In Analytics

You can’t just blindly accept numbers—that’s not scientific. Yet when you rely on black-box analytics, you’re forced to accept that the logic of the unseen algorithms precisely matches your needs.

Opening up the black box gives you three types of confidence in the data that is driving your decisions.

  • Understanding how an analytical value is derived. Even if you couldn’t do the math yourself or were never a mathematician, you should know what inputs and logic went into creating the analytical results on which you are relying.
  • Modifying or creating analytics to fit your specific needs. Although you may be working with an established analytics vendor or product, you may need to modify existing algorithms or even create your own in order to meet your needs. For instance, you may want to add or change the inputs given to an algorithm, or change the weighting given to the inputs used.
  • Auditing the analytics process. If you are using analytics to make significant, data-driven decisions, odds are you will need to show your math at some time to someone: regulators, investors, board of directors, insurance companies. Black box analytics don’t give you this opportunity for auditability and transparency.

Open source analytics packages are increasingly the norm. R and Spark are two leading examples. Open source allows you to create, understand, modify, and audit analytics to match your specific needs and assure your stakeholders.

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  • Micah

    Do you use R and Spark currently? What other open source packages do you use or like? Great post.

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