Category: Uncategorized

Analyze and rectify customer base thanks to geocoding of addresses !

Data quality, a major challenge for business management

Each year, 10 to 15% of the data contained in the Customer databases are subject to changes related to, among other things, addresses, telephone or company name changes.

Improving the quality of the client database also requires deduplication actions and better organization of their business data.

It is always possible to use traditional techniques such as “siretisation” of addresses for French customers, optimization of the management of the DUNS number internationally or even the certification of addresses with the help of specialized third-party companies.

But today, there is a much more innovative and efficient approach, the analysis and the recovery of the addresses thanks to the geocoding!!



 What does that mean exactly ?

The geolocation of its customers involves the geocoding of addresses, namely the addition of X, Y coordinates in order to be able to visualize on the basis of the map, the positioning of prospects and customers.

The geocoding will reveal ‘visually’ and in a very explicit way the problems (Addresses nonexistent, duplicates, …).

Today, geocoding engines allow geocoding addresses in more than 140 countries


How it works ?

When you run a geocoding on an address database, you get two types of results: non-geocoded addresses and geocoded addresses.

In general, non-geocoded addresses are linked to a coding problem: badly-filled postal code, wrong address.

These errors are easy to correct despite a long and tedious side.


What about the addresses actually considered to be correctly geocoded ?

Geocoders do not all have the same approach; Some will provide contact details even if they didn’t found the address, others will provide a bad geocoding rate but will be very accurate for good results.

Several methods are possible to verify the quality of the geocoding according to the “Match rate” obtained:

  • Positioning the points according to their Latitude and Longitude on a graph makes it possible to highlight points that are too offset. Addresses located on the outskirts may be false. This analysis works well on limited areas across a country. It can be refined by removing too eccentric values during the first iteration.
  • Work on points with the same location using the previous graph or via a spreadsheet formula. For example, if a large number of points have the same position, the geocoding did not recognize the city or street.
  • Check the returned standard address. Via a formula in a spreadsheet, it is possible to compare the input address and the standard address returned by the geocoding application. If the address is set to the number but the number is different from the input, geocoding does not have the expected quality.

It will also be possible to “compete” several geocoding tools to increase the quality of the analyzes.


Errors from the analysis have been identified, what to do ?

It all depends on the data involved and the desired result.

On the expected rendering, the necessary precision must be identified; For example, for the identification of the presence or absence of a tourist office in a city, the positioning in the city is sufficient.

The use of another geocoder makes it possible to improve the zone to be geocoded. Consider whether a common point to these addresses would not mislead them as a different city name from the Postal Code. Lastly, manual repositioning should be considered as a last resort, if necessary, by the operational teams.

Pour approfondir Vandy Berten

This article was originally posted on Galigeo blog:

Infrabel choose Galigeo!

Infrabel LogoInfrabel is responsible for the management, maintenance and development of the Belgian rail network. To do this, it has regional teams who intervene in the field in the event of technical problems in terms of electricity, signage, civil engineering works and roads. In addition, Infrabel regularly conducts general inspections. These enable the rapid detection of aged infrastructures and technical defects, so that they can be repaired and / or replaced in good time.

The solution Galigeo for SAP BI allows, thanks to the management and geolocation of the sensors capturing the transition of trains on the rails, a significant improvement of the quality of invoicing to the user companies.

Geo-visualizing the different zones associated to land reserves enable Infrabel to better see and manages them. For example, seeing the price deviations between the different land reserves becomes much more transparent. In the long run, Galigeo Extensions will enable a more effective optimization and valuation of Infrabel’s land tenure.

Finally, the geolocation of the equipment facilitates the maintenance of the various parts and optimizes the planning of the intervention teams.

Infrabel choose Galigeo

The 3 cores of Location Intelligence (part 1 – Location Discovery)

Location Intelligence is known as the use of spatial reference for the improvement of Data Visualization, Analysis, and Event Forecast. Through the connection of alphanumeric and spatial data an additional and intuitive view is created, which makes it possible to recognize spatial patterns, trends and potentials. Through the consideration of spatial conditions, Location Intelligence creates an optimized entrepreneurial decision-making base in Business Intelligence Solutions, and an improved customer interaction, as well as integrating a higher process quality in business processes.


If one is searching the Internet for notions such as Location Intelligence (hereinafter called “LI”), Location Analytics, GeoMapping or other catchwords, one will find a variety of interpretations and definitions. However, there is no clear representation of the necessary elements that determine Location Intelligence. In principle it is possible to describe in a closed loop with the 3 memorable words LOCALIZE-ANALYZE-ACT; WHAT one wishes to reach with LI. However, the question remains of HOW one can usefully establish LI in a company. Basically there are 3 cores to differentiate:


1. Location Discovery (Data Warehouse “Tuning“):

Ideally in B2C companies an integrated information source (Data Warehouse, hereinafter called “DWH”) exists in which relevant data from different sources for the corporate management are brought together in a uniform format. If it is a customer centered DWH, i.e. a dataset which describes processes between businesses and customers, then data or key performance indicators like turnover, customer profitability, customer rankings, site performance, sales, sales performance and customer satisfaction will usually be stored within it.

Traditional BI solutions give simplified answers to the questions “Who, What and How much”. The question of “Where” is however often neither asked nor answered. Yet the “Where” controls often the “When, Who and How much” and then makes possible an objective assessment where all important factors including the spatial references are taken into account.

If one looks more attentively at a customer centered DWH, one discovers that spatial data is already available, such as customer addresses, branch locations, sales areas, marketing target areas, risk areas, supply chain relationships, routes and competitive structures for example. Due to inadequate technology, however, the visualization and analysis in traditional BI solutions are not available.

Location Discovery begins with the so-called Geocoding. Every object with spatial reference will have one or more coordinates (x/y, or Lat/Long) assigned in the database and will thus be made available on a geographic map for the subsequent spatial representations and analysis. This event takes place in the DWH every time a spatial data changes or a new one is added. Once the WHERE is known, once something is located, it is possible through simple intersection of spatial information for every point, every line or surface to inherit attributes (like product-related purchasing power, product affinities, socio-demographic indicators, risk class, area affiliations, etc.). This process is well described and identified as “Data Refining”.

The value and ROI of a dataset in a customer centered DWH is thereby significantly increased. The DWH turns from “single point of truth” to “single point of business value”. Location Intelligence begins therefore with the back end environment and not at the front end.


This post by Olivia Sedant originally appeared on Galigeo Blog

General Electric Healthcare strengthens its collaboration with Galigeo

General Electric Healthcare, specializes in manufacturing medical equipment such as imaging systems, surgical navigation, and monitoring and molecular diagnostics.

With Galigeo For Salesforce, each rep can visualize its leads and customers on a map, and more importantly correlate other data (competition, turnovers, open tickets on equipment, etc.) related to its clients, which are mainly hospitals or private medical practices.

Sales segmentation will be integrated into the process and tool. The use of Salesforce will become much easier for managers (Changing territories according to sales and market indicators, automatic calculation of potential, balancing, scenarios, automatic reassignment of accounts, …).

Several hundred thousand leads and customers are currently being managed.

Thanks to a geomarketing approach integrated with Salesforce, GEH significantly increases the efficiency of its sales team in a highly competitive global economic environment.

more info on

Register to Galigeo for SAP Webinar – Asia (Feb 2nd)

Discover what’s so great about the G17 release.

We’re proud to announce that our most ambitious release to date is now available. Galigeo G17 is a must have update for all Galigeo customers: benefit from improved performance and a large number of new features.

Galigeo location intelligence solution allows users and businesses to leverage their data by visualizing data sets on a map – view your data geographically and better understand and improve the efficiency and effectiveness of your business activities.

Register below to attend our Webinar for Asia Pacific:
2 February @ 3pm Singapore Time
(2pm Indochina Time)

Register to the Webinar

During this webinar we will present from concrete examples, improvements and innovations made to Galigeo for SAP BusinessObjects.

Content of this session:

  • Importing personal data from Excel sheet
  • Smart Mapping: Drag and drop indicators directly on a map
  • Customization of graphical representations: flows, personal labels, heatmaps
  • … and much more!

What’s new in Galigeo G17

Twice a year, Galigeo releases a major version of its Location Intelligence solution. Galigeo G17 (“G” for Galigeo and “17” obviously for 2017) release comes with the biggest change log in its history and is already available as ‘Galigeo for Enterprise’ standalone version and ‘Galigeo for SAP Business Objects’ extension.

You are not yet a Galigeo user? Don’t worry and read this article!

The main changes in the new version concern the adoption of self-service and mapping automation to provide end users more freedom to see to the information they need. Everything has been designed so that non GIS* users can easily do some advanced GIS analysis without needing to be an expert.

(*Geographic Information System)

Most of the new features focus on three aspects: simplicity, self-service and automation. Let’s review some of them one by one.

1. Import your own data

Users can now import their own flat files (CSV, XLS, XLSX) to a map document and visualize the data in a minute.

All data can be managed through a new panel added to the interface.


The “Auto Mapping” feature can automatically detect the best way to visualize the imported file on a map. Each column is analyzed to see if the values correspond to an existing cartographic layer (for example zip codes, region id, etc…) or eventually if they directly contain longitude/lattitude columns.
Imported data is saved with the document. Authors can make it public so that everyone can see it in the document. However, the files imported by the end users are only visible to the same user and won’t interfere with the others.


2. Automatic mapping and mapping wizard

Galigeo G17 introduces a mapping wizard to adjust the way a query or a dataset is represented on a map (the thematic mapping is done afterward). In most cases, the “Auto Mapping” will do the job for you. However, users might want to choose a different dimension or adjust the visibility scales.



3. Self service

The data panel can also be used to quickly visualize a indicator on a map.

Simply drag’n drop an indicator on the map and select what kind of thematic mapping you want to use.
 BusinessObjects G17 Self Service


4. Contextual help

This is another feature that make the application easier to use. The user documentation is now embedded and you will see some new “?” on every panels bringing a contextual help.



5. Indicator aggregation

Mapping a BI dataset on a geographic layer sometimes generates some duplicated records. For example, a zip code data can be defined by year, by typology, etc… In those cases, the geographic dimension appears duplicated in the original dataset. To make sure that the thematic mapping is correct, we need to make sure that the aggregation is correct.


6. Display flows on a map

The screenshot below speaks for itself.



7. Thematic filtering

Each thematic layer can now be filtered on a indicator or a dimension.This is useful to specify some criteria before displaying a specific layer.



8. Operational layers defined from the configuration page

Operational layers represent the external layers provided by Arcgis Server. Previous versions did support operational layers but they needed to be defined in a config file by the administrator.

Now every authors can easily manage the list for each documents.



9. Global usage statistics for administrators

Administrators can now see who’s using what at a glance.
Statistics are displayed as a report from the administration console.



  1. Other improvements

The full list would be too long to go through in details. Here are some quick hits for those who used a previous version:

  • Control labels from the layer panel
  • Activate/deactivate thematic classes by clicking on them in the legend
  • WMTS basemaps
  • Heatmap and cluster visibility saved with the document
  • Link to georeports from the infowindow
  • Support for Google Pro accounts
  • Improved geobubbles
  • Default point generalization now keeps the features with the highest indicators


For more information, don’t hesitate to contact us:

You can also start you 30 days trial here.

Where is Matthew? Or how to easily enrich your dashboards during crisis situations? And maybe get a promotion.

This article originally appeared on Galigeo Blog

With the US Government announcement that Cyclone Matthew was on its way, many were eager to predict its impact. At that moment we recognize that location matters. Where are our infrastructures, customers, suppliers, employees, etc. and what better than a map to represent the situation?

This is the logistic dashboard of “Production Co”:dashboard

In this example with “Production Co”, we have several production sites spread over the world. The supply chain, represented by arcs between sites, is quite complex and the capacity to supply the volumes (represented arcs by arc width) produced by each site affects directly the interdependent system.

Modern BI proposes a vast choice of visualizations. But a map is the best choice to represent the situation. Solutions like Galigeo let you design some creative thematic maps. In our example, we know at a glance and in real time the performance of each site. The thematic mapping is fully interactive and users can show/hide their indicators on demand.

As the news report on the incoming threat:


Hurricane Matthew might affect some of the sites and our dashboard definitely needs to include that information. Ideally we should be able to display the real time position and intensity of the cyclone. But we are most concerned about the potential flooding zones

Fortunately, a quick search on Google gets us to the NOOA website:


The National Oceanic and Atmospheric Administration is part of the US Department of Commerce.


That is good news, their data is available through public services in various formats. We chose to access the data through an Arcgis Server map service. The NWS Forecasts Guidance Warnings is the most appropriate considering what we are looking for:


We now need to associate the NOOA map service URLs to the Galigeo dashboard:


Instantly, the NOOA data appears now in the list of layers:


We conclude here that Production Co is lucky because none of its sites is directly affected by Matthew. That would not be the case if the hurricane had been in the Gulf of Mexico.

Think of going to see your boss with this valuable information even before she asks?

Let’s have a look to the potential flooding by zooming in:


Our site is still in the safe zone! We will have to watch the evolution carefully…

Strategically, the following dashboard represents the zones with a high risk potential:


This example about a logistic dashboard might seem quite specific to a large group having to cope with complex world logistics (that’s not everyone’s case!). However we use to say that 80% of data contains a geographic element. That means that BI report data visualization is ready for maps. And as we can see from the above example, with Location Intelligence (the subject of this article) we can naturally cross reference heterogeneous information on the same visualization support. I am sure that those who read this have experienced difficulties doing data enrichment and trying to match dimensions from various universe or databases. If that’s your case, try to do it with a map, things might be much easier! You may even get a promotion.