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Decoding Data Quality as a Service (DQaaS): A New Era in Data Management

John Muehling
April 12, 2024

Data is the lifeblood of business.  It flows through every aspect of business operations, from strategic planning to customer engagement. When leveraged effectively, it’s a resource that can be the difference between a thriving business and one that simply survives or, worse, collapses over time. Unfortunately, many companies are plagued by inaccurate, unverified, or otherwise "dirty data." To make matters worse, they are perpetually challenged on how to fix the problem, often times leading to a considerable assortment of negative side effects dealing with the issue ineffectively, causing a terrible drain on corporate resources.

Over the past decade, data privacy management has become a factor for consumers and businesses. Now, with AI coming into focus asa revenue-impacting solution and the virtual mandate of data security across the board, companies need to focus on data quality management to produce the“actionable intelligence” needed to make critical decisions and deliver today's go-to-market strategies. Data Quality as a Service (DQaaS), is the answer.

Historically, big companies with robust data teams and bespoke tools have worked hard to maintain clean and effective data. But even their efforts can be in vain. And what about the companies who can’t afford to hire an entire data team? They are left piecing together various technologies or end up manually managing their data as a low-tier priority. 

The result is always dirty data. But DQaaS marks a turning point in data management. It is a cloud-based, service-oriented model that provides businesses with comprehensive data quality management solutions.Particularly for organizations that lack the resources of larger corporations.Because DQaaS offers access to high-quality data management sourced to experts, businesses of all sizes can now harness the power of clean and reliable data to enhance business decisions.

 

With DQaaS, businesses can expect:

●      Enhanced accuracy and reliability of data

●      Improved operational efficiency

●      Better-informed decision-making

●      Increased customer satisfaction

●      Higher revenue growth potential

 

Without it, your company may be left to spend too many hours and too much money cleaning up data that won’t stay clean without the right procedures in place.

DataGence Is Here to Help

DataGence is the pioneer ofData Quality as a Service (DQaaS).

While there are most certainly companies that offer data cleaning tools and even professional services to accompany them, our approach is different because we start by becoming stewards of your data. We work with your team to understand the nuances of your data ecosystem and unearth problems as well as hidden opportunities for enhancement. In this initial phase, our focus is to understand the client’s business objectives and level of “data maturity.” Then, we work to up skill the client team whilst defining the company’s data quality standard sand governance frameworks. Only then can we properly build and implement our VESPAS processing engine and expect to deliver maximum results.

Our expertise lies in identifying inefficiencies in how data is managed, processed, and utilized. We want to be a strategic partner helping you make the best use of the tools you have available. And we will do everything we can to guide you away from suboptimal practices and towards the path of effective data governance.

Once we've grasped the nuances of your data ecosystem, we approach solutions with a proprietary set of tools and processes. Our expertise lies in identifying inefficiencies in how data is managed, processed, and utilized. We want to be a strategic partner helping you make the best use of the tools you have available. And we will do everything we can to guide you away from sub optimal practices and towards the path of effective data governance.

With DataGence, you are embracing a culture of data excellence.

What Makes DataGence Different

Achieving maximum data quality requires processing via a particular order of operations. One particular type of data, what is commonly called master data, is data comprised of company and person records, perhaps vendors, prospects, and customers. This data is the cornerstone of every company. This is data you might encounter in aCRM, like Salesforce, or in the records of people using your products embedded deep in your ERP system.

Managing this master data is vital, but it is far from straightforward. Take, for instance, the all-to-common scenario of duplicate records. Imagine “John,” a contact who appears multiple times in your database. He signs up for a newsletter using his Gmail address, then, intrigued by your product, reaches out to a salesperson with his business email and ends up becoming a customer. Because each record came from a different entry point, it is fairly common for two separate records for the same individual to be residing in your database.

Many companies spend years blissfully unaware of these duplicates until someone either needs to use the data or digs in enough to see the problem.  And, even if the problem is recognized, resolving those duplicates effectively can be a time-consuming and expensive task. However, what most companies don’t recognize is that the consequences of those duplicates can be much more costly. Using our scenario, imagine marketing targets “John” as a new prospect under one record, while the salesperson engages him under another. Messaging to a prospect is different than messaging to a customer. And John’s needs as a prospect might be different than his needs as a customer. After all is said and done, it is “John” who is left confused.  And, who knows, perhaps that confusion leads him to engage as a customer for a shorter period of time?  

DataGence steps in to unravel the tangled web of master data.

Unlike other “data cleaning” products, DataGence delivers a service that handles all aspects of the job, including data cleansing, validation, enrichment, standardization, and governance in one place. We delve into your data, discerning connections and patterns often missed.

And finding accurate data is the kicker isn’t it?

Over the course of the last several years, we did some research of our own to find out how “accurate” these data providers truly were. We subscribed to multiple data enrichment services, asking each to enhance the same profile. The results were a patchwork of inconsistencies. Some records got some things right, and others got different things wrong. I was able to start seeing the inconsistencies in the enrichment sources. The same thing happened when we tested for validation!

How are you supposed to use this information that is supposed to be “accurate” when you have no guarantee it actually is? Especially if you are using this information to make critical business decisions. 

DataGence’s AI-enabled proprietary processing ensures the greatest level of accuracy—we validate the validators and data providers. The bottom line? You can count on the data when you get an enriched record back from us! Our enriched records are something no other provider offers.

Our goal is to provide a unified view of each contact, ensuring that marketing and sales are not only working with accurate data but are also aligned in their approach. We do this through a series of six checkpoints.

VESPAS: The Six Pillars of DataGence’s Data QualityOperations

DataGence's approach to data quality flows through six critical operations, symbolized by the acronym VESPAS. Each of these checkpoints represents a fundamental step in elevating the quality of your data and making it highly actionable for your organization.

V for Validate

The cornerstone of our validation process is ensuring that every record in your database is legitimate. We use a proprietary, multi-source methodology to verify the accuracy and authenticity of the data, setting the stage for further enhancements. Is this an actual person, or did someone or something (abot) submit a form with “mickey@mouse.com” as their email?

E for Enrich

Once validated, we will enrich your records.This step involves adding layers of information to your existing data and using a proprietary multi-source methodology to return accurate and current data. How many times has your data enrichment provider given you outdated information?  When you enrich at scale during complex embedded operations, do you know when this is happening?  Those days are in the past. Customized to your specific needs, DataGence enrichment deepens your understanding of each record, turning basic data into “actionable intelligence.”

S for Standardize

Our standardization process ensures uniformity across your data sets. A key example of this is aligning address formats for standardization country codes. This step is crucial for accurate reporting and analysis. This is also an important step in tackling the challenge of duplicate records. Trying to compare records with varied data standards makes the process more complex, if not impossible.

P for Purify

Anyone who has worked in marketing or sales knows that duplicates are the worst kind of bad data. It can cause poor customer experiences and loss of revenue and introduce risks that your business doesn't need. Maintaining a clean, duplicate-free database is vital, given the ever-increasing emphasis on data privacy is vital. A person"opting-out" may not be aware that they are only opting-out for one of many profiles in the database. If the expectation is "global opt-out," duplicate data presents a fail point. DataGence Parity pares down your data to produce unique (and complete) records and does the heavy lifting of matching people to companies using proprietary matching criteria.And we don’t stop at the “person” level. Company data is just as crucial, especially for those companies executing ABS (Account-Based Strategies). The Parity processing engine also ensures accurate company data and proper corporate hierarchy structures.

A for Authorize

This step involves gaining and managing consent to communicate and maintain consumer and business data. Privacy rules are enabled based on organizational maturity or required standards. We ensure you have the necessary permissions to engage with individuals in your database.We also provide a turnkey structure for maintaining compliance and ensuring the highest ethical standards are practiced in your data interactions.

S for Segment

Data doesn’t mean anything if you can’t access it easily in a report or target your audiences with personalized messaging, offers, etc. Our segmentation process enables you to build targeted marketing segments. This means the data becomes highly reportable for marketing and sales departments.

Through the VESPAS framework, DataGence ensures data quality and sets the stage for data governance, helping you unlock its true potential. See what we mean when we say no one else currently offers it all?

DataGence Handles the Entire Process

Many data management companies focus on specific niches. Some might offer robust data validation services, ensuring email addresses and contact details are accurate and up-to-date. Others might specialize in data enrichment, adding layers of information to existing records. Yet, this fragmented approach creates a gap in the data management lifecycle we discussed above. You need a company trained in the entire VESPAS process and can continually manage your data within these stages.

DataGence's VESPAS processing engine (Validate, Enrich, Standardize, Paring,Authorize, and Segment) presents an end-to-end solution that gives you a competitive edge. We recognize that data quality is not a linear journey but a cyclical, interconnected process. Each aspect of VESPAS feeds into and enhances the others, creating a synergistic effect that elevates the overall quality and usability of data.

Tags
Data
Integration
Validation
Enrichment
Standardization
Authorization
Purification
Segmentation

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