Every passing day, 2.5 quintillion bytes of data are generated. A majority of this is not useful; in fact, some of them can be detrimental to organizations, unless they are enriched and cleansed.
Raw, unenriched data—that are incomplete, outdated, or inaccurate—can lead to faulty insights, missed opportunities, wasted marketing budget, and/or reduced customer satisfaction leading to higher churn rates. Enriching your data can not only help mitigate these but also turn your data into a valuable asset. Enrichment transforms your humble data into a powerful driver of growth, helping your organization run smoothly and efficiently.
Enriched data help uncover insights that lead to better decision-making and enable advanced segmentation of customers. This, in turn, leads to greater personalization resulting in higher conversion and increased customer satisfaction. In what follows, we’ll discuss how data enrichment makes the quality of data better.
Enrichment is the process that transforms raw data into useful information. It elevates data from its raw, basic state to a higher, more valuable form, unlocking its true potential and turning it into a powerful asset. This enables the use of data to make informed decisions, segment & target consumers to provide personalized experiences, and unearth unparalleled business insights.
This transformation involves a number of processes and blends first-party data with second- and third-party data. In common parlance, that means improving, refining, and augmenting raw data with data from other sources. This is achieved using various methods.
One, supplementing missing or incomplete data with, typically but not always, data from external sources. Correcting inaccurate information is another. It may also be done by adding new dimensions and attributes to existing data—for example, adding information such as email and occupation to name and address.
The result of all this is high-quality data—or gold, no less.
Data enrichment is principally to enhance the quality of the data. We have seen briefly how this is done. But this merits greater elaboration. So let us consider how enriching data increases their quality.
Data oftentimes come in fragmented forms, fraught with inaccuracies and incompleteness. These require cleansing and standardization. This eliminates inconsistencies that may arise when using data from sources with varying formats, structures, and conventions. They are then further enhanced by enrichment, wherein the quality of cleansed data is improved by supplementing the information from other sources, internal or external. The enriched data are thus more information-dense, contextually relevant, and better organized.
These two types of information are crucial for any meaningful personalization, either for marketing and advertising or for enhancing customer experience. First-party data, which often lack granularity, is appended with supplementary information gathered from external sources.
Demographic data centers on the characteristics of individuals or populations, such as age, location, and interests. Enrichment of this data is usually, but not always, done by utilizing social media platforms to extract information shared by users.
Firmographic data, on the other hand, focuses on attributes of businesses or organizations, like industry, size, revenue, and geographic location. It can also be information about the individuals within a company, such as their roles, positions, and experience. This type of data is crucial for B2B marketing and sales strategies. Enriching these may involve scouring LinkedIn pages and profiles, scraping data from dedicated data providers such as Crunchbase, and browsing the company’s website.
Data grow old and become less useful over time. Enriching and standardizing historical data, ensuring that they adhere to the latest formats and conventions, will keep them compatible with more recent data. With timely and periodic updates replacing and modifying old and irrelevant information with new ones, enrichment prevents data from becoming obsolete. Consequently, it makes them valuable for ongoing analysis and decision-making.
Periodically checking the database for relevance and enriching the data ensures that data flows with time and stays fresh and youthful. This may involve archiving older versions of data for reference and routinely auditing data to identify and rectify inconsistencies.
Data enrichment provides depth and adds granularity to data, appending layers of information and increasing the level of detail within each data point. This provides a more comprehensive and nuanced view of the underlying information.
Enrichment can also involve categorizing data into finer sub-categories or classes, adding behavioral data, or appending geographic coordinates to addresses. This addition of new attributes or variables to existing data points enables businesses to gain deeper insights, make more informed decisions, and implement personalized & targeted marketing strategies.
Enrichment involves matching and updating existing data with data from external sources. This can help identify gaps and inconsistencies in the original data. External data help fill the gaps and bring new perspectives or dimensions to the existing data.
Incorporating data from external sources provides additional context that helps identify anomalies and outliers in existing data. These, if not addressed, can skew analysis and decision-making.
As the enriched data are more comprehensive and detailed, they facilitate in-depth analysis and help uncover insights that may not be easily derived from the original dataset alone.
Data enrichment is crucial for any business wanting to gain an edge. But it is tedious and the process can be a time sink—of other resources, too.
Besides the variety and volume of data, there are other hurdles. The data sources may be unreliable, and existing data may not have sufficient context or relevance further complicating the enrichment process. In addition, one also needs to keep privacy and regulatory compliance in mind.
Added to all this is cost. Acquiring and integrating external data sources can be expensive. That is assuming you can find them—good data are often hard to come by. And the task itself is strenuous and time-consuming.
So, what to do?
There are multiple courses of action available to us. Let us consider the three most viable solutions.
Using automated tools for data enrichment
Multiple tools exist that can automate (most of) the process of data enrichment. These can greatly enhance and ease the task. They can scour the web, scrape relevant information, and append it to your data.
However, these tools are good only for certain aspects of data enrichment and are far from completely automating the process. Human oversight is required at every stage. They can also cost dearly, that their capability and utility may not justify.
Getting data enriched by in-house experts
There can be a number of benefits to getting data enriched by in-house professionals. They have greater knowledge of and contextual information on the raw data, which would aid in enriching them. Data integrity is also less likely to be compromised.
This is the most obvious approach but it is often not the most sensible. Data enrichment is tedious and time-consuming. The result, though, can be worthwhile, expending the scarce resource of data experts on menial tasks is unprofitable.
Outsourcing data enrichment
A third pragmatic solution is to outsource some or the whole of the data enrichment process. This is, in most cases, more viable, reliable, and cost-effective. Companies that provide data enrichment services generally have the necessary tools and resources with the requisite expertise and access to quality data sources. As seasoned practitioners, they often have tricks up their sleeves that others may not be aware of. Consequently, the data enriched by them are more relevant, better segmented, and of higher quality.
It’s not without pitfalls, however. Outsourcing data enrichment entails sharing sensitive confidential information, which may raise security and privacy concerns. There is often limited quality control and customization can be a hassle. These challenges can be mitigated by choosing partners with a history of trustworthiness & accountability and having requisite certifications, such as ISO.
The approach depends, ultimately, on specific needs and requirements. No one approach works in all situations. And each has its benefits and drawbacks.
Enrichment is what makes data what they truly are—the oil that powers the engine of growth, drives innovation, and makes organizations function smoothly. And as data become more valuable, enrichment is key in transforming them into prized assets.
With all that said, the efficacy of data enrichment rests on the quality of the existing data. It cannot make bad data good; it can only make good data better.