Data Enrichment, or Data Enrichment: As the terms suggest, it is an addition of data to what you already have. But it is an addition in the name of quality: on the one hand, the objective is to optimize, refine, improve the set of raw data to obtain insight, i.e. analysis, useful to the activities of a company (but not only); on the other hand, to keep a database ‘clean’, typically subject to many errors and obsolescence.
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What the date enrichment is for
One of the issues facing companies and organizations when it comes to Big Data is how to turn their data into value and make it a strategic leverage. So-called analytics are now a central part of many business activities such as customer segmentation, competitive intelligence, fraud detection, and the design of new products or services. But the data that each organization acquires through its own channels (technology platforms and processes) is not enough, so many companies are trying to enrich internal data records with data from external sources. The old model of static purchasing of data from one or more suppliers is now considered inefficient due to both the rapid obsolescence of the data itself and the rapid growth of new forms of dynamic acquisition of useful data available online, which can be acquired to company assets through tools, processes and data enrichment solutions.
Typically the process of data enrichment concerns the profiling of users and customers of a company, it is therefore a very useful process in the marketing and development of new products, because it allows to know better the target customers. This also applies in areas such as healthcare or pharmacy, where better knowledge and better results can be achieved through the use of data and data enrichment processes.
Consider the example (see this Accenture Technology Labs paper) where a company’s consumer database has the name and address of its consumers. Being able to use publicly available data sources such as LinkedIn or Facebook to find information such as details and interests can help the company gather additional information for activities such as customer segmentation.
The advantages of data enrichment (examples)
Detailed customer profiling 360-degree client (so-called personas) has become a holy grail in the consumer sector, especially in vertical sectors such as retail and healthcare, according to Accenture report. Companies often invite customers to sign up for reward programs or engage them on social media with the goal of gaining access to basic information about a customer, such as name, email, address and social media account. However, in the vast majority of cases, this information is incomplete and not uniform. For example, for a John Doe customer, a company may have a name, address and phone number, while for Jane Doe, the information available will be name, email and a Twitter account. However, by exploiting the basic information and filling in gaps, profiling has many applications:
– personalized and targeted promotions: the more information a company has about its customers, the better it can customize offers and promotions. But that’s not all: in the insurance field ‘knowing more’ about the customer allows you to act in a preventive insurance perspective, to customize services and pricing, to create alternative models such as peer-to-peer, on-demand, micro policies, etc.;
– better segmentation and analysis: the supplier may need more information about its customers, beyond what they have in their profile, for better segmentation and analysis. For a company, getting to know its customers better means understanding their insurance needs, satisfying them with innovative policies and reaching them through the most effective channels;
– fraud detection: the supplier may need more information about its customers to detect fraud. Providers typically create detailed customer profiles to predict their behavior and detect anomalies. In the insurance industry, claim management is made much more efficient by the use of quality data.
Data Enrichment Tools
Data enrichment is quite a challenge and much depends on the quality of the existing data in the company databases. If the existing information is wrong or too incomplete, it will be much more difficult to find an order in the chaos, model the data, use it for analysis and for specific purposes.
The work to optimize a database with data enrichment processes is painstaking and many times requires manual work, but there are more and more tools, software that have this purpose and differ according to the purpose you want to achieve. The most basic systems are those of web scraping, the most complete, secure and advanced systems are developed by software houses and tech startups (enrichment providers).
The various business and non-business systems through which data is collected should be integrated, and the data themselves mapped and reorganized.
Having in mind the precise purpose to which the data enrichment is addressed is important to understand which data sources to use: to give an example, to enrich the professional profile of the customer, we can use Linkedin as a source of data, but not Facebook; the latter social network will be more useful if we want to know more about personal tastes, hobbies, holidays, habits, sensitivity and even the personality of people.
In the insurance industry it is very important to collect information that contributes to the analysis of behaviors, even the most irrational, which is why a new source of data to pay attention to is gaming.
Data is a key feature of all types of games, not only for the game itself, but for what they can bring to data science outside of the game. During the game you are constantly collecting data, and it has a lot to do with the emotions, attitudes, behaviors and the most instinctive sphere of the players.
Data enrichment in the insurance sector
The processing of insurance business data has traditionally involved demographic data, exposure data or behavioural data, but today, these traditional data sets are increasingly being combined with new types of data, such as Internet of Things (IoT) data, data coming from social networks and online behaviours, bank account and credit card data, in order to perform more sophisticated and comprehensive analysis, in a process commonly known as “data enrichment”. That through artificial intelligence and analytics, provide companies with the tool to revolutionize processes, operating costs, offers, customer relationship.
Privacy, Gdpr and data enrichment
Data enrichment does not concern access to private data. Rather, the focus is on the collection and use of data that are already public and their connection. Suppliers of data enrichment systems generally offer verifiable tracking of how raw data is captured.
The right to monitor and process such data is different, with respect to which it is the provisions contained in the GDPR that determine which types of consent are necessary for their processing.
Correct data management is something, however, that goes beyond compliance and concerns the ethical approach to data that each company intends to promote in its activities.
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