Automation isn’t essentially about technology. It’s a mindset. Technologies are the toolkits with which we automate. In the end, “Building Down” means focusing less on climbing up what’s above you than on re-architecting and re-engineering what’s already in front of and below you so that you can do more with less.
In the digital era, the way we build organizations and grow as individuals, teams, and units within them is changing. In the past, we would build an organization that contains business units. It scaled upward and outward. People joined generally at the bottom of “the ladder” and, with hard work and time, they climbed up that ladder and took on more responsibility across the unit and/or organization.
A mindset shift is in order. Technologies like IoT and AI change what organizational growth and management* means. It’s no longer just about what people can do with other people. Neither is it instead about what people can do with machines. It’s about what people can do with people and with technology. Read More
Daily Archives: June 26, 2019
Use Data Lakes to Bet on the Future of Artificial Intelligence
Artificial intelligence has moved far beyond the stuff of science fiction. And, for all the benefits AI provides today, we can only guess at what the future of artificial intelligence holds. To help ensure that they will be able to take advantage of any and all AI advancements, many companies are making use of data lakes.
Indeed, one of the single largest tech trends of the last five years has undoubtably been the mainstream adoption of artificial intelligence. Within just a few years time, artificial intelligence has gone from being relatively obscure to being used almost everywhere. In many ways, it reminds me of the way that cloud services suddenly gained mainstream acceptance a decade ago. All at once, software vendors collectively felt the need to rebrand their products to reflect cloud readiness. Today, the same thing is happening with AI.
As with cloud services, there are countless use cases for artificial intelligence. One of the main use cases that is driving adoption (at least, in a generic sense) is that artificial intelligence engines can sometimes be used to spot trends and derive meaningful insight from an organization’s existing data. The flip side to that idea, however, is that for the artificial intelligence engine to do its job, it needs access to raw data. There are obviously a number of different ways of making this data available for analysis, but one of the best options may be to create a data lake. Read More
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