Relational Solutions Blog

Analytics as a Service - Opening the Gateway to Insights

Posted by Karen Kurtzweil on Fri, Jul 22, 2016 @ 01:33 PM

It's no secret that data scientists and analysts are in demand. For the thousands of companies looking to hire a data scientist, the line of requests is growing faster than the new ride at any amusement park.  A quick search on LinkedIn or Monster returns thousands of job postings seeking highly skilled data scientists across enterprises worldwide.  After being labeled "the sexiest job of the 21st century" by Thomas H. Davenport and D.J. Patil in their Harvard Business Review article 2012, all eyes are focused on this highly coveted position. Unfortunately, there are not nearly enough trained experts to go around and the talent gap widens.

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Topics: Business Intelligence, Big Data, analytics

Reason #10 What Makes a CPG Analysts Job so Difficult?

Posted by Janet Dorenkott on Wed, Mar 30, 2016 @ 02:44 PM

Requst a Consulatation,

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Topics: Demand Signal Repository, analytics, CPG, enterprise DSR, predictive analytics, prescriptive analytics

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?

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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?

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

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