Better understand DataMaps – a Google Maps analogy

We have seen unbelievable transformations as our lives get more digital but one of the most fascinating and rapid transformations in the recent years is how Maps have changed. Saying Rand McNally or AAA in the context of a road trip is like saying Blockbuster and Hollywood in the context of watching a movie. Things have come a long way from the days of static maps and atlases. The Google map app on our phone provides a great analog for what Meru is doing with DataMaps.

We could think of the old paper maps as a static representation of physical information. In retrospect, these maps were not very user or driver friendly. You could zoom from country level to city or, at best, segment of the city level (downtown map insets for example) but that would require using separate maps of different scales. You could plan a route from starting point to destination and get some information about things along the way. But it was hard to figure out where you were on the map. Taking a wrong turn or misreading directions would require some effort to correct. Similar examples of static DataMaps could be a listing of the systems in your organization maintained by the Procurement department, a configuration management database or a schema description for key databases. There might be richer descriptions of architecture and setup for individual systems, especially for the key systems that guard the crown jewels (akin to the downtown inset map). However, all these are often disconnected point in time representations with no easy ways to reconcile differences, maintain or even use. This is not by any stretch of imagination a true DataMap.

On the other hand, the digital version of the map in the Google Maps App or Apple Maps app is much closer to where we want DataMaps to be. These digital mapping apps seamlessly integrate static data with tons of dynamic data. They provide an ability to zoom in and out effortlessly – all the way from a satellite view to a street view and even inside some buildings. They provide clear directions and optimize routes while driving – courtesy of GPS and integration of traffic and road condition information (closures, jams, flooding, weather etc.). You can find out more about what is nearby and where something you are looking for is available. All of this happens on your phone with a simple to understand interface.

Clearly the gold standard for understanding the data in your organization is an app that works like these digital mapping apps. We want to see where data resides, where it flows, what type of data is being transferred and where there are intersections with different data networks. You should be able to understand transfer points where data moves from internal sources to external sources as simply as we view a travel route that involves a combination of walking, bus and rail. We want to understand whether important data is being securely handled in the few critical systems and be able to zoom into these systems to minute detail.

Google Maps and other mapping apps also provide an analog for how a DataMap should be developed. You do not wonder whether these apps have been built manually or in an automated manner (a typical and false binary choice companies feel compelled to make when developing a DataMap). The key aspect is a seamless integration of data from many disparate sources. Street level images are painstakingly collected by cars with cameras that have traversed the entire road network. Realtime traffic information is obtained from many sources and integrated to provide evergreen information about traffic. Businesses submit forms with their physical location and other information so that they can be found easily in search queries. There is clearly a lot of effort that goes into building these apps but all the complexity is hidden from the user. The end result is a map that is easy to use and intuitive and requires almost no training to use.

Meru has adopted a similar approach to DataMaps – we enable information about data, systems and data flows to be maintained and updated with minimal effort from users by utilizing powerful automation capabilities. Different stakeholders and teams within an organization are able to easily see and understand how their data is used with our DataMaps. Our DataMaps are also used to track and manage risk around data and data flows. And, once a functioning DataMap is available within the organization, we have seen their use expanding beyond the original objectives like how mapping apps continuously grow in capabilities.

Indeed, key insights about DataMaps can be learnt by looking at an app like Google Maps.

  • Integrate data from multiple sources

  • Ensure it is current and evergreen but do this in a manner that is easy and requires little or no effort from users

  • Allow users to provide input as needed in an easy manner

  • Automated or Manual – this is a false binary choice. A smart combination of both approaches is needed to build and maintain a DataMap

  • Incentivize your users to use and own the DataMap. A good way to do this is to provide them insight they would not be able to get otherwise

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