May 12 2020
Data analytics, artificial intelligence, and machine learning have long been among the most hyped-up words associated with digitalization. And yet, despite the abundance of blogs, guides, and features dedicated to these terms, many people are left with only a vague and superficial understanding of what they actually mean. I'd even go so far as to say that, while plenty of us have a basic understanding of what artificial intelligence (AI) and data analytics are in principle, a firm grasp of how they can be applied in practice often remains out of reach.
As is the case with many a notion, precise definitions of these terms tend to vary depending on the person defining them. It's also worth acknowledging that, yes, there is some degree of overlap between them. With a certain degree of acceptance towards terminological ambiguity, the descriptions below have been compiled by Vincit’s data team.
1. Data analytics
In data analytics, data is compiled or presented in such a way that people can interpret it. This might be through visualization, averaging, or other statistical methods. Data analytics is part of a broader field of data science and comprises a toolkit that also includes AI and machine learning. Data science is any activity whereby information or understanding is derived from data, for example, in business development.
2. Artificial intelligence
Artificial intelligence is a program or system capable of independently performing intelligent activities. Artificial intelligence is also characterized by an ability, albeit limited, to independently reason and learn and to utilize any findings reached through these actions when performing tasks.
3. Machine learning
Machine learning is a form of AI, the functioning of which is dependent on the material it is given. This kind of AI relies on the use of a model into which teaching material consisting of certain observational units is entered. Among other things, machine learning models like this can be used to classify data, such as messages sent to a customer service team, into the appropriate, pre-defined categories.
In practice, and slightly crudely stated, businesses can capitalize on data analytics, artificial intelligence, and machine learning in one of two settings:
Data-driven and decision-making business processes are often intricately connected to activities such as optimizing sales, measuring marketing metrics, stock management, supply chain management, or preventive maintenance.
If we look at stock management, for example, AI can be leveraged in the agile management of inventories and in predicting the need for stocking up. This enables suppliers to maintain an optimized level of stock to meet demand. In other words, customers don’t need to be told that their order can’t be fulfilled, nor does the warehouse end up holding surplus stock. When it comes to customer service optimization, one way in which these AI processes can be utilized is in the categorization of messages received by a customer service team. The AI ensures that messages are directed to the appropriate handler. This speeds up the whole message-handling process which leads to better service for the customer. In terms of measuring the effectiveness of marketing, for example, the impact of a multi-channel summer campaign can be assessed by examining the different data sources and combining the results to calculate the awareness and conversions the campaign has generated.
While most of us are able to examine and interpret small amounts of data ourselves, when it comes to any larger body of data, a machine can accomplish this significantly quicker and with a higher degree of accuracy. As humans, we all have certain preconceived notions and biases that lead us to pay more attention to what we expect to see. This can have a dramatic impact on the reliability of data analytics tasks carried out manually. On top of this, machines are far better than us at taking into account the simultaneous effect of several factors, for example, when making forecasts.
As their name suggests, smart products and services are characterized by the addition of some form of AI feature that adds value to the product or service in question.
A good example of this kind of product is the waste compactor produced by Europress, who specializes in waste management solutions. The Europress compactors are equipped with software based on forecast models, which enables them to predict when the dumpsters used by a store will be full and ready for emptying. To give another example the LeakLook water meters utilize AI to identify water leaks and prevent flooding and water damage. Both of these examples also make use of the previously mentioned data-handling techniques to support their business processes and decision making.
Another common type of a smart service is the use of recommendation algorithms by various websites and mobile apps. You’ve no doubt run into these when shopping online or using a streaming service. The algorithm takes the user’s browsing history other aspects of user behavior that the site or app tracks and begins to learn what the user’s needs are in order to recommend products or services that the user may find interesting.
Data analytics, artificial intelligence, and machine learning can be leveraged in many contexts to develop and optimize business operations. I’d like to end this blog by addressing a common misconception. Many people are under the impression that AI and data analytics are super complex and come with an astronomical price tag. In many cases, this couldn’t be further from the truth. In fact, plenty of projects get off the ground with a proof of concept intended to give a tentative understanding of the potential benefit of a solution with relatively little effort and money. There are also other cases in which a customer believes they need a complicated solution when, in reality, all that’s needed to make the necessary improvement is a quick and easy fix. The moral of the story? It’s always worth asking!
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