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Business is
uncertain. The only thing that can bring stability is making the right
decisions, and those decisions must be driven by real-time statistics. Now that
data is extremely significant and widely used, improving the quality of that
data becomes a necessity. This is where data enrichment plays a big role.
Did you know a predicted compound annual growth rate
of nearly
30%, 2023? It clearly indicates that organizations are
embracing data-driven insights rather than intuition. And without
data enrichment, you cannot go ahead and make accurate decisions.
Introduction: Data Enrichment
Data enrichment is a technical process that completes the missing parts of any piece of information. These parts can be extracted from third-party records, which can be from external but trustworthy sources.
Considering these third-party sources, they can be various platforms like social media, mobile applications, IoT devices, etc. This can be any piece of data that you require and is missing from your database.
Why do businesses require data enrichment services?
Well, the reason is
obvious behind adopt data enrichment solutions. It is to get into insights. These insights
can be related to customers, productivity, revenues, the back-office team,
pricing, logistics, or anything else. These are basically the components of any
business. Every entrepreneur likes to learn whatever is happening in his
corporation. In other words, he requires insights into what the business is
gaining, losing, or where it stands. These insights enable him to make further
decisions about doubling or multiplying profits. Precisely, these are the
reasons why every organization leverages enriched records.
·
Knowledge
discovery
However, this term
conflicts with machine learning. It is related to understanding insights and
gaining value from them to further improve decisions related to commercial
activities. In the present scenario, data scientists enrich databases to make
them accurate and filled with consistent and relevant entries. And data mining
experts rely on it to prepare data models to further gain knowledge and make
decisions that actually prove effective in the end.
· Targeted marketing
The very next
reason can be targeted marketing. However, marketing already exists. But to
make it more precise and result-oriented, enriched customer data is required.
It may consist of information related to customer intent, the web journey,
comments and reviews, carts, location, pricing, etc. The insights from these
metrics can help in understanding what customers want and, hence, tailor
messages or offers so that they can be converted into consumers.
· Better customer experience (CX)
The very next
benefit behind data enrichment can be personalizing communication and services.
Let’s say a customer frequently explores your website, puts things in the cart,
and tries to make payment, but bounces out. This can be due to the pricing or
shipping charges. The product in the cart indicates the willingness of the
customer to buy, but for some reason, he leaves it unsold. The deep analysis
can help in discovering missing information like zip codes, locations, or email
IDs. Like this, there may be hundreds of such visitors who intend to buy but
leave without investing. Collecting their data and completing missing pieces of
email IDs or locations can help in drafting lucrative offers. These offers can
encourage them to come again and purchase. So, it can help in personalizing
communication about offers, which drives customers to invest. So, this practice
can help win customers’ hearts by offering intended offers.
· Insights about new opportunities
Insights refer to
complete knowledge. A new market can guide the addition of new opportunities.
This can be possible with a complete database of the potential market, its
customers, and competitors. Once discovered, developing the right products and
services is like a walkover. These right decisions may be related to the unmet
needs of prospective customers. Overall, enriched details can guide in forming
the right vision, which attracts customers, revenues, and recognition.
· Increased productivity
Every business
focuses on enhancing its productivity. Making random decisions won’t do any
good, but the correct and complete datasets can. Such data can show customers’
pain point, interest, purchasing capacity, and average selling cost.
With data
enrichment, these details can be at your fingertips. It means that you need to
waste time looking for vital information to complete the collected database for
customer, intent, competitor, and supplier's analysis.
· Better data quality
Like every mortal
thing, data decays. And it is also true that it happens quicker than anything
else happening around. So, before it’s too late to overcome, you need to enrich
your incomplete data with vital elements from trusted sources. Also, proactively
test their quality by verifying and validating the collected data to narrow
down the possibility of bad and impractical decisions.
Also, refresh or
update databases from time to time to avoid bad decisions that can cost you
heavy penalties. Enriching addresses, phone numbers, and updates can increase
the accuracy of that data, which will save and improve the customer experience
via exclusive data quality.
How does data enrichment work?
After reading a
long narrative on the benefits or uses of enrichment, let’s figure out how this
process works. Basically, it can be split into three segments.
·
Aggregation
The very first
phase is aggregating data from a variety of sources. This process requires
external sources like social media, directories, etc. to extract and collect
data from.
·
Extract,
Transform, and Load (ETL)
This phase formally
ensures extracting data from reputed sources to collect and integrate with the
existing data. Once extracted, the data may appear in XML or any other format.
It requires cleaning, which is to extract the useful parts from the raw data.
And then, convert it into a useful piece of information that relates to an
existing database. Once cleaned, the datasets are loaded.
·
Appending
Data
The final phase is
about appending or adding contextual pieces of information to the existing
data.
Considering the
methods, there are two ways. The first is the manual method, and the second is
automation. For sure, manual processing is a lengthy procedure and has the risk
of erroneous results. In comparison, automation can be faster and free from the
risk of inaccuracies.
Conclusion
So, it’s up to the business which method it relies on. The ultimate goal is to gain value through contextual and enriched databases. The process is helpful and often ends up attracting benefits through complete and accurate data-driven solutions.