How SMBs Leverage Big Corp Technology 

Background of Small Businesses and Data Mining:

Small and midsize businesses (SMBs) consist of businesses operating with less than 1,000 employees, with small businesses being those that employ less than 100 employees. At the end of 2014, there were just over 28 million small businesses in America, employing 56 million workers – making small businesses a foundation of America's economy. 

According to one of the most recent annual surveys on small and midsize businesses in America, only 40% of SMBs collect customer information. While less than half of SMBs use some collect information on customers, about 65% of Fortune 500 countries have data marts and data warehouses with customer information.

Process of Data Mining:  

There is an evolution to the process of knowledge discovery in databases containing multiple steps. The process of data mining begins with raw data, such as customers having buying preferences. Identifying which preferences to look at is the next step in the process, selection�.  Organizing the results into preprocessed data is the next step, followed by refining that data into transformed data. 

The final two steps are what will separate the leading SMBs from the rest of the pack, and those include: the identification of patterns and what to do with the discovered relationships. 

The knowledge gained from screening all this data through data analytics is only useful if applied correctly by small businesses.

Misperceptions of Data Mining in Small Businesses:

With any revolutionary business method there will always be skeptics. Self-checkout lanes at supermarkets are an example of a technique that was not immediately accepted when it was implemented. Customers complained that there was a lack of interaction because these stations are unmanned. While this is a valid point, these stations do not completely replace clerk-serviced checkout lanes. Instead, these stations provide a helpful alternative for customers who are trying to hurry through the checkout process. 

In the same way, the revolutionizing power of data mining is not trying to replace the knowledge gained from a face-to-face interaction with a client, nor attempting to eliminate conversations between staff and customers. This technique is intended to be a new tool for companies to improve these scenarios.

For small businesses venturing into the use of data analytics for the first time, it may seem like a daunting task to try and understand the software and the results provided by the software. Many SMBs may be deterred from this revolutionizing technique because it seems too complicated to be understood by the average small business owner. While large enterprises do need data scientists to be in charge of processing the data analytics, small and midsize businesses can manage without hiring a PhD in data science. Most small businesses should be able to get by without the need to hire anyone new at all.

Examples of Data Mining in Small Business Situations:

One of the most significant areas that data mining can improve is a company's marketing campaign.  There is a similar example for the effects of providing a highly targeted marketing approach. These are exactly the kind of numbers a local business would be looking at if they were using the technique of data mining. Based on their customers preferences, SMBs could narrow down the patrons to send mail. Achieving a higher response rate would not necessarily mean the business would make more sales, but the cost of mailing would be low enough that the company would proportionally have a higher rate of return on investment.

Conclusion:

Small businesses all across the country are missing out on an opportunity to provide better business to all of their customers. If these small and midsize business owners would look past the misperceptions of data mining, they would be introduced to an entirely new approach for their company. Once these owners become better educated on these issues, their companies will have new potential to thrive.