Relational Solutions Blog

Implementing & Managing the Demand Signal Management Process

Posted by Janet Dorenkott on Sun, Aug 2, 2015 @ 06:30 PM

One misconception is that retailers send clean data. This simply is not the case. The cleansing and validation of the data is critical. Invalid data will give you invalid results. Retailers often send incomplete data. They also send duplicate data. In addition, they tend to “recast” previously sent data. Thus, how does one distinguish between duplicate and recast data? How do you identify missing fields? How is it rectified and re-loaded? What are the business rules? How is it modeled for reporting that is specific to Point-of-Sale data? Can it be integrated with internal data such as shipments, forecasts, budgets, etc.? How do you identify and analyze promotions? How can you predict problems before they impact your bottom line? Do you have the time and knowledge to shift through all this data to find only the nuggets that contain actionable information?


Topics: Business Intelligence, Big Data, POSmart, Data Warehousing, Demand Signal Repository, data integration, analytics, data scientist, analyst

How to Convince Your Boss You Need an Enterprise Data Solution

Posted by Janet Dorenkott on Sun, Jul 26, 2015 @ 07:00 PM

Why is it so difficult for consumer goods companies to create reports without an enterprise data infrastructure? How do I make my boss understand? What can we do to solve these problems? Your answer is in "What's the Hold Up?!"


Topics: Business Intelligence, Big Data, POSmart, Demand Signal Repository, blog, data integration, data scientist, E-Book, analyst

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


Topics: analytics, CPG, data scientist

Reason 9 What Makes POS Difficult for CPG Data Scientists & Analysts?

Posted by Janet Dorenkott on Fri, May 29, 2015 @ 04:01 PM

Relational Solutions knows the nature of business intelligence is that users requests constantly change. In addition, data that is available to the data scientist or analyst today will be different than the data needed over time. 

Anytime there are changes to the source data or change requests from end users, a data analyst is subjected to dealing with those issues related to that change.


Topics: Business Intelligence, POSmart, CPG, data scientist, analyst


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