It is critical to use artificial intelligence for data analysis. Digital transformation increasingly dominates business actions. In this scenario, data takes on a central position and must be treated with the utmost attention.
However, this practice is relatively new, and it takes a change in the mindset of managers to understand this need. Everyone knows it is necessary to optimize processes, performance and results, but how to do this with technology?
That’s why we’ve put together a quick and objective post to give you a more accurate idea of what it takes for this intelligence to be used effectively in data analysis. Check out!
How Can Integrating Data Analytics With AI Be Beneficial For Technology Businesses?
AI is a collection of technologies that excel at extracting insights and patterns from large data sets. It can use these insights and practices to predict what drives results. And you can even learn to improve your predictions over time. With AI in analytics, you can get more value from the data you already have, unify that data, and make increasingly valuable predictions.
AI-powered software can automatically analyze data from any source and provide critical insights. Customer data analyzed with AI can be particularly revealing and help influence product development, improve team performance, and tell companies what works and doesn’t. See now 4 points where AI can be beneficial for data analysis.
Predictive analytics happens when a machine uses historical data to make predictions. Whether you know it or not, sophisticated predictive analytics solutions use AI to make these predictions. So when you hear that term mentioned, chances are AI is involved.
Prescriptive analytics means that a machine makes predictions and prescribes what to do next. Rigid analytics tools rely on AI to do this.
Augmented analytics is when AI is used to automate parts of the analytics process that would be performed by a data scientist or data science team. This includes tasks related to preparing data and gaining insights from datasets. This type of analysis uses AI to facilitate the human side of data analysis.
Finally, you might hear the term descriptive analysis. This means that a platform provides historical data. Describe what happened. All AI tools have a descriptive analysis component. But not all descriptive analytics tools use AI.
What Are The Main Benefits Of This?
Well, now that you understand how artificial intelligence can contribute to powering data analysis, let’s see what practical benefits are extracted from this junction. Follow up.
Reduction Of The Error Rate
False positives or distorted vision of problems represent some of the most critical points of traditional analysis. When a human being works on the issue of falling sales, for example, he usually raises a series of hypotheses. He analyzes and tests each one of them to understand what is happening.
On the other hand, data analysis based on artificial intelligence should focus directly on data, exhaustively scouring an entire data warehouse to identify the error. Thus, improving the analysis process and reducing analysis distortions are possible.
Finally, the traditional analysis may even find an error but not know if it is exactly the source of the problem. Still, in the sales example, it may be that human vision discovers that the influence of a bad climate has affected the results but cannot perceive that brand penetration is a much worse problem than this, which would be done for AI analysis.
Digital transformation has exponentially increased the number of data and information in business. As a result, management gained complexity and demands that were difficult to control. At this point, artificial intelligence comes in handy to absorb this huge Amount of work with high speed.
Suppose analytics aims to understand data better so companies can act on it. In that case, AI analytics is like a team of data scientists working 24 hours daily, answering on-demand questions with unparalleled speed and depth.
Integrated View Of The Company
AI finds insights and patterns in large data sets that humans cannot see. It also does this in scale and speed.
AI can delve deeply into analyzing data about your customers and offering predictions about consumer preferences, product development and marketing channels.
Finally, AI can be used to unify data across platforms. This includes using the speed and scale of AI to bring all your customer data into a single, unified view. Artificial intelligence can also unify data across different sources, even those that are difficult to track, such as call data.