Big Data for Small Businesses

Engaging in analytics-driven decision strategies is a wise practice for today’s business operator. By using the right combination of data and analytical methods, it is possible to realize significant value through optimization of customer satisfaction.

While there are many benefits to this strategy, one of the most notable is to entice additional sales after a purchase by offering a next logical item based on the specific profile of an individual customer. More generally, this enables a strategy of customer-based micro-segmentation, whereby it is possible to more accurately anticipate the rising expectations of each individual customer for new products and services.

The misperception is that enterprise systems vendors, offering proprietary platforms at significant cost, must provide the analytics software needed to achieve these results. This is often taken to imply that only large-scale business chains can afford to implement an analytics program for business optimization.  However, the reality is that there are multiple open source software systems available, that can be used free-of-charge to implement a data analytics program for a small business.

Overall, for small business applications, a low-cost data analytics system can be built that encompasses the following data management areas of practice, and their associated free-of-charge open source technologies:

• Data Stream Capture: social media channels, as well as other data streams that are generated in real-time, can be captured and managed using Apache Spark Streaming, as well as Apache Storm.
• Data Storage: storage of Big Data across multiple computer systems can be achieved by utilizing the open-source Hadoop Distributed File System (HDFS), as well as open source distributed databases that include Apache Cassandra and Apache HBase, amongst others.
• Data Analytics: analytical and predictive algorithms can be designed and trained using Apache Spark machine learning library, H2O machine learning library, Microsoft R Open, and Python.
• Data Visualization and User Interaction: graphical user interfaces for data display and user input can be designed using several library packages in Microsoft R Open, Python, as well as JavaFX. Moreover, displays written in Java/JavaFX can directly call Microsoft R Open and invoke statistical analysis procedures on demand via the rJava interface.

Although each of these products have a functional purpose in their own right, the ultimate advantage occurs when they are combined as an ensemble of subsystems in an overall data management and analytics chain. These are all available free-of-charge, and the software needed to facilitate interfacing between these components can easily be designed using Java.

Essentially, the analytics design process in this case would involve specification and training of machine learning algorithms appropriate to the data characteristics and the business intelligence objectives. Additional design would involve specification of data storage attributes in terms of file systems and database table structures, as well as format of user interface for display of data and user command input. All of these activities can be performed by a competent data science provider specializing in analytics software design, such as AlgoTactica.

The advantage of this approach is that it is possible to design an open source data analytics platform tailored to the individualized needs of the small business, without incurring the huge software licensing costs normally associated with proprietary platforms offered by commercial vendors. The only direct costs involved would be associated with the purchase of computer hardware and the contracting of a data science provider following a firm fixed price quotation.