How Traditional Companies Can Utilize AI And Machine Learning To Build Better Products
Have you ever noticed how accurate Netflix’s recommendations are to your taste? And how is Google Maps so confident I’m going home, that it will suggest directions to my house? Even my iPhone suggests what time I should set my alarm clock right before I go to bed. This means it knows when I’m going to bed and tells me the optimal time to wake up, down to the minute, based on my sleep patterns. Amazing, right?
So why do most organizations continue to use their data the same way they would have used it 10, 15 or even 20 years ago? Wouldn’t it make sense for businesses to use this technology to create better customer experiences and operational improvements?
What if you could use this technology to help your customers improve their lives? The good news is that the same machine learning technologies utilized within these large organizations are offered to the general public by various providers, such as AWS, Google Cloud Platform and Azure — for a fee, of course.
At AWS re:Invent 2019, there was a clear, overarching theme: machine learning. And not just machine learning, but the big push for using technology that can make smarter decisions for your business in real time, with minimal involvement from your organization (once it’s set up, of course).
And what exactly makes machine learning and artificial intelligence work? Data — and lots of it. To make this work, you need to experiment with machine learning and data lakes and create an open data culture.
Experiment with machine learning and data lakes.
Let’s say you’re a successful organization looking to uncover your most profitable customer cohort. Knowing this information will give you better insights into what products are working best for which customers. This means you can make more informed marketing and operational decisions based on consumer activity.
You could go into your financial reporting systems and see what products are selling the most, but that only tells you which products are selling, not who buys these products. So you dig further in your separate, disconnected-from-the-financials CRM system to understand the types of customers buying those products. There, with some manual or development effort, you can uncover this information.
And now, what if you wanted to know what marketing campaigns are driving sales to these specific customers?
Well, that’s when it gets interesting. This is possible through the integration of data silos, mostly through manual effort in copying and pasting, formula-driven Excel spreadsheets or specific development effort. This is a lot of work, but still possible in a data silo.
Let’s take it a step further.
What if you want your marketing system to automate personalized marketing campaigns based on purchasing behavior, incident tickets, ratings of products, and your customers’ activity on social media and browsing behavior on your website. Now, life becomes much harder, if not impossible, when all of your data reporting platforms are siloed.
This is why many organizations and cloud vendors are pushing organizations to data lakes. A data lake, unlike a data silo, is natively suited for artificial intelligence and machine learning analysis, as well as building predictive models from disparate, disconnected data sources.
In our example, if all of the data is within a data lake, you can not only uncover insights you were never able to before, but you can build models in real time to send out personalized marketing campaigns at the right time.
Consider moving your data to data lakes to see the impact for yourself.
Create an open data culture.
If Jeff Bezos didn’t mandate that every single department open up access to its data through APIs, then AWS actually wouldn’t exist as we know it today. The culture of departments working together and sharing data between each other was the start of the Amazon S3 storage system.
Traditionally, we build our data warehouses around a type of data, in a data silo. Most data reporting systems are siloed and only accessible by their respective departments. The data is also nicely organized like a typical relational database and is easy to understand.
But, as I mentioned, the data isn’t connected, which leaves an organization with many blind spots. Instead of finance, sales, marketing and operations living in their own worlds, wouldn’t it make sense for teams to be able to make decisions based on connected data systems?
Companies, specifically department leads, should consider changing their mindset and open up their data to departments within the organization. By moving away from a data silo world to a data lake environment, you’re giving your business an extra edge to compete. You’re giving the data scientists permission to find new business opportunities that would never be possible by partitioning your department’s data.
Machine learning is here to stay. It’s accessible, and the technology has improved to where any organization, not just Google, Microsoft and Apple, can utilize these advancements to make a positive impact with their customers and the world. It’s not too late. In fact, it’s just the beginning.