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Data fuels every efficient business asset. But it's always of the best quality.

The average yearly financial cost of poor data quality in 2017 was $15 million. ~Forbes

Businesses collect data from many sources, but raw data contains inconsistencies.  Data enrichment helps companies to transform raw and unstructured data into a structured dataset. That enables data analytics to provide insightful data to enhance and reuse business data.

The article discusses data enrichment, how it works, and the best approach to it. We will also see how you can automate in a few clicks!


What is Data Enrichment?

Data enrichment process combines or merges a company's data with an external dataset to form complete and reliable data.

It is the process of enhancing, modifying, and improving raw data. Brands use this process to improve their decision-making and business data quality.

Source

Any firm can enjoy having enriched data since it makes it more insightful. Adding new categories you derive from existing data is a straightforward form of data enrichment. Organizations can make better business decisions by examining reliable and authoritative data.

Each opportunity is given extra context by adding or improving the lead data. Your chances of converting a lead increase as you learn more about them. Here’s how data enrichment can help your business by adding or improving lead data.

  • Employees can respond to a broader range of inquiries due to the availability of extra information.
  • Data enrichment enables you to label and sort company data for rules making.
  • It enables businesses to process text more and moderate data more.
  • Data enrichment also enables them to provide more insightful responses than they could have with raw data.
  • It increases client satisfaction since richer data enables you to fulfill customers' requirements.

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How Does Data Enrichment Work?

Data enrichment can be broken down into simple steps. You have an incoming stream of data. Your team or software determines the missing or inconsistent data. The third part is integrations, then filling in the missing data to complete your data.

Here are three methods used to enrich enterprise data, and it also explains how data enrichment works.

Web Scraping

Obtaining massive amounts of data from the internet is known as web scraping. It is a scalable, cheap method of enhancing an enterprise database. And businesses can use it to add data from an accessible web source to a CRM or spreadsheet.

Manual Research

Enhancing leads through manual research is a different choice. That entails searching for tips on sites such as Google or LinkedIn. Then they add the information to a database or worksheet. The strategy works fine for small quantities of data. But ineffective for adding hundreds of new prospects to an existing file.

Data Enrichment Tools

A data enrichment tool or service-providing company accomplishes three things.

  • They collect online data from authoritative third parties
  • Arranges, purify, and format the data
  • Combines data from various sources

Data Enrichment tools and services function for businesses. But one drawback is that you can only access the information in their database. You would need more choices to select data points and sources with scraping.


Why should you use data enrichment?

Sorting out the ideal customer record involves effective data enrichment procedures because a single dataset cannot contain all the transactional or behavioral information required to provide a complete picture of the client. Because of this, data enrichment techniques are essential in today’s world.

Here's how data enrichment helps your organization in the long run:

  • Data enrichment improves data reliability and improves business decision-making.
  • By enriching customer data with information from other sources, businesses can gain a complete understanding of their customers.
  • With enriched data, businesses can perform predictive analytics and accurate forecasting.
  • Data enrichment can help to identify potential compliance issues.

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Check out Nanonets workflow software. Extract data from documents & enrich your databases on autopilot!


How to enrich data?

To enrich your business data, use the step-by-step approach for dependable results.

Appending Data

Combining data from various sources can produce an accurate and consistent data set. By merging data from different modules of your business, it will give you a better picture of your client's prerequisites. While it also enables you to generate accurate statistics for use as features in machine learning models (MLM).

Data Segmentation

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

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 Manipulation

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.

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.

Data Categorization

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.

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How to automate data enrichment?

Businesses can automate data enrichment easily using workflow automation platforms like Nanonets. Most data enrichment tasks are rule-based, making it easier to automate.

Nanonets is an AI-based document workflow management platform with in-built OCR software. Nanonets can enrich data extracted from documents( word, CSV, xlsx, Json), scanned images, webpage, emails, or PDFs with no-code workflows.  

Here's how you can automate data enrichment using 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.

Here's an overview of data enrichment on Nanonets:

Nanonets can be used for a variety of tasks including but not limited to

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 it 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.

Scalability

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.

Completeness

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.

Generalization

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.


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Tips to become better at data enrichment

Data enrichment will give your business various advantages. But it is a challenging task requiring Big Data usage. Here are a few helpful tips when you need help with how to enhance your current data.

Set Approachable Data Enrichment Goals for Your Business

Businesses can achieve mighty results by implementing data enrichment processes. And it's possible to elevate your business revenue with data enrichment. But set realistic data enrichment goals you can achieve with your enterprise resources.

Stay Updated with the Latest Enrichment Processes

Data enrichment of your business is not a matter of a few times. But you must stay updated with the changing trends in the data-enriching industry. Pay attention and use all the latest strategies to enrich your business data because this will help your business to stay ahead of your competitors.

Using the Right Tools & Strategies

Suppose your enterprise aims to achieve better revenue and positive outcomes. Make sure you use the best practices or tools for data enrichment of your business. Many data enrichment tools are available but do your research before you settle for one. You can also rely on third-party service-providing companies that offer data enrichment services.


Conclusion

Data enrichment is sometimes neglected, but it is critical to creating suitable datasets. This occurs when developers need to consider the data set criteria for analytics. When it's time to decide what data to capture in apps, the need for analytics data will change over time.

Thus well-developed data transformation tools are the need of the time. They enable team members to change and enrich business data to their unique needs. This empowers the analytics teams to provide accurate insights, promote broader analytics adoption, and be more responsive to the business.


Want to get started with data enrichment?

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