With a solid foundation established, the next step in home building is to assemble the framework of the house, which requires constant collaboration between the contractor, the architect, and the builders. As the architect’s plans are carried out by the builders and the structure begins to take shape, you’ll start to make decisions about the interior flow of the house. Are you building a split-level home or a single-level ranch? Do you want an open-concept layout, or do you prefer separate spaces for cooking and entertaining? The layout you choose will affect the materials and costs down the road. At this point, you’ll also need to run the utilities (like electrical, HVAC, and plumbing) throughout the frame.
When it comes to your data management program, this is the point at which you transition from planning to doing. Use the blueprints you’ve made with the data architect to guide the project as you work with your IT department to implement the data management architecture. As you begin to define the rules and processes for how data will flow through your systems, you’ll likely need to make decisions around cleansing, standardizing, and enriching the data.
A common finding of a full-volume analysis is a lack of standard data formats. In this case, you might find the word "Street" spelled out in some instances or abbreviated "St" elsewhere. Likewise, state names can be spelled out, abbreviated, or truncated (like "Mass" or "Calif"). And this is just the tip of the iceberg! Your data management program will need to have rules in place to cleanse and standardize the existing data and to ensure that new data coming into the system conforms to these rules.
Of course, any rules that transform the data should be vetted by the business users first. Think of your business’s data stewards as the plumbers in this scenario. You wouldn’t move the location of the bathroom without consulting the plumber to see if it’s feasible first, would you? Of course not. As you begin to transform your data, securing feedback from the business users who understand that data is critical. Otherwise, you could end up creating a data management strategy that is as useful as a bathtub in the kitchen.