Improving BI Development Efficiency: Standard Data Extracts

A few years ago, a mission to Mars failed because someone forgot to convert U.S. measurement units to metric measurement units. Miles weren’t converted to kilometers.

I thought of this fiasco when reading a blog post recently that insisted that the only reasonable approach for moving data into a data warehouse was to position the data warehouse as the “hub” in a hub-and-spoke architecture. The assumption here is that data is formatted differently on diverse source systems, so the only practical approach is to copy all this data onto the data warehouse, where other systems can retrieve it

I’ve written about this topic in the past, but I wanted to expand a bit. I think it’s time to challenge this paradigm for the sake of BI expediency.

The problem is that the application systems aren’t responsible for sharing their data. Consequently, little or no effort is paid to pulling data out of an operational system and making it available to others. This then forces every data consumer to understand the unique data in every system. This is neither efficient nor scale-able.

Moreover, the hub-and-spoke architecture itself is also neither efficient nor scalable. The way manufacturing companies …

A few years ago, a mission to Mars failed because someone forgot to convert U.S. measurement units to metric measurement units. Miles weren’t converted to kilometers.

I thought of this fiasco when reading a blog post recently that insisted that the only reasonable approach for moving data into a data warehouse was to position the data warehouse as the “hub” in a hub-and-spoke architecture. The assumption here is that data is formatted differently on diverse source systems, so the only practical approach is to copy all this data onto the data warehouse, where other systems can retrieve it

I’ve written about this topic in the past, but I wanted to expand a bit. I think it’s time to challenge this paradigm for the sake of BI expediency.

The problem is that the application systems aren’t responsible for sharing their data. Consequently, little or no effort is paid to pulling data out of an operational system and making it available to others. This then forces every data consumer to understand the unique data in every system. This is neither efficient nor scale-able.

Moreover, the hub-and-spoke architecture itself is also neither efficient nor scalable. The way manufacturing companies address their distribution challenges is by insisting on standardized components. Thirty-plus years ago, every automobile seemed to have a set of parts that were unique to that automobile. Auto manufacturers soon realized that if they established specifications in which parts could be applied across models, they could reproduce parts, giving them scalability not only across different cars, but across different suppliers. 

It’s interesting to me that application systems owners don’t aren’t measured on these two responsibilities:

• Business operation processing—ensuing that business processes are automated and supported effectively
• Supplying data to other systems

No one would argue that the integrated nature of most companies requires data to be shared across multiple systems. That data generated should be standardized: application systems should extract data and package it in a consistent and uniform fashion so that it can be used across many other systems—including the data warehouse—without the consumer struggling to understand the idiosyncrasies of the system it came from.

Application systems should be obligated to establish standard processes whereby their data is availed on a regular basis (weekly, daily, etc.). Since most extracts are column-record oriented, the individual values should be standardized—they should be formatted and named in the same way.

Can you modify every operational system to have a clean, standard extract file on Day 1? Of course not. But as new systems are built, extracts should be built with standard data. For every operational system, a company can save hundreds or even thousands of hours every week in development and processing time. Think of what your BI team could do with the resulting time—and budget money!

photo by jason b42882

Link to original post