In computing, a mashup integrates/combines data and/or functionality from multiple sources and presents it in a single view. In organizations, mashups are used every day in the form of business (accounting, administration, business development, customer service, engineering, finance, human resources, management, manufacturing, marketing, operations, production, research and development and sales) dashboards and Information Technology (IT) dashboards. These dashboards can ingest simple data and/or even Big Data and then show an overall summarized visualization of what is going on.
In order for mashups to work, there are business processes and data management procedures that need to be followed. By consistently providing relevant data, mashups can reveal great insights and also help in strategizing. At its core, mashups “gather” data from multiple sources where data might have been manually or automatically (e.g., IoT) entered. Since the data is being pulled from various sources, it can create issues in terms of provenance and governance.
Provenance of Mashups
For provenance, since the origin of the data is not always displayed, this can create problems in terms of:
- The authenticity of the data
- Copyright of the data
- Misrepresentation of the data
- Manipulation before displaying the data
- Incorrect correlations of the data
Governance of Mashups
For governance, since policy, organization, and structuring of the data matters, this can create problems in terms of:
- Timeliness of data
- Unintentional avoidance of new data
- Skewed conclusions due to duplication of data
- Deciding if/when data governance should be done by Business or IT or both
In light of the above issues, let’s ask the following questions about mashups in your organization:
| Today | Tomorrow | |
| 1. | Who is responsible for defining and managing data’s lifespan in mashups? | Who should be responsible for defining and managing data’s lifespan in mashups? |
| 2. | What does your mashups data show you? | What should your mashup data show you? |
| 3. | Where does the data come from in mashups? | Where should the data come from in mashups? |
| 4. | When data is relevant? | When should data become relevant? |
| 5. | Why mashups are used? | Why mashups should be used? |
It should be clear by now that the strength of a mashup is directly related to the weaknesses in the underlying data regardless of how pretty the picture of the mashup might look.
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