The Future of Business Analytics
The data era in business
As the possibility of collecting data in all aspects of business evolves and increases, the tools for processing and analyzing it are becoming increasingly obsolete. Therefore, it is necessary to implement new technology to organize and process information that allows us to make the right decisions to compete in a new, technologically modernized market.
Logistics and operations data
When we think of business cost optimization, we first consider general operating costs, which include production, packaging, transportation, and shipping costs, among many others. In this way, companies allocate large amounts of budget and make investments to quantify and measure the entire logistics process to identify critical points for improvement.
Administration and finance data
Another important part of business data can be found in all areas related to financial management, where financiers, accountants, lawyers, and human resources are involved, each working with interrelated and often disorganized information. Much of the business decisions come from analyzing this information, so the relevance of a good data structure and the ability to process it efficiently is essential to achieving good business performance.
Data in marketing and communication
It is no news the ability that companies have today to measure the behavior of consumers about their products and services to improve the entire customer journey. Market research is done practically in real-time, where even commercial experimentation can be done thanks to the help of digital tools, as is the case of the entire e-commerce industry. The ability to process a massive amount of data and give an immediate response is only possible through implementing new technologies such as Artificial Intelligence.
As a result of this data growth, we will witness the emergence of intelligent companies that will transform all industrial sectors using Cloud and AI technologies in the future. In addition, as a consequence of the pandemic, these technological processes have been increasing at an accelerated pace to counteract the global economic impact.
The impact of Artificial Intelligence on business analytics
As previously mentioned, the excessive growth of data collection in industries has made traditional tools obsolete since most require manual work, surpassing the ability to use data simply and effectively. Artificial Intelligence is a solution to this problem since it can process and analyze a massive amount of data in real-time without any effort.
“Artificial Intelligence is growing considerably due to the vast benefits it offers, helping companies fully leverage their business analytics expertise.”
Artificial Intelligence vs Business Intelligence
The main differences between IA and BI are the following:
● In terms of its principles:
The purpose of Artificial Intelligence is to create the reasoning of machines similar to human beings. A clear example is an idea we all have when we imagine a robot. Even though technology does not yet allow us to consolidate them that way, we are getting closer to that reality. On the other hand, the purpose of Business Intelligence is to help analyze business performance through data to understand the context and try to predict the future.
● In terms of its objectives:
The main objective of Artificial Intelligence is to generate intelligence systems that help replace human decision-making, allowing automatic and fast work to be done. On the other hand, Business Intelligence’s main objective is to provide information that can be used to make good decisions and thus improve the efficiency and effectiveness of a business.
In this sense, rather than being two opposing disciplines and technologies, they are complementary since both can identify problems to generate strategies and solutions based on results and data.
Artificial Intelligence and Business Intelligence are complementary technologies. Both can identify problems and generate strategies and solutions based on data.
How does Artificial Intelligence boost business analytics?
1. Accelerates data processing
When you want to perform a business analysis based on data, one of the most time-consuming and labor-intensive steps in the process is the organization and preparation of the data. In this case, implementing technologies based on Artificial Intelligence can help data analysts automate very demanding processes, such as extraction, transformation, and organization, allowing them to obtain higher quality data to work within less time.
2. More accurate results
Artificial Intelligence allows us to implement better tools to identify patterns and behaviors, especially when working with large volumes of data. This technology can understand correlations that would otherwise be impossible or difficult for data analysts to detect.
3. it Can perform predictive analytics
As we mentioned at the beginning, one of the main competitive advantages you can have today is understanding market behavior to provide better shopping experiences. There is no more efficient way to achieve this than through implementing Artificial Intelligence, as it can analyze data in real-time and forecast possible future scenarios.
4. Facilitates data queries
Artificial Intelligence enables the interaction of the business with its data, with the help of tools such as NLQ (Natural Language Querying) and NLG (Natural Language Generation) that allow us to make queries on the data in a simpler way, which results in better decision making for businesses in general.
First steps to boost business analytics with Artificial Intelligence
The race for the future of business
Investment in innovation and technology is one of the main boosters of the financial system, and there are more and more startups and companies that are focusing on developing and adopting technological processes such as Artificial Intelligence to solve business problems; however, they have not always translated into success stories mainly due to the way in which they have been implemented.
Implementing Artificial Intelligence into business analysis processes has to be addressed with a clear strategic direction to prioritize the aspects of the business that most need to implement automation performed by Artificial Intelligence.
Without this focus on strategic objectives, companies will likely get bogged down in tests and pilots without achieving significant results on data analytics projects.
The company must work on a data-driven structure and methodology to develop a successful business analytics project using Artificial Intelligence. This means that much of the business team and other external areas must know how to use data as a daily part of their work, understand that business data is extremely valuable, and build confidence in the results of the automation performed by Artificial Intelligence.
Finally, it is understandable that implementing new technologies in a company’s operations can be overwhelming and complicated. Even big companies with teams and areas specialized in technology have failed to change work processes significantly. At XalDigital, we focus on providing appropriate solutions for implementing such tools according to the needs of each company.
