Saturday, January 31, 2009

Improving Communication on Data Governance Teams

If data governance is about enabling people to improve processes, your team should consider some tools to help communication between the people. Particularly if your data governance team is global, communication software can improve efficiency by working through some of the issues of a diverse team. If teams are in different time zones, it will be difficult for you to hold status meetings at a time that's convenient for all. The good news is that there are some fantastic software tools including Web 2.0 tools that can support communications in a data governance team.

I'm sure you've heard of, and used, most of these technologies. But have you considered using them on your data governance project?

Blogs
Blogs are great ways to provide commentary or news on your data governance project. The writer may use text, images, and links to other blogs written by other team members to inform and foster teamwork. A blog allows for one person's perspective on the data governance project, but readers can leave comments and links to their own blogs. Blogs can educate and inform data governance groups, and they can use them to debate unresolved issues or to continue discussions between meetings.
Data governance teams could designate certain team members to blog about the problems they are trying to solve and the projects they are working on. Over time, this type of blog would help keep a record of the processes used - what works and what doesn't. It can also be used to inform data stewards, data governance constituents and other readers about how the company is working to solve data quality issues.

RSS Feeds
The problem with blogs is that you have to revisit them frequently in order to keep up on the latest news. RSS feeds are a great way to push crucial data governance information to the team benefits them by improving communication.

Wikis
Wikis can hold the latest corporate data policies. Wikis can be opened up to the corporation and provide communications across the enterprise.
There are a lot wikis to choose from. Your best bet is to check out the matrix at www.wikimatrix.org

Workflow
Let’s not forget workflow tools. Workflow software is genre of powerful tools for collaboration and should be considered to improve efficiency into your data governance process. With workflow tools, teams can manage the processes and coordination of the data governance team. The processes managed with workflow tools might include any of the following:

  • work progress of a person or group
  • business approval processes
  • challenges of specific data governance technical processes like ETL or data profiling
  • financial approval processes
Much of the work involved in data governance is meeting and discussing status. Workflow software can save of the time and human capital investment that goes into holding status meetings by covering status and progress in an application. Employees update their status on specific task while managers can see what is on schedule and what is behind.
Some examples of workflow tools include Attask, Basecamp, Clarizen, Sharepoint

Friday, January 9, 2009

Starting Your Own Personal Data Quality Crusade

As I talk to people in the industry, many folks comment on their organization's lack of interest when it comes to information quality. People have the tendency to think that responsibility for information quality starts with someone else, not themselves. In truth, we all know that information quality is the responsibility of everyone in the organization, from the call center operators to the sales force to IT and beyond.
So why not start your own personal crusade, your own marketing initiative to drive home the power of information quality? Use the power of the e-mail signature to get your message across.

Use these graphics in your signature file to drive home the important of IQ to your organization.

I may knock out a few more banners this weekend, but if you have your own ideas for a custom "Information Quality" banner, let me know and I'll post it.




Friday, January 2, 2009

Building a More Powerful Data Quality Scorecard

Most data governance practitioners agree that a data quality scorecard is an important tool in any data governance program. It provides comprehensive information about quality of data in a database, and perhaps even more importantly, allows business users and technical users to collaborate on the quality issue.

However, if we show that 7% of all tables have data quality issues, the number is useless - there is no context. You can’t say whether it is good or bad, and you can’t make any decisions based on this information. There is no value associated with the score.

In an effort to improve processes, the data governance teams should roll-up the data into metrics into slightly higher formulations. In their book “Journey to Data Quality”, authors Lee, Pipino, Funk and Wang correctly suggest that making the measurements quantifiable and traceable provide the next level of transparency to the business. The metrics may be rolled up into a completeness rating, for example if your database contains 100,000 name and address postal codes and 3,500 records are incomplete, 3.5% of your postal codes failed and 96.5% pass. Similar simple formulas exist for Accuracy, Correctness, Currency and Relevance, too. However, this first aggregation still doesn’t support data governance, because business users aren’t thinking that way. They have processes that are supported by data and it's still a stretch figuring out why this all matters.

Views of Data Quality Scorecard
Your plan must be to make data quality scorecards for different internal audiences - marketing, IT, c-level, etc.

The aggregation might look something like this:You must design the scorecards to meet the needs of the interest of the different audiences, from technical through to business and up to executive. At the beginning of a data quality scorecard is information about data quality of individual data records. This is the default information that most profilers will deliver out of the box. As you aggregate scores, the high-level measures of the data quality become more meaningful. In the middle are various score sets allowing your company to analyze and summarize data quality from different perspectives. If you define the objective of a data quality assessment project as calculating these different aggregations, you will have much easier time maturing your data governance program. The business users and c-level will begin to pay attention.

Business users are looking for whether the data supports the business process. They want to know if the data is facilitating compliance with laws. They want to decide whether their programs are “Go”, “Caution” or “Stop” like a traffic light. They want to know whether the current processes are giving them good data so they can change them if necessary. You can only do this by aggregating the information quality results and aligning those results with business.

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Disclaimer: The opinions expressed here are my own and don't necessarily reflect the opinion of my employer. The material written here is copyright (c) 2010 by Steve Sarsfield. To request permission to reuse, please e-mail me.