Relational Solutions Blog

Reason 10 What Makes POS Data Difficult for Data Scientists & Analysts

Posted by Janet Dorenkott on Mon, Jun 22, 2015 @ 02:00 PM

The lack of an integrated, enterprise demand signal management process is the reason it’s so difficult to work with point of sale & syndicated data. With a truly open, flexible and manageable process in place, new data, new retailers, changing data elements, various data types, and so forth, all become much more valuable. An enterprise demand management process should integrate with your existing data warehouse to leverage value that already exists. With a sound architecture, CPG companies will realize a fast ROI and put an infrastructure in place that will address todays reporting and analytics needs, as well as on-going insight requirements. Sometimes referred to as a DSR, Demand Signal Repository, however, it should be considered a process that leverages an open architecture.

The lack of an integrated, enterprise demand signal management process makes life more complicated than necessary for data scientists and any analyst to perform business intelligence & gain insights

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Topics: analytics, CPG, data scientist

Reason #8 Frequency of Data Feeds

Posted by Janet Dorenkott on Mon, May 25, 2015 @ 08:18 PM

External data comes from various sources at different frequency rates. Analysts might get scanned point of sale, POS data, via EDI feeds directly from a retailer every day. Data Scientists could buy syndicated data provider information every week, but it might be last months data. You might also be getting data weekly from another data provider for different retailers.

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Topics: Business Intelligence, POSmart, analytics

Reason #7. What Makes POS Data so Difficult to work with for CPG Data Scientists?

Posted by Janet Dorenkott on Tue, May 19, 2015 @ 10:04 AM

 

  Point of sale data comes in many different formats. Those formats all need to be transformed into a common data type in order to report off that data. An enterprise architecture needs to be able to extract data, transform the various formats into the same data type and load the data into a format that is easily accessible.

Depending on the database you select for analysis, The various formats listed above need to conform to one single data format. In that process, data needs to be mapped, hierarchies need to be aligned, calendars need to be addressed.

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Topics: Relational Solutions, Business Intelligence, POSmart, Demand Signal Repository, data integration, analytics, CPG, data scientist, analyst

Reason #6 for What Makes a CPG Analysts Job So Difficult

Posted by Janet Dorenkott on Wed, May 13, 2015 @ 08:30 AM

Retailers make decisions based on their own profitability. Not that they don't consider the consumer goods manufacturer, but ultimately, they have an obligation to their shareholders. The problem for CPG analysts is that they are subject to whatever changes the retailers make. When a retailer decides to add new stores, data integration needs change. When a retailer puts new contractual obligations in place, the CPG manufacturer must accommodate those changes and their reports and analytics needs to be reflective of those changes as well. This is our 6th of a 10 part infographic, blog series on what makes using point of sale data so difficult for business analysts and data scientists in the consumer goods industry. 

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Topics: POSmart, analytics, CPG

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