Business Analytics Machine Learning: Applications & Analytics

Data is a fundamental resource for important business decisions, setting goals for the years ahead, and leveraging your company for the future market. It’s safe to say that using basic analysis, empty reports, and outdated information won’t get you anywhere. 

Data-driven businesses are between 1 and 3% more profitable and up to 5% more productive than their competitors. These rewards are within reach without involving a highly-skilled and expensive analytics team. It’s about time you grasped them.

Leveraging artificial intelligence (AI) is an effective and affordable alternative to data science. Specifically, the combination of analytics and machine learning algorithms. Let’s address what this is, why it’s a popular technique, and where it could fit into your business. 

What is machine learning? 

AI has been making headlines across the world, so you probably have a good idea of what it is. But there are many fascinating and complicated avenues to explore. 

Machine learning (ML) is one of them. This subfield focuses on helping machines “learn”, hence the name. The approach is defined by algorithms that explore imputed training data. They describe what they’ve been fed, provide appropriate solutions, and make predictions. 

The heading says "Machine Learning Process". Underneath, there are 5 icons with arrows to illustrate the process. The first one is "training data", "algorithm", "learning", "trained model", and finally "results".

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Naturally, this has had a far-reaching effect, you can see some of them below. 

  • Diagnosis in healthcare
  • Motion control in robotics
  • Self-driving cars
  • Customer service chatbots

This technology has found its place in the business models of champions such as Google and Amazon. As leaders of innovation, it makes sense for your company to follow in their footsteps. So, let’s investigate how this implementation puts you on the path to success. 

The benefits of using machine learning for business analytics

Data analysis is hard work. Combining business strategy skills and technical knowledge isn’t something everyone can do. Beyond that, they take a lot of care, time, and effort to conduct, regardless of team size. Below is a simplified list of what it takes. 

  1. Present, time series, and historical data sets are taken from many different departments of a company, including marketing, finance, sales, and HR.
  2. Exploration into all of this information is conducted to uncover any valuable insights, patterns, and correlations.
  3. The findings give clarity on a business’s performance and help evaluate what’s changed, where improvements can be made, what the next steps should be, and which metrics need to be kept track of.
  4. Data visualization, most commonly graphs or charts, can make the information easier to understand.
The infographic shows that there are three parts to business analytics. Descriptive to describe what's happening right now, predictive to predict what will happen next, and prescriptive which decided what should be done.

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We broke a sweat just reading that. 

However, there’s no need to engage in such strenuous mental exercise when artificial intelligence can do it for you. Machine learning enables businesses to do everything in the list above with the capability of an expert team, making them the ideal business analyst’s virtual assistant. 

Let’s look at where this fascinating technology proves the most useful and why you should consider its benefits before developing your next strategy. 

1. Making better decisions

Taking risks and making decisions are two crucial aspects of running a business. But these aren’t easy to do by any means. Your choices depend on factors such as the market, economy, technological developments, and customer behavior. 

Unfortunately, none of these are known to be the most stable or predictable. They could change at a moment’s notice, leaving you to wonder what went wrong. You don’t have to make a decision you don’t feel confident about when you have data on hand.

The white text over an orange background says "Highly data-driven organisations are 3x more likely to report significant improvement in decision-making.

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Machine learning algorithms are able to sift through datasets at an impressive pace, analyzing for errors as they do so. You’ll be able to create effective action plans for business problems based on scrutinized information, establish long-term strategies, and better distribute resources like inventory, time, and money so they don’t go to waste.

Here are some of the specific applications of machine learning models to keep in mind.  

  • Making data mining possible. The algorithms can discover trends in much larger datasets than traditional methods, which means the insights into your company operations are more reliable because they’re based on more information. 
  • Streamlining predictive analytics. When patterns are uncovered, you need to know what to do with them. The regression models will use their findings to make predictions about future outcomes, which can be used in business forecasting. Having this knowledge leads to more effective and confident risk management and can optimize supply chain operations.
  • Maximizing the value of data. The patterns in data are sometimes hidden from the human eye and wouldn’t be found without machine intervention. This discovery could be key in decision-making if it showed a correlation no one expected.  

2. Seeing the bigger picture

Every organization has data in some form. It is important to have, but you won’t be able to optimize it unless you understand the story it’s telling. When you use machine learning algorithms in your business analytics, you’ll get the most insight into what’s going on, why, and how. 

Machine learning models are capable of working with big data, which many traditional software applications are not. The more information at your disposal, and the more accessible it is, the more of the picture you’ll see. You’ll be able to improve everyone’s time management and will be more likely to achieve your ambitious goals. 

Let’s take a look at some machine learning applications.

  • Making data accessible through natural language. You may not have data scientists on your team, employees might not be fluent in Python, and the information you have could be overly complicated. However, machine learning algorithms will map certain words to translate them from their confusing coding.
  • Providing more scope for data analysis. These incredible models can handle huge amounts of data. This means you can get a broader and more holistic view of your business analytics which will guide actions toward achieving end goals. 
  • Creating data visualization. AI makes it possible to visualize our data, which is the preferred way of learning for 65% of individuals. Not only will this make the information easier to interpret, but it will reduce issues of bias and subjectivity.
There are three statistics across the page, each one has a corresponding icon. The facts are: "Almost 50% of your brain is involved in visual processing", "70% of all your sensory receptors are in your eyes", and "we can get the sense of a visual scene in less than 1/10 of a second".

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3. Solving problems 

Business data is used to make improvements and changes when necessary. Not acting on this information quickly enough can damage your reputation, ruin relationships with customers, and wreak havoc on your profits. Being quick to solve problems is imperative. That’s why you need to utilize the speed of machine learning algorithms. 

Your team can address time-sensitive issues and make necessary changes before you begin to see any consequences. It will also be easier to prioritize the next steps because you can see in real-time where the most important complications are occurring. Your business will always feel one pace ahead.

Here are some of the specific ways machine learning technology can help you act on the results of your business analytics. 

  • Flagging errors in data entry. Data corruption is more common than you might think.Models can flag missing values, typos, and formatting errors, so you can rest assured knowing your decisions are based on the most accurate and trustworthy sources. 
  • Identifying areas of higher risk. Due to these applications being able to learn, they can even identify potential problems before they become apparent. This can include changes in customer buying trends, new competition, or an economic downturn.  
  • Removing human error. Humans can sometimes misinterpret information, make the wrong connection, or come to the wrong conclusion. It’s just a part of life. But this isn’t ideal when dealing with data analytics. Machine learning methods remove the guesswork because the result is clear and actionable insight.  

4. Automating tasks

The duties of a business analyst are seemingly endless. They will communicate with project managers, conduct constant research, convince stakeholders that the company is doing the right thing, and create documents for every department. This isn’t even half of it. 

The heading reads "A Day in the Life of a Business Analyst". There are four icons that represent different tasks and a cartoon man standing in the middle. These tasks are documenting processes, gaining an understanding of an organization's business processes, brainstorm solutions, and work in corporation with project managers.

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The automation of repetitive tasks would vastly improve their job performance. They would have an opportunity to explore new directions, be hands-on, and make more innovative recommendations. They’re capable of more when they’re free to think about the big things. 

One incredible advantage of using machine learning applications is that they can adapt to new information independently. The models learn without much human intervention and improve over time. Amazingly, they can even make decisions based on their findings. 

As you can imagine, this has huge benefits for companies that implement it. Let’s examine some of the ways it helps. 

  • Minimizing human involvement. Algorithms automatically detect anomalies or trends, and the findings are given to business users without them having to seek them out. This process means the team can focus on human-oriented tasks like documentation and communication. The analytics will therefore improve as a result because more time is dedicated to their delivery. 
  • Standardizing processes. When a process is automated, each phase will be recorded and traceable. This ensures that important parts of the operations aren’t forgotten, everyone is accountable for their role, and it will be easier for the individual in charge to document things like use cases, case studies, or reports. 
  • Applying what they know. Pre-trained machine learning models can apply what they’ve learned to new data sets. If this were a manual task, it would be time-consuming and eat away at resources. Instead, it can be automated with very little supervision. 

Successful business just got easier

Whether you like it or not, it’s impossible to catch up with tomorrow if you’re only using the technologies of yesterday. The future doesn’t wait for anyone, and the only way to chase it is to introduce the latest advancements into your processes. 

That being said, don’t assume machine learning models can take over the role of your business analyst. While they can improve the process, it is sensible to have a professional on hand who has a grasp of context and can communicate better with your team.

There will always be a place for humans in data. These technologies are groundbreaking, but they still require supervision and revisions. If anything, the use of machine learning will only bring more projects, new roles, and upskilling opportunities for business and data analysts. 

It’s not always what a business has that’s important, but what they have access to. Check out some of the best AI-powered applications on Top Apps and what they can do for you.