, databases, and clouds. Traditional ETL workflows are often rigid, slow to change, and costly to maintain. Composable data pipelines offer a different path. They break data movement into small, reusable functions that you can combine, test, and scale as needs evolve. The result is faster time to value, cleaner data for analytics and AI, and greater control over where your information lives. At Sesame Software, we build pipelines with modular components that do one job well. Producers pull data from sources like Salesforce or NetSuite. Transformers normalize, enrich, or redact fields. Consumers write data to warehouses, object stores, or analytics tools. Because each function is independent, teams can prototype quickly, add custom logic, and scale without reworking the whole pipeline. Why Composable Data Pipelines Matter Now Business leaders want results fast. Composable data pipelines let you spin up proof of concepts and production flows without rewriting large jobs. When a new use case appears, you can reuse existing functions and add only what’s necessary. This approach lowers risk and shortens delivery cycles. Scaling is another significant advantage. Modern architectures rely on serverless and distributed compute to process high volumes without huge infrastructure overhead. With a modular approach, you can scale individual pipeline stages independently. This is more cost-efficient and easier to monitor. Compliance and data quality matter more than ever. Pipelines can include validation, normalization, enrichment, and PII redaction steps before any dataset reaches analytics or archives. This makes audits simpler and reduces exposure. Finally, AI readiness depends on clean, tagged, and consistent data. Composable pipelines automate wrangling, add metadata, and produce reliable training sets that improve model performance. How Composable Data Pipelines Work Our model separates responsibilities into small, pluggable pieces. Producers connect to sources such as Salesforce, NetSuite, JDBC, or third-party APIs. Reusable functions perform transformations: normalize data types, add metadata tags, remove unnecessary fields, enrich addresses, or redact sensitive values. Consumers persist results to Snowflake , S3 , SQL databases , or BI tools. Because each step is auditable and independent, you get clearer lineage and easier troubleshooting. You can substitute or update a single function without affecting the rest of the flow. This also lets teams version functions and test changes safely. Prebuilt vs Custom Functions for Composable Data Pipelines Sesame provides prebuilt functions for common tasks like string normalization, default values, and PII handling so you can move quickly. For advanced needs, you can add custom functions for AI enrichment, third-party API calls, or serverless scaling using AWS Lambda or equivalent. Mix and match prebuilt and custom functions to create pipelines tailored to your architecture. Start with low-risk, high-value flows (for example, Salesforce → Snowflake) and iterate toward richer enrichment and governance over time. Business Benefits of Composable Data Pipelines Faster proofs of concept and lower time to value. Improved data quality and audit readiness. Elastic scale without oversized infrastructure. Easier maintenance and faster feature delivery. Better datasets for analytics and machine learning. These translate to faster insights, lower operational costs, and a stronger compliance posture. Data pipelines should be flexible, auditable, and ready for what comes next. Sesame Software’s composable data pipelines help you move, clean, and govern data in near real-time. This ensures analytics and AI teams get trustworthy inputs while engineering teams benefit from simpler, safer flows. If you want a quick checklist or a short demo, click the button below. Next Steps See our full range of pipeline capabilities to design modular, auditable data flows. Learn which connectors match your architecture and scale needs. Book a demo to validate your architecture and prioritize a pilot. Download a quick evaluation checklist to share with your team. Composable Data Pipeline FAQ What are composable data pipelines? Composable data pipelines use small, reusable functions (producers, transformers, consumers) that connect to form end-to-end flows. They replace monolithic ETL with modular parts you can reconfigure for new use cases. How do composable data pipelines differ from traditional ETL? Traditional ETL is typically a single, rigid process. Composable pipelines are modular, easier to test, and faster to change — which reduces time-to-value for POCs and improves maintainability. Are composable pipelines secure and compliant? Yes. Functions can include built-in normalization, redaction, and metadata tagging so sensitive fields are handled before data reaches analytics or archives. Sesame supports GDPR/HIPAA controls and audit logs. How long does it take to implement a composable pipeline? Times vary by use case. A simple replication pipeline (e.g., Salesforce → Snowflake) can be configured in days; advanced enrichment or custom functions can take a few weeks. We help prioritize quick wins first. Can composable pipelines prepare data for AI? Absolutely. Automated wrangling, metadata tagging, and consistent formatting create high-quality training datasets for ML models. Found this post helpful? Share it with your network using the links below.

SANTA CLARA, Calif., Oct. 15, 2025 /PRNewswire/ — Sesame Software and DataNovata announced a collaboration delivering an end-to-end solution to a common problem: historical NetSuite data is costly and difficult to access for reporting, audits, and eDiscovery because maintaining live NetSuite access requires expensive licenses and fragile, time-consuming workflows. The combined offering exports and preserves NetSuite records while providing a searchable, NetSuite-like read-only user interface so teams regain fast, auditable access to historical records without ongoing NetSuite licensing. Sesame Software performs full and incremental NetSuite exports that preserve transactional records, metadata, and relational structure and provides flexible endpoints to databases or cloud object stores. Built for scale, the export process minimizes system impact and keeps archived data compliant and under customer control. Incremental exports keep archives current without disrupting production systems, and configurable endpoints to S3 or cloud databases let organizations choose storage patterns for operations and retention. Export full-fidelity NetSuite data with Sesame Software, then view, search, and report on it with DataNovata’s NetSuite-like web UI — secure, read-only access that keeps you in control. DataNovata analyzes the exported schema and generates a secure HTML5 web UI that mirrors NetSuite relationships while preventing modification through read-only access. Business users, auditors, and IT teams can search, report, and visualize historical NetSuite data in a familiar interface without touching live instances. Typical use cases include month-end reporting, regulatory audits, legal eDiscovery, and historical analytics. The solution supports role-based access control, encryption at rest and in transit, and audit logs to satisfy internal and external compliance requirements. Customers can deploy the workflow in weeks. The combined solution integrates quickly into existing environments and requires minimal training. Finance, compliance, and operations teams gain immediate visibility into historical records and the ability to run reports without the friction of live NetSuite access. “DataNovata has worked ideally with Sesame Software’s NetSuite data extracts to help customers incorporate reporting customizations in their NetSuite implementations. We welcome our ongoing collaboration with the Sesame Software team,” said Jon Sherrington, SVP, DataNovata Ltd. About DataNovata DataNovata transforms exported application databases into secure, searchable web applications for reporting and archival access. The company’s tools simplify schema analysis and create a NetSuite-like read-only experience for business users, auditors, and IT teams. About Sesame Software Sesame Software provides enterprise data replication and export solutions that help organizations take control of their data. View this release on PR Newswire. About NetSuite Data Archive Sesame Software’s NetSuite Data Archive solution enables businesses to securely export and preserve their complete NetSuite dataset—including transactional records, metadata, and relationships—while maintaining full audit and reporting capability even after offboarding from NetSuite. This ensures compliance, accessibility, and peace of mind, giving organizations lasting visibility into their financial and operational history without relying on costly NetSuite licenses. A common use case for the NetSuite Data Archive is supporting audit readiness and business continuity after a system migration. Companies often leverage it to retain access to years of financial transactions, customer data, and historical reports while transitioning to a new ERP or reducing license costs. With archived data structured for easy retrieval and compatible with analytical tools, teams can continue reporting and analysis seamlessly, long after their live NetSuite instance is closed.

For many organizations, NetSuite is the backbone of financial and operational data. But reporting often lags behind business needs. Manual exports, spreadsheet reconciliations, and long waits for insights leave teams working with yesterday’s numbers instead of today’s reality. By the time reports are ready, the moment to act has often passed. The Problem with Delayed Reporting In today’s fast-moving market, slow reporting creates costly setbacks: Missed opportunities  when shifts in demand or performance aren’t visible fast enough. Bottlenecks  as finance and operations teams chase down spreadsheets. Compliance risks  when reporting data is incomplete, outdated, or inconsistent. When decisions depend on lagging reports, organizations lose both agility and accuracy. Real-time analytics is no longer a luxury, it’s a necessity. Why Real-Time Analytics Matters Real-time analytics transforms NetSuite from a system of record into a live engine for insights. With always-current data: Executives view dashboards built on today’s numbers, not last week’s. Finance teams reconcile faster with fewer errors. Analysts focus on strategy instead of wrangling exports. In short, real-time analytics shifts teams from reactive reporting to proactive decision-making. How Sesame Software Helps Near Real-Time NetSuite Analytics Sesame Software eliminates reporting delays with near real-time replication of NetSuite data  into the analytics platform or data warehouse of your choice. Our pipelines are designed for scale and reliability, so your team can: Streamline reporting workflows  by automating exports. Centralize data across systems  for a single, trusted source. Empower decision-makers  with accurate insights at the moment they’re needed. Whether you rely on Snowflake , Redshift , BigQuery , or another warehouse, Sesame Software ensures your NetSuite data is always up to date and analytics-ready. If NetSuite is your system of record, don’t let outdated exports and spreadsheets hold you back. Real-time analytics is no longer a luxury - it’s a competitive necessity. Next Steps See our full range of NetSuite solutions to unlock real-time insights. Explore our NetSuite Export solution  for fast, accurate replication. Talk to a Data Expert  about building a real-time reporting strategy tailored to your business. Near Real-Time NetSuite Analytics FAQ Can NetSuite do real-time reporting on its own? NetSuite includes built-in reporting tools, but most reporting relies on batch exports or saved searches. These often introduce delays. For truly real-time analytics, data must be replicated into a warehouse or BI platform. What are the drawbacks of manual NetSuite exports? Manual exports create bottlenecks, increase the risk of errors, and leave teams working with outdated numbers. They also slow down reconciliation and decision-making. How does Sesame Software enable real-time NetSuite analytics? Sesame Software replicates NetSuite data in near real time into your chosen data warehouse (such as Snowflake, Redshift, or BigQuery). This ensures executives, finance teams, and analysts always work from current data. Which platforms can NetSuite data replicate into? Sesame Software supports leading cloud warehouses and BI platforms including Snowflake, Amazon Redshift, Google BigQuery, and others. This gives organizations flexibility in where they run analytics. Why is real-time analytics a competitive advantage? With live access to NetSuite data, organizations can act on insights faster—whether it’s spotting market shifts, improving financial visibility, or staying compliant. Faster decisions mean a stronger competitive position. Found this post helpful? Share it with your network using the links below.