登录查看更多内容

How can data mining and OLAP support decision making in a dynamic and uncertain environment?

由人工智能和领英社区提供技术支持

Data mining and OLAP are powerful tools for extracting insights from large and complex data sets. They can help you make better decisions in a dynamic and uncertain environment, where you need to adapt to changing customer needs, market conditions, and business opportunities. In this article, we will explain how data mining and OLAP work, what benefits they offer, and how they can be integrated and enhanced with other technologies.

在这篇协作文章中查找专家回答

由社区从 2 条内容中精选。了解更多

1 Data mining basics

Data mining is the process of discovering patterns, trends, and relationships in data that are not obvious or easily accessible. Data mining can help you understand your customers, competitors, products, and processes better, and find new ways to improve your performance, efficiency, and profitability. Data mining involves applying various techniques, such as classification, clustering, association, regression, and anomaly detection, to analyze data from different sources and perspectives.

添加您的观点

Michael Kerr

Lead Data Engineer | ETL and ELT | R | Python | SQL | Azure Synapse | AI Engineering
(已编辑)
举报内容
Data mining is indeed a powerful tool for businesses and organizations, allowing them to make more informed decisions and drive innovation. It's important to note, however, that the quality and accuracy of insights derived from data mining heavily depend on the quality of the data being analysed. Therefore, investing in robust data collection and preprocessing methods, as well as ensuring data privacy and security, are essential for maximizing the potential of data mining and leveraging it for a sustainable competitive advantage.

已翻译

赞

2 OLAP basics

OLAP stands for online analytical processing, and it is a way of organizing and querying data in a multidimensional structure. OLAP can help you perform complex and interactive analysis on large amounts of data, such as sales, inventory, finance, and marketing data. OLAP allows you to slice and dice data along different dimensions, such as time, location, product, and customer, and drill down or roll up to different levels of detail. OLAP also enables you to compare and aggregate data across different scenarios and time periods, and create reports and dashboards.

添加您的观点

Michael Kerr

Lead Data Engineer | ETL and ELT | R | Python | SQL | Azure Synapse | AI Engineering
举报内容
OLAP is an invaluable tool for businesses to extract valuable insights from their data in a highly efficient manner. As we move towards an increasingly data-driven world, it's important for organizations to adopt scalable and performant analytics solutions like OLAP. A key advantage of OLAP is its ability to precompute and cache aggregations, which significantly reduces query response times for complex analyses. Additionally, the integration of machine learning and AI algorithms with OLAP systems can further enhance the decision-making process by enabling predictive and prescriptive analytics. This empowers organisations, not only react to past trends, but also proactively plan and strategise for the future.

已翻译

赞

3 Data mining and OLAP benefits

Data mining and OLAP can be powerful tools to support decision making in a dynamic and uncertain environment. Through these techniques, you can gain valuable insights and actionable information to identify new opportunities and trends in your market, segment customers for personalization, optimize pricing and promotion strategies, detect fraud and anomalies, enhance forecasting capabilities, and monitor performance.

添加您的观点

4 Data mining and OLAP integration

Data mining and OLAP can be integrated to create a comprehensive and flexible analytical environment. This can be done in various ways, like using data mining to prepare and transform data for OLAP analysis, or using OLAP to explore and visualize data mining results. Additionally, data mining can be used to enhance OLAP analysis, while OLAP can be used to enhance data mining analysis. By leveraging the strengths of both approaches, a more comprehensive and flexible analytical environment can be achieved.

添加您的观点

5 Data mining and OLAP enhancement

Data mining and OLAP can be enhanced with other technologies and methods to improve their performance, functionality, and usability. For instance, cloud computing can be used to store and process large and distributed data sets more efficiently and scalably. Big data platforms and tools can help handle diverse and unstructured data sources and formats. Artificial intelligence and machine learning can automate and optimize data mining and OLAP processes, such as data preparation, feature selection, model selection, parameter tuning, and evaluation. Natural language processing and generation allow users to interact with data mining and OLAP systems using natural language queries. Finally, data visualization and storytelling can present findings more effectively and engagingly.

添加您的观点

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

添加您的观点

Data Mining

+ 关注

给文章评分

我们借助人工智能创建了此文章。您认为这篇文章怎么样？

很棒不太好

举报此文章

查看全部

How can data mining and OLAP support decision making in a dynamic and uncertain environment?

1

2

3

4

5

6

1 Data mining basics

2 OLAP basics

3 Data mining and OLAP benefits

4 Data mining and OLAP integration

5 Data mining and OLAP enhancement

6 Here’s what else to consider

Data Mining

给文章评分

感谢您的反馈

更多Data Mining相关文章

更多相关阅读内容

How can data mining and OLAP support decision making in a dynamic and uncertain environment?

1

2

3

4

5

6

1 Data mining basics

2 OLAP basics

3 Data mining and OLAP benefits

4 Data mining and OLAP integration

5 Data mining and OLAP enhancement

6 Here’s what else to consider

Data Mining

给文章评分

感谢您的反馈

查看其他技能