Business Intelligence and Data Science
Business intelligence is more than just numbers. Finding ways to unlock the underlying meaning and value embedded within the numbers is critical for sustained success. Data science is the study of trends and patterns. Researchers leverage information to graph predictive models and illustrate current activity. In business, data science and analytics is applied to everything from investment decisions, hiring processes, and more. However, people, unlike numbers, can be unpredictable.
When data scientist develops a dashboard, they need to have a profound understanding of the problem they want to solve. They have to prove that their collection of data accurately quantifies the variables involved. They need to implement their model to translate the collected data and convert the insights into an effective decision-making tool. A simple discussion about the graphic can help confirm the model and predictions are directed at the right research or business intelligence.
When a problem has defined key indicators for progress, data analytics becomes significantly easier to implement. When you can measure the performance, success rate, or actions of your current strategy, a data dashboard can optimize the process. MicroStrategy, a business intelligence and software company, based in Virginia, released a report in 2020 disclosing that at least 52% of companies worldwide implement predictive analytical tools at their respective enterprises. A few examples where businesses rely on data dashboards are for marketing research, supply chain and logistics management, and stock performance.
Businesses also use a data dashboard to track their employees. Conversations about predictive models can help bring to light methods of handling outliers. There is a certain level of irrationality, which can be hard to gauge from studying a data dashboard. Emotions can be tricky. A personality test can only tell you what the applicant thinks of themselves. A discussion can give the employer, coach, or interviewer another perspective of how the applicant, client, or individual actually behaves off the paper.
A data dashboard or data model can only know as much as the human has shared with it. Data science cannot consistently make an accurate prediction on the unknown. While data science can be helpful at detecting fraud, improving current operations, or reducing risks, there are other challenges a data dashboard might struggle with. When challenges like emotions, personalities, or the intricacies of human intelligence arise, a departure from artificial intelligence may be required.