H Data Analytics
H Data Analytics
H Data Analytics
H Data Analytics
H Data Analytics

What is data enrichment and how can it benefit manufacturers?

Nov. 22, 2022
"Data enrichment is essentially filling in the gaps in your data set."

By Gergo Varga, product evangelist at SEON

In the age of big data, manufacturing companies work with a large influx of information about both their products and their customers. Data enrichment can help you aggregate your data so that you can organize your data, improving accuracy and relevancy while creating a more complete picture for your data sets.

What is data enrichment?

Data enrichment is essentially filling in the gaps in your data set. The aim is to make sure that data sets are as accurate and up-to-date as possible. When data is aggregated properly into a set, it’s more likely to be meaningful than if you were to look at it by itself, or as part of an incomplete set. Data enrichment is one dimension of data quality; low-quality data can result in issues like higher processing cost, unreliable analysis and more.

For example, imagine you only have a few primary data points (that is, data points that you can enrich with other sources). Take for example, a customer’s phone number or email address can be a primary data point. Data enrichment involves taking these primary points and enriching them with related, relevant data that creates a more complete picture. In the case of someone’s phone number or email address, you can enrich primary data using data enrichment tools to find out what social-media accounts they’re linked to, as well as their history as a customer. Finally, you can use data enrichment to combine pre-existing data sets together that work well together in the same database.

So, where can these data sources come from? Primary data can be enriched from both internal and the open internet (and open sources on the internet). These can be databases online which perhaps aren’t easily searchable using Google, but are still public domain. With the right tools, like data enrichment or other open source intelligence tools (OSINTs), you can collect this data and combine it into a set with your pre-existing primary data.

Data enrichment is just one way that you can extract data from the web, but data-enrichment tools are particularly useful in that they enable you to clean and organize the data you collect as well. What’s more, you can merge these sources together with relative ease (so long as the data is relevant and fits into other data sets). This makes data enrichment relevant to a broad range of businesses, whether that’s in manufacturing or any other company that’s looking to keep their data fresh and relevant.

How can data enrichment help?

Case study 1: Data enrichment in the world of manufacturing

Below, we run through a few applications of data enrichment that can contribute to the efficiency of your systems. Firstly, we’ll look at RFID tags and how they can be enriched so that you can keep track of materials in your warehouses, whether that’s moving them in or out, or so that you can scan them to find out more about a specific material. RFID tags enable you to store information on your products, plus the data on them can be updated with relative ease—with the help of data enrichment.

RFIDs are smart labels that can be communicated or updated. As SpotSee says, RFID tags can “optimize your supply chain by improving material flow. The more recent passive RFID tags serve as intelligent monitors that deliver accurate track and trace details throughout the supply chain.”

By placing an RFID code on items in your stock, you can enrich the data on the chip based on its number or code in order to update the information contained on it. There are several ways that this can be useful to manufacturers who are constantly moving items. You can enrich data on an RFID tag to show when your materials have moved in and out of your warehouse. Often, you’ll need to update records like pricing, weight or any other enriched data; this might include information on the kinds of materials being used, whether there are any hazardous substances being moved and how much of that material is in stock (including how many pieces there are perhaps if it needs to be sold in bulk).

You can also enrich data on tags to provide crucial information about expiration dates, certification and whether a product needs to be recertified or inspected. By aggregating data using enrichment, you can also group similar materials together on your database—particularly useful if you are working with a range of different raw materials with different functions, or by expiry date.

Data enrichment and manufacturing analytics

Here’s another idea. Data enrichment is a good first step before you use data analytics to make key manufacturing decisions. In case you didn’t know, data analytics is the ability to read, interpret and use data in order to communicate and make informed decisions. In their guide to manufacturing analytics, Tibco says that most data needs to be merged, cleaned and filtered before it is ready for analysis.

How is data enrichment relevant to this process? Firstly, it can be used to provide a richer and more complete data set to analyze. This could be very useful for manufacturing companies who are looking to utilize this to promote efficiency and strategy. By building a more comprehensive data set on areas like customers, you can analyze the data more deeply to find out what your customers need from your products. As a manufacturer, this can help you to update your products or product supplies to better accommodate your customer’s needs. These insights can also help you forecast changes in demand—you’ll be able to manage your supply chain more efficiently.

Case study 2: fraud prevention

The fact that data enrichment can help you complete a data set makes it a key tool in the world of fraud fighting. All online businesses run the risk of being exploited by cybercriminals looking to make fraudulent transactions. It’s therefore worth familiarizing yourself with how data enrichment can be used to stop them before they have a chance to make a purchase.

When customers take actions on your website, they leave behind a small data trail. Upon registering with you, they might provide you with a phone number or email address. Their ISP and IP address will also be part of this trail.

By itself, this might not look very useful if you’re trying to spot a cybercriminal. But by enriching primary data sources like a user’s email address, phone number or IP with data from sources online, you can find much more about them. One data-enrichment tool that’s particularly useful here is a reverse phone lookup tool. How they work is that they take data from open online sources, including information like whether a number is disposable and/or even real in the first place. As so many cybercriminals use disposable or fake numbers, if the data points to this being the case this provides you with some indication that a user is suspicious. So does if their phone number isn’t linked to any social media accounts, or if their social media accounts aren’t very old.

In SEON’s guide to reverse phone lookup tools, they suggest that key features to look out for include price, integration, data-privacy compliance, scalability, data freshness and which countries are covered. At the end of the day, which tool is right for your business will depend a lot on your own needs.

That’s not the only potential use of customer data, however, as we describe in our third case study—sales.

Case study 3: sales

Not only does data enrichment help keep your records accurate, but it also can tell you more about a customer based on enriching snippets of primary data like their email address or phone number. By enriching this data with a data-enrichment tool, you can find out more about a customer’s social-media accounts linked with their email or phone number. This can give you clues about their pre-existing shopping habits—if they are a long-time user of Airbnb for instance, then you can target ads related to travel.

When used a lot to build a profile of your customers, data enrichment can help you to segment your customers based on demographics, shopping habits and interests—telling you not only a lot about one individual customer but about your customer base’s online habits on the whole. If you are a manufacturing company looking to target certain products to customers’ needs better, then this can be a great option for you. This kind of customer segmentation can help target the right kinds of advertising and information to specific customers can translate to increased sales—according to a survey by NotifyVisitors,“80% of audiences tend to do business with a brand that personalizes their experience with it… segmentation makes firms 60% more likely to understand customer’s challenges.” In summary: data enrichment helps you to create the general picture that you need of your customers in order to start segmenting your customers based on needs and responding to this with personalized marketing.

Wrapping it up

As you can see from the above, data enrichment is highly versatile. It can keep your data fresh, relevant and comprehensive. Data enrichment can help you update information about materials kept in a warehouse, which is particularly useful if you need to regularly update RFID tags with info on expiry dates, certification data and other data relevant to manufacturers. What’s more, data enrichment is also incredibly useful in spotting cybercriminals—using the very same tools can be used to segment customers according to demographic.