A Data Mapping Specification is a special type of data dictionary that shows how data from one information system maps to data from another information system. Creating a data mapping specification helps you and your project team avoid numerous potential issues, the kind that tend to surface late in development or during user acceptance testing and throw off project schedules, not to mention irritating your stakeholders.

Primary Source Strategies
Primary Source Strategies: Maps Nwea
Primary Source Strategies: Maps Satellite
Primary Source Strategies: Maps Atlas

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Data Mapping Specifications are particularly valuable in the following types of projects:

Data Migration – When source data is migrated to a new target data repository.
Data Integration – When source data is sent to a target data repository on a regular basis and the two data sources do not share a common data model. The integration can happen hourly, daily, weekly, monthly, or even in real-time as is typically required for a system integration.

These might sound similar, and they are. The primary difference between the two is that after a data migration project is complete, the original source data is no longer used or maintained while after a data integration project is complete, both data sources are maintained.

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Strategy, Sources and Sites for Map Section- History Optional Paper I This section is a very grey-area in case of our Optional paper. I have had interaction with many of my peer aspirants. So, if you are an engineer by academic background - this section might seem a cake walk for you.

However, strategy is not needed in just one instance. Or even just a couple of instances. We’re talking about strategy at every single stage of the recruitment process! There are two reasons for this. First, recruiting is a sales profession. Second, there are people at both ends of the sale.

The Key Elements of a Data Mapping Specification

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Essentially a data mapping specification will analyze, on a field-by-field basis, how to move data from one system to another. For example, if I were to orchestrate a data feed from the Bridging the Gap article repository to a search engine, I would want to map key attributes of the article, such as the title, category, and content to the attributes specified by the search engine. This analysis exercise would ensure that each piece of information ended up in the most appropriate place in the target data repository.

To achieve this goal, a Data Mapping Specification contains the following elements:

List of attributes for the original source of data (often with additional information from the data dictionary)
A corresponding (or “mapped”) list of attributes for the target data repository (again, with additional information from the data dictionary)
Translation rules defining any data manipulation that needs to happen as information moves between the two sources, such as setting default values, combining fields, or mapping values

Data Mapping is About Resolving Potential Issues

Creating a Data Mapping Specification requires discovering and resolving potential issues prior to the data mapping being implemented. In data migrations and integrations, any number of differences between how data is stored can cause data to be lost or mis-represented.

For example, it may be that your source data has a text field and your target date repository uses an enumerated list. Without analyzing the data and providing logic for mapping the text values to the allowable list values (or initiating appropriate data clean-up efforts), you are likely to experience unexpected errors during the system migration.

A Data Mapping Sample Template

Here’s a simple data mapping template and example you can use to see how this works in action. It’s a hypothetical example assuming that we’re sending a data feed from the Bridging the Gap article repository to a search engine.

As you can see, even a simple data mapping exercise encounters potential data mapping issues that would best be handled proactively in a business-centered way than retroactively in a technically-focused way.

Solid business analysis through data modeling will prevent these issues before they happen, by discovering them in advance, collaborating with business and technical stakeholders to find feasible solutions, and initiating any appropriate data clean-up and normalization efforts.

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Satellite images are like maps: they are full of useful and interesting information, provided you have a key. They can show us how much a city has changed, how well our crops are growing, where a fire is burning, or when a storm is coming. To unlock the rich information in a satellite image, you need to:

Primary Source Strategies: Maps Nwea

Look for a scale
Look for patterns, shapes, and textures
Define the colors (including shadows)
Find north
Consider your prior knowledge

These tips come from the Earth Observatory’s writers and visualizers, who use them to interpret images daily. They will help you get oriented enough to pull valuable information out of satellite images.

Primary Source Strategies: Maps Satellite

Look for a Scale

Primary Source Strategies: Maps Atlas

One of the first things people want to do when they look at a satellite image is identify the places that are familiar to them: their home, school, or place of business; a favorite park or tourist attraction; or a natural feature like a lake, river, or mountain ridge. Some images from military or commercial satellites are detailed enough to show many of these things. Such satellites zoom in on small areas to collect fine details down to the scale of individual houses or cars. In the process, they usually sacrifice the big picture.