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Three Common Misconceptions about AI and Big Data

by Richard Lynch, on May 10, 2017 8:36:10 AM

3 misconceptions about dataFor those of us who view statistics more like “sadistics,” artificial intelligence and big data are suspect, intimidating, and big money. Here’s why we are wrong.

Misconception #1

The data can’t be trusted

Not anymore. Solutions like Absolute Insight come with the tools needed to clean data simply, efficiently, and effectively. In addition to streamlining the data cleaning process, the process can be re-used as data is refreshed and renewed for additional analyses.

Another relief is that there is no need to worry about sample size and related statistics — the larger the data sources the better the models.

Misconception #2

Big Data is too complex to be used by operations staff

Big data has traditionally been viewed as work for data scientists. Today, that’s no longer true. Operations staff such as fraud unit Investigators and auditors can have their personal data scientist in a box to combat issues like healthcare fraud, waste, and abuse. There’s no need to master the programming and mathematical underpinnings associated with AI and predictive analytics. With a little tool training, line staff start providing their subject matter expertise to best leverage big data for business insights and actions in a matter of weeks.

Misconception #3

AI isn’t financially feasible

AI can be expensive and user adoption low when a full-blown solution gets “implemented.” This misconception is now exposed.  Alivia Analytics architecture utilizes lower cost Apache’s Spark technology and state of the art visualizations to bring predictive analytics to the desktop. By constructing libraries of templates and wizards, it allows users to employ the complex algorithms by merely selecting a course of action. This saves money, increases speed, and improves overall organizational efficiency, including training workers for today’s digital workforce.

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