The importance of enriching data

During his recent mission aboard the International Space Station, astronaut Thomas Pesquet reminded the importance and enrichment of data to provide accurate studies and reports. The field of data science has seen rapid progress in recent years: from financial analysis to climate risk prevention, in most companies, managers have become aware of its importance. They realize the critical benefit of exploiting internal and external data backed by advanced analytics.

Data plays a central role in any activity, from packet delivery to space mission path development, and defines the structure of activities. Business leaders are increasingly realizing that machine learning and artificial intelligence (AI) will be sources of significant competitive advantage in the coming years. For many, the difficulty is knowing where to start.

Understanding data enrichment

Data enrichment can be defined as The process of merging company data with various data from other systems or third party data sources. The ultimate goal of data enrichment is to provide qualitative, complete and comprehensive information.

A typical example is a company that seeks to better define its customers. By integrating the data it has on its customers with external demographics and behavioral information, a company can gain a richer and more accurate understanding of its customer base. In fact, data enrichment increases the reach and value of your existing business data by integrating it with high-quality data from external sources.

Towards the future of data analytics

Many companies have started collecting their data and making sure it is stored somewhere where it can be used in the future. This often includes text data, semi-structured and unstructured data including service tickets, user reviews, and social media posts, as well as more traditional sources such as ERP transaction data. By collecting and organizing this information while ensuring that it is stored in a way that it can be used later, these companies lay the foundation for reaping future benefits from data analytics.

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What is more, Data enrichment makes it possible to effectively map the company’s business landscape. It really provides valuable information to companies which in turn can generate instant intelligence.

This is why some companies are already using external data. Many also plan to start using external data within the next year. The trends surrounding geospatial data follow a similar pattern. About a third of companies use location data in some way, and the remaining companies plan to start using geospatial data within the next year.

Thus, these trends reflect the fact that companies seeking to understand and use their information are increasingly realizing that data enrichment is a logical starting point. It’s about identifying how companies can begin to harness data science more effectively to ultimately deliver more relevant business.

The unique role of geospatial data

Geo-referenced data provides a particularly interesting opportunity to create value. They allow access to information about the location of a particular entity in time and space, including its physical characteristics, what is close to it, the type of traffic or even the weather conditions at a particular time. Geospatial data helps maintain similar sets of information for different countries and regions around the world. When combined with an organization’s existing business data, it provides rich content and particularly valuable insights.

However, geospatial data still poses particular difficulties, as it escapes the standard models and definitions that typically characterize structured data. The difficulties of working with geospatial data can be compared with the information recorded in a spreadsheet, where one tries to put squiggly lines on it.

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In the case of an address, for example, the use of location information consists of calculating distances, average driving time, or exposure to certain types of extreme weather events or other natural disasters. This type of analysis, as well as many other examples in the field of site intelligence, require specialized tools and capabilities. However, location intelligence has the potential to generate significant intellectual value.

Enriching data: a team effort

In order to generate concrete interest in collecting and evaluating information, it is important to recognize the different interests of data enrichment. This usually involves interviewing several stakeholders to determine which goals are most important. This information should be cross-referenced with organizational data to identify high-value opportunities.

Thus, the company may have set important goals for further analysis, but if the data set required for this analysis contains serious reservations, the company must first address data quality or governance issues. anyways, The process should include a variety of skillsFrom data scientists to business users and policy makers. Data science is already a team sport.

In short, today’s businesses benefit from a full range of capabilities that help them manage and integrate their data, maintain high data quality while enriching that vision with selected external data, and expose context for using geospatial data. Ultimately, integration, data quality, enrichment, and location intelligence offer value and, when combined, form a data integration platform that provides a significant insight advantage to his company, his goals, and his customers.

by Steve van den BergSEMEA’s Vice President specially

Read also:

> Democratize data science and make AI interpretable and transparent
> Data science and ML: the small ‘thumb’ reigns against the two giants
> Building strong governance to improve your data
> Building a data strategy, a hard road

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About the Author: Irene Alves

"Bacon ninja. Guru do álcool. Explorador orgulhoso. Ávido entusiasta da cultura pop."

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