Why should companies combine AI and data analytics? The answer – fast and effective digital transformation. Learn more about the ways businesses are moving forward!
You’re likely no stranger to the concept of artificial intelligence (AI). Throughout the past decade, this technological innovation has become more prevalent in industries across the globe and for good reason – AI simply can work faster and more effectively than its human counterparts can.
At the same time, we live in a time where real world data is generated constantly. We know that achieving digital transformation requires leveraging insights from data to some extent – but what is the best approach? Further, how can data engineers arrive at desired insights more quickly? Where should we begin?
Increasingly, AI is being applied to data analytics to allow businesses to understand products they should offer or effective ways to market to their target audience. In 2021, the strategy is clear – the path to innovation combines both AI and data analytics/data science for truly transformative effects.
Analytics and AI – one of these things is not like the other! AI technologies have become so prevalent in everyday life, we hardly notice the assistance it provides. For instance, every smartphone includes facial recognition that unlocks your phone with your face (AI that sees), voice assistants (enabled by natural language processing), and predictive text (AI that writes). This technology has become so advanced that many AI systems rely on neural networks, or a series of algorithms that imitate the way human intelligence works to recognize relationships between amounts of data.
Forbes breaks down some of the key features that lay the groundwork for successful applications of AI to data analytics:
When it comes to marketing, data is everywhere – from user-tracking data on apps and websites, newsletter conversion rates and online advertising click-throughs, to CRM data analysis. How can someone who isn’t a data scientist make sense of it all?
One tried-and-true methodology is leveraging a process called data mining, which delivers vast quantities of (often unstructured) data. Typically, marketers interact with data via dashboards that structure data to deliver an analysis of commonalities, like percentages, ratios, and averages. Through data collection, whoever is performing the analysis can search for a pattern, report a result, and find relationships between variables. Humans make assumptions, and the data is then searched to test that hypothesis. If valid results are found, testing may continue on additional data.
One important point: because it is based on past events, data analysis is descriptive rather than predictive. It does not predict the impact of a change in a variable.
Predictive analytics is easy enough to define – it’s all in the name. Simply put, with predictive analytics, predictions are made based on data that is collected. These judgments are made based on historical data; further, they rely on human interaction to question data, validate patterns, and create and test assumptions.
In this technique, assumptions informed by past experiences speculate that the future will follow the same or similar patterns. Yet, it is important to note that predictive analytics are limited because they are informed by a human understanding of past events. Therefore, the technique’s capability is limited by the volume, time and cost constraints of human data analysts.
Yet, that observation should not discount the usefulness of predictive analytics. Marketers find predictive insights derived from data analytics extremely useful. By using data drawn from past experiences, marketers can predict campaign effectiveness, make more informed decisions on collateral, and more effectively choose geographic markets and demographics to target. Yet, there is a limit to how quickly a human can work. For instance, the more detailed a business’ desire to target and segment is, the higher the time and cost demands. Therefore, achieving successful, hyper-personalized campaigning becomes nearly impossible.
When encountering AI, you’ll likely also come across the term ‘machine learning.’ AI and machine learning is a continuation of the concepts set forth by predictive analytics. Yet, there is one important distinction: AI machine learning is able to make assumptions, test, and learn on its own, or autonomously.
With AI machine learning, this system can makes assumption, reassess the model and reevaluate data, all without human intervention. The most important part? The speed at which AI machine learning systems can work. Similar to the fact that AI can code for each and every possible action/reaction a human engineer may encounter, AI machine learning is able to test and evaluate data to predict every possible customer-product match, faster and more effectively than a human can work.
Now that we live in an era where data is constantly produced, we need to find ways to glean insights quickly and effectively. That’s where data analytics comes in. How is AI used in analytics? According to the Marketing Artificial Intelligence Institute, broadly, there are three categories:
1. Find new insights: Where the human eye falls short, artificial intelligence can fill in the gaps. One of the greatest capabilities of AI is the fact that it is well known for being highly effective in finding insights and patterns in large datasets at scale and at speed. Today, AI-powered tools can answer any questions you have about your website data. It can also recommend actions based on opportunities it finds in your analytics.
2. Predict outcomes: With AI systems, you can use analytics data to predict outcomes and plot successful courses of action. By analyzing data from hundreds of sources, these AI-powered tools can offer predictions about processes or methods that work and those that do not. AI systems can go even further, diving deep into data about your customers and offering predictions about product development, consumer preferences, and marketing channels.
3. Unify customer data and analytics: With AI, data can be unified across platforms quickly and effectively, allowing you to view all of your customer data in one place. AI can even unify data across different sources – even hard-to-track ones like call data.
With an influx of data in the digital era, your company needs a way to compile, analyze, and leverage it to help your business. Properly analyzing large amounts of data can help you understand trends and identify opportunities for your business. But it can be overwhelming if you don't know how to analyze data. That's where Stefanini's analytics solution can help.
Our industrial analytics solutions combine our machine learning and predictive modeling expertise, root cause analysis, loss costs predictive modeling, early warning failure modeling, process optimization and an agile analytics lab. We implement an agile, cost-effective approach to complete lifecycle analytical support, so you can use big data analytics findings to help you better understand and cater to your customers.
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