Database

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 Dirty Data Moved As-is Migrating incomplete, outdated, or inconsistent records only relocates the problem without clean and structured data.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.

XML stand for Extensible Markup Language which is easy to read by human and machine both, it is saved with.xml extension and have markup symbols to describe its file contents like HTML. XML file should be well structured and have proper opening and closing tags, it is considered as a kind of database in itself. It always start with <?xml version=”1.0″ encoding=”UTF-8″?> which contains its version and the encoding, changing the encoding will let XML to treat special character differently. JSON stand for JavaScript Object Notation, it is language independent data format and used in exchanging data between a browser and a server. It is text based representation of structured data which is based on key-value pairs. We can convert any JSON into JavaScript and vice-verse. Note: Before reading any file make sure it is not password protected. I am reading below file               tFileInputXML tFileInputXML component Reads an XML structured file row by row to split them up into fields and sends the fields as defined in the schema to the next component. tFileInputXML component has a few basic properties that needs to be check/uncheck to process data for proper formatting. In ‘Edit Schema’ we need add one column with type, ‘Document’. Then in ‘Loop Xpath query’ option we need provide tags within XML file, e.g “/”, a simple backslash means file will be read from beginning to end or we can also provide “/root/value” now under ‘mapping’ in “XPath query” we can provide similar “/” node value to fetch values of all tags. tXMLMap TXMLMap is similar to tMAP component, it is an advanced component fine-tuned for transforming and routing XML data flow (data of the Document type), especially when processing numerous XML data sources, with or without flat data to be joined. In tMap component if we already have XML file, we can import it by right click on doc and select ‘import from XML file’ the schema will be automatically created. In this we have to set loop element, in the above image loop element is ‘value’, so iteration will happen based on ‘value’ tag. tAdvancedFileXMLOutput tAdvancedFileOutputXML outputs data to an XML type of file and offers an interface to deal with loop and group by elements if needed. tAdvancedFileOutputXML can be used in place of tXMLMap. In above image ‘entidad’ column is set as loop element, so iteration will happen on this tag. ‘@id’ is called attribute which means it is sub-element of entidad and we can’t add sub-element under it whereas ‘direction’ is also sub-element of entidad but we can add sub-element under it as we can see in above image. tFileInputJSON tFileInputJSON Extracts JSON data from a file and transfers the data to a file, a database table, etc. JSON stand for ‘JavaScript Object Notation’ is a lightweight data-interchange format and It is based on the JavaScript programming language. ‘Edit schema’ will contain all columns. ‘Read By’ will have 3 options out of which we are taking ‘JsonPath’. We can check ‘Use Url’ if Json file need to be fetched from any website else keep it uncheck. ‘Loop Json query’ is appearing because we have selected ‘JsonPath’ in ‘Read By’ property above, it will have path of tabs in file, please see Json file before this. In the ‘book’ tag we have 4 attributes which needs to be extracted. tFileOutputJSON tFileOutputJSON receives data and rewrites it in a JSON structured data block in an output file. Below is the file format that we are going to convert into JSON file. ‘Name of data block’ is what comes in JSON at top, see below image. Edit schema will have all column that need to be mapped. Output JSON file: While working on Talend if in case we came across some issue which is not possible to resolve at our end we can raise it to Talend community on this link. Their team will help in solving the problem. About Girikon:  Girikon is an IT service organization, headquartered in Phoenix, Arizona with presence across India and Australia. We provide cutting-edge Salesforce consulting services and solutions to help your business grow and achieve sustainable success.   

Data visualization is the act of taking data and placing it into a visual context, such as a map or graph to bring the Information out of it. Visualization also makes it easier to detect patterns, trends, and outliers in groups of data which can define the next strategy for a business. Good visualizations should extractinformation from complicated datasets so that theinformation is clear and concise. Now the question comes which Tool can we use for a better understanding of Data? The Answer is Tableau because Tableau has more flexible deployment options compared to Visualization Tools. Tableau, along with on-premises deployment also supports cloud services as well. Tableau connects to many different data sources and can visualize larger data sets than any other BI tool can. The inbuilt AI gives it more power than any other tools, through which you only needs to drag and drop the data and Tableau Engine will display the most suitable visualization for your data, which definitely you can change and customize as per your need. Also customization is way better than any other BI as it can be formatted to the slightest detail. Tableau is very good with creating processes and calculations. For example, while creating calculations in tableau, the formula can be typed once, stored as a field and applied to all referencing that source. This makes it easier to create and apply recurring processes. Tableau’s flexibility allows users to create custom formulas that can be applied to a filter or a field. Data storytelling is also one of the unique and easy to use features in Tableau which makes it different from other. Another major thing is Apart from Real Time data, it also allows using extract for fast retrieval and display of data which can be refreshed as per the need of the user. About Girikon: Girikon is a reputer provider of end-to-end IT services including Salesforce consulting, implementation and Salesforce support. Their commitment to excellence has made them a preferred choice among their customers.