Finding it hard to keep up with this fast-paced industry?
In today’s digital economy, data empowers businesses with the ability to find valuable insights that improve marketing campaigns, help them better understand customers, and streamline operations. With higher volumes of information than ever building up in internal systems, there seems to be no end to what’s possible.
The best insights, though, often come from enhancing existing information to make it more valuable, rather than adding more raw data points to your datasets. This article describes data enrichment, how it benefits businesses, and some common data enrichment processes to help your organization augment its existing information sources.
What is data enrichment?
Data enrichment improves the usefulness of your data by enhancing it with extra context merged from another source. Usually, the data enrichment process merges external data from third-party sources with an existing internal database. For example, you might acquire customer demographic data from external sources, such as income level or marital status, and combine that with the raw data about customers stored internally to come up with more fine-grained insights into customer behavior and expectations.
It’s also possible that in large businesses with many data silos, the data enrichment process merges two disparate sources of data from different internal systems. For example, information on customer experience might be in one system while other customer records are in another. By merging data fragmented across different teams and their systems, you get a more complete picture and can extract better insights.
Whatever shape data enrichment takes at your business, view it as an effort to improve data quality. Always start with the business purpose for enrichment and work backward. Whether a legal requirement calls for more detail in an existing database (e.g. ZIP codes), a marketing campaign has incomplete information, or an important internal calculation requires extra context, these are all examples of business purposes for data enrichment.
There are many valid business purposes for wanting to enrich data, but it’s important to exercise caution and make sure there is at least some common factor that links any two distinct data sources. This common factor could just be an email address or IP address; the point is that you can’t enrich data unless there is some linking factor that shows the third-party data source or internal dataset adds actual context to the data you want to enrich.
What are the benefits of data enrichment?
Here is a non-exhaustive list of the potential benefits to your business from data enrichment:
Increased likelihood of providing the right data for audits, legal proceedings and data subject requests
For organizations managing large volumes of records within highly regulated industries, records are often needed for evidential purposes. It’s important to be able to identify and find records when needed.
Metadata that encompasses your end-to-end data landscape can help with data discovery.
Enriching records with metadata from systems external to the source system, or simply enriching typically metadata-poor content sources such as emails and google docs increases the quality of your records metadata. By increasing the quality of records’ metadata, it increases the likelihood of accurate categorization and providing the right data, at the right time for audits, legal proceedings, Freedom of Information (FOI) requests, or Data Subject Access Requests (DSAR).
Streamlined data minimization and preservation
Enriched metadata can add additional context that can then be used to help categorize and apply your retention schedules to records. For example, let's say you have a project management team who use a project management system. When the Project Manager sets a project close date in that project management system, metadata enrichment can ensure records from all authoring systems have that close date applied in the metadata.
Another example where metadata enrichment can really help with data minimization is an employee termination event. This typically occurs in a Human Resources Information System (HRIS), but documents for the employee often sit in a SharePoint repository. The employee termination date in the HRIS system can enrich the metadata on the employee documents and allow for defensible disposition after the relevant retention period (typically seven years).
In a world where 52 percent of enterprise data is “dark” information with unknown value and where it costs $650,000 to store one petabyte (1000 terabytes) of data, the price of accumulating more and more data without extracting value from it continues to increase. Data enrichment shines a light on this dark data and uncovers insights using augmentation from other sources.
Furthermore, part of the enrichment process is to evaluate existing data stores and discover redundancies, errors, or duplicate information. This evaluation not only primes data for enrichment but it also saves money through reduced storage space requirements.
Improved IT security
Beyond the more obvious benefits, data enrichment also has uses for improving IT security. One example is to improve threat detection by enriching an application’s security log files with additional sources of information about a user’s role, behavior, and access privileges from internal Active Directory or other Identity and Access Management databases.
More personalized customer communications
Improving customer experience and crafting meaningful customer relationships are crucial for recurring business. Data enrichment facilitates these improvements through more personalized customer communications. Armed with enriched customer data, you can more acutely predict and understand the preferences, wants, and needs of customers. Tailoring your communication strategy with this understanding drives better relationships and more recurring business.
Common data enrichment processes
We’ve already mentioned how you need to start any data enrichment endeavor with a clear business purpose. Beyond this starting point, here are some common data enrichment processes that play an important role in any enrichment project.
Cleanse existing data
The enrichment process starts with evaluating the quality of the existing data that you want to enrich and cleansing it. This step removes errors, duplicates, obsolete information, and redundancies. The aim of cleansing is to have high-quality data that is primed to meet the business use case for data enrichment.
Data segmentation helps to tighten up datasets both before and after combining with other sources. This process is most helpful when enrichment is being used to improve marketing or sales efforts, for example, by identifying segments of customers to nurture through their specific customer journeys. Segmentation helps to better categorize and describe the data you want to enrich so that the best type of information that will enhance this data can be more easily identified.
Append and enhance
The actual process of appending or enhancing data using a third-party or internal source involves several smaller steps. First, you need to source quality data that links in with whatever data repository you want to enrich. Second, you’ll want to evaluate and cleanse the sourced data to ensure data quality. Then, you’ll need to use data preparation tools and data matching techniques to blend the data sources together. An enrichment service can handle this step of the process if you lack internal resources.
Loading the data
The final step, which completes a typical extract, transform, and load (ETL) pipeline is to load the blended data into the system or location where it’s required for analysis. This makes the enriched data usable and accessible for end users to find better insights.
How RecordPoint can help
The RecordPoint Data Trust Platform allows you to inventory all your data, both structured and unstructured, as part of a continuous inventory process. A big part of that process includes metadata enrichment.
All data passes from your connected sources (e.g. SharePoint) through the RecordPoint platform’s intelligence engine. This engine detects metadata from the source system, such as data size, type, location, author, as well as extra custom fields you may have set up in the source system.
Source system metadata alone sometimes may not provide important context, allowing risk to creep in. RecordPoint has an External Metadata Enrichment module that adds consistent sets of additional metadata from external systems, which can be used for categorization and defining retention or disposal. It also allows you to enrich typically metadata-poor sources such as Exchange Online, Teams or Dropbox.
Watch the video below to hear our VP of product Kris Brown explain how it works.
RecordPoint can also enrich your records with privacy signals such as Personally Identifiable Information (PII) and Payment Card Industry (PCI). This is another way of enriching your records as it enhances existing information allowing you to better identify risk.
A guide to data governance principles and best practices
Creating data governance is crucial to ensure a company's data is relevant, timely, and secure. Learn the essential principles and best practices for managing data through the entire lifecycle.
Benefits of centralized data records management
Three reasons why record and information management for your data pipeline, across platforms and the cloud, would benefit your organization.