Dickeson--The Analytical Power of OLAP
June 1998
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Diapers and beer sell best after 6 p.m. After six, husbands are sent out to get diapers, and often grab a six pack for themselves. So the rack with diapers should be located next to the beer storage to increase impulse buying by men.
Web breaks increase on a web press as relative humidity in the pressroom decreases.
People on the third shift decrease the density of cyan ink on a running press form because of a loss of natural light perception incurred while they sleep in the daytime.
Are these statements true? Perhaps. Perhaps not. To find the answer, do some "mining" on your "data warehouse." Use OLAP.
OLAP (online analytical processing) is a hot topic these days, and with good reason. OLAP enables you to "slice and dice" the sales or operating data in your computer databases in an almost infinite variety of ways to find relationships that can provide support for decisions that need to be made.
A study was conducted by the GATF that identified, as I recall, more than 1,600 variables that affect press operations. Chaos theory teaches us that slight changes in any variable can have substantial impact on output—productivity. We all know this. One press manufacturer provides a database with 2,600 event codes (op codes) to analyze press operations.
When we try to quantify the impact of all those thousands of variables and take affirmative action, we throw up our hands in frustration. No way to analyze this mass of data. We revert to anecdotes: "Let me tell you what happened on press 22 last Thursday..."
Maybe we're getting a tad closer to finding significant and relevant relationships by using the analytic power of our personal computers to establish such relationships and trends. With OLAP, we take a multidimensional view of the data via MDBMS (multidimensional data- base management).
Until recently we've mostly used two-dimensional databasing (RDBMS—relational database management systems), where the elements appear in rows and columns like a spreadsheet. With the RDBMS applications, we're confined to OLTP (online transactional processing). OLTP is optimized for creating, updating and retrieving individual records, such as our accounting systems, for receivables, payables, payrolls, etc.
In contrast, OLAP is used by analysts and managers who frequently want a higher-level, aggregated view of the data. OLAP databases are optimized for analysis.
The data world of RDBMS is two-dimensional—like a flat spreadsheet. The MDBMS multidimensional world is a cube, three-dimensional—like a Rubik's Cube. With it you can drill up, down and across. The RDBMS data can become a "virtual" cube for OLAP inquiries with certain software, but with restricted speed and efficiency. Or the data can be stored as a true cube in a multidimensional server set of "arrays" that are interlinked by dictionaries and pointers.
Web breaks increase on a web press as relative humidity in the pressroom decreases.
People on the third shift decrease the density of cyan ink on a running press form because of a loss of natural light perception incurred while they sleep in the daytime.
Are these statements true? Perhaps. Perhaps not. To find the answer, do some "mining" on your "data warehouse." Use OLAP.
OLAP (online analytical processing) is a hot topic these days, and with good reason. OLAP enables you to "slice and dice" the sales or operating data in your computer databases in an almost infinite variety of ways to find relationships that can provide support for decisions that need to be made.
A study was conducted by the GATF that identified, as I recall, more than 1,600 variables that affect press operations. Chaos theory teaches us that slight changes in any variable can have substantial impact on output—productivity. We all know this. One press manufacturer provides a database with 2,600 event codes (op codes) to analyze press operations.
When we try to quantify the impact of all those thousands of variables and take affirmative action, we throw up our hands in frustration. No way to analyze this mass of data. We revert to anecdotes: "Let me tell you what happened on press 22 last Thursday..."
Maybe we're getting a tad closer to finding significant and relevant relationships by using the analytic power of our personal computers to establish such relationships and trends. With OLAP, we take a multidimensional view of the data via MDBMS (multidimensional data- base management).
Until recently we've mostly used two-dimensional databasing (RDBMS—relational database management systems), where the elements appear in rows and columns like a spreadsheet. With the RDBMS applications, we're confined to OLTP (online transactional processing). OLTP is optimized for creating, updating and retrieving individual records, such as our accounting systems, for receivables, payables, payrolls, etc.
In contrast, OLAP is used by analysts and managers who frequently want a higher-level, aggregated view of the data. OLAP databases are optimized for analysis.
The data world of RDBMS is two-dimensional—like a flat spreadsheet. The MDBMS multidimensional world is a cube, three-dimensional—like a Rubik's Cube. With it you can drill up, down and across. The RDBMS data can become a "virtual" cube for OLAP inquiries with certain software, but with restricted speed and efficiency. Or the data can be stored as a true cube in a multidimensional server set of "arrays" that are interlinked by dictionaries and pointers.
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Best Practices for Print Automation
Printing Production Management
