Machine Learning for Business Decisions

A brief explanation of machine learning and how an organization can benefit from incorporating this new-ish technology into business decisions.

Artificial intelligence (AI) and machine learning (ML) might seem like foreign concepts to some of us, but in reality they’re incorporated into the technologies all around us and we’re most likely using them in our everyday lives, without even realizing it.

Some frequented areas in which applications of AI and ML can be found include:

• smartphones
• email
• social networking
• online shopping
• banking and finance
• healthcare
• education
• commuting

Just for the sake of giving a couple basic examples of how AI and ML are helping us every day, let’s consider how we travel from one place to another.

Any time you use a global positioning system (GPS), a ridesharing app like Uber or Lyft, or fly to get to where you need to be, you are reaping some of the many benefits found in AI and ML. Applications like Google Maps and Waze use AI to incorporate data from our smartphones and other users to determine the best routes for us to take, while Uber and Lyft find optimal routes for their drivers and implement ML into pricing, reducing passenger wait times, selecting additional riders that will fit into the routes adequately, and detecting fraud. Commercial pilots utilize autopilot (made possible with AI) more often than they actually steer an aircraft on their own, which just goes to show us how reliable the technology is.

Now some of you may be wondering…. 🤔 What’s the difference between artificial intelligence and machine learning? Well here’s a brief overview of the main differences between AI and ML:

Artificial Intelligence vs Machine Learning

First, it’s important to know that while all machine learning is AI, not all AI is machine learning. Artificial intelligence in and of itself essentially enables machines to adapt, reason, and provide solutions. Machine learning, on the other hand, is an application of artificial intelligence that enables systems to learn and improve from experience, all on their own. And by experience, I mean leveraging lots of existing data (and sometimes synthetic data) of course!

Okay, so let’s get down to business, which is probably why you’re reading this blog post in the first place:

The Benefits of Using Machine Learning in Business

For a little clarification, in a business context, AI and business analytics come together to create machine learning algorithms that analyze massive amounts of data to find patterns and make predictions that have an extremely high probability of being accurate. These predictions can be employed to improve both an organization’s scalability as well as its operations. ML can be incorporated into business practices in every industry imaginable. So if your organization has data of any kind, there are nearly infinite ways the technology can help guide you in making effective business decisions.

With a plethora of data available, machine learning is more accurate than it’s ever been and the continuous influx of data all around us only ensures its accuracy will continue to grow. And, just to give you a little idea of how much data there actually is, consider the following information:

• 1.7MB of data was created every second by every person during 2020
• 2.5 quintillion bytes of data are produced by humans every day
• By 2025, we will generate an estimated 463 exabytes of data every day

Source: https://techjury.net/blog/how-much-data-is-created-every-day/#gref

To get your wheels turning about how you might take advantage of machine learning in your business….

Business Applications of Machine Learning

You can implement machine learning technology to analyze your customers’ purchase history to predict their behaviors, allowing you to provide them with appropriate product recommendations and special offers that are likely to lead to additional sales. The technology can help keep technicians up to speed in the service sector by handling many of the complexities involved in scheduling like selecting the best routes and prioritizing appointments, while it can also easily forecast the costs associated with major infrastructure projects. In the transportation field, ML can project things like the amount of fuel and mileage a fleet can expect to incur along its routes within a specific timeframe. It can significantly improve predictive maintenance measures that mitigate risks in manufacturing and enable medical providers to make more accurate diagnoses based on historical data. Aside from permitting a highly personalized customer experience, machine learning also offers banking professionals with much more precise risk assessment and gives them the ability to provide advanced fraud detection and prevention. Ultimately, using ML will free up a lot of your employees’ time so they can spend it on other high-value tasks that machines can’t handle yet, making things run much more efficiently in general.

With all that being said, how you decide to put your data to work is up to you (or possibly someone else within your organization), but it is highly recommended that you incorporate machine learning into your processes in order to make well-informed business decisions that will have an extremely high probability of resulting in desirable outcomes.

I know all of this sounds great, but I bet you’re probably wondering how you should even begin the process of getting ML into your company’s data strategy.

This is where we come in!

DataLakeHouse

DataLakeHouse harnesses the power of the data cloud and combines it with advanced machine learning capabilities to offer data-driven suggestions and incredibly useful business analytics, all at a cost much lower than other providers, at that. But that’s just a small portion of what the platform has to offer. And, did we mention, it’s 100% Snowflake focused. Additional features include, but are not limited to:

• Data sync bridge (ELT) that enables you to bring all of your data together
• Customizable financial calendars for accurate reporting & analytics
• A meta-data dictionary, where users can enter logic for future reference
• Data check validation, ensuring data was landed properly at the target
• Pre-built industry-specific analytical models to simplify analyses by providing specific insights that are commonly sought after
• Single customer view data structure and aggregation, enabling personalized marketing and segregated insights
• Data sharing, allowing users to choose which data they want to share with other internal and external users

Having the ability to make data-driven decisions is invaluable and an absolute necessity these days. Although the platform hasn’t officially launched yet, you can sign up for our beta program and access waitlist to become a part of this game changing approach to discovering new opportunities with your data on our website- https://www.datalakehouse.io/