Best Data Consolidation Guide In 2024 For Driving Data-Driven Business Growth

Don’t let big data mismanagement come in the way of impactful decision-making. Combat data silos and make insights-backed decisions using data consolidation today!

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Data consolidation is ideal for forward-thinking companies that want to leverage business intelligence and unlock the next level of their success journey. 

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Read this guide to understand the benefits, techniques, use cases, best practices, and real-world applications of data consolidation.

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What is data consolidation?

Data consolidation is when you combine data from multiple sources into a unified system and optimize it for analytics, reporting, and regulatory compliance.

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Sources include data warehouses, databases, spreadsheets, or cloud-based systems. 

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Consolidating data ensures that your data is standardized, cleansed, and organized for better analysis and decision-making. It involves gathering data, verifying its accuracy, eliminating redundancy, and standardizing the format to make it easier to use. 

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Different types of data consolidation:

Navigating your tech stack and making sense of your data becomes a walk in the park with data consolidation. Here’s an overview of the different types of data consolidation principles that are currently used by your respected peers in the industry:

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Benefits of data consolidation: Why is it important?

Without consolidating data, businesses are left with isolated data that cannot be accessed across different departments or systems. This results in data silos that can lead to inefficiencies, poor decision-making, and misalignment between teams.

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Data consolidation is helpful if you run a large or growing organization with multiple departments generating and storing data independently. 

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Here are some more reasons why data consolidation is important:

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1. Improved decision-making: When all data is centralized and accessible, outdated data cannot influence your business strategy as you have access to complete, accurate, and up-to-date information. This allows you to make more informed business decisions
2. Enhanced data quality: Data consolidation includes cleansing and standardizing data sets, eliminating duplicates, inconsistencies, and errors, and improving overall data quality
3. Operational efficiency: Consolidating data reduces the need to collect and integrate data manually. It simplifies reporting, reduces the time spent gathering information from multiple sources, and streamlines business operations
4. Cost reduction: Maintaining multiple data storage systems is costly. Data consolidation can reduce infrastructure costs by eliminating unnecessary databases and minimizing the overhead associated with managing scattered data
5. Compliance and security: In industries governed by strict regulatory standards such as healthcare, finance, etc., consolidating data ensures that data is easily traceable and secure. You can easily protect sensitive data, maintain audit trails, and meet compliance

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Data consolidation best practices:

Here are some data consolidation best practices you should follow to enhance efficiency, improve data quality, and ensure data security.

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1. Define Clear Objectives

Every successful project starts with outlining clear, measurable objectives. These might include improving data analytics, eliminating data silos, or enhancing decision-making. 

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By defining objectives, you can choose the right tools and strategies to help you achieve your goals.

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2. Choose the Right Tools

Some businesses require simple Excel data consolidation, while others need more complex tools. 

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Choose the right tool that suits your goals. The tool you choose should be able to handle your data volume and variety and ensure they can integrate seamlessly with your existing systems.

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• For small-scale projects with specific customization needs, hand-coding may be enough.
• For large enterprises looking for efficiency and scalability, an ETL software is ideal. 
• For businesses leveraging cloud infrastructure, cloud-based ELT tools are a good fit.
  
  

3. Prioritize Data Quality

Establish quality standards before you start consolidating data. Prioritize cleaning and validating data, remove duplicates, and standardize the data format to facilitate easy analysis later.

Once complete, monitor and improve data quality consistently.

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4. Plan for Data Security

Another best practice is safeguarding your data and protecting it against threats.

Data consolidation can expose your business to new security risks. Securely store your data and ensure every transaction complies with data privacy regulations. 

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Once the basic checks are in place, audit your data for potential security risks regularly. This could be once a week, once every two weeks, once in six months, or once a year – the choice is yours.

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5. Monitor and Update Your Data Consistently

To maintain the relevance and accuracy of your data, monitor and update it at a timely interval. Tools that automate updates and create links to source data can help facilitate this process.

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For example, 5X auto-updates data in real-time so you don’t have to. 

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You may want to look for similar tools in the market, but you might not find a tool that is as scalable, flexible, and cost-effective as the 5X data management tool.

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6. Leverage Automation

If your organization deals with big data, automation is key. 

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By automating the data consolidation process, you will save time and reduce errors that happen from manual interventions. Use ETL tools to automate data extraction, transformation, and loading. 

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7. Encourage Collaboration

Data consolidation is not a one-man effort, it involves teamwork. 

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Encourage collaboration between analysts, data engineers, IT staff, and business teams to ensure a smooth and successful data consolidation.

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What are data consolidation techniques?

There are several data consolidation techniques and each has its unique advantages:

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1. ETL (Extract, Transform, Load):

ETL is a traditional method for consolidating data.

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This process involves extracting data from multiple sources, transforming it into a standardized format, and loading it into a central location or database. In the transformation phase, data is cleansed and standardized, ensuring that it meets the requirements of the destination system.

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Two main approaches to ETL include:

• Custom coding: Skilled developers write custom scripts and programs to perform the ETL process. This method is flexible but time-consuming.
• ETL tools: These are specialized software tools you can use to automate and manage the ETL process. These tools often have user-friendly interfaces, pre-built connectors, and strong data-handling capabilities.

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Use case: A company like Mc. Donalds that operates in different countries and needs to consolidate sales data from its regional branches into a single data warehouse for global reporting.

2. ELT (Extract, Load, Transform):

ELT is a modern approach to data consolidation.

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Using this process, you can load data into a target system or warehouse and transform it within that system. This consolidating technique is ideal for large data sets and complex analytical queries.

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Use Case: A streaming platform extracts clickstream data, loads it directly into a data lake like Amazon S3 or a cloud warehouse like Snowflake, and performs transformation when querying the data for behavioral analytics.

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3. Data Virtualization: 

Using this technique, you can access real-time data without physically moving it from the source.

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Data virtualization avoids the complexity of data migration and allows users to access, query, and integrate data from various sources over a unified view.

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Use case: Ideal for financial institutions or banks as it can easily integrate real-time data from multiple touchpoints without moving it to a new database. Sources include customer data, transaction records, risk assessments, consumer behavior patterns, etc.

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4. Data Warehousing:

Storing data in a central database or data warehouse is the primary goal of data warehousing.

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It often involves batch processing, where data from various sources is periodically consolidated into the warehouse. This serves as a repository for analytics and reporting.

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Use case: Ideal for retail businesses that use a data warehouse to store and analyze their e-commerce, point-of-sale, and marketing data, to optimize their marketing strategies.

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5. Data Lake: 

Data lakes allow organizations to store structured and unstructured data in its raw form. Using it, you can ingest data and analyze it in this repository. 

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Use case: A media company that stores raw audio, video, and text data from various platforms in a data lake to support machine learning algorithms for audience engagement analysis.

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Data consolidation use cases:

Some of the most common applications of data consolidation include:

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• Retail and e-commerce: Consolidating sales, customer, and inventory data from multiple channels (online, in-store, and third-party platforms) to gain a unified view of performance and make informed decisions on marketing, pricing, and inventory orders
  
• Healthcare: Combining clinical data, patient records, billing information, and operational data from different systems to give healthcare providers a holistic view of patient care, improve decision-making and patient outcomes
• Financial services: Ensuring accurate financial reporting, risk management, and regulatory compliance based on the transaction data, customer data, risk profiles, and compliance data that the financial company has unified into a central repository 
• Supply chain management: Gain real-time visibility into the supply chain, improve demand forecasting, and optimize production and distribution processes based on data unified from suppliers, manufacturers, logistics partners, and retailers

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Conclusion

By understanding the importance and techniques of data consolidation, you can unlock the full potential of your data to drive growth and innovation. It also helps you improve operational efficiency, ensure compliance, and enable faster and more insightful decision-making.

As an all-in-one data management platform, 5X specializes in streamlining data consolidation and gives you the tools and expert guidance you need to centralize data, eliminate silos, and unlock actionable insights. Sign up now to try our data consolidation tool for free today!

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