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Accurate, real-time, and reliable data can work wonders for any organization, but finding reliable data is difficult.
Relying on poor-quality data can be a very costly mistake.
The average yearly financial cost of poor data quality in 2017 was $15 million. ~Forbes
On top of this, a large proportion of enterprise data is stored in data silos, which makes it difficult to collaborate across teams. This leads to incomplete information, low productivity, and inefficient collaboration.
It is estimated that companies can lose $1.5M annually due to siloed communications.
How can you ensure high-quality data flows into all your databases in real-time? The answer is data enrichment.
Data enrichment can enhance the quality of data by combining data from multiple sources to provide a complete unified view. Let’s see what data enrichment is, how to do it, along with the use case, benefits, and ways to automate it easily.
Automate data enrichment with no-code workflows in 15 minutes!
What is data enrichment?
Data enrichment is the process of combining data across multiple sources like first-party data (company web forms, leads) with third-party data (information databases like apollo). Data enrichment helps in improving data quality and is mainly used to collect data from multiple sources and get complete information for a particular entry.
For example, Your CRM has a customer entry that shows information about the customer name, company, customer source, and more. Whereas your financial accounting software has information to scout customer invoices and payments. Merging these two data can help you perform detailed customer analysis about the highest ARR by customers, and form a better customer profile to perform deeper behavioral analysis.
Why do you need it?
On average, companies use 17 disparate software & over 28 data sources to generate customer insights and improve customer experience. 
Managing a such high number of data sources without proper instrumentation is bound to have errors or gaps. Data enrichment tool helps you get the best of all data sources by combining them. Apart from this:
- Data enrichment improves data reliability and enables data-driven decision-making.
- It allows organizations to sync data in real time from multiple sources automatically.
- It improves customer understanding by combining customer data from multiple sources without disturbing the customers.
- It ensures uniform data quality, which helps in performing predictive analytics and accurate forecasting.
- Using a Data enrichment tool can help identify potential compliance issues.
What are the different ways to enrich data?
Now that we’ve seen what is data enrichment and the benefits of data enrichment. Let’s move on to how you can implement it. There are three different ways to do it.
- Using scraping tools
- Manually searching and updating data
- Using automatic data enrichment software
Using Scraping tools
Scraping tools allow you to extract data from different sources, which can be later integrated into your systems.
Email parsing tools can extract data from incoming emails. Web scraping tools can extract data from web pages. Whereas OCR software, like Nanonets, can help you extract data from documents, excel, CSV, or more.
Manually searching and updating data.
Another way to enrich data is to do it manually. Simply find the kind of data you want to enrich and enter the data into a worksheet or database. The main drawback is that it takes up a lot of time, and ROI can be on the lower side.
Using automatic data enrichment software
If you have a clear idea of what data you need to extract, the sources, and the databases to connect, you can use data enrichment software. Such software can automate:
- Data collection from multiple sources
- Data formatting, cleaning, and data aggregation
- Data sync across multiple sources
This is the best choice when you’re working with high volumes of data on a daily basis. Here’s an example of how Nanonets helps in customer data enrichment by combining invoice data with CRM data.
Automatically import invoices from different sources.
Extract data from invoices using OCR.
Perform data enhancement tasks automatically using no-code workflows.
Upload the final data to the ERP or CRM of your choice.
Want to automate repetitive data enrichment tasks?
Check out Nanonets workflow software. Extract data from documents & enrich your databases on autopilot!
Nanonets for data enrichment
At Nanonets, we work towards making unstructured data from documents & multiple sources usable for advanced analytics. Armed with OCR software, 5000+ integrations, and advanced workflow automation, you can use Nanonets for extracting, connecting, and syncing data across disparate sources.
Nanonets can extract data from any kind of data type: PDF, excel, CSV, emails, webpage, databases, images, scanned documents, or more. You can perform a variety of data automation tasks like cleaning, collection, verification, aggregation, formatting, and more.
With seamless integrations, you can sync data across multiple data sources in real-time. Here are some use cases for which you can use Nanonets:
- Data Extraction
- Accounts Payable Automation,
- Invoice Processing,
- Email parsing,
- Barcode data extraction
- Financial Automation
- Healthcare automation
- Logistics automation,
- Purchase order automation
Here's how you can automate data enrichment using Nanonets.
- Login or create a free account on Nanonets.
- Select the incoming source of data from the data import options.
- Check the data that is extracted from the document. You have to do it once. Once set, this will be automated.
- You can either select from one of the data enrichment options on Nanonets, or get help from our team to write a custom code for your use case.
Once done, you can use the export options to sync or update data in relevant business platforms.
Put data enrichment on autopilot with Nanonets. Try it for yourself
Data Enrichment Techniques
In general, here are the 6 steps to enrich data:
Select the sources to combine data from. The next step is to set up data collection and merge the received data into one single location.
Data segmentation enables you to separate or arrange a dataset following particular parameters. Utilizing statistical, regional, technological, or behavioral values is a prevalent segmentation method. The segmentation is then used to categorize and characterize the entity better. While if we talk about marketing use cases, segmenting is also used for targeting.
Derived attributes are not part of the initial data set. But these fields are built from a single domain or a group of areas. Since derived characteristics usually contain reasoning applied during analysis, they are helpful. To determine the age, the tactic subtracts the birthday from the current date, which is the derived property that is most considered.
Data imputation is the process of replacing values for missing information across fields. Instead of treating the missing number as zero, the estimated value examines your data. Calculating a lacking field's price based on other matters is a good example.
Named Entity Extraction
You can add many data values within a single field when using complex semi-organized, or unstructured data. Entity extraction allows you to identify different entities, such as people or businesses. The values should belong to one domain and then be blasted into one or more fields. This strategy will make your business data more meaningful.
It is the process of grouping data into two categories to organize and analyze it better. You can use either of these approaches to analyze unstructured data to make it more sensible.
- Sentimental Analysis: The method of removing sentiments from text is known as sentiment analysis, such as analyzing whether consumer feedback is favorable, unfavorable, or neutral.
- Topication: The process of determining the "topic" of the text is topication. Such as identifying the genre of the article, if it is about tech, sports, or travel.
What are the challenges in data enrichment, and how to overcome them?
Here are some common challenges while enriching data and ways to overcome them:
- Inconsistent data structure - When you’re working with different data sources, the way data is stored might be different. Due to different schema or storing patterns, it becomes difficult to identify and merge them. The solution is to identify such differences and use no-code workflows to clean and convert data into a uniform structure.
- Tackling data privacy- Data extraction can be a challenge if you’re extracting sensitive data. Make sure you’re following proper guidelines before extracting data from any source.
- Inefficient integrations- Sometimes there are differences in how data is stored in different software which leads to incorrect entries. Make sure data integration across multiple systems follows proper naming conventions for better data synchronization.
If you have another use case in mind, please contact us. We can help you automate data extraction, processing, and archiving using no-code workflows at a fraction of the cost.
What Are The Best Practices For Data Enrichment?
Data enrichment is the process of adding more information to existing data sets to make them more valuable. Sometimes it needs to be done only once, but in other cases, it needs to be done regularly, especially when new data is constantly being added.
To ensure the data is high-quality and accurate, it's essential to use the best methods and techniques for data enrichment. These practices will help improve the overall quality of the data and the insights it provides, which can ultimately benefit the business.
When designing a process for data enrichment, it's crucial to ensure it can handle more data as your business grows. The process should also be efficient and not require too many resources.
Automating the process is a good idea as it can handle more data without increasing costs. This is especially important if you are part of a mutual business where there are limits on how much data can be processed.
Stability & Replication
Every data enrichment process should be repeatable and produce the same final results. All your data enrichment processes should be rule-driven. This ensures the results will remain constant.
Indisputable Evaluation Criteria
There needs to be a defined evaluation standard for every data enrichment operation. You must be able to judge whether the procedure has been satisfactory and has run as expected when you compare initial successes with those from the very first tasks. You can see that the outputs are what you would expect from them.
You should ensure that the results have the same qualities as the data that went into the system. You should also consider possible outcomes for every variable, including unknown result scenarios.
Being detailed, you input new values into the system will allow you to be confident. This will ensure that the enrichment process results will always be reliable.
The activity of data enrichment ought to be adaptable to many data sets. Make sure that the procedures you apply can be applied to many datasets. So you can use the same logic for various tasks.
You can also use the same method to remove any entry from the data field. This strategy connects all your business needs and data throughout all business domains.
How to select the best data enrichment software?
There are many data enrichment tools available in the market, but the challenge is to find the one that fits your requirements and budget. Here are a few things to look for before finalizing your next data enrichment software:
- Integrations: What are the different integration options available? See if they have native integration or via APIs.
- Data extraction process: Check the data extraction accuracy. You don’t want to extract data with a lot of inconsistencies only to clean it later on.
- Data hosting: Do you want cloud or on-premise hosting?
- Rule-based automation:
- Data quality and legality: How fresh is the data you are acquiring? And does the company delivering it meet legal requirements for data protection like the GDPR?
- Pricing: There shouldn’t be a ton of variation here, as most third-party data enrichment companies charge a micro fee for each check.
And last but not least, Middleware options, which I’ll explain in more detail below.
Want to get started with data enrichment?
See how you can implement your use cases with Nanonets easily.
Read more about data processing on Nanonets:
- How to improve data insights with data aggregation?
- Improve data consistency with efficient data matching
- Turn raw data into structured data with data enrichment
- Find the best data extraction tool in 2023
- Eliminate data inconsistencies with data wrangling
- How to clean data easily?
- What is data merging?
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