Posted by Michele Goetz on August 16, 2012
Let’s face it: managing data is not an easy task. The business certainly wishes, and may even think, that this is the case. So, we cut corners on fulfilling data requirements to meet short-term demands. We lay aside more strategic investment that would best support our strategies, have a wider value across the business, and build toward a proper foundation for the long term.
Today, our data architecture gets held together with duct tape. Even if we have used the new “pretty” duct tape that comes in colors, camouflage, and animal patterns, it is still duct tape.
What we are now faced with is more data silos, inconsistency in data quality, and challenges to provide a single view of your business. Investments made to provide a strong data foundation have either withered behind business as usual or have been collecting cobwebs from lack of use. I call this data technical debt, and it is holding your business back both in getting information the business needs and allowing for agility to meet the increasing variety of use cases.
To move forward, what are things we can do?
1. Make sure there is a strong vision for a desired state.
2. Recognize milestones needed to achieve the desired state.
3. Continuously align project requests to milestones to ensure progress is made on the vision.
4. Align and consolidate projects with similar milestone contributions to expand the value of vision widely and faster.
Sounds simple. But, this all requires discipline. Overcoming pressure from business units will be the toughest hurdle. But it is an easy case to make to your CEO and CFO that you want to leverage investments made and stick within the time, resources, and budget for approved data projects. Exceptions may be made. However, you can show increases in time, cost, and resources to your larger strategy by not sticking with the plan. Addressing those four bullet points ensures exceptions don’t become the rule, allowing you to put duct tape in the trash and overcome data technical debt.
What are you doing to reduce data technical debt?
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