#artificial learning

Given the magnitude of AI usage in digital marketing, it is hard even to believe that once marketers frowned upon the thought of integrating AI with digital marketing. But as every technology takes time before it is accepted, AI and machine learning are now used widely by all marketers. In recent years, marketers have put an incredible amount of trust in these technologies and reaping the many benefits it brings.

But AI is not the only technology digital marketing is benefiting from. It's subsets like the Internet of Things, Machine Learning and big data that are helping marketers to realize the full potentials of Artificial Learning. Given the edge and ease AI provides to marketers, there is no denying the fact that the usage of this technology would be more widespread.

Embark on a journey into Prompt Engineering, a nuanced art form poised to redefine the future of AI interactions. This transformative force empowers businesses to craft precise queries, unlocking applications in content creation and code generation through the prowess of GenAI. Delve into the intricacies of vulnerabilities and discover best practices, ensuring ethical use and optimal results.

Problem Statement

Why Traditional Business Intelligence techniques are failing to resolve today’s Business problems?

Possible Solutions

The goal of Business Intelligence is to provide right-time information to management and function as a decision support mechanism. Below are few challenges in using Traditional Business Intelligence techniques.

IT spends months or even years building out dashboards and reports based on user requirements. Users get their hands on the dashboards and reports and immediately request changes based on what they see. IT then spends more weeks or months implementing those changes. Users spend time with the new dashboards and reports, and then come up with more requirements as new questions come to mind. It’s a long, painful cycle.

The extracted and filtered data need to be transformed into operational data warehouses by ETL (Extract, Transform and Load) processes. Then, techniques such as OLAP cubes are used to enhance the data storage to deliver better analytical performance. BUT as long as they are query- and cube-based solution, maintaining associations in the data requires hand coding — and therefore lots of time and money which results as higher cost solution.

If business user wants to get deeper insight into an aspect of the business which required to add a new data source, then developer would have to go back and re-code the associations by hand all over again. Business users failed to explore data, make discoveries, and uncover insights that can be used to help them solve their problems in new ways.

The performance gains are overshadowed by the enormous complexity and additional delays imposed by an ETL process.

Traditional BI required Specialized consultants to perform ETL tasks and set up the Data Warehouses that few decision-makers will use.