When it comes to metadata management, organizations progress along a standard path, driven by the individual organization’s specific needs and how much it values metadata. As an organization matures, it applies a higher priority and more resources to extracting value from metadata in order to make the organization more agile and to improve its data and its processes.
In the “Infant” stage, a project team (or a department, or even the entire organization) recognizes that documenting metadata is important. Fields in a system need to be defined before they can be created, source-to-target mappings have to be developed and communicated to developers and QA, and appropriate approvers need to sign-off on everything before it is deployed. Unless the project team has robust tools handy, it resorts to using spreadsheets or a collection of isolated tools for maintaining their metadata during the life of the project.
Business requirements (with their definitions) are written in a Word doc or an Excel workbook. Data models are developed in modeling tools with the DDL output directly applied to the databases. Data analysts maintain the business rules and mapping logic in another spreadsheet and QA develops the test cases in its own testing software. Teams are constantly passing spreadsheets around by email and using VLOOKUP’s to link everything together.
When the email and VLOOKUP’s get unmanageable, someone suggests that “Collaboration” is what’s needed, so the organization moves to the “Child” stage where it keeps its Word documents and Excel workbooks in SharePoint (or some other collaboration software). The PMO develops standard templates and policies for how SharePoint and the documentation should be maintained. This stage is definitely an improvement over the email headaches of the “Infant” stage and collaboration and standardization make projects more efficient and less risky.
At the “Child” stage, metadata documentation is usually focused on project execution. End users typically don’t have access to the information and the metadata isn’t readily available for data quality or data governance initiatives.
Moving from the “Child” stage to the “Adolescent” stage is where things get really interesting. There’s a “Metadata Gap” that the organization has to cross. In order to start generating value from metadata, the metadata developed during the project lifecycle must be (re-)used in different ways and with different tools. Crossing the “Metadata Gap” requires acquiring and utilizing new tools and developing the resources and processes to make them effective. It requires time, effort, and prioritization.
Once an organization realizes that prioritizing metadata improves data quality and makes users more effective, it has to make choices about exactly how cross the gap. How much should it spend? What new tools should it purchase? How many people should be devoted to the effort? What disruption will it cause to the current initiatives? Is the improvement worth the cost?
Once the organization has crossed the gap by allocating time and resources to metadata management, it has entered the “Adolescent” phase. In this phase, metadata across the organization is being managed centrally. It’s accessible to a wide audience of both technical and business users. A business glossary is established that provides the common vocabulary that everyone can speak. Reference data and valid values are developed for key data elements. Data lineage is established and the organization now has tools to see the impact of upstream and downstream changes. History is maintained to provide a rear-view look while new or changing business rules are folded into the process.
As the processes and tools mature and become operationalized, the organization is ready to enter the “Adult” phase of metadata management. In this phase, data governance initiatives begin to establish standards and prioritize efforts to improve data quality. With the results of these efforts, the business makes better decisions and customers have a better experience as a result of how the organization manages data.
And they all lived happily ever after . . .
If only it were that easy.
It’s not. But at MapWorks, we’re providing the tools that make it easier. To many organizations, especially smaller or newer companies with limited resources, the gap between the “Child” and “Adolescent” phases is very difficult to cross. The value of robust metadata management and data governance is often out of reach because of the cost (time, budget, etc.) of moving away from spreadsheets to something better.
MapWorks makes that gap a lot easier to cross. Our software gives companies the benefits that more expensive solutions provide, but at a fraction of the cost and in a fraction of the time. We’re cloud-based – no hardware to purchase, no software to install. Just sign up and get started. Right out of the gate, our users get:
- Business Glossary – Align your organization to a common vocabulary and definitions
- Business and Technical Metadata – Manage business names, technical names, formats, business rules and mapping logic.
- Data Lineage and Impact Analysis – Quickly see upstream and downstream impacts for every data element.
- Reference Data Management – Maintain your LOV’s in a single place.
- Import from External Sources – Import metadata from your existing databases and tools and avoid manual data entry.
- Works for Small Teams and Large Organizations – Collaborate with users across the company.
- Successful Data Governance – MapWorks gives your team the tools to get your data governance program moving.