Unleashing the full value of your data by enriching it with external data

Across sectors organizations are collecting increasing volumes of data and making these datasets easily accessible and searchable by creating data portals. However, while the benefits of leveraging data are well known, data enrichment processes are still largely underdeveloped.

Enriching your data with external datasets is an essential step to providing complete, high-quality data to users, and helps generate new, value-creating insights.

Find out more about the use of external data by our customers to enrich their portals:

• ICF Habitat
• Lamie mutuelle
• The Caisse des dépôts

Over recent years, organizations have focused on better collecting, analyzing, and leveraging their data.

However, using this data on its own can fail to deliver sufficient value because:

• information required to contextualize the data is missing. For example, a dataset of companies needs to be enriched with legal and financial information (company registration number, address, turnover, etc.)
• data cannot be visualized effectively. To create understandable map-based visualizations it needs to be enriched with geographical references
• required data does not exist within the organization.

Also known as third-party data, external data offers a wealth of value-added information. It can come from different sources:

• Partner data
• Open data (public data, weather data, satellite data, etc.)
• Social media data (customer reviews of a product for example)
• Data bought from specialists in a sector or category
• Data from private companies, etc.

Some of the most commonly used data sources are:

• Databases of zip codes
• Databases of company registrations and activity/finances
• Census data
• Databases of electoral/municipal/state and country boundaries
• Mapping data
• Weather and temperature data

While third-party data can help enrich internal data, too few companies are making full use of it. Indeed, 92% of analytics experts believe that their organization does not make sufficient use of external data, despite the many benefits it provides. These include:

In order to gain a deep understanding of their industry, organizations need to take into account a range of external factors when analyzing the data they produce. Third-party data helps deliver this additional insight.. 

Whether it’s adding information for further analysis or predictive models, or leveraging data repositories to create data visualizations, external data provides a complete view of the organization’s and the industry’s challenges and produces richer insights. The knowledge generated in this way enables better decisions to be made, both to limit risks and to seize new opportunities. The use of external data can thus provide enormous competitive advantage over rivals who rely solely on their own data.

With external data, organizations can access a wealth of useful data to better understand their target audience. For example, Who are they? What are their issues and needs? What are their communication preferences?

By answering all these questions, companies can adapt their approaches and launch much more personalized marketing campaigns. External data can also shed light on the evolution of consumer behavior, allowing you to anticipate coming trends.

In some cases, the use of external data can underpin new data services that would be impossible to implement based solely on internally generated data.

External data can therefore complement your data to create specific visualizations, or to provide deeper analysis of your industry.

Organizations already have a lot of data. Before integrating new data from external sources, it is important to know if it meets a real need.

Start by imagining use cases based on your organization’s needs. This will allow you to understand what data you need and enable you to set objectives and desired results.

Opendatasoft offers its customers prepared and packaged datasets, by sector and based on the use cases already implemented by our customers. These ready-to-use datasets allow you to enrich your data to meet your specific needs and the wider challenges of your sector.

The sheer quantity of data available can make it difficult to select reliable datasets that provide high added value for your business. At the same time different companies may offer the same dataset. Which is best for you? You should therefore rely on trusted partners to identify the most reliable data source to use.

Opendatasoft partners with its customers through the entire data sourcing lifecycle. Based on 12 years+ experience across all sectors, we are able to quickly identify the data sets that will be most useful for your business.

Many organizations trust us to source their external data. Our teams have worked on many public datasets in order to offer quality sources in real-time to the Opendatasoft ecosystem.

External datasets, especially open data, are not always of sufficient quality. For example, the data schemas created by their producers do not always allow them to be easily exploited.

Additionally, the structure and quality of a dataset can evolve with each update, requiring regular monitoring and in some cases, a reprocessing of the data to match your specific needs.

Opendatasoft allows you to clean and correct data simply thanks to more than 50 processors. It also enables you to select just part of the data or to modify the columns of the dataset to focus only on information that is useful to you.

Once all external data has been selected, it is important to centralize it in a single place to provide a comprehensive view of the available data. Otherwise there is a risk of duplicate or useless datasets being created as data is scattered across the organization, rather than being in one data portal.

Opendatasoft allows data portals to be created very quickly, without requiring technical expertise. You can therefore gather your data and allow all teams to quickly create and distribute data experiences digitally to feed both internal and external ecosystems.

To complete the virtuous circle of data sharing, it is essential to put in place good practices so that users of your portal or data services can reuse data, whether it is by analyzing metadata, choosing from multiple download options, or reusing it in real-time through APIs.

With the Opendatasoft platform, you can complete the metadata of your datasets based on standard models (INSPIRE, DCAT, etc.) and generate APIs for each dataset of your portal.

ICF Habitat, a real estate subsidiary of the SNCF Group, has been in business for almost 100 years and manages a portfolio of 95,000 homes. The company has created a data portal to provide its employees with multiple dashboards and data visualizations to support the Group’s objectives:

• Steering the Strategic Development Plan (SDP) projects: The SDP is a tool for decision-making around ICF Habitat’s development policy. It aims to support the company at a local level.
• Thematic analysis: on past and future operations, on landowners in France, family ranges and comparative views on strategic actions.
• The social landlord fabric dashboard: this dashboard allows ICF Habitat employees to see the overall French social housing stock, across all landlords. They can filter down to specific areas and look forward to compare current and future states.
• A map of correlations between housing developments and rail networks, allowing employees to analyze the situation and make better decisions.
• A dashboard for suppliers to make it simpler for them to work with ICF Habitat.

To create these different dashboards, ICF Habitat reuses several external datasets from the Opendatasoft Data hub: including the consolidated French business database (SIRENE V3), the official zip code database, municipalities and municipal districts and even the results of 2020 local elections.

Also by adding geographic data from the Data hub, teams spend less time adapting their data so that it can be shown in map-based visualizations.

Created more than 70 years ago, Lamie Mutuelle is a specialist in health and property insurance. In order to exploit the full value of its member data, Lamie Mutuelle wanted all of its data to be reusable and usable.

The organization has therefore created a data hub that feeds several decision-making dashboards, 360° views, its CRM solution, and customer apps such as its web-based member area.

The use of external sources was essential to create these different solutions. Data used includes the complete list of French companies, the official classification of medical conditions, and data on all deaths in France since 1970.

By integrating these external data cross-references, Lamie Mutuelle is able to enrich customer files with significant additional information that is not produced or collected internally. This adds significant value, and improves internal customer knowledge.

This French public organization has created an internal portal accessible to its 6,000 employees. The aim is to share datasets in the form of insights, enabling everyone to improve their daily working performance, by easily accessing and sharing data. In addition, the work carried out on the data within Caisse des Dépôts has made it possible to automate essential processes, recording information and automatically making it available to all employees.

As part of its business operations, French public sector financial institution Caisse des Dépôts collects, produces and maintains a large amount of data. This covers datasets that are of general interest (for example around housing, social policies, investments and financial activities) but also those that are specific to its work (such as stimulus plan, social housing, old age and retirement, and professional training).

The organization publishes open data on its public portal, but also internally to support the work of its employees.

To enrich data, it uses multiple external datasets:

• Public data datasets that are essential to employees and their roles
• Geographic reference data to create maps and provide lists of municipalities.
• Data to enrich municipality datasets, such as adding population, location, whether the municipality is participating in a program supported by the organization, and local diagnosis datasets on the elderly.
• Data relating to public contracts and legal professions to automate and support in-depth analysis.