In the era of vast amounts of digital information, effectively utilizing and sorting through data can be challenging for businesses. This is where data enrichment comes into play. Let’s explore how data enrichment can help us make better use of big data.
Understanding the Process of Data Enrichment
To fully grasp the benefits of data enrichment, it’s important to understand how it works. So, what makes it so valuable for analyzing big data?
The process begins with collecting digital information from various sources such as online forms, tracking software, and third-party data. This can include details like names, emails, and phone numbers.
The next step involves incorporating additional data sources to enhance existing datasets. These sources can be internal or external, public or closed, with the latter requiring permission to access.
The goal is to gain deeper insights and more accurate intelligence about customers, brands, associates, and other elements of your network. This process helps strategize better, streamline workflows, and enhance online security.
Some companies rely on their internal big data sets for this purpose, but there are also other methods. For example, using Open Source Intelligence (OSINT) tools as a form of data enrichment can be highly beneficial for marketing, sales, fraud analysis, HR professionals, journalists, and law enforcement agents.
Data Enrichment Offers Multifunctional Capabilities
As per SaaS Scout, internet users produce a staggering 2.5 quintillion bytes of data every day. To dive into this sea of information and extract meaningful insights, reliable tools, techniques, and resources are essential. Here are some key factors to consider:
OSINT, or Open Source Intelligence, is one approach to data enrichment. It involves utilizing and cross-referencing publicly available data from sources like online posts, published books, press releases, census data, and more.
For access to closed data sources, companies partner with third-party entities and their databases. This allows the funneling of data, which can then be combined with open-source insights or used independently if applicable.
But where does this data come from? Common sources include search engine results, publications, social media, subscriptions, utility logs, first-party company data, and third-party information.
In practice, there are various ways to search for information that can enrich your data sets. First, identify the type of details you need, whether demographic, geographic, company-related, behavioral, or others. Additionally, understanding why you’re enriching your data is crucial.
The clearer your goals are, the more efficient the process becomes, both in terms of tools and methodology. Several prominent brands have heavily invested in big data analysis, recognizing its value. For example, ADMA explored how Coca-Cola uses data to supercharge its brand status, highlighting the importance of convenient ways to structure and manage overwhelming amounts of data.
Data enrichment software and services are increasingly popular in the corporate world, with the market size projected to reach $2.67 billion by 2027, according to Maximize Market Research’s global report.
To expedite and enhance the process, developers are incorporating machine learning and other forms of artificial intelligence. Machine learning, in particular, simplifies the complex task of analyzing big data by automating certain aspects. This enables the integration of computer and human intelligence to gain deeper insights.
Applying Data Enrichment in Real-World Scenarios
Data enrichment extracts valuable information from the massive amount of big data available and adds it to your primary datasets.
Let’s consider a practical scenario. Suppose you only have names and emails, which provide limited information. Data enrichment can be useful in various ways, such as segmenting customers or leads, providing actionable insights on whom to target and when.
This could mean having a clearer picture of potential job candidates in HR, receiving informative data for making decisions in mortgage underwriting, or gaining a deep understanding of a client’s reliability in fraud prevention. Adding digital footprints to the equation provides valuable information in all of these contexts.
In the realm of online fraud prevention, data enrichment works behind the scenes to identify and gather information related to our primary data. For example, a specific email address or phone number provided by a user, irrespective of their trustworthiness, can be used to collect a wealth of data from various OSINT sources. The results enrich our primary data and combine to form a 360-degree profile of each user, which fraud analysts can study to make informed decisions or trigger specific actions within the system, such as blocking or flagging suspicious users.
With sufficient knowledge and powerful tools at your disposal, you can conduct similar big data analysis independently, whether you’re a freelancer or a smart city company. However, thanks to a range of data enrichment services available, you don’t have to.
The purpose of data enrichment is to enhance datasets and add value to them. This value can have implications for management, sales, fraud prevention, cybersecurity, customer relations, and more. By leveraging data enrichment techniques and tools, businesses can improve their overall performance.
However, perfecting your data enrichment process requires effort and careful planning. Aim for high data quality and utilize automation and purpose-built modules to prevent big data from overwhelming you.
Data enrichment methods and tools are here to assist, so take the time to understand how you can benefit from them, especially if you’re a decision-maker in the corporate world.
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