Data mining's value continues to grow in private sector

Data mining’s value continues to grow in private sector

on Mar 10, 15 • by Chris Bubinas • with No Comments

Data mining is quickly becoming a standard resource for countless organizations, as more success stories come to light...

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While data mining is not exactly a new technology, it’s definitely assumed a whole new level of importance in recent years. In the past, data mining was used predominantly by research institutions and only the most advanced public and private sector organizations. Now, businesses of all sizes and industries are increasingly coming to realize that they can experience tremendous benefits by leveraging these solutions.

Data mining is quickly becoming a standard resource for countless organizations, as more success stories come to light. As the trend grows, it will be imperative for firms to invest not only in the data mining solutions themselves, but also the necessary foundational tools, such as embeddable math and statistical libraries.

Data mining success

To understand why data mining is becoming such an important and popular resource for private sector organizations, it’s worth considering how some businesses have used these tools to revitalize their brands and operations.

Consider Timberland, for example. As The Washington Post recently highlighted, the footwear and apparel company saw its revenue stagnate between 2006 and 2012 as it lost out on market share in North America. The brand remained well-known, but this was not translating into profits. As Stewart Whitney, the company’s president, acknowledged, “[t]he brand had become stale in many ways and the focus wasn’t there.”

Timberland was able to revitalize itself by leveraging data mining tools. The company conducted a two-year study of data from 18,000 people spread across eight countries. By mining this data, the organization was able to determine that its ideal customer lived in a city and had a casual interest in the outdoors. With this newfound target demographic in mind, Timberland refocused its marketing and product development, eventually leading to a 15 percent increase in sales in the most recent quarter. This growth is even more impressive in that it came at a time when the retail industry as a whole experienced only slight growth. All told, Timberland’s profit margin increased from 8 percent in 2011 to 13 percent in 2014.

By mining customer data, Timberland discovered its ideal demographic.

A broader shift

According to The Washington Post, Timberland’s move to embrace data mining is not an isolated decision. On the contrary, the retail industry as a whole is coming to appreciate that shoppers now have more power than ever before. By utilizing review aggregate websites and product reviews, consumers can make smarter purchasing decisions, which puts pressure on businesses to become more competitive. Data mining represents a powerful means for teams to gain the insight they need to reach this higher level of competitiveness.

“LinkedIn revealed that data mining expertise was among employers’ most desired skills.”

This trend can be seen well beyond the retail industry. Notably, LinkedIn recently revealed the most highly sought-after skills among Indian employers on the popular professional networking site. As the Economic Times reported, data mining expertise was among the most desired skills, along with statistical analysis. The website concluded that this list was powerful evidence of industries’ efforts to take advantage of big data technology.

Going forward

As organizations continue to increase their reliance on data mining efforts, they will need to ensure that they have a solid software foundation needed to maximize results, accuracy, and efficiency.

The foundation of any analytics application is its math and statistical code. As these functions can be highly complex and hard to prove accurate, it makes sense to use embeddable algorithms built from years of expertise, testing, and real-world use, such as IMSL Numerical Libraries. These functions allow organizations to reduce development costs for their data mining and analytics efforts, while at the same time providing consistent, repeatable results. Just as importantly, embeddable math and statistical libraries can scale up along with software, avoiding the risk of software crashes that can otherwise compromise data mining efforts.

By embracing these resources as part of their data mining endeavors, organizations can position themselves to enjoy a key advantage in their industries for years to come.

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