How to Clean, Prepare, and Move Your Enterprise Data: A Simple Guide to Data Access, Integration, and Migration
Introduction
When enterprise data lacks consistency and structure, it slows down the efficiency of the system. This unclean enterprise data gradually reduces trust and performance and compromises long-term business success. The uncleaned data affects the operational platforms and threatens revenue, strategic plans, and potential growth of an organisation. For enterprises to access and migrate data, it needs to be cleaned and prepared.
The impact of getting this right extends far beyond IT operations, empowering the entire business with the ability to meet, integrate, and migrate your enterprise data. This blog presents how to clean data and data pipelines and how to prepare data. The blog also provides an explanation of the data migration workflow and the current challenges that organisations are facing.
Enterprise data
Enterprise systems manage a lot of data in different formats—structured, semi-structured, and unstructured, which includes customer details and data from smart devices. These differences increase complications and make it difficult to handle quality data and standardization across platforms. When the data is poor or when it is hard to access, organizations suffer from system failure, unreliable analytics, and faulty reports. These issues can put the operations at risk, and they can slow the decision-making process. Ensuring clean, well-organized data is necessary because it enables the right insights, reliable automation, and efficient processes. These elements collectively enable faster, more responsive business operations.
Challenges of enterprise data
Enterprise data comprises information that is gathered from several departments, like sales, finance, HR, supply chain, and customer service. These are stored in various systems like ERP, CRM, and custom applications—each with its own rules, formats, and access settings.
The key challenges are Data Silos across business units, different formats and naming conventions, repeated or outdated information, and intricate integrations between old and cloud systems. These issues must be resolved before any major digital transformation or data migration, as clean, consistent data is critical for ensuring accuracy, minimising disruption, and achieving long-term success.
What is Data cleansing, and why is it important?
Data cleansing is the process of fixing and filtering inaccurate data. This process is the first and foremost step in any operation, especially during migration.
Migrating poor data leads to multiple problems – breaks your applications, slows down your system, and delays project deliverables. It also leads to financial and reputational damage.
During migration, data must be thoroughly checked and validated. It includes checking for consistent formats, removing duplicates, verifying entries, and correcting errors from various sources. Clean data ensures a smooth migration process and sets the stage for reliable system performance.
Upsides of Data Cleansing
- Improved productivity
- Cost optimisation
- Enhanced data consistency
- Informed decision-making
- Data security and compliance
Data Pipeline vs. Data Preparation: What’s the Difference?
The data pipeline streamlines the flow of data from application to data warehouse. It takes care of the extraction, transformation, and loading of data, making it easier to clean and prepare data for migration. By constant integration and ensuring reliability, data pipelines help in insights, upgrading data quality, and improving scalable operations. This reduces manual efforts, cuts operational costs, and sets the stage for advanced analytics and automation.
Data preparation is the process of cleansing, combining, and organising data to use it for business analytics and data applications. This process involves knowing the null values, data field mapping, enriching data, and standardising units. This process can be manual or automated, and speed and consistency are improved by modern pipeline integration using AI/ML.
Navigating the Data Migration Workflow
Data migration is not just about transferring or moving data—it’s about moving the appropriate, accurate, and efficient data with minimal business disruption. This procedure usually happens when a company moves to the cloud, upgrades software, or integrates systems. Here’s a simple breakdown of the flow:
1. Discovery
Start by discovering the data and sorting the data to be moved. Access the data and check for its quality, relevance, and stability. This feature helps identify non-migrant data, duplicate records, and hidden dependencies across platforms.
2. Cleansing and Preparation of Data
Choose the right data for migration—clean and enrich the data. Cross-check that the data is in the right structure and good format, and fill in all details where necessary. This procedure helps make sure that only clean, usable, and reliable data goes to the new system.
3. Transporting the Data
With the help of an automated platform or integrated platforms, the transfer begins. Data is transferred to match the target schema, locked in for security, or sent via different paths to see the workflow. Stakeholders are informed prior to ensure that there is minimal downtime. Keep monitoring the transferred data to check whether there are any real-time issues and take quick action.
4. Validation
After the transfer process is complete, we will test and validate the integrity and wholesomeness of the data in its new system. Once the monitoring and checking are done, we can shut down the old system to save money and space.
Blueprint – Migration plan
- Access data
- Mapping Source to Target Infrastructure
- Tools and Platforms for Data Transfer
- Transporting data
- Migration Timelines and Fallback Protocols
- Validating the migrated data
- Optimising the system
- Ensuring Compliance and Upholding Governance Policies
Data Migration – Types
- Storage migration involves moving files to another storage infrastructure to upgrade to better storage solutions, increase capacity, or shift to different platforms.
- Database migration is the process of shifting data from one database to another. E.g., Oracle.
- Application migration is the shifting of software to another platform (ERP/CRM).
- Cloud migration is the process of moving digital assets like data and applications to another cloud computing environment.
Data migration supports beneficial decision-making, long-term durability, and accurate information. It ensures a smooth flow of data across modern platforms.
Current challenges
Many companies face similar issues with their data being trapped in silos, such as Salesforce, SAP, or MongoDB. When these systems aren’t integrated, insights are not completed, and it slows down the processes.
With the right tools, enterprises can
- Connect SaaS, SQL, NoSQL, and legacy systems.
- Data is collected at one analytical layer.
- Improve cross-team decision-making and streamline operations.
Data integration not only connects systems but also makes data a shareable and secure resource.
Your Comprehensive Solution
As a trusted expert in digital transformational solutions, SquareOne harnesses Qlik’s AI-based platform to bring intelligence to full data cycle management from cleansing and preparation to integration and optimisation. They empower businesses to automatically access data, check the quality and performance, connect data, and collect datasets that support insights. Seamless decision-making, clean data pipelines, and integrated platforms are achieved when SquareOne and Qlik work together. Change your business with the powerful tools that give you faster and smarter data.
Final Thoughts
Successfully organising your enterprise data requires a clear process that starts from cleansing to migrating the data. These steps will make sure that your data is accurate and accessible, and they will also help you with operations. Businesses that invest in clean, integrated pipelines will have better speed and better customer satisfaction. The combination of innovative platforms like Qlik and dependable partners such as SquareOne enables organisations to successfully execute digital transformation and maintain leadership in an increasingly data-driven environment.
