Step two to becoming a data-driven organization: use automation to unify data

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July 7, 2020

In the second in a series of blogs focusing on the six steps organizations should take to become data-driven, we will look at the increasing importance that artificial intelligence and machine learning play in the process of automating data integration.

In the first blog, we defined what we mean by a data-driven organization and examined the importance of a modern data architecture as a foundation for providing business users with immediate access to relevant insights.

That modern data architecture needs to ingest data from the many sources, and this is often a complex mixture of data types, locations including on-premises and cloud data, application programming interfaces (APIs), and myriad edge and personal data stores. A complete picture of business performance that cuts across the system and organizational boundaries is a vital step in becoming data-driven. In the past, surveys have shown that 77% of companies believe they have lost revenue due to incomplete data.

If an organization does not try to integrate its important data sources centrally, then that burden falls upon the data analysts and business users. This can mean weeks, even months, are spent creating data mash-ups before any analysis is undertaken. Not only is this a terrible use of their time, its often prone to errors and inconsistencies due to a lack of technical and data domain knowledge.

The traditional fix has been to invest complex data integration software, but now new technologies such as artificial intelligence (AI) and machine learning (ML) are transforming this time and resource-intensive approach. AI and ML can be used to identify the relationships in data, considerably speeding up the process of creating analytical data stores. Gartner predicts that “through 2022, data management manual tasks will be reduced by 45% through the addition of machine learning and automated service-level management.”

You can read about the increasing role that AI and ML are set to take in our best practice guide on the six steps to becoming a data-driven organization. Discover how you can transform your business by ensuring every person who makes decisions has access to the data they need when they need it and learn more about Infor Birst’s automated data refinement capability here.

In the next blog on the topic of how to become a data-driven organization, we look at the role of data-as-a-service.

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