Choosing Data Integration Strategy and Aligning it with your Business Objectives

By
Vladimir Kubikov
Client
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To understand data integration, imagine these two scenarios:

Scenario 1: The marketing department in a retail company is compiling its monthly reports. To do so, the team requires data from the company’s emailing system, CRM, Google Ads, Facebook Ads, and website. However, since data has been gotten from various sources, it often has different formats, and consolidating it is a headache.

Scenario 2: In retail, a company with multiple outlets needs to manage inventory, sales, and labor hours across its network. All this data needs to be brought together in one place to get a 360-degree view of the business to greatly enhance future planning and profits.

The solution? Data integration.

Without an advanced data integration system, most business operations will be chaotic, which will negatively impact revenue and overall business efficiency.

Let’s explore how to implement a data integration plan successfully.

What Is Data Integration?

Data integration is the process of combining data from different sources to a centralized destination known as a data warehouse to help obtain a unified view of the business.

Its ultimate goal is to allow data consolidation which makes a company data ready. A solid data integration strategy ensures that data platforms and tools for data analysis and creating reports can make better data-driven decisions.

In today’s competitive business world, it’s critical to derive value from data. Making sense of it through an advanced integration system is also essential.

How to Succeed Your Data Integration Strategy

Now, let’s look at tips on how to build the right data integration strategy for your business.

Choose a Data Integration Technique

Data integration is a process that can be accomplished using various methods. It’s crucial to identify which method will bring the most benefit to your business and require the least processing time. Highlighted techniques are:

ETL

ETL (Extract, Transform, and Load) involves extracting data from the sources, transforming it along the way, and finally loading it into a specified data warehouse.

ETL is best used for synchronizing several data use environments and migrating data from legacy systems.

Use cases:

  1. Companies that are merging may have multiple consumers, supplies, and partners in common. This data can be stored in separate data repositories and formatted differently. ETL works to transform the data into a unified format before loading it into the data warehouse.
  2. You are a small Ecommerce firm wanting to generate insights from customer data. If you have never invested in data integration before, ETL is a great starting point. You will find plenty of data engineers and tools that help you incorporate this technology into your organization.
  3. Your Ecommerce firm wants to perform data analysis but doesn’t want to break data governance legislation when moving potentially sensitive customer data to an external system. The Transform stage of ELT ensures data remains compliant with the law before it enters a repository like a warehouse (s a type of data management system that is designed to enable and support business intelligence (BI) activities, especially analytics).

Implementing ETL

The implementation of an ETL process requires specialized software (such as Microsoft’s SQL Server Integration Services, AINSYS, or Talend) that can extract, transform, and load data. The process typically involves the following steps:

  1. Extraction: The ETL tool extracts the required data from various source databases. These sources could be relational databases, CSV files, Excel files, or any other data storage systems. The extraction process should ensure minimal impact on the performance of the source systems.
  2. Transformation: Once the data has been extracted, it’s transformed to fit the needs of the destination database. Transformation can involve various operations such as cleaning, filtering, validating, aggregating, and integrating data. These operations ensure that the data adheres to the business rules of the target system and is suitable for analysis.
  3. Loading: The final step is loading the transformed data into the target database, usually a data warehouse. Depending on the requirements, this could be done in batches or in real-time.

Benefits of ETL

  1. Efficiency: ETL processes automate the task of gathering and transforming data, making it more efficient than manual processes. This is especially true when dealing with large volumes of data.
  2. Accuracy: ETL tools provide various validation and cleaning processes to ensure that the data being loaded into the data warehouse is accurate and high-quality.
  3. Consistency: By transforming different data formats into a unified format, ETL processes promote consistency, which is crucial for accurate data analysis.
  4. Integration: ETL enables businesses to integrate data from various sources into a centralized data warehouse. This greatly enhances the data’s usefulness for analysis and decision-making.

ELT

Another common data technique is ELT (Extract, Load, and Transform). Here, data is extracted from various sources and stored in a database (unline in the previous case, when data is transformed right away). Transformation of the data is done at a later time when needed for particular uses.

ELT is best used in high-volume data sets or real-time data use environments.

Use case: Organizations with large volumes of data. Let’s take an example of meteorological systems like weather services. They collect, collate and use large amounts of data regularly. The extract, load, and transform process allow for quicker transfers of source data.

Implementing ELT

The ELT (Extract, Load, and Transform) process is fundamentally different from the traditional ETL process in the sequence of operations. Here’s how it’s typically implemented:

  1. Extraction: The initial step of ELT is the same as ETL; data is extracted from various source systems. These sources can vary widely, from structured databases to unstructured data sets.
  2. Loading: The second step is where ELT differs from ETL. Instead of transforming the data immediately after extraction, in the ELT process, the raw data is loaded directly into the target system, which is often a data lake or a high-capacity data warehouse.
  3. Transformation: The transformation process in ELT takes place within the target system. The data is transformed as and when required for analysis. Modern data warehousing solutions are capable of handling complex transformations within the data warehouse itself.

Benefits of ELT

  1. Scalability: ELT is designed to handle big data environments. Because data is loaded before transformation, the process can handle larger volumes of data more efficiently since it saves time.
  2. Agility: With ELT, data analysts and scientists can work with raw data immediately, without having to wait for the entire data set to be transformed. This allows for more agile data exploration and quicker insights.
  3. Flexibility: Because transformation takes place within the target system, ELT offers greater flexibility. Users can decide how and when to transform the data based on their specific needs.
  4. Real-Time Processing: ELT supports real-time data processing, making it a good choice for businesses that need up-to-date insights, such as restaurants or hospitality businesses.

Change Data Capture

Change Data Capture (CDC) is a design pattern that identifies and tracks changes in a database so that actions can be taken using the change data. Implementing CDC involves several steps:

  1. Monitoring: This is the first step in CDC, where the system monitors source databases for any changes. These changes could be insertions, updates, or deletions.
  2. Capture: After identifying the changes, CDC captures the change data. The capture might involve simply noting the type of operation (like an update or a deletion) or it could involve capturing the complete before and after images of the changed data.
  3. Delivery: The captured change data is then delivered to the target systems. This could be a data warehouse, a data lake (a centralized repository designed to store, process, and secure large amounts of structured, semistructured, and unstructured data), or any other system that needs to consume the changed data.

Benefits of Change Data Capture

  1. Real-Time Data Integration: CDC supports real-time data integration by continuously capturing changes in the source databases. This allows for up-to-date data in the target systems, which is crucial for real-time analytics.
  2. Lowered System Impact: CDC minimizes the impact on source systems by avoiding batch loads. This improves the overall performance of the source systems.
  3. Historical Data: CDC allows for maintaining historical data in the target systems by keeping track of all changes. This is valuable for trend analysis and auditing (for example, Ticketmaster that tracks concert prices).
  4. Improved Data Quality: CDC ensures that the most recent changes are always reflected in the target system, improving the quality and accuracy of the data.

Data Replication

Data Replication involves creating and managing multiple copies of a dataset across a database environment. The implementation steps usually involve:

  1. Setting Up the Environment: This involves preparing the source and target databases for replication. The databases could be located in the same data center or distributed geographically. It also involves configuring the network for data transmission.
  2. Establishing a Connection: Once the environment is set up, a connection is established between the source and target databases. The connection ensures that data can be replicated in real-time or at scheduled intervals.
  3. Replicating the Data: Data from the source database is then copied to the target databases. The replication can be in one direction (unidirectional) or both directions (bidirectional), depending on the business requirements.

Benefits of Data Replication

  1. Data Availability: Data Replication ensures that data is available for use at all times. In the event of a system failure, data can still be accessed from the replicated databases.
  2. Load Balancing: By distributing data across multiple databases, data replication allows for load balancing, thereby improving system performance.
  3. Data Protection: Replication provides a means of protecting data from hardware failures, system crashes, or other unforeseen circumstances. With multiple copies of data available, data loss is minimized.
  4. Improved Decision Making: Replication helps in faster decision making as data is available across various geographical locations instantly.

Data Virtualization

Within this process, data from different systems are virtually combined to create a unified view rather than loading data into a new repository. It allows an application to retrieve and manipulate data without requiring technical details about it, such as how it is formatted or physically located.

Implementing Data Virtualization

Data Virtualization is the process of providing an abstract layer that allows clients to access data from various formats and sources in a seamless manner. Here are the steps involved in implementing data virtualization:

  1. Identify Data Sources: The first step is to identify the various data sources in your organization (for example, client data from your CRM or tasks from your task tracker). These could be structured databases, semi-structured datasets, unstructured data sources, or even real-time data streams.
  2. Create Data Abstraction: This involves creating an abstract layer that unifies all the data sources. This layer enables users to access and manipulate data without worrying about its format or physical location.
  3. Implement Metadata Management: Metadata management involves cataloging all available data, including its source, format, and relevant characteristics. This metadata forms the basis of the abstract layer, allowing users to retrieve relevant data when required.
  4. Implement Security Measures: Data security is paramount, especially when dealing with multiple data sources. Security measures should be implemented to ensure that only authorized individuals can access the data.
  5. Continuously Monitor and Update: Once the system is up and running, continuous monitoring and updating are necessary to ensure that the data remains accurate, timely, and reliable.

Benefits of Data Virtualization

  1. Improved Access to Data: Data Virtualization provides a single point of access to multiple data sources, reducing the need for data movement and storage.
  2. Real-Time Data Availability: It allows for real-time data integration and immediate access, which is crucial for businesses that rely on up-to-date information.
  3. Cost-Effective: It reduces the need for physical data storage and the associated costs.
  4. Flexibility: With data virtualization, businesses can be agile, adapting to changes quickly. New data sources can be added, and unnecessary ones can be removed without impacting the business operations.
  5. Enhanced Data Quality: It provides the ability to handle inconsistencies among different data sources, enhancing data quality and reliability.

Streaming Data Integration

This is a real-time data integration technique that implements a data pipeline to ingest, filter, transform, enrich and then store the data in a target database or analytics system to be analyzed later.

Streaming Data Integration (SDI) involves real-time data collection, transformation, and loading from various sources. Here’s a step-by-step guide on how to implement it:

  1. Identify Data Sources: Determine the data sources that will feed into your streaming data integration process. These could be web servers, application logs, IoT devices, social media feeds, and more.
  2. Choose the Right Tools: Utilize appropriate data streaming tools, such as Apache Kafka, Amazon Kinesis, Google Pub/Sub. Your choice depends on your specific use case, the types of data you’re dealing with, and your preferred cloud platform.
  3. Establish Data Pipelines: Build data pipelines to transport data from the source to the destination. Ensure these pipelines are capable of handling real-time data.
  4. Filter and Transform Data: As data flows in, it’s crucial to filter and transform it as necessary before loading it into the target system. This can involve removing unnecessary data, standardizing formats, or applying specific business rules.
  5. Monitor and Manage: Continuously monitor the data pipelines to ensure they’re functioning correctly. You’ll also need to manage and maintain the system to cater to the evolving needs of the business.

Benefits of Streaming Data Integration

  1. Real-time Insights: Streaming data integration enables businesses to analyze and react to data in real time, providing immediate insights that can be used for quick decision making.
  2. Scalability: Streaming data integration is highly scalable and can handle large volumes of data effectively. This makes it ideal for industries such as social media, finance, or IoT where data volumes can be enormous.
  3. Improved Efficiency: By automating the process of data ingestion, transformation, and loading, SDI can significantly improve the efficiency of data handling processes.
  4. Reduced Data Latency: Streaming data integration reduces data latency, ensuring that the most recent data is always available for analysis.

How to align your Data Integration Strategy with your Business Objectives

To successfully align your data integration strategy with your business objectives, you should follow these key steps:

  1. Understand Your Business Objectives: Before choosing the data integration technique, it is crucial to have a clear understanding of what your business objectives are. These objectives will define your data needs and how they should be processed. Understanding your business needs will guide you to choose the right data integration technique and tools.
  2. Assess the Data Landscape: Map out your existing data landscape. Where is your data coming from, and in what formats? How much data are you dealing with, and how quickly does it need to be processed? This assessment will help you decide the right data integration technique for your specific needs.
  3. Define Key Metrics: These metrics should be aligned with your business objectives. For example, if your objective is to improve customer satisfaction, a metric could be the reduction in customer complaints or repeat sales following the implementation of your data integration strategy.
  4. Involve Key Stakeholders: Data integration is not just an IT project but a business-wide initiative. Therefore, involving key stakeholders from across the business can help ensure that the data integration strategy meets the needs of all departments and aligns with the overall business objectives.
  5. Implement the Right Tools and Technology: Based on your data needs and business objectives, select the right tools and technologies that can support your data integration process. This may involve investing in new data integration platforms or software, or upskilling your team to handle the new technologies.
  6. Review and Iterate: Use your key metrics to track the success of the strategy and make any changes needed to better align with your business objectives.

Remember, a successful data integration strategy is not a one-size-fits-all solution. What works best for one company might not be the best fit for another. Tailor your strategy to your business’s specific needs and objectives.

How to choose the right Data Integration Tool

Selecting an effective data integration tool is pivotal for any successful business operation and will help any company grow they profits. Your choice should be guided by several factors:

  • Supported Data Sources: Every tool comes with its own set of supported data sources. It’s crucial to ensure that the tool you select can handle all necessary data extraction from your required sources.
  • Scalability: The chosen tool should facilitate growth, allowing the expansion of your data warehouse as your requirements evolve over time.
  • Infrastructure: A well-structured and efficient data integration tool is paramount.
  • Performance: Prioritize a tool that offers high-speed processing and reliable performance for your day-to-day operations.

AINSYS sets itself apart as a superior data integration tool, thanks to the following features that help create robust and efficient data pipelines:

  • Extensive Connectors: AINSYS facilitates the consolidation of your databases and files in a single location. With an impressive number of connectors, AINSYS can integrate various business applications, social media platforms, cloud and on-premise databases, spreadsheets and flat files, web services, and APIs.
  • Integrated Data Warehouse: With AINSYS, you can swiftly set up an integrated and intelligent data warehouse. This feature aids in forming relationships between different data sets and systems, thereby enabling comprehensive exploration, analysis, and reporting of all your data.
  • Integrated ETL: AINSYS aims to reduce the time analysts spend on data preparation, enabling quicker data comprehension. The integrated ETL is user-friendly, quick, and sophisticated, ensuring a seamless user experience.

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