Data Management

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 implanted in Salesforce, which means a single source of customer data is available across all the Cloud platforms. Profiles updates occur uninterruptedly; AI models and automations can immediately act on the data, and insights don’t just sit in reports. Rather, they drive real-time actions throughout the customer journey. In other words, Data Cloud transforms scattered data into actionable intelligence that influences customer interaction. 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 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 openings rather than searching for them. Since the segments endlessly update as customer behavior changes, this approach scales way beyond static campaigns and constantly drives higher revenue. 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 relationships, 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: Seamless 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.

Are you dealing with duplicate data?  Does your data not fall under exact match?  Are the duplicates in your data not consistent for an exact match?  Are you struggling with cleansing of different types of data duplicates?  If you have answered yes to most or all of the aforementioned questions then the solution to your problem is Fuzzy Matching. Fuzzy matching allows you to deal with the above mentioned problems easily and efficiently.  What is Data Matching? Data Matching is the process of discovering records that refer to the same data set. When records come from multiple data sets and do not have any common key identifier, we can use data matching techniques to detect duplicate records within a single dataset.  We perform the following steps: Standardize the dataset Pick unique and standard attributes Break dataset into similar sized blocks Match and Assigning weights to the matches Add it all up — get a TOTAL weight What is Fuzzy matching? Fuzzy matching allows you to identify non-exact matches of your dataset. It is the foundation of many search engine frameworks and it helps you get relevant search results even if you have a typo in your query or a different verbal tense. There are many algorithms that can be used for fuzzy searching on text, but virtually all search engine frameworks (including bleve) use primarily the Levenshtein Distance for fuzzy string matching: Levenshtein Distance: Also known as Edit Distance, it is the number of transformations (deletions, insertions, or substitutions) required to transform a source string into the target one. For example, if the target term is “book” and the source is “back”, you will need to change the first “o” to “a” and the second “o” to “c”, which will give us a Levenshtein Distance of 2. Additionally, some frameworks also support the Damerau-Levenshtein distance: Damerau-Levenshtein distance: It is an extension to Levenshtein Distance, allowing one extra operation: Transposition of two adjacent characters: Ex: TSAR to STAR Damerau-Levenshtein distance = 1 (Switching S and T positions cost only one operation) Levenshtein distance = 2 (Replace S by T and T by S) How to Use Fuzzy Matching in TALEND? Step 1: Create an Excel “Sample Data” with 2 columns “Demo Event 1” and “Demo Event 2”. Demo Event 1: This column contains the records on which we need to apply Fuzzy Logic. Demo Event 2: This column contains the records that need to be compared with the Column 1 for Fuzzy match. Step 2: In TALEND use the above Excel as input in the tfileInputExcel component and provide the same file again as input to the same component as shown in the diagram. Step 3: In the tFuzzyMAtch component choose the following configurations as shown in the below diagram.  Step 4: In the tMap we need to choose the following column to take an output. Demo_Events_1 MATCHING VALUE Step 5: Finally, you need to select an tFileOutputExcel component for the desired output.  In the final Extracted file, the Column “VALUE” shows the difference between the records and matches the records to their duplicate.  Conclusion: In a nutshell, we can say that the use of TALEND’s Fuzzy Matching helps in ensuring the data quality of any source data against a reference data source by identifying and removing any kind of duplicity created from inconsistent data. This technique is also useful for complex data matching and data duplicate analysis.  About Girikon Girikon is a reputed provider of high-quality IT services including but not limited to Salesforce consulting, Salesforce implementation and Salesforce support.

Girikon boasts of a strong Data Management Practice when it comes to handling CRM data. With a strong team of Data Architects, Data Specialists, ETL Experts and Data Stewards, we have successfully walked hand-in-hand with our clients in helping them define & implement custom-fitted Data Management Strategies. We aim at CRM deployments that are high performing, scalable and adhering to security protocols, data privacy and third-party compliances that matter in your industry like Payment Card Industry (PCI), the Health Insurance Portability and Accountability Act (HIPAA) etc. We have extensive experience in Data Extraction, Transformation and Loading (ETL) data from a wide variety of sources including legacy applications, ERP systems, CRMs & other web content, Standard relational databases, NoSQL Database (MongoDB), on premise/cloud-based applications, Files (e.g. XML, Excel, CSV, flat files) and web service APIs. Our Enterprise Data Integration skills extends to cutting-edge ETL tools including Talend, Informatica etc. and support delivery of reliable data integration solutions to our clients across the globe. Girikon’s Data Services include: Data Integration Services using Talend/Informatica – Girikon’s team of experts provide scalable data integration and data quality solutions for integrating, cleansing and profiling of all kinds of corporate data using Talend & Informatica. Master Data Management Services – Our MDM services include consolidation of data across various businesses in an enterprise using Talend or otherwise. We help create a single “version of the truth” for our customers. Application Integration Services – Using Mulesoft, we specialize in providing a common set of application integration tools to build a service-oriented architecture, to connect and manage services in real-time. Data Preparation Services – These services include manipulation of data into a form suitable for further discovery, visualization, processing and enrichment. Data Migration from various orgs in Salesforce – We have successfully completed several enterprise-wide business consolidation projects for our customers. Along with the Salesforce system development to meet the required business needs, we have gained thorough experience in migration of the related Data to enable synchronized business. Data Migration from different CRMs like Sugar, MS Dynamics to Salesforce – In addition to inter-org migration of Data within the Salesforce environment, we are also adept in migrating data from other CRMs like Sugar, MS Dynamics etc. to Salesforce. Data Stewardship Services – Of late, we have seen a surge in demand for Data Stewardship services requiring resources to be responsible for maintenance and quality of data required throughout the organization. By scaling up to serve the needs of our existing accounts in these areas, we now have developed a dedicated team of Data Stewards who are ready to become custodians of your organizations data in a way that would facilitate your growth. End-to-End ETL (Extract, Transform and Load) Services – While as mentioned above, we can take up activities in parts if that is the business need, what we exceptionally excel at is end-to-end ETL processes. Using Talend, Informatica etc., we would love to help you to eliminate the silos in your business, bring in data from multiple sources and Load to Salesforce for a consolidate view resulting in good, well-analyzed decision making. How Girikon, as a Salesforce Consulting Partner Helps Organizations looking at any kind of Data Services in relation with Salesforce can reach out to Girikon for assistance with consulting, design, execution and training in the above mentioned areas and rest assured of quality deliverables. ETL – Extract data from multiple, varied sources, transform it as required to meet the need and then Load data to Salesforce (Tools – Talend, Informatica etc.) Data preparation (or data preprocessing) – We can help prepare and deliver clean, usable data for use as per business requirements Data Stewardship – Girikon’s Data Stewards possess the required expertise & experience to be responsible custodians of your business data’s quality & maintenance CRM Master Data Management & Data Migration – We are experts in making your data much more usable in a very cost efficient manner. We have our in house de-dup & data merging application which makes the process much more simpler than it would be otherwise. Any specific Training & Support – Girikon’s Data consultants have so much exposure and experience of varied systems, situations & solutions that they would love to share some of the knowledge gained with your teams to bring in various perspectives. Additionally, we can also help with specific processes and tools related trainings.