To understand how SAP HANA uses machine learning algorithms, it is crucial to revisit the principles of predictive analytics within the context of Data Science.
Predictive analytics is a branch of advanced analytics focused on forecasting future events, behaviors, and outcomes. It leverages historical data and patterns to make these predictions. This process has become increasingly critical, especially in today's business landscape characterized by significant trade and supply chain disruptions.
Navigating Business Challenges with Predictive Analytics
In today's rapidly evolving business landscape, the ability to anticipate future trends and outcomes is more vital than ever. Predictive analytics serves as a powerful tool in this regard:
Predictive analytics helps businesses look into the future and peer around corners with a reasonable degree of accuracy. This capability has always been important - but it has never been as critical as it is right now.
Companies have had to navigate major trade and supply chain disruptions, sudden spikes (or nosedives) in demand, brand new risks and challenges, and overall uncharted waters. That’s why predictive analytics has shot to the top of priority lists for organizations around the world.
Predictive analytics has become increasingly important for organizations facing greater business challenges. By accurately predicting future events or trends, organizations can proactively identify risks, seize opportunities, optimize operations, and make informed decisions to mitigate potential impacts.
Note
Businesses that base decisions on data are 19 times more likely to be profitable.
Source: McKinsey
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Basic Steps in the Predictive Analytics Process
To gain a deeper understanding of this process, please watch the video illustrating the six steps of the predictive analytics process in detail.
Importance of Data in the Decision-making Process
Both predictive analytics and machine learning use historical data to forecast future outcomes. In the context of machine learning, these outcomes are used to retrain and improve the model. Data plays a pivotal role in the decision-making process of organizations, especially in the domain of business analytics.
Here’s how businesses leverage the power of data to drive informed decision-making, optimize operations, and secure a competitive edge in the market:
- Business analytics derive insights from data, guiding strategic and operational decisions, optimizing processes, and mitigating risks.
- Leading companies rely on analytics for fast, data-driven decisions, driving revenue growth and innovation aligned with market demands.
- Data-driven decision-making enhances customer experiences through personalized interactions, which promotes loyalty.
- Data-driven insights empower employees to innovate, make informed decisions, and drive efficiency.
- Leveraging data analytics provides a competitive edge by anticipating market trends, optimizing operations, which leads to sustainable growth.
Note
94% of companies say analytics is important to their growth and digital transformation 1
59% of organisations are currently using advanced and predictive analytics 1
Source:
https://www.sap.com/uk/products/technology-platform/cloud-analytics/what-is-analytics.html