C-Store technology is applied to POS data using analytics and artificial intelligence (AI) to pinpoint specific problems. High performance C-store operators leverage the technology to save time, sell more and reduce theft. POS analytics and AI allow supervisors to pinpoint problems, recognize top performers and coach cashiers on the most important issues.

The pressure of c-store operations demands tools that leverage data to save the time need to follow-up. Hiring, training, meetings and the normal day to day activities consume most of every day. Finding problems quicker is a big help. It just makes sense, experienced managers have fixed almost every possible problem. The solution is not usually the challenge. Rather, the issue is finding or even awareness of the problem. For example, knowing which stores are underselling promotions, seeing too many voids or even learning that cashiers are using their birthdate for every age check.

Three results that define 'Big' in Big Data

Defining the ‘Big’ in Big Data

January 25, 2014 — 

Redefining ‘Big’ in Big Data – Action, Ideas and Results. Matthew Shoup of LinkedIn provides a wonderful set of ways to interpret the ‘Big’ in Big Data. It is not so much the quantity of data that is important for many companies but rather what is done with it. While having lots of data is

Never use SQL again for ETL – Pentaho Data Integration reusable flows instead

Never use SQL again for ETL – Pentaho Data Integration reusable flows instead

October 7, 2013 — 

Since Pentaho is free and PDI is so easy, our team no longer will use any stand-alone SQL as part of our extract transform and load (ETL) process. Many times we have determined that we only need to extract the data ‘one time’. Inevitably, we need to run it again or something very similar. It