Analytical data integration is the process of connecting disparate data for analysis.Why is Data Integration Important for Business Analytics?Data is the most critical asset an organization owns. In a blog titled, “Top Trends in Data and Analytics for 2021,” Anayltics8 CTO Patrick Vinton noted, “If you aren’t embracing data-driven decision making and using your data as a strategic asset, you’re going to be left behind.” Most organizations have multiple data sources that they use in isolation for a variety of different reasons. But businesses are not able to maximize the value in their data when the data is siloed. Siloed data poses several challenges including data quality issues, information out of context, and duplicate efforts that waste valuable time and resources. Breaking down data silos allows users to add context and perspective to data which makes it much more valuable.Data integration enables your organization to meet business demands, save time and money, reduce errors, increase data quality, and deliver more valuable data to your business users, who in turn can shift their focus to provide valuable analysis.What is the Best Data Integration Approach?Every organization is going to have unique data integration needs, even if they share the same source systems, because they have varying business needs. It is critical that organizations view their data integrations as business solutions, not just technical solutions.A data integration solution allows an organization to strategically determine how users access data and makes it easier to add context where it matters most. The right approach to data integration helps answer the questions who, what, where, how, and why so that users translate the data into something more meaningful and actionable. Depending on where you expect your users to access data, here are a couple of approaches you can take with a data integration solution:Persistent staging layer/base layer: You can integrate your data into a centralized location such as a data lake or a persistent staging layer in a data warehouse. The benefits of using a data lake for data integration is that you can replicate your data as-is and store it in one place for ease of access and clear lineage. In this case, the data is not highly governed in terms of business rules and how everything fits together; the point is you’re allowing your business users to figure it out and add context at the point of analysis.Transformation layer: For a more governed approach, you can integrate your data into a dimensional data warehouse. The main difference in this situation is that your data will be transformed to combine disparate sources together into a business context. This requires business users to understand the logic and governance around the data before they can optimize it for analytics. A data transformation layer is very important because it’s establishing the rules and adding context, and then translating that data into information in a reliable, consistent, governed way.Having a two-tiered approach where you have a staging layer for your data sources before the data warehouse layer will give you the best of both worlds. It’s important to define, however, who will use each layer and for what purpose. Business requirements should determine the best way to proceed with a data integration solution.What Are the Best Practices for Implementing A Data Integration Solution?Data integration should not be a bottleneck to your business operations. Your north star should be that your data architecture always supports the ability of your people to access data. When done correctly, data integration provides a ton of value to your organization. Here are some of our data integration best practices:Build the business case: You need to define the “why” before building a data integration solution. It is not enough to say that you want to reduce risk and increase efficiency by reducing manual touch, even though that is a real benefit. Everyone working on the integration needs to know what business problem it will solve, and what the true value of the integration is in business terms. Knowing why an integration is necessary will affect all the little decisions that need to be made to meet your current and future needs.Let the principles guide the process: When you’ve answered the “why”, then you can begin to think about the how. A common mistake is over-engineering a data integration solution. When you don’t identify the “why”, you run the risk of making the solution too complicated or too simple where it is not scalable, secure, or stable. Having principles for data integrations helps define a baseline of expectations and will help guide technical decisions made along the way.Some examples of principles are that your data integration solution should always protect against data loss, should always have a component of automation, and should always reduce risk. If an integration does not meet your principles, you need to go back to the drawing board. Another principle should be to manage your metadata. Metadata allows you to keep track of what happens to your data through an integration, what was sent and where, and helps identify issues along the way. It helps you understand areas of improvement, measure data quality, and mitigate risk. You can leverage similar metadata logging processes and patterns across data integrations to speed up design. This prevents developers needing to recreate the wheel every time you have a new interface.Assign roles for accountability: You want to have clearly assigned roles for a data integration—a stakeholder who owns the integration, as well as a steward who manages the day-to-day of a data integration solution. Having stakeholders who will be held responsible for whether the solution is working and a steward who is responsible for maintaining it will ensure that it is successful in the long run.Select the right tools: There are lots of options when it comes to selecting the right tools for data integration. Analytics8 experts put together some best practices on how to find the right solution, but ultimately it comes down to understanding your business needs and business requirements to pick tools that you can grow into.As data continues to play a critical role in decision making, it becomes more critical for business leaders to enable the paths data takes to do so.