How BI Bridges The Gap Between Data Science And Decision-making?

The two core factors of optimizing business decision-making are data science and managers.

The relationship between Business Intelligence and Data Science

Since its inception, business intelligence (BI) has evolved from silo dashboard-based reporting to modern self-service analytics with real-time data access and fast insight to infer business decisions. It focuses on using the information to create decisions that are timely and effectively informed by the appropriate insight. And among its primary duties are

• Determine business issues
• Clarifies business use cases
• ingest the knowledge and insight produced by data science
• Describes business KPIs
• Keep track of changes in business indicators and deal with shifts in decision-making
• Recognize the biases, assumptions, and boundaries of the model.
• Explain the essential elements that were utilized and their significance or contribution.
• Track model performance, data drift, and concept drift

How to bridge the gap?

Many decision-making processes rely on the relationships and insights produced by data science. Data science and BI can occasionally produce conflicting messages, and it can be challenging to explain what actually happened in the real world using data science. This makes the choice more difficult.

We must be aware of the primary causes of these disparities in order to close the gap and improve both the speed and quality of decision-making. Technology and human factors are two ways we might look at this. Technology contains methods for ensuring data consistency, such as creating semantic layers that enable both parties to access and observe the same version of the data or defining the same measure to track concept and data drift and calculate the effects of model performance degradation on KPI.

Additionally, it involves using BI to automate decision-making so that BI and data science may be integrated into the overall process. The data science team gives seamless prescriptions in addition to making predictions. To enable smooth collaboration between the two, the core is to build a BI solution around a semantic layer. When data drift and model performance deteriorates, BI can automatically detect the gaps between the two, identify the underlying causes, and send out alerts.

The second factor is data science leaders who are mainly on communication and collaboration.

Conclusion

BI service providers are bridging the gap between data science and decision-making by taking the visualizations to the next level by empowering everyone with quick data-driven decisions with BI solutions. BI solutions enable easy collaboration on reports among the team and shares insights across the application.

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