Data Management

Financial institutions must handle a significant amount of data, including but not limited to loan records, customer histories, compliance documents, and more. The data is scattered across various systems, making it difficult for Financial Services providers to obtain consistent information. The result is operational bottlenecks, slower service, redundant efforts, and audit exposure leading to both financial, as well as reputational damage. Salesforce, and particularly its Financial Services Cloud, is a solid choice. Instead of working around old system limitations, it provides institutions with one unified platform where they can handle customer data, manage relationships, and meet regulatory requirements — all in the same place. The real-world case for digital transformation in banking is explained here, and in this blog, we will explore what a Salesforce data migration for banking specifically entails for the banking sector. In addition, sharing a practical guide on how to migrate banking data to Salesforce. 5 Benefits of Banking Data Migration to Salesforce 01 Unified Client Data Across Departments Teams get access to customer information, chat history and sales report from one platform, thus removing the issues of mistakes happening due to having data from different sources. Now they can see the same information as other members of their team with no inconsistency or inaccuracy. 02 Stronger Regulatory Compliance Capabilities Unlike legacy systems, Salesforce Financial Services Cloud migration offers built-in audit trails, control over documentation and compliance tracking. Industry regulations and KYC/AML standards are followed properly. 03 Scalable Infrastructure for Business Growth Legacy CRM systems have scalability issues. Enhancements are always very costly when new product lines are added; new clients are acquired, or the number of transactions increases. However, when integrated with a top-of-the-line CRM system such as Salesforce, there’s no need for that burden — a burden that allows institutions to expand without the platform ever being a limiting factor. 04 Customer Experience and Retention Agents have instant and accurate client information at every touchpoint, so they respond faster. Queries are resolved quickly, no delayed outreach, and the number of inaccuracies also drop, which leads to high CSAT score and the retention of the client. 05 Advanced Analytics and Reporting Financial services data migration into Salesforce lets leaders do more than just data analysis. It covers portfolio performance, service gap analysis, real‑time insights, and client behavior trends, helping them move from reactive decisions to proactive planning. How to Migrate Banking Data to Salesforce: 7 Steps to Know 1 Audit Core Banking Data It’s hard to determine what changes or fixes may need to be made when there is no direction or plan regarding what you’re going to do. With a comprehensive review you can track data categories, stores and files format as well as data quality from all the sources. At this point, deduplication and format checks, error correction and record validation occur, and it’s best to correct these errors at this time than after migration. 2 Define Scope and Data Mapping You won’t migrate everything to Salesforce. You need to decide what gets migrated and what doesn’t. There will be some gaps or inconsistencies in the source data. Resolve these issues before the banking CRM migration to prevent future challenges with data analysis. 3 Set up the Salesforce Environment Before Salesforce data migration for banking, your Salesforce ecosystem needs to be configured to receive it properly. For Salesforce banking implementation, it includes components such as custom objects or field definitions, and workflow logic should define how the system needs to work in reality. Also, offering any regulatory data structures the institution requires. 4 Run Pilot Data Migration Start with a controlled transfer of a smaller dataset as these phases move, allowing you to verify that the migration logic performs as expected. Issues discovered at this stage remain contained, making it easier to implement corrections before scaling. In addition, teams can prevent system overload since each phase transfers only a manageable volume of data, while also refining strategies based on insights gained during each step. Leveraging salesforce support services throughout the process can further enhance migration accuracy, minimize risks, and ensure a smoother transition. 5 Confirm Data Accuracy with Testing If testing verifies that quality of migrated data, then stakeholders reviewing usage, ensures it supports business processes. Errors are found and fixed before deployment, preventing rework and also developing reliable systems and boosting confidence in the migration outcome. 6 Execute Full Data Migration The migration to Salesforce is done in batches, scheduled during quieter periods to limit disruption. Salesforce migration consulting for banks guides the process, keep watch for complications, and prepare fallback options if needed. These experts apply data‑migration practices so the migration stays secure, compliant, and meets business requirements while daily operations run without disruption. 7 Optimize Post‑Migration Process Post migration, the system should be checked to ensure it runs smoothly, and the data must be reviewed regularly. Next step is to offer teams training to use it effectively. Early issues are expected, but a clear support framework ensures they are resolved quickly. This builds confidence in the environment and provides a stable base for future upgrades. Checklist Before Banking CRM Migration During a Salesforce Financial Services Cloud migration, the quality of the plan tends to predict the quality of the outcome. Challenges like data loss, budget overruns, and low user adoption are usually because of ignoring early signs of failure. Remember, getting the fundamentals right early is far less expensive than correcting them. So, here are few do’s and don’ts for a successful migration: Do’s Cleanse and standardize data before transfer. Run a pilot migration with limited records. Get stakeholders in the discussion early for validation. Document mapping rules and workflows. Train users ahead of rollout. Don’ts Don’t skip compliance checks. Don’t migrate duplicate or unused fields. Don’t ignore integration dependencies. Don’t rush without phased planning. Don’t overlook post‑migration support. Conclusion The banking sector cannot compromise on data accuracy, client trust, and regulatory compliance. This is why the decision to migrate banking data to Salesforce requires more than just technical expertise. Organizations must ensure that the migration plan does not disrupt teams, slow down operations, or create compliance risks. As discussed in the blog, banking institutions should approach the Salesforce data migration process in phases and work with qualified Salesforce migration consultants. Partnering with a salesforce development company in usa can provide the expertise needed to create an effective migration strategy that aligns with business requirements, safeguards sensitive data, and enables a smooth transition from legacy systems to Salesforce with minimal losses and operational disruptions. :root { --accent: #1a73e8; --accent-light: #e8f0fe; --text-main: #1f1f1f; --text-body: #2a2a2a; --text-muted: #6b7280; --rule: #e5e7eb; --tbl-border: #dde3ec; --bg-highlight: #f3f7ff; --bg-light: #f8f9fa; --white: #ffffff; } /* ── Body ── */ .blog-body { font-size: 17px; line-height: 1.78; color: var(--text-body); width: 100%; } .blog-body p { margin: 0 0 20px 0; } .blog-body h2 { font-size: 24px; font-weight: 700; color: var(--text-main); margin: 48px 0 14px; line-height: 1.3; } .blog-body strong { font-weight: 700; color: var(--text-main); } .blog-body ul, .blog-body ol { margin: 0 0 20px 0; padding-left: 22px; } .blog-body ul li, .blog-body ol li { margin-bottom: 8px; line-height: 1.72; } /* ── Benefits Panel ── */ .benefit-list { margin: 8px 0 36px; border: 1px solid var(--tbl-border); border-radius: 10px; overflow: hidden; } .benefit-item { padding: 22px 24px; background: var(--white); border-bottom: 1px solid var(--tbl-border); } .benefit-item:last-child { border-bottom: none; } .benefit-item:nth-child(even) { background: var(--bg-highlight); } .benefit-header { display: flex; align-items: center; gap: 12px; margin-bottom: 10px; } .benefit-num { font-size: 11px; font-weight: 800; letter-spacing: 0.06em; color: var(--white); background: var(--accent); border-radius: 4px; padding: 3px 8px; flex-shrink: 0; } .benefit-title { font-size: 16.5px; font-weight: 700; color: var(--text-main); } .benefit-item p { margin: 0 !important; font-size: 16px; line-height: 1.75; color: var(--text-body); } /* ── Step List ── */ .step-list { margin: 8px 0 36px; display: flex; flex-direction: column; gap: 0; } .step-item { display: flex; gap: 20px; align-items: flex-start; } .step-marker { display: flex; flex-direction: column; align-items: center; flex-shrink: 0; padding-top: 2px; } .step-num { width: 34px; height: 34px; border-radius: 50%; background: var(--accent); color: var(--white); font-size: 14px; font-weight: 800; display: flex; align-items: center; justify-content: center; flex-shrink: 0; } .step-line { width: 2px; flex: 1; min-height: 28px; background: var(--tbl-border); margin: 6px 0; } .step-line-hidden { width: 2px; min-height: 0; } .step-content { padding-bottom: 28px; flex: 1; } .step-title { font-size: 16.5px; font-weight: 700; color: var(--text-main); margin-bottom: 8px; padding-top: 6px; } .step-content p { margin: 0 !important; font-size: 16px; line-height: 1.75; color: var(--text-body); } /* ── Do's & Don'ts ── */ .dos-donts { display: grid; grid-template-columns: 1fr 1fr; gap: 16px; margin: 20px 0 36px; } @media (max-width: 580px) { .dos-donts { grid-template-columns: 1fr; } } .dd-col { border-radius: 8px; overflow: hidden; border: 1px solid var(--tbl-border); } .dd-head { padding: 11px 18px; font-size: 13px; font-weight: 800; text-transform: uppercase; letter-spacing: 0.06em; } .dd-col.dos .dd-head { background: #e8f5e9; color: #2e7d32; border-bottom: 1px solid #c8e6c9; } .dd-col.donts .dd-head { background: #fff3f3; color: #c62828; border-bottom: 1px solid #ffcdd2; } .dd-col ul { list-style: none; margin: 0 !important; padding: 12px 18px 16px !important; background: var(--white); } .dd-col.dos ul { background: #f9fef9; } .dd-col.donts ul { background: #fffafa; } .dd-col ul li { font-size: 15px; line-height: 1.65; padding: 7px 0; border-bottom: 1px solid var(--rule); color: var(--text-body); margin: 0 !important; display: flex; gap: 8px; align-items: flex-start; } .dd-col ul li:last-child { border-bottom: none; } .dd-col.dos ul li::before { content: "✓"; color: #2e7d32; font-weight: 800; flex-shrink: 0; margin-top: 1px; } .dd-col.donts ul li::before { content: "✕"; color: #c62828; font-weight: 800; flex-shrink: 0; margin-top: 1px; } /* ── Pull ── */ .pull { background: var(--bg-highlight); border-left: 4px solid var(--accent); padding: 16px 20px; font-size: 16.5px; line-height: 1.75; border-radius: 0 6px 6px 0; margin: 28px 0; color: var(--text-main); }

If you’ve been anywhere near enterprise data conversations lately, you’ve probably heard people casually comparing platforms that… honestly, weren’t designed for the same job in the first place. And yet, here we are. Consider Salesforce Data Cloud vs. MDM comparison—not because they’re identical, but because organizations are under pressure to manage customer data in ways older systems never anticipated. As a result, Salesforce data cloud implementation is increasingly being evaluated alongside traditional MDM strategies to support unified, real-time customer insights. Let’s break this down properly. Why This Comparison Even Exists Not too long ago, the boundaries were actually pretty well understood. MDM (Master Data Management) stayed behind the scenes, doing the kind of work most people don’t notice unless something breaks. It focused on consistency. Clean records. A single, trusted version of data across systems. Not exciting, sure. But absolutely critical. Then CDPs entered the picture — and things started shifting. Customer Data Platforms didn’t only organize data, they kinda made it usable right then. With real-time insights, immediate activation and ongoing updates across touchpoints, it turned data from something you just parked inside a system into something you actually used pretty much as it showed up. Still, getting that kind of responsiveness usually hinges on collaborating with the right salesforce consultant, not just any specialist. Ideally they can connect your CDP strategy, the data pipelines, and your customer engagement workflows in a way that turns it into measurable value in near real time. That’s really where the lines began to blur. Because now companies are asking: Do we still need MDM? Can CDP replace it? Or are we comparing apples to… slightly smarter apples? You can see why architects, marketers, and data teams end up in the same room arguing about the “right” direction. What MDM Actually Does (And Still Does Well) We shouldn’t rush to write off MDM. It solves a very specific, very real problem. At its core, MDM is about control. It creates a “golden record” by: Consolidating data from multiple systems Standardizing formats and definitions Removing duplicates Applying strict governance policies to keep data reliable Picture it like a records manager who never cuts corners. Everything labeled, verified, cross-checked. Where MDM shines Data accuracy across enterprise systems Industries where regulatory expectations are high, like banking or healthcare Managing core entities such as customer, product, or supplier records Backend system alignment But here’s the thing. It’s not built for speed, personalization, or high-frequency digital engagement. Batch jobs, overnight syncs, and heavy governance are still the norm in most MDM setups. And that’s becoming a problem. What a CDP Brings to the Table Now let’s flip the lens. A customer data platform focuses less on control and more on continuity — connecting signals across every customer touchpoint. It ingests data from web activity, mobile apps, CRM systems, email platforms, support tools — pretty much anywhere interactions happen — and brings them together into unified profiles. Not static snapshots, but continuously updated views that reflect what’s happening right now. And honestly? That matters. Because customers move fast. Expectations move faster. What CDPs are really good at Real-time or near real-time data ingestion Identity resolution across channels Behavioral tracking and event streams Audience segmentation and campaign targeting Activation into marketing, service, and analytics tools That’s where most organizations are focusing their attention now. Customer Data Platform vs MDM in Practice Instead of overanalyzing it, here’s a straightforward way to compare Customer Data Platform vs MDM: Dimension MDM CDP Core purpose Enterprise data quality and governance Customer understanding and activation Data scope Reference data: customer, product, supplier, etc. Behavioral, transactional, and interaction data Data model Canonical, structured, slower to change Flexible, event-driven, designed for journeys Processing Mostly batch, scheduled updates Streaming plus batch, close to real time Governance Strong stewardship and controls Lighter governance, more focused on agility Primary users IT, data governance, operations Marketing, customer experience, analytics, growth teams Where Salesforce Data Cloud Fits In This is where things get interesting. Salesforce Data Cloud isn’t just another CDP. It’s positioned as a broader data layer that extends CDP-style capabilities across the full Salesforce Customer 360 and beyond. Which is why you’ll hear more and more teams debating Salesforce data cloud vs MDM in architecture meetings. Data Cloud aims to deliver: Unified profiles that blend CRM data with external sources Real-time ingestion and harmonization of events and records Built-in identity resolution across channels and systems Native activation into Sales Cloud, Service Cloud, Marketing Cloud, and custom apps with the expertise of Salesforce Marketing Cloud Consultants. In simple terms, it tries to act as connective tissue between traditional CRM data, streaming data, and activation use cases. That doesn’t mean it automatically replaces your existing MDM. But it does change the conversation about what “master” customer data needs to look like going forward. The Real Question: When Does CDP Start Replacing MDM? This is where things shift from theory to reality. Organizations aren’t just comparing anymore — they’re actively evaluating when to replace MDM for some parts of the stack. And the honest answer: it depends heavily on your priorities. When CDP starts to take over We usually see CDPs taking center stage when: Customer experience is the top KPI, not just data accuracy Real-time personalization and journeys are business-critical Marketing, product, and CX teams want direct access to unified data There’s a high volume of behavioral and interaction data across channels In these situations, a traditional MDM can feel slow and rigid. It’s great at maintaining order, but less great at powering real-time decisions in the middle of a customer interaction. Where MDM still holds its ground MDM is relevant when: Regulatory and audit requirements are strict “Golden record” accuracy has financial or legal implications You manage multiple entity domains beyond customers (product, supplier, location, etc.) There are established stewardship and governance practices you can’t just bypass So CDP doesn’t walk in and shut down MDM overnight. The shift is more nuanced than that. A Simple Decision Lens for Enterprises If you’re sitting in front of a whiteboard trying to figure out the right mix, a few practical questions help frame the discussion: What’s the primary outcome we care about: governance or activation? Are we mostly managing reference data, or rich behavioral data? Who needs to use this data most? How fast do we need to react — hours, minutes, or seconds? How many legacy systems and domains are involved in our core processes? This isn’t just a technology choice. It affects org design, ownership, and even how quickly experiments can move from idea to production. How to Think About an MDM–CDP Replacement Strategy Let’s get into the “how,” because this is where things tend to get risky without a plan. If you’re exploring an MDM replacement strategy, jumping straight from legacy MDM to a CDP-only model is usually too abrupt. A phased approach tends to work better. Phase 1: Coexistence Keep MDM as the backbone for core entities and compliance Introduce CDP (or Data Cloud) for customer-facing personalization and analytics Synchronize only the data that truly needs to flow between the two Phase 2: Gradual Shift Move more identity resolution and profiling logic into the CDP/Data Cloud Let marketing, CX, and product teams rely primarily on CDP data Broaden real-time applications across journeys, campaigns, and in-app experiences Phase 3: Consolidation Reassess which governance responsibilities can be safely handled by the CDP/Data Cloud Retire or narrow the scope of MDM where it no longer adds unique value Keep MDM for cross-domain, heavily regulated, or non-customer master data if needed It’s rarely a big-bang cutover. It’s more like responsibilities shifting from one system to another over time. Where Salesforce Data Cloud Changes the Conversation With Salesforce Data Cloud in the mix, some organizations are reevaluating how much traditional MDM they need for customer-centric use cases, leveraging Salesforce Data Cloud for business success through unified customer data and real-time insights. Data Cloud can: Combine CRM master data with streaming events and external sources Run identity resolution natively across Salesforce apps Feed insights directly into flows, bots, and AI-driven recommendations That’s where questions about when to replace MDM get more concrete — especially if your CRM is already Salesforce and your teams live inside that ecosystem. A Simple Real-World Scenario Imagine a retail bank. Before CDP/Data Cloud: MDM maintains clean customer records across core banking, CRM, and billing systems Marketing works mostly off periodic data extracts and batch lists Updates propagate overnight or via scheduled jobs After introducing a CDP or Data Cloud: Behavioral signals from mobile apps, websites, and ATMs flow in close to real time The bank can trigger personalized offers during or immediately after key interactions MDM still anchors core identity and compliance, but CDP powers the “in-the-moment” layer Over time, more CX-facing use cases move onto the CDP/Data Cloud, while MDM narrows its focus to the most critical and regulated master domains. Nothing dramatic. Just steady evolution. Common Misconceptions About CDP vs MDM You’ll hear a few recurring myths in these discussions. “A CDP completely replaces MDM.” In most enterprises, they address different layers of the problem. “MDM is outdated.” It’s not outdated; it’s just focused on long-term consistency and governance rather than activation. “You’ll always need both.” Some organizations do, some don’t. It depends on domains, regulations, and long-term architecture goals. “Rolling out a CDP is quick and easy.” Integrations, data quality, and governance still require serious effort — just in a different context. Keeping these in mind helps avoid overpromising what any single platform can do on its own. The Subtle Shift in Ownership One underappreciated shift is who actually “owns” these systems. Historically, MDM was driven and owned by IT, data management, and governance teams. CDPs are often championed by marketing, digital, or customer experience leaders. That means introducing a CDP or Data Cloud isn’t just a tooling decision. It’s a change in decision rights — who can create audiences, define segments, trigger journeys, and use data in near real time. And that naturally creates some tension between governance and speed. Getting that balance right is as important as getting the architecture right. So Where Does This Leave Us? We’re not really looking at a simple “CDP replaces MDM” story. We’re looking at a redefinition of roles. In some organizations, CDPs (and platforms like Salesforce Data Cloud) will take over most customer-data-centric responsibilities: profiles, identities, and activation pipelines, often with support from a Salesforce Consulting Partner in Dallas to accelerate implementation and adoption. In others, MDM will remain the central reference layer, with the CDP acting more as an activation surface on top of it And in quite a few cases — especially where Salesforce is already strategic — the boundaries between the two will keep getting less clear over time as Data Cloud expands. Which, naturally, can feel a bit messy. But also necessary, because customer expectations and data patterns have changed faster than traditional data architectures. Final Thought Modern enterprises usually need elements of both — but not always in the same proportions, and not always with the same platform mix. MDM was designed for consistency and control. CDP was designed for insight and action. And figuring out that balance — where governance ends, where activation begins, and how Salesforce Data Cloud implementation fits into the middle — that’s where the real work (and the real advantage) shows up. :root { --accent: #1a73e8; --text-main: #1f1f1f; --text-body: #2a2a2a; --text-muted: #555; --rule: #e5e7eb; --tbl-border: #dde3ec; --tbl-row-alt: #f7f9fc; --bg-light: #f8f9fa; --bg-highlight: #f3f7ff; --white: #ffffff; } .blog-body { font-size: 17px; line-height: 1.78; color: var(--text-body); width: 100%; } .blog-body p { margin: 0 0 20px 0; } .blog-body h2 { font-size: 24px; font-weight: 700; color: var(--text-main); margin: 44px 0 14px; line-height: 1.3; } .blog-body h3 { font-size: 19px; font-weight: 700; color: var(--text-main); margin: 32px 0 10px; line-height: 1.35; } .blog-body ul, .blog-body ol { margin: 0 0 20px 0; padding-left: 22px; } .blog-body ul li, .blog-body ol li { margin-bottom: 8px; line-height: 1.72; } .blog-body strong { font-weight: 700; color: var(--text-main); } /* ── Table ── */ .tbl-wrap { overflow-x: auto; margin: 20px 0 28px; border: 1px solid var(--tbl-border); border-radius: 6px; box-shadow: 0 1px 4px rgba(0,0,0,0.05); } .af-blog-table { width: 100%; border-collapse: collapse; font-size: 15.5px; background: var(--white); } .af-blog-table thead th { padding: 13px 18px; text-align: left; font-weight: 700; font-size: 13px; text-transform: uppercase; letter-spacing: 0.04em; background: var(--bg-highlight); color: var(--accent); border-bottom: 1px solid var(--tbl-border); } .af-blog-table tbody tr { border-bottom: 1px solid var(--tbl-border); } .af-blog-table tbody tr:last-child { border-bottom: none; } .af-blog-table tbody tr:nth-child(even) td { background: var(--tbl-row-alt); } .af-blog-table tbody tr:hover td { background: #ddeaf8; } .af-blog-table tbody td { padding: 13px 18px; vertical-align: top; color: var(--text-body); border-right: 1px solid var(--tbl-border); line-height: 1.6; } .af-blog-table tbody td:last-child { border-right: none; } .af-blog-table tbody td:first-child { font-weight: 600; color: var(--accent); } /* ── Pull / callout ── */ .pull { background: var(--bg-highlight); border-left: 4px solid var(--accent); padding: 16px 20px; font-size: 16.5px; line-height: 1.75; border-radius: 0 6px 6px 0; margin: 28px 0; color: var(--text-main); }

In today’s digital age, businesses require instant access to real-time customer data. However, even after investing heavily in CRM systems, service platforms, and analytics, businesses have to deal with fragmented data, disjointed processes, wasted spend and lost revenue. This is where Salesforce Data Cloud Implementation comes to the rescue. By unifying disparate data sources into a single one, this innovative solution drives customer engagement, boosts sales and drives efficiency. However, the true value comes from how this cloud platform must be implemented and the use cases that deliver business impact at scale. What Does Salesforce Data Cloud Actually Do? Salesforce Data Cloud collates data from various Salesforce applications, mobile apps, websites, data warehouses, call centers, and more into a unified customer profile. Unlike conventional data lakes, Data Cloud is embedded within Salesforce, ensuring a single source of customer data is accessible across all Cloud platforms. Profile updates happen continuously, allowing AI models and automations to instantly act on the latest information, while insights fuel real-time actions instead of remaining static in reports. As a result, Data Cloud transforms scattered data into actionable intelligence that improves customer interactions and business outcomes. Many businesses also work with a hubspot crm consultant to align their CRM strategies and create a more connected customer experience across platforms. Why Most Data Cloud Projects Usually Fail? Despite its worth, several organizations fail to make the most of Salesforce Data Cloud for Enterprises as they approach it with restricted vision. Rather than leveraging it as an intelligence platform across the enterprise, most organizations use it only as a marketing tool, a database, or a Salesforce data cloud integration project. This approach leads to weak adoption, disconnected initiatives, and an ROI much lower than the true potential of Data Cloud. Salesforce Data Cloud Use Cases that Scale Real-Time Lead Intelligence for Sales Most sales teams rely blindly on CRM records that miss critical signals like website activity, usage of product, email engagement, support tickets, marketing communications, and business behavior. Salesforce Data Cloud brings all of these touchpoints into a continuously updated customer profile. For instance, when a prospect visits your pricing page numerous times, attends a webinar, and immediately has an open support ticket and an forthcoming renewal, Data Cloud instantly unifies this activity and surfaces it inside Sales Cloud, Einstein scoring, and lead and account records. This offers sales reps a clear view of purchasing intent, risk factors, level of engagement and upsell opportunities in one place. Smarter and Faster Customer Support Customer support teams are usually last in the line to get access to insightful customer data, though they rely on it the most. Salesforce Data Cloud provides agents a real-time view of every client, as soon as a conversation begins. When a client reaches out, the agent can see their buying history, previous interactions across various channels, subscriptions, marketing assignation, loyalty position, and product usage. This allows them to move right into solving the real issue rather than asking basic questions. At the same time, Einstein AI leverages this data to forecast risk of churn, suggest next-best actions, and suggest upsell offers in the flow of service. Since Data Cloud acts as the intelligence platform behind the entire operation — it enables quick resolutions, tailored support, and better outcomes at scale. Revenue Growth Via Cross-Sell and Upsell Most organizations, especially in financial services, have unexploited revenue within their present customer base. However, they lack the insight to identify who and when to target. Data Cloud unifies buying history, product usage, client lifecycle stage, support communications, and appointment data into a real-time view. It then identifies by default customers ready for upgrades, accounts that require other products, and users who are not fully utilizing their licenses. These segments flow directly into clouds, Agentforce, or Einstein automations, enabling teams to act on opportunities rather than searching for them. Since the segments continuously update as customer behavior changes, this approach scales far beyond static campaigns and consistently drives higher revenue for financial services organizations. Personalization Beyond Marketing For many personalization translates to something as simple as an email subject line. However, true personalization rests on behavioral data that moves across every customer touchpoint. This becomes possible by Salesforce data cloud that links actions like browsing a product, abandoning a cart, and opening a mobile app into a unified customer profile. With this shared source of truth, all the cloud platforms work from the same live data. This would enable a customer to use the email received as a reference to what they just viewed, the support agent can view their abandoned cart, the website can showcase a relevant offer, and the mobile app can instantly update. Since the data model is used across all Salesforce clouds, personalization can scale without maintaining distinct engines for each channel. Einstein and Agentforce for AI-Powered Decision Making AI is powered by the data that backs it, and Salesforce Data Cloud makes Salesforce AI truly operative. By unifying actual customer behavior across systems, Data Cloud allows Einstein and Agentforce to create tailored emails, endorse next-best actions for teams, predict churn, lifetime value, the chances of conversion, and automate workflows using updated data. Without Data Cloud, AI is confined to fragmented CRM records. And since the intelligence layer grows like other systems such as product usage, billing and support, the AI becomes more accurate inevitably, enabling decision-making to scale across the complete organization. How to Implement Salesforce Data Cloud? Begin with the Outcome Success with Data Cloud relies on strategy rather than on software. High-performing teams begin their journey with a clear, outcome-driven roadmap, defining three to five experience-focused use cases before any data is connected. This ensures every integration supports quantifiable business impact. Connect What You Need Make sure to connect just the data that right away supports your priority use cases. Make sure to focus on the sources that will instantly drive the outcomes you care about most. Create an Integrated Data Model Make sure to align products, accounts, discourses into a single model. This lays the foundation that enables Data Cloud to deliver insights throughout the business. Activate Within Salesforce Data generates value when it is used. If data isn’t driving any value, it’s simply unused potential. Expand Across Teams Once your key use cases are up and running, Data Cloud should be scaled across various channels, regions and products to burgeon its impact across the organization. Final Words Salesforce Data Cloud converts raw data into actionable insights. It empowers business heads to turn every client interaction into an instant of intuition, engagement, and revenue. Organizations that put their data to work across the entire customer journey will be at an advantage. So, if you are considering implementing this innovative platform then you must consider availing Salesforce Data Cloud Implementation Services.

Service leaders in the US are staring down a packed 2026. With customer expectations skyrocketing and tech evolving faster than ever, it’s not just about keeping up – it’s about getting ahead. We’ve all seen those headlines: budgets tight, talent scarce, and digital demands exploding. So, what service leaders should focus on? Honestly, it’s a mix of smart tech adoption, team empowerment, and ruthless efficiency. Let’s break it down into seven actionable items every operations leader needs to nail this year. 1. Embrace AI for Service Operations to Cut Response Times in Half AI for service operations isn’t some distant dream anymore – it’s table stakes. Think about it: customers hate waiting. A Gartner report from late 2025 pegged average resolution times at over 24 hours for many enterprises, and that’s just not cutting it. Here’s the thing, we’re talking predictive analytics that spot issues before they blow up, chatbots that handle 80% of routine queries (per Forrester data), and automated ticketing that routes problems intelligently. Does anybody really prefer long email chains anymore? Nah. Quick AI Starter Framework: Audit your stack – Map out where AI can plug in, like sentiment analysis on support tickets. Pilot small – Test on one channel, say email, and scale what works. Train the team – No one’s getting replaced; AI frees them for high-value stuff. Operations leaders in USA who skip this? They’ll watch competitors lap them. Kind of makes you think. Enterprise Service Management: Unifying Your Fragmented Tools Enterprise service management (ESM) is the glue holding it all together. You’ve got IT handling tickets, HR drowning in requests, and customer service juggling a dozen apps. Sound familiar? ESM platforms centralize this chaos into one dashboard – think ServiceNow or Jira Service Management on steroids. To be fair, not every org needs a full overhaul. But if your teams are siloed, you’re losing hours daily to manual handoffs. A 2025 McKinsey study showed ESM adopters slashing operational costs by 20-30%. ESM Pros vs. Old-School Silos Aspect Traditional Silos Enterprise Service Management Visibility Limited to one department Full org-wide dashboard Efficiency High handoff delays Automated workflows Scalability Breaks under growth Handles 10x volume easily Cost Hidden redundancies 25% lower long-term TCO Anyway, start by mapping your current tools. Integrate, don’t replace. You’ll thank us later. 2. Build Intelligent Service Management with Predictive Insights Intelligent service management takes AI a step further – it’s proactive, not reactive. We’re seeing platforms that forecast service disruptions using machine learning on historical data. Over 60% of Fortune 500 service teams now use this, according to IDC’s 2025 Service Operations report. You know the drill: A spike in login issues? The system flags it before calls flood in. Or it predicts agent burnout from ticket volume trends. Here’s why it matters for priorities for service leaders in 2026 – margins are thin, and downtime costs thousands per hour. Three Ways to Roll It Out: Data hygiene first – Clean your logs; garbage in, garbage out. Partner smart – Tools like Zendesk AI or Freshworks do heavy lifting. Measure obsessively – Track MTTR (mean time to resolution) pre- and post. It’s fast. And it turns customer service from being a cost center to a revenue driver. 3. Tackle Head-On: Talent and Retention Top Service leadership challenges 2026? Top of the list: keeping skilled agents amid The Great Resignation 2.0. Burnout’s real – agents handling 100+ tickets daily aren’t sticking around. Deloitte’s 2025 survey found 45% of service pros planning to jump ship. We need to flip the script. Empower teams with self-service portals so they focus on complex stuff. Gamify performance with leaderboards. And yeah, flexible shifts – remote work’s not going away. Rhetorical question: Why burn out your best people on rote tasks when AI can handle them? Short answer: Don’t. 4. Optimize Strategy Around Customer Channels Service operations strategy has to mirror how customers actually connect. Phone? Declining. Messaging? Exploding. Twilio’s 2025 data shows 75% of consumers prefer text or app chat over calls. Prioritize omnichannel: WhatsApp, SMS, email, all in one view. Integrate with CRM for context – know the customer’s history instantly. Channel Comparison: Old vs. New Channel Pros Cons 2026 Priority? Phone Personal touch Slow, expensive Low Email Detailed records Delayed responses Medium Messaging Instant, 90% open rate Less formal High You wonder why more companies don’t push WhatsApp for support. It’s cheap, global, and customers love it. 5. Leverage Tools Like the Salesforce Inspector Chrome Extension for Smarter CRM No service stack is complete without Salesforce tweaks, right? Enter the Salesforce Inspector Chrome extension – a free powerhouse for debugging and optimizing your Service Cloud setup. It lets you inspect records, export data on the fly, and spot config issues without endless clicks. Here’s the deal: Service leaders waste hours fumbling in Lightning. This extension pulls metadata, logs API calls, and even bulk exports opportunities. Perfect for auditing workflows before the big 2026 rollouts. Pro tip: Install it today. Pair with AI overlays for next-level personalization. We’ve seen teams cut setup time by 40%. 6. Prioritize Cybersecurity in Your Service Layer Cyber threats? They’re service killers. Ransomware hit service providers hard in 2025, with IBM reporting average breach costs at $4.5 million. Zero-trust models, multi-factor everywhere, and AI-driven threat detection – non-negotiable. Train agents on phishing. Encrypt tickets. And integrate service desks with SOC tools. Short para: One breach, and trust evaporates. 7. Measure and Iterate: Data-Driven Decisions Only KPIs like CSAT, FCR (first contact resolution), and NPS aren’t optional. Dashboards that update in real-time? Essential. 2026 Success Metrics Table Metric Target for 2026 Why It Matters CSAT 90%+ Direct customer loyalty gauge FCR 75%+ Cuts repeat contacts by half MTTR Under 4 hours Speeds revenue recovery Agent Utilization 85% Maximizes ROI on headcount Review quarterly. Adjust. Repeat. Final Words For service leaders in the US, 2026 is less about experimenting and more about executing with intent. The organizations that win will be the ones that align technology, people, and process around clear outcomes—not trends for the sake of trends. Whether it’s AI-driven service operations, unified enterprise service management, or smarter channel strategies, the common thread is focus. Pick the priorities that matter most to your customers and your teams, measure relentlessly, and iterate without hesitation.

Businesses who intend to use advanced AI-powered features like Salesforce Einstein and Agentforce, unified, clean, and structured are non-negotiable. Legacy systems aren’t sufficient, and they need to migrate data to Salesforce. But data migration isn’t about moving just numbers or names from one system to another. Salesforce data migration is a complex and challenging process that needs proper attention for a smooth, secure transfer without disruption to your existing processes. Poor Salesforce data migration plan leads to broken workflows, lost data, and waste of resources, therefore you must follow best practices for data migration in Salesforce. So, if you’re also wondering about the steps you need to know for a successful data migration to Salesforce or understand the issues during the process, then this blog is for you. Here, we’ll discuss steps for the Salesforce data migration plan and share tips to avoid challenges for effective Salesforce data migration services. 4 Common Failure Patterns Seen in CRM Migrations Salesforce offers a variety of benefits to businesses, and this is why they often migrate their data to it. However, there are certain common issues that make the Salesforce data migration process full of errors and costly setbacks. So, let’s understand these CRM migration failure patterns to ensure smoother adoption: 1. No Data Ownership Defined This is the most common reason for failure as when no one owns data decisions, conflicts go unresolved. Teams argue over field meaning, duplicates multiply, and migration timelines slip while everyone assumes someone else will decide. 2. Dirty Data Moved As-is Migrating incomplete, outdated, or inconsistent records only relocates the problem without clean and structured data. Therefore, Salesforce becomes harder to trust, reports lose credibility, and users quickly revert to spreadsheets. 3. Business Logic Ignored Data is migrated without understanding how teams actually sell, support, or report. As a result, fields exist, but workflows break because relationships and dependencies are never mapped or clearly defined for all. 4. Testing Treated as Optional Limited or no testing hides errors and performance issues until go-live. By the time users notice missing records or incorrect histories, rollback is no longer realistic, leading to confidence being damaged, and both reputational and monetary loss. Best Practices for Salesforce Data Migration: Tips for a Successful Implementation Here are the best practices for Salesforce data migration plan that you must follow to ensure you successfully migrate data to Salesforce: Define Scope with Impact There’s no need to transfer all the data from your previous system into the Salesforce CRM. Focus on what is needed for your present workflow, reporting and compliance requirements. Don’t move everything without any scope, in doubt, archive the data you don’t presently need. It will assist in preventing crowding of data and ensure your Salesforce CRM system is organized, clean, and efficient. Assign Data Ownership Early All Salesforce objects and significant areas require individual business owners. Without clear ownership, it’s easy to lose sight of essential data or information. This applies to all relevant stakeholders and not just tech people. A business owner must ensure that decisions concerning any conflict (data) or the relevancy of field or post-migration problems are taken fast and effectively. Audit Data Quality First Did you know poor data quality costs for organizations at least $12.9 million a year on average? So, assess the quality of your data before you start with the Salesforce data migration plan. Identify problems such as redundancy, absence of values, old information and inconsistent formatting as these impact the nature of your data. When you already know the quality of your data, you can avoid unexpected problems down the line and keep the migration process on track. Clean & Standardize Pre-Migration Process Once data is live in Salesforce, it’s so difficult to clean and make corrections, so ensure you maintain standard formats, pick-list values and naming conventions before migration. In doing so, you start with a clean uniform dataset to operate as opposed to trying to make sense of everything that has made it live. Map to Real Salesforce Usage The legacy systems have old data structures, which always show old business processes. This is why you need to ensure that during Salesforce data migration, consider how your business works now, not the way it used to be. To ensure the objective meets, you need to adjust objects or retire fields that do not meet your requirements, making sure everything on Salesforce is operating as intended. Preserve Relationships & History Ensure you keep the data relationships, activity history, and ownership information intact; any break between these leads to confusion and lack of confidence in the new system. Therefore, it’s essential that you understand how things move such as linked records, timestamps, and dependencies, and plan accordingly. Doing so, you preserve the full context of your data and can test it after it’s in Salesforce. Use Phased Migration Approach In the case of large datasets or complicated organizations, it is advisable to divide the migration/ implementation into stages. This allows you to minimize risk, learn from each phase, and record any issues at an early stage before going through a complete migration. In addition, it allows your teams time to change and to improve throughout the process. Build Validation into Process Validation should not be left to the last step; therefore, establish validation conditions, such as count checks, inter-system data comparison, and verify fields to monitor the data during migration. This will assist in having correct data all along the way as opposed to a final check which may overlook problems. Test with Real Scenarios You should test migrated data with the help of actual user cases, so perform operational tasks using the actual users such as report generation, dealing with cases, as well as forecasting. Doing so helps you identify any issues or gaps that cannot be spotted through technical testing and ensuring that the migration is suitable to be put into practice. Document Decisions & Assumptions Keep a track on decisions that you took during the migration process, such as the type of data that can be transferred and the reason behind it. Recording such vital information is a good source of references or guides for teams who may need it later to understand what was moved, what was left, and why you made a particular decision. When teams have clear knowledge of the process or decision made earlier, they can work efficiently and be more collaborative and strategic. 5 Common Salesforce Data Migration Mistakes and How to Avoid Them Migrating everything to avoid conflict: Teams often transfer all the data to avoid tough decisions, but this clutters the information. So, you should define relevant fields and criteria before you start the process and convey the same to stakeholders. Underestimating custom object complexity: Custom objects carry hidden dependencies, review workflows, validation rules, and integrations tied to them. This will help you avoid broken processes before you go-live. Ignoring reporting requirements: Data loads that overlook reporting logic result in broken dashboards. Ensure the data you need to migrate supports existing KPIs and regulatory reports before final sign-off. Rushing go-live without reconciliation: Without comparing source and target data to meet deadlines means silent data loss. Always reconcile record counts and critical fields between source and Salesforce before launching. Treating migration as a one-time task: Post-migration fixes are inevitable; you must plan such situations so that any issue or concern is timely resolved. How to Find the Right Salesforce Data Migration Expert in 5 Steps Step 1: Look For Migration-specific Experience Not every Salesforce consultant understands large-scale data movement. Ask for examples through client testimonials or case studies where they handle legacy CRM or ERP migrations with complex data models. Step 2: Assess their data strategy approach A strong expert asks about data relevance, ownership, and quality before mentioning tools. Remember, strategy-first conversations signal maturity, expertise, and lower long-term risk. Step 3: Evaluate validation and testing methods Both validation and testing are crucial to ensure your data migration to Salesforce happens without any issue or loss of data. The reliable experts give equal importance to reconciliation frameworks and automate testing, and not manual checks or assumptions. Step 4: Check collaboration with business teams Migration succeeds when technical and business teams align and aren’t scattered. Cohesiveness allows Salesforce consultants to facilitate decisions, not just execute instructions with no objective in mind. Step 5: Review post-migration support plans Once the migration is live, there will be instances where your system may face data or performance issues. In that case, you need proactive, structured post-migration support from the consultants and not disappearing to act once data is loaded. Quick Salesforce Data Migration Checklist in Phases Phase 1: Pre-migration Define migration scope and exclusions clearly Assign data owners for all key objects Audit and clean source data Finalize field mapping aligned to Salesforce usage Document assumptions and decisions Phase 2: During migration Migrate in controlled phases where possible Preserve relationships, ownership, and history Run validation checks alongside data loads Test with real business scenarios Track issues and resolutions centrally Phase 3: Post-migration Reconcile record counts and critical fields Validate reports and dashboards Address user feedback quickly Lock deprecated fields and objects Archive legacy data securely Closing Remarks on Salesforce Data Migration Salesforce CRM has completely changed the way businesses deliver digital experiences to customers. It’s more consistent, personalized, and seamless. However, this is possible because your team, especially the sales team, can extract value from customer data across multiple sources, build smart automation based on customer activity, proactively work with contacts, and manage relationships. This is why it’s essential to have a solid Salesforce data migration practice in working as poor data in CRM means lost opportunity in terms of creating a more personalized experience or contributing to your revenue growth. Hopefully this blog has given you an insight into how to build a Salesforce data migration plan, key challenges to overcome and ensure your CRM enables you to become a customer-centric organization. If the process seems overwhelming, we recommend you consult an expert Salesforce data migration service provider. These firms have certified Salesforce Consultants that would streamline the process, help you focus on your core activities as they manage the complexities of data migration in Salesforce.

Are you looking for the best Salesforce SMS app in 2026? If so, we’ve put together a list of worthy solutions that you must evaluate to simplify communication workflows and enhance customer engagement. In this guide, we compare leading Salesforce SMS apps based on features, automation capabilities, analytics, and scalability for different business needs. As SMS remains one of the most effective and personal ways to reach customers; it's time for you to leverage its full potential while combining it with Salesforce. Not just the Salesforce SMS integration makes messaging more powerful but also offers automation and ensures real-time tracking. More than that, it lets you enable two-way conversational texting while sending bulk promotional messages and delivering timely service updates. Here are the top options for Salesforce SMS app, their features, and how they streamline business communication. Consider reading throughout to learn more. How We Evaluated These Salesforce SMS Apps Native Salesforce integration Support for two-way and bulk messaging Automation and workflow capabilities Analytics and reporting depth Scalability and multi-channel support Top 7 Salesforce SMS Apps to Streamline Business Communication While Salesforce can power your Customer relationship, the right SMS app can transform the way you connect, engage and respond in real time. And the amalgamation of these two can help you make an impact in 2026; however, it depends on what you choose among these options. GirikSMS GirikSMS is a robust SMS solution for Salesforce designed to provide businesses with seamless, scalable, and intelligent customer communication. Unlike any tool/software that is generic, GirikSMS integrates deep into Salesforce to ensure that all SMS conversations are recorded as part of the customer's 360-degree journey. Beyond simple texting, it proceeds with bulk campaigns, personalized templates, and automatic workflows, assuring businesses of cut through communication noise and instant reach to customers. As a native communication engine linking interaction processes and boosting engagement, it may facilitate communication and engagement via individual text, automated notification, or bulk campaign. These capabilities reduce siloing data pairs, eliminate the need for third-party tools, and allow teams to have complete visibility on their customer conversations without leaving Salesforce. Organizations that want more than just messaging can have the arsenal of reliable, customer-first, and intelligent communication powered by GirikSMS—thus building lasting customer relationships directly within Salesforce. Well, till now, we have found only the tip of the iceberg, to explore more, check out the dynamic features within the best Salesforce SMS app. Key Features Include: Salesforce Integration GirikSMS is designed to work natively with Salesforce Sales Cloud, Service Cloud, and Marketing Cloud. This makes it log all messages under activities, providing teams with full visibility into the customer journey. User Friendly Interface The intuitive user-friendly dashboard of the best Salesforce SMS app—GirikSMS, allows the team to track replies, monitor analytics, and manage campaigns, all without the need for technical know-how. Automated Follow-Ups GirikSMS also features automated sequences triggered by client behavior and predefined events like payment reminders, case updates, and appointment confirmation. Inadequate, automatically generated sort of communication provides him/her with perk in a way of being timely, personalized, and consistent. Personalized Reminders & Alerts  GirikSMS allows an enterprise to create reminders and alerts that are relevant and meaningful to each customer depending on user data such as preferences, locations, and demographics, thereby providing greater engagement. Multi-Channel Support The tool would make it possible for you to seamlessly work with communication channels such as Facebook Messenger, WhatsApp, SMS, and so on within a single interface so that you do not have to switch between multiple platforms. Best for: Salesforce-first organizations that need a native, scalable SMS solution with automation, bulk messaging, analytics, and full customer conversation visibility inside Salesforce. 360 SMS App It is yet another Salesforce SMS app that empowers the user to communicate effectively with customers, prospects, and business partners worldwide. Using its single and bulk MMS/SMS, organizations can connect and manage communication with everyone no matter if they are individuals or in groups. It also features link building, SMS templates, and automated workflow, making it appear on our updated list of top Salesforce SMS apps. Best for: Businesses looking for a simple and reliable Salesforce SMS app to send individual and bulk messages globally with basic automation and templates. Mogli SMS Mogli SMS comes with the power of automation that helps businesses create complex text surveys to collect important data and engage customers. It even branches out significant features like chatbot, voice messages, and Text-to-Pay, making it perform seamlessly no matter what the size of organization is. Beyond traditional text messages, the platform can send MMS and WhatsApp, letting you reach international audiences with compelling text content. Best for: Companies that want advanced messaging automation, surveys, chatbots, and multi-channel support (SMS, MMS, WhatsApp) for customer engagement at scale. Twilio Twilio for Salesforce SMS enables businesses to send and receive text messages from Salesforce. It supports messaging groups of any size and helps you reach contacts and person accounts. Featured with rapid setup, 1:1 chat, automated communication flow, and group inbox, it allows businesses to not just text but track and provide accurate responses, With the power of its reporting, you can analyze deliverability, activity, and team performance like a pro. Best for: Teams that need flexible, API-driven SMS capabilities within Salesforce with strong reporting, global reach, and custom workflow control. Avochato Avochato provides efficient, effective, and effortless business messaging. It integrates well with the system you already use every day, allowing you to message directly from a Salesforce record and automatically logging all messages as activity. Using tags and filters within Avochato, businesses can easily segment audiences, thus sending personalized promotions, updates, and alerts at the right moment. Moreover, they can leverage charts and reports to evaluate clicks, replies, and open rates in real time. Best for: Sales and support teams focused on personalized one-to-one messaging, audience segmentation, and real-time performance tracking from Salesforce records. ValueText ValueText comes with one-to-one conversations, letting you text through SMS or WhatsApp from records stored in Salesforce like opportunities, contacts, and leads. With its chat console, you can manage and respond to customers’ queries like a pro. You can even use dynamic templates, send files, facilitate audio messages, set up auto responses, and much more while having this Salesforce SMS app   Best for: Businesses that require conversational SMS and WhatsApp messaging directly from Salesforce records with templates, file sharing, and auto-responses. SMS Magic SMS Magic is a well-established Salesforce SMS app that reminds and alerts users about meetings and events. It's CRM driven automated multichannel text messaging supports SMS, WhatsApp, and other messaging platforms. With features like conversational AI, intelligent routing, and advanced compliance tools, it turns out to be an ideal option for organizations that handle large-scale communication. Best for: Enterprises managing high-volume, compliance-driven communication across SMS and WhatsApp with automation, routing, and conversational AI support. To Sum it Up Sending SMS through Salesforce is not just about texting but about creating data-driven, timely, and personalized communication that actually strengthens customer relationships. From the more feature-rich solution from GirikSMS to the more versatile platform of Mogli SMS, businesses now have many options from which to choose the best solution applicable to their scale, goals, and industry needs. However, the app chosen must integrate seamlessly with Salesforce and also provide the team with a good measure of analytics, automation, and multi-channel support. If you want more than just basic texting, an intelligent and Salesforce native app like GirikSMS will provide you with the leverage needed in customer engagement, followed by communication that really speaks to its customers.

 Organizations rely on different data sources to capture information for making smart business decisions. A lot of the information gathered is for compliance purposes. Many organizations have discovered that they not just lack the right policies to capture the data but also lack a robust technology infrastructure to manage and understand the data.  Over the past couple of decades, Data has grown in volume and type, which has forced organizations to finally address the issue of dark data. This excessive amount of data has not just increased the storage cost but continue to remain unutilized. What is Dark Data? Contrary to what the name suggests, there is nothing dark in dark data neither it is scary. Organizations collect a vast amount of data to make logical decisions for their benefit but most of the collected data is never used for making a business decision, and this unutilized data is known as dark data. Where does Dark Data come from and what is its type?  Dark data could be found in log files, data archives, website log files, emails, etc. of an organization. Data is very similar to the iceberg where the visible part is the data that is being utilized whereas the data that is submerged and is invisible is the dark data. Dark Data is usually categorized into two different types. Let’s try and understand each type with an example  Type 1: For example, let’s take chat messages or customer emails, the content in the message can turn into dark data if the organization doesn’t extract the meaning from the message in a way that the data analysis tools can analyze it. Type 2: The metadata which comes along with the chat message or from the customer’s emails like the time at which it was sent, sender name, receiver name, device used to send it, location, attachments (if any), etc. become dark data when the email or message gets archived. Data for both the types reside in the databases but they are not used to derive any insights. It is stored in the database so that it can be retrieved in the future if required. Real-Life Example One of the restaurants of a famous food chain wanted to identify the reason behind the decreasing footfall. Any restaurant will typically try and collect feedback on the quality of food, the quantity of food, pricing, presentations, taste, ambiance, service, etc. There is a good chance that the primary reason behind decreasing footfall in the restaurant is due to the limited or no parking facilities. Information about the limited and no parking facility was always there with the restaurant but they never used it to identify the problem. This kind of data that is available with the organizations which they never consider to look at for any query is referred to as “dark data”. Is Dark Data available only in the unstructured data?  Dark Data can be there in both Structured Data as well as in Unstructured Data. Often unstructured data becomes dark data as organizations don’t know how to analyze the data to get the insights. However, structured data could also be a part of dark data. When data is stored in the structured format in the database but it is not being used by the organization to obtain the insights in that case stored data becomes dark data. Problems Associated with Dark Data Organizations often capture much more data than they are capable of. Most of the captured data stay in the dark because most organizations do not have the required tools and capabilities to process the data efficiently.  “According to IDC, organizations fail to analyze 90% of the unstructured data.” Most organizations don’t have access to tools that can manage and utilize all the captured data. It’s being observed that most organizations want to capture as much data as they can but they don’t have enough resources to analyze all the captured data. Organizations are looking for tools that can look inside their data and can reveal insights that can provide them with a business advantage. How can we leverage Dark Data? Drawbacks of storing dark data are often more than their benefits. Lack of data security associated with dark data could even lead to cyber-attacks, non-compliance issues, etc.  The best way to tackle dark data is by utilizing it well. It may not be easy for most of the organization to utilize all the captured data as it requires both the considerable investment of time and the money. There are some ways with the help of which organizations can reduce/use most of their dark data. Organizations should regularly audit their databases. They should eliminate such data points which are not useful for them this will eventually save a lot of space. Organizations should even try to keep their data in a structured format.  Even if the business decides to dump dark data even then they should keep the data encrypted and in a secure manner. Organizations should label their unstructured data so that it is easy for them to find in the future for analysis. Organizations should have their data retention and data disposal policies in place so that data can be retained and disposed of with ease.  Organizations should use Artificial Intelligence tools as they have capabilities to make documents discoverable through search. AI has the ability to crawl through the data to understand and classify them automatically.  Conclusion Dark Data represents unused opportunities that organizations are unable to utilize because of the investment and technology constraint. The investment required to deal with dark data is costly but the outcome is worth the investment made. If organizations opt to sit on the dark data and do nothing about it then it could eventually lead them to several risks like cyber-attacks. The key is to do something about the dark data rather than treating it to use fewer data. About Girikon Girikon is a reputed name in the IT service space with a focus on Salesforce consulting and Salesforce implementation services. Besides being a Salesforce Gold partner, the company has multiple accreditations to its credit.