Big data refers to an astounding volume of digital information. Sorting through it is difficult, as is pinpointing specific details for companies to use – especially when said data has flaws or errors. That’s why data management techniques such as data enrichment are so important.
Let’s take a closer look at exactly how data enrichment can help us put big data to better use.
How Data Enrichment Works
It’s important to first understand how data enrichment works. What makes it so useful for big data analysis?
- Data enrichment starts with digital information that a company collects via forms, tracking software, and even third-party data. Details can include names, emails, phone numbers, and so on.
- The next step involves engaging with additional data sources to embellish existing sets of information—about employees, clients or users, for example. These data sources can be external or internal, public or closed, the latter requiring permission to access.
- The goal is to get more in-depth and more accurate intelligence regarding customers, brands, affiliates, and other parts of your network. As a result, the process helps strategize better, streamline your workflow, and boost your online security, for instance.
Some companies will use their internal big data sets for this purpose. But that is not the only way. In terms of openly accessible information, an article at SEON explains how we use OSINT tools as a form of data enrichment. Marketing, sales, fraud analysis, and HR professionals, as well as journalists and law enforcement agents, can find the results very useful and insightful.
Data Enrichment Is Multifunctional
SaaS Scout explores big data facts and finds that internet users produce 2.5 quintillion bytes of data per day. So, reliable tools, techniques, and sources are essential to diving in and coming up with the right insights. Here are some important factors to keep in mind.
OSINT, short for Open Source Intelligence, is one approach to data enrichment. It utilizes and cross-references data that’s publicly available, whether from online posts, published books, the digital or printed press, census records or elsewhere.
For access to closed sources of information, companies or data enrichment platforms partner up with third-party entities and their databases. It’s then possible to funnel data from them and combine it with open-source insights – or use it on its own, if appropriate.
But where exactly does this data come from? Popular sources include:
- Search engine results
- Publications
- Social media
- Subscriptions
- Application logs
- First-party company data
- Third-party records
In practice, there are different ways to search for information that can enrich your data sets. First, you decide what kind of details you need – demographic, geographic, corporate, behavioral, or other. Why you’re enriching your data is also important.
The clearer the purpose(s), the more efficient the process can be made with both the tools and methodology and the information to be filled in. Some of the world’s biggest brands have invested heavily in big data analysis.
For example, ADMA looked into Coca-Cola’s data use in an interview that includes discussion of how “overwhelming for data scientists and executives” big data can be, and how important it is to have convenient ways to structure and recall it.
Data enrichment software and services are increasingly popular in the corporate world, their steadily growing market size expected to hit $2.67 billion by 2027, according to Maximize Market Research’s global report.
To make the process faster and more powerful, developers are employing machine learning and other forms of artificial intelligence for everything from analysis and reports to workflow automation. With machine learning in the picture, channeling data into profitable action plans is easier as smart technology automates parts of the complex process. This means you can combine computer and human intelligence to master big data.
Data Enrichment in Practice
As a method, data enrichment finds nuggets of useful information in the mass of big data out there and adds it to your set of primary data.
Let’s look at how this works in practice. Let’s say that all you have is names and emails, which doesn’t give you much information. Data enrichment can be of use here for several different reasons, such as segmenting customers or leads in a way that can give you actionable insights on who to target and when.
This could, for example, mean you have a clearer picture of candidates for a position if you work in HR, that you receive useful information to help make decisions in loan underwriting, or that you can get a good understanding of how legitimate a user is in fraud prevention. Adding digital footprints into the fold gives you further information that is very valuable in all these contexts.
In the world of online fraud prevention, data enrichment works under the hood to identify and collect information linked to our primary information: the few givens we have about a user. For instance, they may have provided a specific email address or a phone number, which we know is theirs no matter if they are legitimate or a fraudster.
So, a wealth of information is identified and gathered from across OSINT sources, starting from this email or number. The results of this process enrich our primary data, combining to form a 360-degree profile of each user that can be studied by fraud analysts to help them make decisions or trigger specific actions in the system, such as blocking a user or flagging them as suspicious.
With enough knowhow and powerful solutions at your disposal, it’s possible to conduct similar big data analysis alone, whether as a freelancer or smart city company. However, the range of data enrichment services available means you don’t have to.
Conclusion
The goal of data enrichment is to enhance data sets and add value to them. This worth can relate to administration, sales, fraud, cybersecurity, customer relations, and more. Either way, a business’s performance improves.
That said, it takes work and careful planning to perfect your data enrichment process. For best results, you aim for high data quality and employ automation and purpose-built modules to stop big data from overwhelming you.
Data enrichment methods and tools are here to help, so get to know how to make the most of them – especially as a decision maker in the corporate world.
