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The How-To Guide to Data Migration, Integration & Everything In-Between

Publication date: 7 December 2021

Data integration is the process of combining data from different sources into a single database or data warehouse. This is often done to improve data quality, data analysis, and support decision-making. 

On the other hand, data migration is the process of moving data from one location to another. This may be necessary when switching data systems, updating data structures, or assembling data from several different data sources.

Both data integration and data migration are necessary for businesses to thrive. In this article, we explore the challenges of both data integration and migration, as well as how to resolve them.

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Most Common Data Integration Challenges

Data integration can be a complex process, and businesses often encounter the following challenges:

  • Data quality: data is often incomplete or inaccurate, making it difficult to make informed decisions.
  • Data latency: data is not always up-to-date, making it difficult to respond quickly to changes in the market.
  • Data variety: data is often in different formats, making it difficult to combine and analyze.
  • Data volume: data is often large and unwieldy, making it difficult to process and store.

How to Resolve Data Integration Challenges

There are several ways to resolve data integration challenges:

  • Data quality improvement: data cleansing, data enrichment, and data scrubbing can improve data quality, increasing data accuracy and enabling data-driven decisions.
  • Data latency resolution: data integration tools can combine data from multiple data sources, making it faster to create a single source of truth.
  • Data variety resolution: data integration tools can combine data from different sources in a common schema, reducing the number of formats required for data storage. Data warehouses also reduce data variety, as data is stored in a single place.
  • Data volume reduction: data warehouses can compress data, store data at lower granularity (i.e., summarizing data), and incorporate data storage optimization strategies, improving data processing efficiency.

Most Common Data Migration Challenges

Whether you are migrating data from a small or large number of sources, you might encounter some of the following challenges:

  • Data complexity: data is often spread out across multiple systems, making it difficult to consolidate.
  • Data volume: data is often large and unwieldy, making it difficult to move.
  • Data accuracy: data may be inaccurate or incomplete, making it difficult to use for decision-making.
  • Data timeliness: data may be outdated, making it difficult to use for decision-making.

How to Resolve Data Migration Challenges

Here are some ways to resolve the above-mentioned data migration challenges:

  • Data complexity resolution: data profiling can simplify data and make it easier to consolidate into a single system.
  • Data volume reduction: data compression and data deduplication can reduce the size of data, making it easier to move.
  • Data accuracy improvement: data scrubbing can improve data accuracy, ensuring that data is ready for decision-making.
  • Data timeliness improvement: data staging can ensure that data is up-to-date for data use.

The data integration and data migration processes can be complex, but data availability is essential to business success. With the right tools, managing data in a single system or from multiple sources can quickly become easier.

At HICO-Group, we implement state-of-the-art business intelligence concepts that are customized for your company to help you build future-proof data solutions.

Are you looking to transform the way your organization handles data? Whether you are building a data-driven enterprise from scratch, improving existing KPIs, or still trying to connect the dots, we are the team of business intelligence experts in your corner.

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