These two terms are bordering on the same concept in theory because both do collect “intelligence” or data and take action based on that collected data. The difference between AI and BI is that AI bridges the gap for those who don’t have a deep technical understanding of the data. AI can analyze massive amounts of data and deliver recommendations based on that data. From everyday life to the industrial space, we’re bordering on creepy sci-fi level AI capabilities- as a consumer, it’s a bit unsettling, but business owners are chomping at the bit to adopt AI technologies to enhance their BI solution.
The driving force behind BI is to use various technologies and tools to collect and analyze data to aid decision-making. AI on the other hand explores using computer systems to mirror various attributes of human intelligence, such as problem-solving and learning. The defining difference between AI and BI is that with BI, the decision-making is left in the hands of a human, whereas AI can enable computers to make those decisions themselves. By embracing AI and BI, businesses can work with mass quantities of data and turn it into actionable plans that will benefit the organization. Gartner predicts that natural-language generation and artificial intelligence will be a standard feature of 90 percent of modern BI platforms within the next two years- so how is AI going to work with your current BI strategy?
Bringing predictive features to reporting
BI systems are being modified with artificial intelligence to bring predictive features to reporting. Today, BI systems make it easy. Users have the ability to click a few data points, choose the desired variable for predictions, and those predictive models will be generated without the hassle of a data scientist writing code. Regardless of the industry you’re in, incorporating predictive features into reporting optimizes the business and makes it easier to automatically act on that data whether it is customer behaviors or predicting risk. Improving data analytics and insights is one of the top use cases for artificial technologies.
Enhanced data points
With AI-powered BI dashboards, more data is being collected. Rather than just collecting numbers and structured data, there’s a lot of insights that can be added to the reporting such as notes or images within records that can benefit the overall analysis. AI is going to inject BI solutions with powerful insights and as a stakeholder for any business, you can’t afford not to realize how AI is impacting modern data management and process management. AI-powered BI has the power to discover patterns from unstructured data from external sources and enterprise data in real-time. The growth of the big data market makes it difficult to act on strategic decisions, and there’s not enough data experts out there who can keep up with the amount of data that an organization is producing. AI helps break all of that data down into actionable insights automatically that will in turn, enhance prescriptive models.
Take action with AI-powered dashboards
So yes, AI clearly can enhance BI dashboards and the data that is collected, but that’s only the beginning. AI-powered dashboards also have the ability to then make suggestions and advice based on the analytics in the dashboard and take action. A real-time automated detection system using machine learning methods can free your data analysts from constant monitoring so they can focus on more critical anomalies. An automated AI analytics solution can map out millions of metrics rather than just collecting the information from a few KPIs. It’s a way for a business to take a step back to look at the whole picture -their business- rather than focusing on just one small piece of it. It’s a step way beyond descriptive business analytics and it definitely opens up the door for new business opportunities. According to Accenture, 40 percent of industry leaders agree that AI is going to impact their companies in the next 3 years – and they absolutely should be thinking that way.
Most companies collect more data than they know what to do with- and that’s a problem. Organizations need to manage all of that data in a way so only quality data is being filtered through for reporting purposes- queue AI tools. AI-driven tools have the power to automatically sift through that data based on set algorithms to ensure high-quality data processing without an actual person checking up on it. Data quality management is set to be one of the biggest tech trends for this year. According to Ovum Research, poor quality data is costing businesses at least 30 percent of revenues- that’s not a percentage to ignore! Further, it’s not only costing money, but it also costs a lot of valuable resources their time to validate the analytics. Forrester reports that nearly one-third of analysts spend more than 40 percent of their time vetting and validating their analytics data before it can be used for decision-making. These are areas where AI-powered tools can come into play and beef up an organization’s overall BI solution and making it more efficient.
A struggle CEOs are having with AI is that the solutions AI tools are coming up are great to optimize the organization but how exactly is it being done? It needs to be more transparent and as organizations are using more AI-powered tools, more is being done to not only look into the level of transparency, but the ethics behind all of the data that is being collected so organizations can understand and make the most out of the valuable data that will make their business thrive.
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