Data has a better idea™ - Learn why and how machine learning can discover new ideas with your data

December 08, 2021 | By HIVERY
Data has a better idea™

As you might have guessed we are so passionate about data and believe it has its own ideas that it is in fact our registered trademark tagline Data has a better Idea. At HIVERY it has actually been our philosophy from the day we started. You can learn more about this in the section: So, why HIVERY? We are just different.

In this blog, we will discuss how machine learning specifically learns and uses data to discover these new ideas.

Why is “data” in Data has a better idea™ in important?

Machine learning can detect epilepsy in children, with a 73% success rate. This test method wasn't applicable to children's developing brains until now.

Machine learning, a sub-area of Artificial intelligence (AI) is actually the Computer Science discipline that looks at algorithms that “experience” data to find new ideas. More specifically it's the practice of machine learning algorithms that finds patterns and connections that human intelligence can't. In fact, data is so important now that it is the competitive differentiator for successful companies.  You might have heard expressions like data is the new oil or data is the new currency or data is the new gold! 

Put in another way, some reports suggest that over 90% of data in the world was generated over the last few years, yes 90%!  Further, according to Forbes report, 2.5 quintillion bytes of data is being created each day.  It's no wonder that data is an important asset and an important part of decision making. It's also no wonder why if there is a new and better way to analyse this data using computers has generated a lot of interest.   Machine learning algorithms are good at finding patterns and connections that we can't. They are essentially good at communicating “data’s ideas” to use.  Machine learning algorithms are modern age “interpreters”.  They provide a new competitive advantage. Industries; from airlines, mining, finance, insurance to health and retail are able to discover data indeed has a better idea. 

Keep reading to learn about how data has a better idea for business intelligence and decision making.

What is the thinking behind: Data has a better idea™?

As mentioned above machine learning (ML) is a method of analyzing big data using carefully designed models or algorithms. This is what a data scientist key job is. Designing and testing algorithms that can look at data patterns without needing much human interaction and that can learn over time -  get better.

Data has a better idea™

Cheaper, more powerful computational processing and affordable data storage make this possible.

A wide variety of industries now make use of this type of AI, from healthcare to retail, to transportation. Business intelligence (BI) helps vendors identify important data points and patterns. It helps reduce costs, increase efficiency, and identify opportunities for a business.

This is due to the fact that datasets have become too large and complex traditional data analysis and modelling approaches are often used by Business intelligence (BI) teams have become ineffective.  ML algorithms help you understand customers on a deeper level, and they learn associations more quickly and thoroughly than traditional BI approaches.

You could analyze a smaller dataset with an Excel spreadsheet. However, to understand larger patterns, you must use ML to process big datasets, find patterns and create sophisticated demand forecasts.

 In retailing, for example, any retailer or category management professional knows when assortment is done right, they enjoy more sales, higher gross margins, leaner operations, and most importantly, more shopper loyalty 

In reality, this is hard than it sounds.  Trying to get assortment planning right means you are constantly monitoring, strategizing and managing SKU performance. 

ML can help with SKU rationalization and SKU optimization allows businesses to only keep the products that generate ideal revenue. An ML can show which specific stores an SKU introduction will perform better, what SKUs should be removed and the impact on revenue and volume taking into account demand transference and cannibalization, all in a few minutes, not months.

Data has a better idea™ with machine learning 

There are many ML methods to design the right algorithm model. Each has its own advantages and disadvantages.  Let's review, at a high level, four popular ones.

Supervised Learning

Supervised learning trains an algorithm using labelled examples. It already knows the desired output. The model modifies itself by comparing its outputs to the correct answers. You should use this where historical data consistently predicts future events.

Supervised learning is especially useful for binary classification, multi-class classification, and regression modelling.

Unsupervised Learning

Unsupervised learning works against data that has no historical labels. There is no predetermined right answer. The system must find a pattern and figure out what is happening on its own. This works well to find groups of customers with similar attributes.

Simply put, it looks for any meaningful connection in the dataset. It's useful for clustering, anomaly detection, association mining, and dimensionality reduction.

Semi-Supervised Learning

Semi-supervised learning combines both labelled and unlabeled data for training. The labelled data points the algorithm in the right direction. Semi-supervised learning is more affordable than fully supervised learning.

This method is best for machine translation, fraud detection, and labelling data.

Reinforcement Learning

Reinforcement learning discovers through trial and error which actions yield the greatest rewards. Its goal is to learn the best policy within a set of defined rules. The algorithm gets positive or negative cues as it tries out various ways of completing a task.

Reinforcement learning is often used for robotics, video gameplay, navigation, and assortment planning.

Machine learning can be leveraged by retailers and consumer package goods when conducting assortment optimization. It finds the sweet spot between operational capability and maximum profit.

Data has a better idea™

Machine Learning for Retailers

Machine learning is a great way to optimize your operating strategy.

Data has a better idea™ for your business! Find out what it can do for your business by contacting us for an AI-powered solution.

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