Due to recent developments in technology, it is evident that machine learning has become vital to processing massive amounts of business-generated data that humans are unable to do on their own. In fact, Maryville University reports that the data created worldwide will skyrocket to 180 trillion gigabytes by 2025, with the U.S. business data analytics market predicted to be valued at more than $95 billion by 2020.
Here are some key examples and applications of how AI can power data analytics:
Sifting through data can be an extremely time-consuming prospect when done manually. Now that industries across the globe collect what is termed as “big data”, how can we derive useful insights from it? That’s where AI comes in. An article on The Enterprisers Project called ‘How Big Data and AI Work Together’ explains that “Historically when it comes to analyzing data, engineers have had to use a query or SQL (a list of queries). But as the importance of data continues to grow, a multitude of ways to get insights have emerged. AI is the next step to query/SQL.” These previous statistical models have now become integrated with computer science, becoming AI and machine learning, leading to the efficient analysis of data at breakneck speeds.
Harnessing the data is not an easy task, especially for big data. A typical business or an organization will have several data sources such as sales records, purchase orders, customer data, etc. The challenge here is data ingestion — to consolidate all these data together, bring it under one umbrella so that analytics engines can access it, analyze it and deduct actionable insights from it. Our previous article on Big Data Ingestion explains how to choose the right data ingestion tools.
Data analytics in the past used to be based on historical analysis. ‘Based on this previous occurrence, this is probably going to happen when we do this’, which is basically a linear prediction. However, AI and machine learning have opened up new predictive avenues on an exponential scale. Machine learning algorithms can now learn how to make decisions or actions based on insights from the past. By starting with process and data mapping, we can eventually make our way to predictive analytics and gradually transition into prescriptive analytics. On the other hand, it’s also important to be aware of having accurate data to base your insights on. Using AI to predict outcomes based on flawed or insufficient data can lead to truly catastrophic outcomes.
Forbes explains how AI can prevent some common human mistakes such as “budgets getting missed, integrations breaking and features forgetting to be turned on” which can lose your business a great deal of money. In order to catch problems before they occur, anomaly detection is a key feature of AI’s data-driven process. Comparing current data and past data to find any points that stand out allows you to find flaws in cybersecurity, advertising, or even marketing.
According to Medium’s exploration of recent data analytics and AI trends, these two concepts are vital to the development of ‘digital twins’, which they define as “an exact digital replica of a product, process or service” that “acts as a mirror of the real world to provides a means to simulate, predict, forecast, service, and self-heal.” An example of this is how weather forecasts use sensors to gauge the weather and represent these conditions through digital formats and predictions. This comes in handy for predictive maintenance for instance, because sensors can detect when equipment is about to break down. As a result, AI can also help improve safety and reliability for employees while reducing the costs of operations.
Ultimately AI is an invaluable tool in your arsenal when it comes to powering the process of data analytics. However, it’s up to you how effectively you choose to use it, and what decisions you make to transform the future landscape of your business.