Here’s a hypothetical situation: Your leadership team is on a conference call, and the topic of conversation turns to operational reports. The head of each line of business (LOB) presents a conflicting set of insights, but each one is convinced that the findings from their analytics platform are the gospel truth. With data segregated across the LOBs, there’s no clear way to determine which insights are correct or make an informed, unbiased decision.

Data Integration Best Practices

What Do You Do?

In our experience, the best course of action is to create a single source of truth for all enterprise analytics. Organizations that do so achieve greater data consistency and quality data sources, increasing the accuracy of their insights – no matter who is conducting analysis. Since the average organization draws from 400 different data sources (and one in five needs to integrate more than 1,000 disparate data sources), it’s no surprise that many organizations struggle to integrate their data. Yet with these data integration best practices, you’ll find fewer challenges as you create a golden source of insight.

Take a Holistic Approach

The complexity of different data sources and niche analytical needs within the average organization makes it difficult for many to hone in on their master plan for data integration. As a result, there are plenty of instances in which the tail ends up wagging the dog.

Maybe it’s an LOB with greater data maturity pushing for an analytics layer that aligns with their existing analytics platform to the detriment of others. Or maybe the organization is familiar with a particular stack or solution and is trying to force the resulting data warehouse to match those source schema. Whatever the reason, a non-comprehensive approach to data integration will hamstring your reporting.

In our experience, organizations see the best results when they design their reporting capabilities around their desired insight – not a specific technology. Take our collaboration with a higher education business. They knew from the outset that they wanted to use their data to convert qualified prospects into more enrollees. They trusted us with the logistics of consolidating their more than 90 disparate data sources (from a variety of business units across more than 10 managed institutions) into reports that helped them analyze the student journey and improve their enrollment rate as a whole.

With their vision in mind, we used an Alooma data pipeline to move the data to the target cloud data warehouse, where we transformed the data into a unified format. From there, we created dashboards that allowed users to obtain clear and actionable insight from queries capable of impacting the larger business. By working toward an analytical goal rather than conforming to their patchwork of source systems, we helped our client lay the groundwork to increase qualified student applications, reduce the time from inquiry to enrollment, and even increase student satisfaction.

Win Quickly with a Manageable Scope

When people hear the phrase “single source of truth” in relation to their data, they imagine their data repository needs to enter the world fully formed with an enterprise-wide scope. For mid-to-large organizations, that end-to-end data integration process can take months (if not years) before they receive any direct ROI from their actions.

One particular client of ours entered the engagement with that boil-the-ocean mentality. A previous vendor had proposed a three-year timeline, suggesting a data integration strategy that would:

  • Map their data ecosystem
  • Integrate disparate data sources into a centralized hub
  • Create dashboards for essential reporting
  • Implement advanced analytics and data science capabilities

Though we didn’t necessarily disagree with the projected capability, the waiting period before they experienced any ROI undercut the potential value. Instead, we’re planning out a quick win for their business, focusing on a mission-critical component that can provide a rapid ROI. From there, we will scale up the breadth of their target data system and the depth of their analytics.

This approach has two added benefits. One, you can test the functionality and accessibility of your data system in real time, making enhancements and adjustments before you expand to the enterprise level. Two, you can develop a strong and clear use case early in the process, lowering the difficulty bar as you try to obtain buy-in from the rest of the leadership team.

Identify Your Data Champion

The shift from dispersed data silos to a centralized data system is not a turnkey process. Your organization is undergoing a monumental change. As a result, you need a champion within the organization to foster the type of data-driven culture that ensures your single source of truth lives up to the comprehensiveness and accuracy you expect.

What does a data champion do? They act as an advocate for your new data-driven paradigm. They communicate the value of your centralized data system to different stakeholders and end users, encouraging them to transition from older systems to more efficient dashboards. Plus, they motivate users across departments and LOBs to follow data quality best practices that maintain the accuracy of insights enterprise wide.

It’s not essential that this person be a technical expert. This person needs to be passionate and build trust with members of the team, showcasing the new possibilities capable through your data integration solution. All of the technical elements of data integration or navigating your ELT/ETL tool can be handled by a trusted partner like 2nd Watch.

Schedule a whiteboard session with our team to discuss your goals, source systems, and data integration solutions.