Enterprise data is growing dramatically; billions of records are being added to enterprise databanks everyday. Choosing the right strategy to gather and display this data, is one of the biggest problems enterprises face. This is where Business Intelligence (BI) comes in. However, BI technology can't make intelligent decisions based on that data. Integrating Artificial Intelligence (AI) with Business Intelligence can enhance your data strategy to make smart business decisions.
How AI Improves Business Intelligence
Businesses are adopting various solutions including apps, processes and other tools, where large amounts of data is gathered and stored in a company database. Business Intelligence tools convert that data into understandable formats such as graphs and dashboards and can generate a roadmap to help managers stay in line with their goals.
Business Intelligence dashboards provide in-depth analytics but BI tools fall short in providing inferences from these analytics to drive business decisions. Here is where AI comes in.
According to CIO Magazine, "Business intelligence is descriptive, telling you what's happening now and what happened in the past to get us to that state." BI is not a predictive tool and cannot make decisions on its own.
AI applications can take this data and predict trends and suggest actions to take. Business leaders can make decisions based on the AI insights and calculate the ROI of these decisions. Without AI, businesses would have to employ a number of data scientists to get the same predictions and intelligence out of the BI data, which is time consuming and costly.
Using AI to Reduce Data Bias
Even though we have tools in place to generate data insights, the data sourced from BI tools can still be biased. If data is biased, an organization's decision-making will be misled.
To cleanse data and avoid data bias, AI-based data governance tools can be implemented. Businesses can be proactive and strategic with data sourced from multiple areas. Based on metrics, data cleansing models can be trained with machine learning algorithms to screen and cleanse data. AI solves the data bias problem for BI tools.
Adding NLP to Analyze Customer Data
Another application of AI in BI is using a Natural Language Processing (NLP) Engine to enhance data coming from customer service management tools. These BI tools with NLP interfaces can gather user interaction data and analyze it to develop a behavioral map and other visualizations for each customer. Based on this data, customer interactions can be customized and customer experience can be improved.
Conclusion
To be successful with BI and AI integration, it is necessary for organizations to understand and define how they want AI to be applied to solve their specific problems and reach their specific goals.
