How Conventional Companies Can Use AI And Machine Learning To Boost Better Products

How Conventional Companies Can Use AI And Machine Learning To Boost Better Products

How Conventional Companies Can Use AI And Machine Learning To Boost Better Products

Perhaps you have noticed how accurate Netflix’s recommendations are to your taste? And how is Google Maps so confident I am going home, it is going to suggest directions to my house? Even my iPhone indicates what time I need to set my alarm clock right before I go to bed. Wonderful, right?

Why would most organizations continue to utilize their information exactly the same way that they would have used it 10, 15, or even 20 years back? Can not it make sense for companies to utilize this technology to create better customer adventures and operational improvements?

The great thing is that the same machine learning technology used within these large organizations is provided to the general public by various providers, such as AWS, Google Cloud Platform, and Azure — for a fee, of course.

And not only machine learning, but the huge push for utilizing technology that can make smarter decisions for your company in real-time, with minimal participation from the organization (once it’s set up, of course).

And what precisely makes machine learning and artificial intelligence operate? Data — and lots of it. To create this work, you need to experiment using machine learning and data lakes and produce an open data culture.

Experiment with machine learning and data lakes

Let’s say you’re a prosperous organization seeking to discover your most profitable customer cohort. Knowing this information will give you better insights into what products are working best for those clients. As a consequence, you can make more informed marketing and operational decisions based on consumer action.

You might go into your financial reporting systems and determine what products are selling the most, but that merely lets you know which products are selling, not that buys these products. So you dig further on your different, disconnected-from-the-financials CRM system to understand the types of customers buying those products. There, with a few manual or development effort, you can discover this information.

And today, what if you wanted to understand what marketing efforts are driving sales to those specific customers?

This is a lot of work, but nevertheless possible in an information silo.

Let’s take it a step farther

What if you want your marketing system to automate personalized advertising campaigns based on purchasing behavior, event tickets, event tickets, ratings of merchandise, and your clients’ activity on social media and surfing behavior on your website. Now, life gets much more challenging, maybe impossible, when most of your data reporting programs are siloed.

That is the reason why many organizations and cloud vendors are forcing organizations to information lakes. An information lake, including a data silo, is suited to artificial intelligence and machine learning analysis, in addition to building predictive models from disparate, disconnected data resources.

Also Read: Is Machine Learning The Quantum Physics Of Computer Science?

In our instance, if all the information is within a data lake, you can’t just uncover insights you were unable to before, but you can build versions in real-time to send customized advertising campaigns at the ideal moment.

Consider moving your information to data lakes to observe the effect for yourself.

Create an open data culture

If Jeff Bezos didn’t mandate that each and every department open up access to its information through APIs, then AWS really would not exist as we know it now. The culture of departments working together and sharing information with each other was the beginning of the Amazon S3 storage method.

Traditionally we build our information warehouses around a kind of information, at an information silo. Most information reporting programs are siloed and only accessible by their respective departments. The data is also nicely organized like a normal relational database and is easy to understand.

But, as I said, the data is not linked, which leaves a company with many blind areas. Rather than finance, sales, marketing, and operations living in their own worlds wouldn’t it make sense for groups to be able to make decisions based on linked data systems?

Businesses, specifically section leads, should consider altering their mindset and start-up their data to sections within the organization. By moving away from a data silo world into a data lake surroundings, you are giving your business an extra advantage to compete. You’re giving the information scientists permission to find new business opportunities that will not be possible by partitioning your section’s data.

Machine learning is here to remain. It’s available, and the technology has improved to where virtually any organization, not just Google, Microsoft, and Apple, can utilize these improvements to make a positive impact with their customers and the world. In fact, it’s only the start.

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