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Machine learning is transforming industries, but businesses should be afraid of it. Transferring systems or integrating systems can lead to losses instead of gains. In some cases, businesses crumble when trying to innovate because their operations come to a screeching halt.
Implementation is Everything
There are a thousand offerings that can be sold to a business. Promises of automating expensive workflows sound great. Remember that when you are messing with important workflows there is much to lose, however. Businesses today know the importance of technology, but it’s good to keep in mind the importance of business as usual. Implementing new systems takes careful planning. Serving the customer today ensures the business will continue to serve customers tomorrow.
That being said, there is much to gain from implementing the technology that already exists. Amazon has many awesome tools that, with that careful planning mentioned earlier, can maximize the potential of your business.
Now is time to talk about a few of them, and the implications that they bring. Keep in mind that this business (Sage Market) is all about innovating to use existing technologies to their maximum potential. I hope you will contact us through any of the channels below the header image to talk more about this.
When I first heard about NoSQL databases, I was grateful that I had found a reason to slack off about learning SQL (I am happy to say I understand SQL databases).
Basically, a NoSQL database is a distributed database that is supposed to make it easier to query data, and I was taught in business school that it would have implications when it comes to unstructured data.
I think that a good analogy for this is Google. Google indexes and organizes lots of unstructured data and then decides what you mean when you search for something. When I took my second business analytics class they said that NoSQL was supposed to allow querying of data without using SQL, like Google.
Let me back up a bit. What is unstructured data, and how does it differ from “structured data”? I back up like this because this is a very important point not only to the idea of NoSQL, but to the application of machine learning to querying databases. Extracting the truth from the data is easy when the labels give you the answer, but becomes much more difficult when the algorithm has to label the data itself.
ONLY TWO FUNCTIONS
The real reason I got so excited when I heard about NoSQL was that it seemed that to create a NoSQL database (as the hype misinformed me), the unstructured data would need to somehow structure, or label itself. Otherwise, how could you query the data in a number of different ways and receive the same information? The query would need to contain a kernal of truth that the system could search for in the data.
Enter Amazon Kendra... (again)
So my interest in NoSQL has really just manifested itself in Amazon’s Kendra product. Instead of putting a bunch of effort into labeling and organizing your data so that it is available to the people who need to communicate it, gain from Amazon’s machine learning algorithm. Let it do as much heavy lifting as possible, and only after you find out what the tech can do should you invest more time and energy.
Clarify with an Example
I used to work for a Credit Union, answering the phone and talking to customers about their accounts. Surprisingly, customers would ask me, being paid close to minimum wage, about esoteric financial regulation. We were supposed to say, “Sir/Mad’am, I am not a financial advisor/accountant, and therefore cannot advise”, but I like to talk about esoteric financial regulations, strangely enough.
One day, a woman called and asked me about the title on a boat that she was going to purchase. It was different from a normal loan, however, because it had been owned by the Coast Guard. It had a special lean on it, called a Ship’s Preferred Mortgage, or something like that. You might think, there is no way anyone could know about that, but my boss pulled it out of thin air. I was jealous, but impressed. I found out later that he had just entered a slightly different search into our internal intranet, and it was written there in plain English.
You might be thinking to yourself, Hayden how does this relate to using machine learning to improve business processes? I’ll tell you how. When that person asked that question, a voice recognition software could have triggered a search into the internal database, using Amazon Kendra’s artificial intelligence to query and return the right information. Then, it could have just popped up on my screen.
I want to stress that this sort of technology could be possible for your application, too! With a little imagination and the right research, any business can implement these awesome tools with little to no risk. Then, benefit from the profits, give back to the community, and go down in history as generally awesome.
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Thanks for reading this! I hope that you contact me to talk about how machine learning can be used to improve you business effectively and ethically. Also like us on Facebook and Instagram, or e-mail me directly.
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