Conceptually, data integration sounds like a simple task: taking data from multiple sources and combining them to help inform business decision-making. This idea has existed since enterprises have been using datasets.
However, as data continues exploding in both volume and complexity, enterprises can no longer rely on manual integration processes. Instead, they must use the right integration tools for the job – which is complicated by the myriad vendors and solutions, each with their own purpose-built capabilities.
To properly tackle data integration, business leaders must be clear on what they hope to achieve with a data integration strategy. Without a solid view of goals and desired outcomes, businesses can run into issues like duplicate data, data governance, and compliance issues. After all, not all data is created equal.
To avoid this, we will examine how businesses can mitigate some common data integration challenges and kickstart their data integration strategy.
Eliminate any data silos. Businesses need to have solutions in place that can sync all datasets from across the company, creating a master record of data that spans all of the different systems within an organization. This requires the use of a data integration tool that can ensure data quality and possesses some workflow capabilities to automatically check for duplications. These tools should easily locate and remove duplicates, standardize formats, and share data from one system to another.
Leverage your partners for better integration. In the past, partners may have simply faxed the relevant information, and enterprises would re-input it into their systems. But this method is time-consuming and error prone. While many organizations still rely on electronic data interchange (EDI) to integrate their data, modern technology offers several alternatives, such as data transfer via Web services that rely on XML files or extensive use of APIs. Other companies use more than one method to transfer data between partners.
Find the right data ingestion product that can pull data from a variety of sources. Across industries, organizations have one or more repositories for the data from which they hope to glean valuable insights. However, all of this data must first be collected in one place, then properly cleansed and formatted for analysis. Many companies rely on a data warehouse, data lake, or combination of the two to store data, and the type of data integration will depend on which platform you are using. For example, you may need ETL tools (extract, transform, load) if you are using a data warehouse; a data lake will need a different data migration product to pull data from different sources.
Getting Integration Underway
To ensure that integration goes smoothly, there are some parameters that must be first met, specifically around governance and data management. Enterprises must ensure that data that is meant to be integrated meets privacy regulations, such as local data privacy laws and internal standards.
Additionally, there must be appropriate rules in place for who can use the data, as well as a plan for risk reduction when moving the data between systems. Once the proper governance structure is in place, then data stewards must ensure that the quality of the integrated datasets is well-regulated and managed according to the needs of both the business and its customers.
Most importantly, they need to have a plan for how users will access usable and accurate data. Data is of little value if it cannot be accessed by those needing to make decisions. With this information clearly spelled out, businesses can set themselves up for success as their data integration plans are underway.