This blog’s title is very similar to the name of a famous Rod Stewart album that contains a signature song of the same name at its core. When it comes down to it, there is a lot of truth to both the title of the song and the title of this blog. When you look at a brilliant picture, you can almost always tell when the photographer is trying to tell you a story. In this blog, I will prove that the message in the song’s title also applies to the data governance discipline by using a picture (graphic) that I have shared often with clients and in articles, presentations and webinars.
There is a tool that you can develop yourself that will immediately, and at little or no cost, assist you with your data and information governance program. The tool is called the Common Data Matrix (CDM), a basic two-dimensional spreadsheet that can store, in almost infinite ways, specific information about the data in your systems, and who is accountable for the management of that data.
Recently, I was assisting a friend in the development of an “intro to data” slide deck for a client and I was asked to simplify the typically large and complex Matrix down to something that was easy to digest by people that were not necessarily “data people”.
At first, I was hesitant to eliminate information (metadata) from my original CDM tool. I decided that I could simplify the tool and use a snap-shot (picture) of the tool to tell a series of stories about why organizations need to govern their data. I am going to show the trimmed down version of the CDM tool in this blog. A larger and more complex version of the CDM is a topic covered in my book Non-Invasive Data Governance, my Real-World Data Governance webinars, as well as other articles I have authored. Take a peak at the larger CDM toward the end of this piece.
Simplified Version of a Common Data Matrix (CDM)
Click on the image to see a larger version.
Even this slimmed down version of the tool tells us many important things, such as:
- EMPLOYEE is a subject area that is important.
- Two of the sub-subject areas associated with EMPLOYEE data are EMPLOYEE BENEFITS and EMPLOYEE PAYROLL.
- The person that is tactically responsible for the data as the SUBJECT MATTER EXPERT (SME) for overall EMPLOYEE data is MCMASTER.
- PANTOK is considered the tactical SME for EMPLOYEE PAYROLL data.
- Both sub-subjects of EMPLOYEE data are maintained in SAS, MDM, and the DATA WAREHOUSE.
- There are different IT people named here that are TECHNICAL OWNERS of each system and database.
- The BUSINESS OWNERS of the data in SAS and MDM are also the SMEs except in the case of the DATA WAREHOUSE where WATERS is the BUSINESS OWNER for the DATA WAREHOUSE.
- HR BENEFITS are a stakeholder and DATA STEWARD of EMPLOYEE BENEFITS data in all three systems and EMPLOYEE PAYROLL data only in MDM.
- HR PAYROLL is a stakeholder and therefore a DATA STEWARD of EMPLOYEE PAYROLL data in all three systems and EMPLOYEE BENEFITS data only in SAS.
- HR PAYROLL manages its own version of EMPLOYEE PAYROLL data in the BUSINESS OWNER’s personal data base labeled PANTOK DB.
- If you have technical questions about EMPLOYEE data in SAS you should ask SPANARCA, about the data in MDM you want to ask BUSHKOFF, and for answers about EMPLOYEE data in the DATA WAREHOUSE you should ask WILLIAMS.
- If you have business questions about EMPLOYEE BENEFITS data in SAS or MDM, you should ask MCMASTER, but business questions about EMPLOYEE BENEFIT data in the DATA WAREHOUSE should be directed at WATERS.
- Business questions about EMPLOYEE PAYROLL data in SAS and MDM should be directed at PANTOK, but business questions about EMPLOYEE PAYROLL data in the DATA WAREHOUSE should be directed at WATERS.
- Of course, this diagram implies that you have formalized your data subject areas, defined your data governance roles and responsibilities, inventoried the systems that house these subject areas of data, … all as a start to defining how you will govern EMPLOYEE data.
- I am certain that this diagram describes many more things. I hope you get my point.
From a Data Governance perspective, this same picture also tells us important stories:
- If the HR PAYROLL and HR BENEFITS functions are asked to provide statistics about EMPLOYEEs, it becomes obvious that these two functions are getting their data from different places and therefore their results may be different. That is, unless the data and the understanding of the data are exactly the same.
- There may be a difference of opinions from the two SUBJECT MATTER EXPERTS around EMPLOYEE PAYROLL data. MCMASTER may be more of an authority than PANTOK but the decisions made about EMPLOYEE PAYROLL may need to be escalated from the tactical ranks to a strategic rank known as a Data Governance Council (or advisory group) for a strategic decision..
- WATERS is the BUSINESS OWNER of the DATA WAREHOUSE for this EMPLOYEE data, but they are not the SME. Governance issues become apparent when WATERS is not expected to define the data in the DATA WAREHOUSE exactly the same as the data in SAS and MDM.
These are just the simple stories that come from the slimmed down version of the Common Data Matrix.
Now consider the matrix as being more complete.
- Consider that you expand the Common Data Matrix to include all of the systems where EMPLOYEE data is managed and not just the three mentioned here.
- Consider that there are many more subject areas that will need to be covered including CUSTOMER, PRODUCT, SUPPLIER, LOCATION and more.
- Consider that there are many more sub-subject areas of data associated with EMPLOYEEs and that there are many more sub-subject areas for all of the SUBJECT AREAs beyond EMPLOYEE.
- Consider that there are many more parts of the organization beyond HUMAN RESOURCES that are defining, producing and using these subjects of data are stored in many different locations.
- Consider that there may be important people in each of these different functions that is tired of depending on other people’s data and has their own personal data sources for answering corporate critical answers and making very important decisions.
- Consider that the data has not been governed thus far meaning that data is defined differently in the different locations.
- Consider that the size of your Common Data Matrix will grow to include the data SUBJECT AREAs that are being governed. Some of the Data Governance tools on the market can capture this information – however not all of the tools have the ability to represent data accountability in this way. In a picture.
- Consider that many of my clients have display only the most relevant portion of the CDM to the audience they are addressing.
Wouldn’t it be great if we knew where all the data is stored, who has responsibility and accountability for the data, the quality and dependability of that data, the definition and consistent understanding about the data, the rules associated with the data, etc.?
Now lets take a peek at a picture of a larger version of the CDM. Click on the image below to see a more readable version. Let me know if you want to talk about how a CDM can assist you in your quest to build an effective Data Governance or Information Governance program.
The Common Data Matrix is a tool that I use to inventory, recognize and most importantly take a picture of accountability for data. It is not a perfect tool and there are many different ways the tool can be set up to meet a specific purpose. I hope that you will share how you are using the CDM as part of your Non-Invasive Data Governance program and the value it is bringing to your organization. If you aren’t using the CDM to house some of the metadata about your Data Governance program, please request a generic copy. The Common Data Matrix provides great pictures of your data, and as we know … Every data picture tells a story … maybe a few.
