Outsourcing Analytics: Reducing Initial Capital Investment

With the rise of big data and analytics, it is often difficult for companies to keep pace in developing big data departments. They want the big data initiative but often don’t have the capital to invest in a new department. What is a company to do at this point? That is the purpose of several new start up companies that offer data mining as a service to their companies. These start up companies, such as Mu Sigma, are designed to take a company’s data, and upon applying data mining principles and strategies, deliver meaningful information to their customer. Mu Sigma specifically has a five step process for their data mining strategy. This process includes :

1.       Input of data

2.       Application of data engineering

3.       Extraction using data sciences

4.       Informatics using decision sciences

5.       Providing information using decision support

(Source 1)

By applying the strategy of outsourcing their data mining, companies are able to reduce their overhead while still gathering meaningful information from their data that they have collected. In an inter connected world, it is often a valuable strategy to explore leveraging other company’s resources and enterprises before starting from scratch in house.

Mu Sigma is not the only data mining as a service start up company, yet it has received the highest level of investment from venture capitalists. Another data mining as service company that has received a high level of outside investment is Opera Solutions. Opera solutions focuses on defining signal hubs for their customers which is where the data is stored to be mined. By gleaning data, Opera Solutions can then offer their processing products to their customers as a subscription service and gain more capital.

Overall, I see the data mining as a service as a leading innovator in the data mining field. By providing an alternative to high investment in IT infrastructure and employees, these companies are able to target the majority of businesses in their target audience.

Sources:

11.  http://www.mu-sigma.com/analytics/platforms/overview.html

h2. http://www.forbes.com/sites/gilpress/2013/03/18/top-10-most-funded-big-data-startups/

33. http://www.operasolutions.com/solutions-and-services/