Businesses operate in a world of data. We collect it from our websites, market research, internal records, customer feedback and more. Businesses even spend a lot of money buying third-party data. While collecting all of this data is great, it is only as useful as what you do with it.
Traditionally, the work of data analytics fell to data scientists. However, artificial intelligence is starting to do much of this work. With AI, data scientists can be more effective, and it can even provide analytics capabilities to people with no data science experience. This is what many people call augmented analytics.
As more organizations recognize the value of AI-powered BI tools, the market for augmented analytics will grow. The market for augmented analytics was over $5 billion in 2020. That number is expected to grow to $25 billion by 2026.
This growth has many business leaders wondering what artificial intelligence can do for their analytics. Read on to learn about some of the ways AI can be applied to business analytics.
Delivering Actionable Insights
Artificial intelligence is really good at taking large datasets and finding patterns in the data. Once it finds those patterns, it can then turn that information into insights. It can also do this work in less time than it would take human data scientists.
As an example, AI analytics could be used by a manufacturing facility. The augmented analytics platform could analyze data to find inefficiencies or problems with quality control. With that information in hand, plant managers could then go to work finding solutions that will save the plant money or improve the quality of the product.
Predicting Outcomes
AI can be effective when working on predictive analytics. With this, the AI-analytics platform identifies patterns in historical data to make predictions based on the current conditions. This can be used to identify and respond to issues before they cause a problem. It can also help you prepare for future opportunities so you can position your business to take full advantage.
Inventory management could be one example of using predictive analytics. The AI could find a pattern in the data that might suggest demand for a product is going to decrease in the near future. This could be a signal that you should order less of that product or that you need to develop a strategy for increasing the demand.
Providing Solutions
Being able to predict outcomes is great, but you might not always be sure of the correct course of action based on a prediction. This is where prescriptive analytics can be beneficial. With prescriptive analytics, an AI-powered BI tool can look at various solutions and develop models that will help business leaders make the right decision.
Artificial intelligence can do this by building models based on data. It can then take each potential course of action to see how it will play out. It can then provide this information to the decision-makers to help them find the solution that will be most beneficial to the business.
Processing Data
Processing data from different sources can be one of the most time-consuming parts of analytics. You have data from various sources, and they all need to be formatted for the analytics tools you may use. With augmented analytics, the AI can do this work faster and with fewer errors.
Some AI systems can actually pull data from a variety of sources and unify them in one set. The more you can integrate different data sources together, the more powerful your analytics will be. Along with that, some of these systems can even do things like read invoices or text documents so they can be used by the analytics platform.
AI is the future of analytics. That is why business leaders need to start making a plan for using AI with analytics. The businesses that adopt AI analytics platforms now will have a clear advantage over those that do not.
