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Top Business Intelligence Trends to Watch Out for This Year

Story Highlights
  • Bursting the Cloud Bubble
  • Reduction of SQL
  • Compilation of Best Practices

Business intelligence, or BI, is a new topic that is emerging in the IT circuit. It is giving rise to various assumptions that can be eye-opening facts or mere rumors. In this article at Transforming Data with Intelligence, Monte Zweben shares the business intelligence trends to watch out for this year.

Discovering Business Intelligence Trends

Upside had interviewed various IT executives all over the country and found out the growing opinions around BI. Discover the three top business intelligence trends to watch out for this year:

Bursting the Cloud Bubble: Organizations have realized that cloud has not been the best option for their business growth. The same applications for which they invested heavily are unable to integrate with emerging technologies. The upkeep of these tools costs them more than they have saved through layoffs a few years back.

For example, you can purchase numerous applications on AWS. However, the responsibility of integrating each application for a connected environment lies with you. So, one of the business intelligence trends is companies no longer believe cloud computing is their one-stop solution.

Reduction of SQL to NoSQL Conversion Projects: Since distributed SQL is available, organizations will abandon SQL to NoSQL projects. Besides, NoSQL tools do not support SQL, and no tool converts SQL to NoSQL. Getting rid of an established language like SQL and its applications for NoSQL is a damaging approach. The safest policy would be to replace your legacy database with a scalable one.

Compilation of ML Best Practices: Organizations will get on board with leveraging machine learning in their processes. Below are the best practices they might follow:

  • Build feature factories where data scientists can experiment. They will have the liberty to combine different data points to build or reject features for the ML model.
  • Instead of working in isolation, collapse all departmental barriers. Project teams will consist of data engineers, application developers, and subject matter experts.
  • Work on application building than solving complicated data lake issues.
  • Use new tools to track data projects. Stakeholders will want companies to closely monitor the progress of each project and report all discrepancies.
  • Get rid of Lambda architecture. Use SQL data platforms that will enable an integrated environment with legacy applications.

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