According to the Data Management Body of Knowledge, data management is “the development, execution and supervision of plans, policies, programs and practices that control, protect, deliver and enhance the value of data and information assets.” In our opinion this is a very good definition, unfortunately the implementation of data management strategies tends to be challenged in practice due to the traditional, documentation-heavy mindset. This mindset tends to result in onerous, bureaucratic strategies that more often than not struggle to support the goals of your organization.
Having said that, data management is still very important to the success of your organization. The Disciplined Agile framework promotes a pragmatic, streamlined approach to data management that fits into the rest of your IT processes – we need to optimize the entire workflow, not sub-optimize our data management strategy. We need to support the overall needs of our organization, producing real value for our stakeholders. Disciplined agile data management does this in an evolutionary and collaborative manner, via concrete data management strategies that provide the right data at the right time to the right people.
There are several reasons why a disciplined agile approach data management is important:
- Data is the lifeblood of your organization. Without data, or more accurately information, you quickly find that you cannot run your business. Having said that, data is only one part of the overall picture. Yes, blood is important but so is your skeleton, your muscles, your organs, and many other body parts. We need to optimize the whole organizational body, not just the “data blood.”
- Data is a corporate asset and needs to be treated as such. Unfortunately the traditional approach to data management has resulted in data with sketchy quality, data that is inconsistent, incomplete, and is often not available in a timely manner. Traditional strategies are too slow moving and heavy-weight to address the needs of modern, lean enterprises. To treat data like a real asset we must adopt concrete agile data quality techniques such as database regression testing to discover quality problems and database refactoring to fix them. We also need to support delivery teams with lightweight agile data models and agile/lean data governance.
- People deserve to have appropriate access to data in a timely manner. People need access to the right data at the right time to make effective decisions. The implication is that your organization must be able to provide the data that an individual should have access to in a streamlined and timely manner.
- Data management must be an enabler of DevOps. As you can see in the following diagram, Data Management is an important part of our overall Disciplined DevOps strategy. A successful DevOps approach requires you to streamline the entire flow between delivery and operations, and part of that effort is to evolve existing production data sources to support new functionality.
In future blog postings we will explore the goal diagram of the Data Management process blade and the associated workflow.