If you notice, I’ve been running a series of related entries for more than a week now. One vein running through these entries is the concept of data—digitized date, managed data, used data. Today, I found a very interesting article about certified data.
The article titled, The Partnership of Six Sigma and Data Certification at isixsigma talks about how Six Sigma can be applied to data quality processes.
Let me start by defining the term certified data. It is “data that has been subjected to a structured quality process to ensure that it meets or exceeds the standards established by its intended consumers.” These standards are documented via service level agreements (SLAs) and administered by an organized data governance structure. In other words, data certification aims to reduce defects to achieve customer satisfaction. By virtue of that, it is therefore inevitable that Six Sigma be applied to such undertaking.
To start working, we go back to DMAIC and DMADV methodologies. The former provides incremental improvements to existing processes, while the latter develops new processes or make radical changes to existing processes. Both work toward continuous improvement using a feedback loop.
Now, let’s assume that you have the data. The next logical step is to determine data certification requirements. An important point here is that requirements for a particular data depend on the intended use for that data. Just to illustrate this point, take this situation: Steel used to manufacture aircraft bolts will have more stringent structural quality requirements than steel used to manufacture household appliances.
It is therefore important to examine your data. The following data characteristics can help you determine its use:
* Accuracy/Precision — Accuracy refers to how closely the data value agrees with the correct or “true” value. Precision is the ability of a measurement or analytical results to be consistently reproduced, or the number of significant digits to which a value has been measured or calculated.
* Completeness — Completeness measures the presence or absence of data.
* Reliability — Reliability is the relative measure of how much confidence one can place in the data values.
* Availability — Availability is the ratio of the amount of time data is available to the amount of time data is needed for access.
* Timeliness/Freshness — Data almost always has an associated “timing” or “freshness” attribute or component for it to be relevant.
* Consistency — Consistency is the common definition, understanding, interpretation and calculation of a data element.
* Uniqueness — Data must have a unique identity and definition to calculate the lifetime value of large customers.
The importance of Six Sigma methodology in data management certification are:
* You have a framework and methodology that you can apply to improve data quality.
* You have the Six Sigma tools and techniques as support.
* You can successfully improve manufacturing and other processes by achieving data quality objectives.
Undeniably, managing large amounts of data—from gathering, storing, and retrieving—is a daunting task. Many companies see this undertaking as untenable. So they try to skirt around it for a while, until they feel the bullwhip effect too late. A bullwhip effect is a result of an unmanaged supply chain. One of its causes that can be linked to unmanaged data is the amount of delay times during information retrieval and material flow.
Unmanaged data is also costly, especially in the IT structure. With a growing databank, your databases need to process more data, overloading database servers. As scheduling and managing data become more complex, you may miss project deadlines. As a result, the overall performance of your organization deteriorates. More information about application data management here.
Successful companies, however, have a different story to tell altogether. They know that managing data, especially large amounts of it, is something they need to do; otherwise their overall processes get drowned along with their unmanaged ever-increasing data. So they invest time and money to manage data every step of the way.
