Relational Solutions Blog

Janet Dorenkott

Recent Posts

Before Big Data, IoT & Omnichannel

Posted by Janet Dorenkott on Mon, Aug 17, 2015 @ 09:07 PM

Before the terms, big data, unstructured data, omnichannel, Iot, Internet of Things & SOMOLO were coined, we had mainframes, ERP systems and data warehouses (and by the way, we still do). You could make the claim that big data started in the 1950’s with IBM’s “Big Iron” and “Big Data Processing” to handle mixed work-loads.


Topics: Business Intelligence, Big Data, Data Warehousing

Data Warehouse or Data Mart? What's the Difference

Posted by Janet Dorenkott on Mon, Aug 10, 2015 @ 05:05 PM

What’s the difference between a Data mart and a Data warehouse? Why are they so often confused.

Simply put, an enterprise data warehouse is the union of all marts being fed by a single source. That single source is a staging area where data is cleansed and harmonized and is often referred to as an ODS or Operational Data Store.

 A data mart can be stand alone reporting solution or it can be soundly integrated into an enterprise data warehouse.


Topics: data mart, data warehouse

What Is Business Intelligence and Why Do We Need It?

Posted by Janet Dorenkott on Wed, Aug 5, 2015 @ 12:47 PM

Business Intelligence, what is it and why do we need it?


Topics: Business Intelligence, Data Warehousing, analytics

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


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