Tossing artificial intelligence at your data to answer business questions resembles using a tornado to blow out a match.
In other words, just because artificial intelligence tools can provide answers doesn’t mean you should use them. If good old business intelligence tools do the job just fine, stick with what you know.
But AI is a great way to uncover information hidden within vast amounts of data analaysis as long as you’re willing to use the information that surprises you, according to Jana Eggers, CEO of Nara Logics, a synaptic intelligence company based in Cambridge, Mass.
“If you aren’t willing to learn, don’t do an AI project. Do a regular data analytics project,” Eggers said during her presentation at the TDWI Accelerate conference in Boston earlier this year.
That’s sound advice in a time when all we hear about is the power and promise of AI technologies like Data warehousing, congitive computing, natural language processing and machine learning.
Using AI judiciously can save companies a whole lot of time and money on a tech that’s exciting but may not be appropriate for the job. It’s also important to carefully consider where and when to use AI because artificial intelligence affects nearly all areas of BI, along with the people, processes and corporate culture underlying the business.
AI technologies requireimmense measures of data, collected from sensors, applications or the smatphones or gadgets in users’ hands. Collecting all that data to feed AI systems requires a tremendous amount of storage so much so that companies are moving their data warehouses to the Hybrid cloud or cloud.
Companies also need staff members with data science skills to make senses of the data, developers who know how to work with AI the list goes on.
The articles in this guide provide deep insight into the dos and don’ts of AI, how AI affects the data center and BI staffing, tips for figuring out the ROI of AI, and more.
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