/ The techniques that ensure data integration success

The techniques that ensure data integration success

Data integration is one of the most complicated and, for many businesses, the most annoying elements of a BI implementation. However, it’s the most important.

Businesses need to manage separate data integrations for each of the tools that they use. This number can easily reach the double digits, even at smaller organizations.

Your BI tool relies on data integrations to connect with your other pieces of business software. Without a stable, accurate data integration, there’s no way for your BI tool to access your business data in an automatic way.

To keep their data integrations working correctly, businesses need to manage their data integrations as a whole. The techniques and tricks businesses use to manage their data integrations are called a data integration strategy.

Businesses need a clear, consistent data integration strategy to effectively manage their data connections. Otherwise, they’ll end up with gaps and mistakes in their data, resulting from flawed, broken, or badly implemented connections.

This problem only grows as a business does. The larger a business gets and the more that it expands its operations, the more software solutions users need to connect to their BI tool, and the more data integrations need to be managed.

Start adding custom data integration solutions, on-premise data connectors, and connections to legacy systems, and it’s almost too much to handle. One simple flaw in one connector can derail a business’s entire data strategy, and without some sort of system to manage it all, they may not even be able to fix it.

 

 

Why are data integrations important?

Data integrations are the code bridges between two pieces of business software that allow them to communicate. In a business intelligence context, this generally means the solutions that businesses use to connect their business software to their BI tool.

Business intelligence tools are designed to connect to all of your software solutions, collect all your relevant data from them, and then enable data analysis with them. Without data integration, you can’t even get past the first step.

In many cases, a poorly implemented data integration can be just as harmful as having no data integration at all. With bad data integration, data can flow, but it won’t flow correctly, and information will get edited or dropped entirely.

If a business wants to make valuable data-driven decisions, it needs to make sure that the data people access is timely and accurate. That’s only possible with correctly configured and implemented data integrations.

 

Methods of data integration

There are three main techniques that businesses use to integrate their data. These techniques can be thought of as the different ‘types’ of data integration. They each have their own strengths and weaknesses.

Native integrations

The simplest and error-free method for data integration is first-party integration, or ‘native’ integration. These integrations exist between two tools that use the same data infrastructure. That makes it very easy to communicate information between those two tools.

Usually, these integrations only exist between tools owned by the same company, which is why they’re called first-party integrations. They’re powerful and effective, but very rare; businesses shouldn’t plan on these integrations making up any substantial part of their integration strategy.

Pre-built integrations

Pre-built, or ‘second-party’, integrations are the most common type of connection, and make up the core of most BI tools’ integration suite. These integrations are built by BI vendors to facilitate communication between BI tools and other common pieces of business software.

In many BI systems, users can simply download a pre-built data connector and implement it quickly. They can rely on the expertise of their BI vendor to provide them with an effective connection. These connectors are the most common ones in most integration strategies.

Custom integrations

In some cases, though, pre-built or in-built connectors don’t exist between two tools. In these cases, businesses generally have to build their own data integration solutions. These custom solutions are called custom, or ‘third-party’ integrations.

These integrations offer much more customizability and flexibility than other options, but they’re also much harder to build and implement. These sorts of integrations should only be used in cases where other integration methods won’t work or don’t exist.

 
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Data integration strategies

Businesses need clear and consistent guidelines for implementing data connectors. Otherwise, their data integrations will be put together in an ad hoc way, and data won’t get transferred correctly.

Even worse, if there are no guidelines or documentation on how data integrations were implemented in the first place, then if something goes wrong, there’s no way to know what to fix.

With clear rules on how data should be integrated, what sorts of tools should be used, and when pre-existing integrations should be reexamined, businesses can build a better data integration strategy.

Best practices for data integration

Businesses don’t need to come up with their data integration strategies by themselves. Many other businesses have already done the hard work of figuring out which data integration strategies work and which don’t.

By looking at the techniques that have already worked for other organizations, businesses can implement effective data integration strategies the first time around, instead of through trial and error.

Centralize the data integration role

At many businesses with ad-hoc integration management, there’s no employee or team that’s specifically assigned to data integration issues. That means data integration is both everyone’s job and no one’s responsibility.

Businesses don’t necessarily need to make data integration someone’s full-time job, but there should be people specifically assigned to manage data integrations. This way, when they fail, employees know who they need to go to to fix it.

Communicate about data integration needs

When multiple different teams and departments use the same business tool, they often end up each building their own data integrations for that tool. Since no one’s communicating about data needs, each person who needs data just builds a new data integration ad-hoc to get it.

This is a very easy way to overwhelm your digital resources. Instead, consider your business-wide data needs before implementing an integration and build one single integration that meets those needs. If you’re looking for data from a business tool, see if there are already existing integrations that can meet your needs before you implement a completely new one.

Have specific goals in mind

For many data integrations, users need to be very specific about what sort of data they want to access. If they just build their integration without focusing on their data needs, they’ll end up with way too much or way too little data.

This is why it’s so important to figure out your company-wide data needs before you implement any data visualization. If a company knows its data goals when it begins the data integration process, they won’t need to redo their integrations later to collect the data they’re missing.

Curate a list of your data integrations

At many businesses, there’s no central list of data integrations and integrated tools. This means there’s no way to know if a given data source is connected or not unless you specifically start looking for integrations. In these cases, it’s very hard to get a broad, holistic view of your integration strategy.

With a simple list of what tools are connected and for what purpose, it’s much easier to manage integrations in a consistent way. Using this list, employees can find integrations they need or spot places where an integration might be useful.

Integrate in simple ways

With data integrations, the simplest method to connect data is usually the most effective. The simpler an integration is, the fewer places there are where things can go wrong.

This idea extends to the data integration strategy as a whole. If a business implements a complex data integration strategy, it’s much harder to maintain and run correctly. A simpler one takes fewer resources and has fewer points of failure.

 

Building better BI with a data integration strategy

Data integrations are very important to the ongoing success of any BI strategy. Without good integrations, BI systems can’t connect business data with the users who need it.

The key to ensuring stable, effective data integrations is through a data integration strategy. When businesses deal with their integrations in a consistent way, it’s much easier to manage them.

Using best practices in data integration strategies, businesses can implement effective data integration techniques without relying on lived experience or trial and error. It allows businesses to skip the hard parts and get right to the insight.

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