In an increasingly digital business environment, companies are generating and have access to a higher volume of data than ever before. But most aren’t equipped to fully leverage it, leading to raw and incomplete datasets being found in every function of every company.
While they’re collecting digital dust, they could be boosting operational efficiency and improving strategic decision-making. This underutilized data should be considered an important asset, one that can boost operational efficiency and empower key stakeholders to make more informed decisions.
The key to turning underutilized raw data into a strategic asset is data enrichment.
In this mini-series, we’ll discuss three dimensions of data enrichment:
- Why does your data need to be enriched? What are the best ways to enrich data?
- What is the difference between good and bad data quality? Why is this important to your business?
- Which types of companies are most susceptible to poor data quality and would benefit from data enrichment?
Let’s dive into the first dimension. We will cover dimensions 2 and 3 in later posts.
Why Is Data Enrichment Necessary?
Leading enterprises procure data through a process of data extraction from several internal and external sources, as shown below:
Each original data source may have a completely different schema than the others, resulting in errors and omissions in the combined database. Restructuring this data to be usable can take hundreds of FTE hours; time that should be spent on higher functioning tasks.
What is Data Enrichment?
Data enrichment transforms the resulting dataset into a strategic asset. It involves taking "first-party" data collected directly from a source like audiences or customers and supplementing it with "third-party" information from external sources.
The goal of the data enrichment process is to improve data quality and value, which yields stronger decision-making. Here are the key elements of data enrichment:
Cleaning: Removing or correcting erroneous data. This can include identifying and eliminating duplicate entries, correcting misspellings, and verifying data accuracy.
Augmentation: Adding third-party data or additional data sources to existing data. One example: enriching customer data by adding demographic or behavioral information from other databases.
Integration: Combining data from different sources to provide a more comprehensive view. This could mean merging datasets or linking related data points across datasets.
Transformation: Converting data from its original form into a form more suitable for analysis. Transformation can be as simple as QOL improvements like the ease of filtering, or calculations, aggregations, or other methods to derive new data points.
Validation: Verifying that data is accurate, relevant, and useful. Usually, this includes checking data against known values or validating it against set rules or requirements.
At Invisible, we’re uniquely positioned to provide scaled data enrichment across a multiplicity of use cases. Through a unique combination of AI and a skilled workforce, both enabled by our process platform, data operations are our bread & butter.
We break down data enrichment tasks into their smallest steps. We then leverage strategic expertise to identify steps that can be performed by automation tech or AI, as well as where to apply our skilled operators through our process engine.
For high volume data-enrichment processes, we utilize our extremely agile recruiting engine that scales to meet demand. As we define a process, we add automation and AI where possible to increase efficiency, improving quality while simultaneously decreasing unit cost.
In this post, we have described what data enrichment is, and why it’s a requirement for leading enterprises. Read part 2 and part 3 of this mini-series to learn more about good vs. bad data quality, as well the types of companies who are positioned to benefit the most from data enrichment.
Bogged down by incomplete and messy datasets? Get in touch