Are you struggling to gain insight into your business due to scattered data across various systems, data silos, and formats? Well, put your mind at ease! In this article, I’ll explain a 7-step process that you can use to seamlessly integrate data from diverse sources, giving you that unified view that you need.

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 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.

What Are The Steps To Integrate Data For Your Business?

Data integration projects can be very simple or extremely complex to implement. Worst case, the costs, both startup and on-going costs, can be millions 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 integrate data no matter the size of your project.

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1. Identify Your Data Integration Need.

Here, you focus on the outcome. Specifically, what are the business deliverables. For example, is it 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. Next, 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.

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Without a doubt, a Data integration solution must meet a critical business need and budget. Moreover, data integration can easily get very complicated, so keep the solution simple. Specifically, a data integration solution must answer the “so what?”. For instance, don’t pick a solution that has all the right buzzwords, pick a solution that will actually integrate data that you need in a cost-effective manner.

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. For more discussion on integration methods, see my article The Best Ways To Access Data – Tech Solutions To Unlock Your Data Silos.

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 a deep technical background while others may be handled outside the IT department. For more tips on IT outsourcing, see my article, Outsourcing IT Services – Best Advice On The Advantages And Risks.

5. Design A Detail Data Integration Solution.

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This is where you get into specifics to include the use of application programming interfaces (APIs), subscription services, replication, middleware, etc. Also, 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.

Also, you need to make sure that the data to be transmitted will be understandable to the receiving system. This is called semantic interoperability. In this case, you need to work with your business sponsor and their counterpart to make sure the data is understandable. Many times, this means having both a good business glossary and data dictionary to assure what is sent is understood. For a more detailed discussion see my article, Semantic Digital Interoperability: This Is The Ultimate Way To Make Supply Chains Seamless.

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. In many cases, data integration projects will result in a set of reports and dashboards. On the other hand, 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 fairly 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. Especially when it comes to large 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. Hence, this makes for a robust data interface that is timely, accurate, and complete.

More References.

See Alexsoft’s Data Integration Approaches for more details on how to implement a data integration project. Also, see Talend’s What Is Data integration?.

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Need help with an innovative solution to make your supply chain systems work together? I’m Randy McClure, and I’ve spent many years solving data interoperability and visibility problems. As a supply chain tech advisor, I’ve implemented hundreds of successful projects across all transportation modes, working with the data of thousands of shippers, carriers, and 3rd party logistics (3PL) providers. I specialize in proof-of-concept and operational pilot projects for emerging technologies. If you’re ready to modernize your data infrastructure or if you are a solution provider, let’s talk. To reach me, click here to access my contact form or you can find me on LinkedIn.

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