BI Trends

Top 10 Benefits of Data Mining

Data mining is critical to success for modern, data-driven organizations. An IDG survey of 70 IT and business leaders recently found that 92% of respondents want to deploy advanced analytics more broadly across their organizations. The same survey found that the benefits of data mining are deep and wide-ranging.
 

In fact, respondents identified no less than 30 different ways that data mining positively impacts their businesses. Here are the top 10:

1. Improved decision-making (56%)

2. Improved security risk posture (47%)

3. Improved planning and forecasting (44%)

4. Competitive advantage (41%)

5. Cost reduction (41%)

6. Customer acquisition (40%)

7. New revenue streams (40%)

8. New customer acquisition/retention (34%)

9. Improved customer relationships (31%)

10. Development of new products (31%)

Data mining has the power to transform enterprises; however, implementing a process that meets the needs of all enterprise stakeholders frequently stands in the way of successful data mining investments—78% of respondents say they are struggling to find the right data mining strategy or solution. 

This struggle partially stems from the wide range of data mining and BI tools available to analysts, including R, Python, Azure ML Studio, SAS, and traditional tools like Excel. Additionally the diversity and complexity of data mining tools and algorithms are the root causes of some key challenges organizations face. For example, 38% of the respondents say data mining tools are not intuitive or conducive to self-service, and 31% say they lack the skill sets needed to leverage the tools.

Despite these barriers, businesses that are able to effectively capitalize on the benefits of broadly distributed data mining have key practices in common. Successful companies:

-  Know the core business needs, both tactical and strategic, that data mining can address;

-  Identify and evaluate the data sources that data mining tools will use for accuracy and relevance;

- Determine the applications, including the BI systems, which data mining tools must interoperate with;

- Identify which of the available data mining solutions addresses the full scope of the organization’s requirements, from budget to technical ability of end-users;

- Use one standard data mining tool that meets the needs of IT, data scientists, and analysts, while also meeting the consumption and visualization needs of business users.

Ready to read more key best practices and statistics on this topic? Download a complimentary copy of the IDG Data Mining Quick Poll: Democratizing Data Science and Predictive Analytics.

Start putting these best practices to use. Discover how MicroStrategy makes it easy to deploy data mining, predictive analytics, and machine learning applications here: https://www.microstrategy.com/us/products/capabilities/advanced-analytics

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