People & Technology: A Marriage in Applied Analytics

Now that it has, however, businesses find themselves in an economic landscape that's far more prone to fast-paced change than traditionally. In an e-commerce-fueled economy, customer demands, expectations, and loyalties change almost daily — meaning all companies need to be able to act on these changes quickly and decisively.

Luckily, this digital world has also brought us an abundance of information — countless data sets. Businesses can unlock crucial actionable insights and retain their grip on their core markets.

Technology and Decision-making

The increased need for advanced data analytics has ushered in a new age of interest and focus on AI and machine learning. And the reason for that is relatively simple — artificial intelligence can handle more extensive and more complex data sets, at least compared to the computing power of the human brain or more traditional data processing methods.

Of course, while some of the technologies we now possess were once considered sci-fi — we're not quite in the realm of Captain Picard and his android buddy, Data. In other words, AI is nowhere near the ability actually to think for itself and make complex decisions — and it may never be able to surpass the simple yet vital nature of human intuition.

People and technology remain equally indispensable in 21st-century business — but there's an increasing number of ways technology will help us become more efficient at what we do best.

With that in mind, let's look at some essential AI technologies and how they work with data analytics in the real world.

Detecting Anomalies

Anomaly detection involves identifying events or items that don't fall into a generally expected pattern. And machine learning algorithms for anomaly detection can be a powerful tool, mainly when predicting and preventing events like bank fraud. Plus, it's applicable in all kinds of sensor networks, system health monitors, fault detection, and even disturbances in ecosystems.

Extrapolation

Extrapolation estimates the value of certain variables based on how they relate to other variables. Yep, it's wordy — but quite powerful in practice. For example, if some business data set exhibits a long-term trend, there are things that company executives will want to know about it.

How will it affect the company in three months if this trend stays the course? Extrapolation can answer questions like this. And sure, machine learning isn't always necessary for these deductions — at least for linear trends. However, non-linear trends are far more complex, and extrapolation helps greatly.

Automating Human Behavior

There are plenty of ways people can analyze vast amounts of data — we've become quite good at that over the past century. However, there's one problem: it's exhausting, especially over time.

Artificial intelligence can help with this. Rule-based systems can extract and manipulate knowledge practically, producing insightful results. And these rule-based systems, often created through sets of "if-then" triggers, can be used to design software capable of answering simple questions instead of human experts — freeing up their valuable time to deal with more complex problems.

If this sounds familiar, you've seen many companies employ a chatbot on their website in the past decade. And as the machine learning tech behind them develops, these bots will be capable of handling more complex tasks.

Pattern Recognition

You may have deduced it from the name — but pattern recognition is a technique for detecting regularities within data. And it's one of the most commonly used forms of machine learning. These systems require vast amounts of training data to "teach" them the correct answers, referred to as "supervised learning." Remember all of those traffic signs that Google makes us recognize in pictures from time to time? Google uses those responses to train machine learning algorithms for driverless vehicles.

And if you think pattern recognition is only helpful for tech giants, think again — companies in all industries find the ability to recognize patterns (and their anomalies) functional. For instance, if customers start buying one item and another in huge numbers, such practices will interest an e-commerce business.

Wrapping Up

As the robustness of artificial intelligence and its ability to interpret Big Data grows, technology will grow in importance in decision-making in high-stakes business environments. More and more devices are constantly appearing and generating new data sets.

However, possessing that data doesn't automatically mean having valuable, actionable insights. That gap is where platforms like ProcureVue™ come in.

ProcureVue™ transforms your spend data into measurable outcomes enabling you to evolve your company's supply chain. Founded by a team of sourcing executives and data scientists, our hands-on advisory approach combined with our proprietary software delivers maximum outcomes faster than industry standards. ProcureVue™ blends best-in-class data harmonization, enrichment, visualizations, and supplemental data offerings, providing intelligence that seamlessly integrates into any organization.

We empower our clients to realize impactful outcomes utilizing innovative and dynamic technology. Our industry expertise and consultative approach provide enriched insights to attain a competitive advantage. Together, they deliver unprecedented clarity to maximize results in the evolving global marketplace quickly.

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