Need to get a unified view of your data to gain visibility of your business? The problem is your data is in different systems and data formats? This article will provide you with 7 steps to integrate data from different data sources.
What Is Data Integration and Why Do It?
The term data integration is fairly self-explanatory. Today, data integration projects can consist of a wide variety of data formats. Specifically, data integration initiatives can include “big data” and a variety of data sources to include web data, social media, machine-generated data, and data from the Internet of Things (IoT).
What is important to know about integrating data is what it takes to integrate the data and why. Basically, what businesses are looking for with data integration is a single, unified view of their data. This is because the end goal of a unified data view is to provide decision makers actionable business intelligence (BI). For example, with actionable information business leaders can improve operational execution, reduce errors, save time, save labor, and overall deliver more valuable information to their business.
See Talend’s What Is Data integration? for more details.
What Are The Steps To Integrate Data For Your Business?
Data integration projects can be very simple to extremely complex to implement. Worst case, the costs, both startup and on-going costs, can be millions, if not billions of dollars. For business leaders there are several key implementation considerations. Specifically, you need to identify the size of the data sets, the quality of the source data, the number of data sources, the frequency of data updates, data transfer method, and data formats. Below are the key 7 steps to integrating your data no matter the size of your project.
1. Identify Your Data Integration Need.
Focus on the outcome. Specifically, what are the business deliverables – ex. A specific report or BI dashboard? Without a doubt, the end goal when dealing with data is actionable information. This is because actionable data is the key information to support a business intelligence (BI) report or related function where information is needed. In brief, the goal of data integration is to deliver the right data, at the right time that is complete and accurate. Lastly, the deliverables for this step is a scope document, business scenarios / use cases with visual mockups.
2. Determine What Data To Integrate.
Based on the data requirements, identify the potential data sources and providers of the data needed. Specifically, get sample data and validate it to see if it will meet the business need. Identify feasible solutions that meet the critical business needs. For many data integration projects, data is structured and can go into a relational database such as a data warehouse. In other cases, you could be dealing with more diverse data like images, PDF files, emails, etc. In this case, the data would need to go into a different type of data repository such as a data lake.
3. Choose Best Data Integration Solution.
Any solution must meet the critical business need and budget. Undeniably, data integration can easily get very complicated, so keep the solution simple. Specifically, a data integration solution must answer the “so what?” and not just be visually appealing. For instance, don’t pick a solution that has all the right buzzwords.
Furthermore, key components of the solution should include the following: integration methods (applications involved, data format, communications method, data transfer size, error catching), costs (initial and on-going), buy / build / outsource, data storage, and data management. Additionally, when selecting a particular technology to use, make sure it will still be viable five years from now. This is because you do not want to build something that will soon be obsolete.
4. Determine Available Resources.
Once you start locking into a solution, it is key that you confirm that you have the technical resources to do the job. For example, some data integration solutions require deep technical background while others may be handled outside the IT department.
5. Design A Detail Data Integration Solution.
This is where you get into specifics to include the use of application programming interfaces (APIs), subscription services, replication, middleware, etc. Specifically, you will need to identify the specific connectors, objects, and data fields for the solution. Furthermore, this includes mapping source data to target data fields, detailed design of data flows, configurations of system connections, and data storage needs. Additionally, your team will need to identify an effective data sync solution to assure that data is complete, accurate, and timely to meet business needs. Further, this includes error catching.
6. Design User Interface (UI) or Data Interface Solution.
Now that you have a plan for the data, you need to identify how the data will be presented as actionable information. This goes back to the first step, the business requirements and use case scenarios. Usually, data integration projects will result in a set of reports and dashboards. Or this could be a data interface such as an API into an existing system.
7. Build, Test, Deploy.
If you can get the data requirements locked down and the final deliverables finalized, implementation will be very budget-friendly. On the other hand, data integration projects can easily be drawn out due to involvement of multiple IT departments in different organizations.
To maximize efficiency where possible, leverage existing data interfaces such as APIs or web services (and even EDI) that IT departments know. Definitely, do not “reinvent the wheel” if you do not have to. As a result, this will minimize time and budget. Where feasible, maximize opportunities to emulate existing interfaces instead of making changes on both ends of the data interface. Also, use operational prototypes and pilot projects to get the data interfaces tested in a real-world environment. Hence, this makes for a robust data interface that is timely, accurate, and complete.
Especially when it comes to data integration projects, build the software solution in increments. Further, make maximum use of operational prototypes and pilot projects. As a result, this will enable you to avoid, where possible, elaborate test environments involving multiple IT departments and elongated testing cycles.
For more information on data integration, see Supply Chain Tech Insights’ The Best Ways To Access Data – Tech Solutions To Unlock Your Data Silos..
Writer and Supply Chain Tech Expert. Passionate about giving actionable insights on information technology, business, innovation, creativity, and life in general.