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Extract, transform and load is a standard data procedure used by businesses keen on leveraging their data. However, as artificial intelligence becomes entwined with data processes, the standard ETL integration model has been forced to evolve. Here are three reasons why ETL processes are becoming outdated and how you can move beyond the old standard.

Is Your Organization Moving Data or Integrating Data?

If your organization has processes in place that allow data to move securely between different branches of your organization, then your current ETL process is probably doing what it was designed to accomplish. However, if your organization depends on data that is pulled from the cloud or from centers outside of your organization, then you need to look at processes for integrating new data with existing data. This is a transformative process that requires all collected data to exist in a unified format regardless of origin. AI and machine learning processes are capable of cleaning data and performing summary statistics, and frequency analysis on new and existing data. In this way, when new information enters your organization it can integrate with legacy data in ready-to-use and meaningful ways.

Does Your Organization View Data as a Byproduct?

Traditional organizations tend to view data as a historical byproduct and not as a dynamic collection of meaningful metrics capable of driving business growth. If your organization’s leadership uses data to track historical trends with the goal of applying predictive analytics that informs decision-making, then your organization is on the right track. The next step is to define and implement a data integration strategy across the organization. AI and machine learning help accomplish this task by continuously making decisions based on current data. Human decision-makers can then take the advice of AI or not. Either way, the organization is capable of remaining highly competitive in fast-moving industries.

Is Your Team Proficient in Data Management?

Your team can hit the ground running with data integration, but smaller steps may need to be taken first. Teams that are not highly data literate, conversant with data governance methods or knowledgable of data management programs will benefit from additional professional development. Of course, AI and machine learning will pick up any slack, saving your organization money and time.

There is no doubt that the ways organizations acquire, access and leverage data are changing. Competitive organizations are taking steps beyond data transfer and looking at AI and machine-learning technologies to integrate and manage information.