Data integration methods and strategies

Data Integration Methods and Strategies
May 20, 2021 | Data Integration

Data integration methods and strategies

The ability to create massive amounts of data is mind-blowing. If only the ability to harness insights from this data kept pace with the ability to make it. Now, with exciting advancements in data integration such as DQLabs.ai, that gap is narrowing.

Our earlier blog article detailed four main reasons you need an augmented data integration tool. Data integration is defined as taking data from multiple sources and combining it to achieve a unified view. The product of the consolidated data avails users with consistent access to their data on a self-service basis. It provides a complete picture of key performance indicators (KPIs), customer journeys, market opportunities, etc. This article gives you detailed data integration methods and strategies.

Data Integration Methods

Manual data integration

The analyst owns every stage of integration, doing everything manually. The price is low here as this approach requires little support, involves just a few data sources, and you get complete control over the whole process.

Some of the benefits are:

  • Reduced cost: This technique requires little maintenance and typically only integrates a small number of data sources.
  • Greater freedom: The user has total control over the integration.

Some of the cons are:

  • Less access: A developer or manager must manually orchestrate each integration.
  • Difficulty scaling: Scaling for larger projects requires manually changing the code for each integration, and that takes time.
  • There is more room for error: A manager or analyst must handle the data at each stage.

Middleware data integration

Middleware is a type of software that acts as an interpreter between legacy systems and modern ones; it connects those applications to exchange information. This is one of the data integration methods that improve the speed of data streaming, allowing systems to connect easily.

Some of the benefits are:

  • Better data streaming: The software conducts the integration automatically and in the same way each time.
  • Easier access between systems: The software is coded to facilitate communication between the systems in a network.

Some of the cons are:

  • Less access: The middleware needs to be deployed and maintained by a developer with technical knowledge.
  • Limited functionality: Middleware can only work with specific systems.

Application-based integration

In this approach, the software will handle everything, including finding, extracting, cleaning, and integrating information from various sources. The information that was separated before and had different formats will be transferred from one point to another.

Some of the benefits include:

  • Simplified processes: One application does all the work automatically.
  • Easier information exchange: The application allows systems and departments to transfer information seamlessly.
  • Fewer resources are used: Because much of the process is automated, managers or analysts can pursue other projects.

Some of the cons include:

  • Limited access: This technique requires special technical knowledge and a data manager and analyst to oversee application deployment and maintenance.
  • Inconsistent results: The approach is unstandardized and varies from businesses offering this service.
  • Complicated setup: Designing the application(s) to work seamlessly across departments requires developers, managers, or analysts with technical knowledge.
  • Complex data management: Accessing different systems can lead to compromised data integrity.

Uniform access integration

This technique accesses data from even more disparate sets and presents it uniformly. It does this while allowing the data to stay in its original location.

Some of the advantages are:

  • Lower storage requirements: There is no need to create a separate place to store data.
  • More accessible data access: This approach works well with multiple systems and data sources.
  • A simplified view of data: This technique creates a uniform appearance of data for the end-user.

Some of the difficulties are:

  • Data integrity challenges: Accessing so many sources can compromise data integrity.
  • Strained systems: Data host systems are not usually designed to handle the amount and frequency of data requests in this process.

Standard storage integration (sometimes referred to as data warehousing)

This approach is similar to uniform access, except it involves creating and storing a copy of the data warehouse data. This leads to more versatility in the ways businesses can manipulate data, making it one of the most popular forms of data integration.

Some of the benefits include:

  • Reduced burden: The host system isn’t constantly handling data queries.
  • Increased data version management control: Accessing data from one source versus multiple disparate sources leads to better data integrity.
  • Cleaner data appearance: The stored copy of data allows managers or analysts to run numerous queries while maintaining uniformity in the data’s appearance.
  • Enhanced data analytics: Maintaining a stored copy enables the manager or analysts to run more sophisticated queries without worrying about compromised data integrity.

Some of the cons include:

  • Increased storage costs: Creating a copy of the data means finding and paying for a place to store it.
  • Higher maintenance costs: Orchestrating this approach requires technical experts to set up the integration, oversee, and maintain it.

Reasons to Have a Data Integration Strategy

Keeps up with the evolution of data

Sensors, networking, and cloud storage are cheaper, resulting in a large amount of available information. AI and ML technologies can make sense of all of that, with capacities far beyond those of humans. All that is required is to unite data from all sources, and the algorithms will work!

Makes data available

Accessible data is a major boost for your business; it’s as easy as that! Imagine that all employees in your company, or even your business partners, could access centralized data. It will be easier and encouraging for your personnel to make reports and keep all processes up to date.

Eliminating security issues

Having access to all possible types of regularly updated and synchronized information makes it easier to avail a higher level of security AI and ML solutions to analyze any dubious activity and have the chance to deal with it, or even set automated algorithms.

Improve data transparency

With a data integration plan, you can enhance all your interfaces and handle complexity while achieving maximum results and the best information delivery.

Makes data more valuable

Data integration adds value to the data. Data quality approaches are becoming more common in DI solutions; these techniques detect the issues that need fixing and improve data characteristics making it cleaner, more consistent, and more complete. The resulting datasets become more valuable than raw data because they are aggregated and calculated.

Simplifying data collaboration

Integrated and available information opens up an entirely new world of possibilities for collaboration in and out of the company. Anyone relying on your statement will have a much more effective impact on the processes with the available data in the correct format.

Fueling smarter business decisions

Organized repositories with various integrated datasets will help you to obtain an impressive level of transparency and understanding across the entire organization. Never before accessible nuances and facts about data will now be in your hands, helping you make the right moves just in time.

Conclusion

While some businesses are still producing more data than they can effectively analyze, data integration strategies are helping close that gap. As these strategies become more refined and elaborate, it can be challenging to pick the right one for your business. The stakes, however, have never been higher. The correct data integration methods and strategy can translate into insights and innovation for years to come. Consider your needs, your goals, and which type of approach matches both, so you make the best decision for your business.

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