Putting Data-as-a-Service to Work Improving Data Quality

What is Data-as-a-Service?

Data-as-a-Service (DaaS) incorporates both an on-demand data delivery offering with usage- and subscription-based pricing.

Delivery Model

As a delivery model, DaaS provides relevant data in real time. For instance, a phone number or physical address, while being entered into a web form, can be verified on-demand. Likewise, a phone number can be checked against the U.S. Federal, State and Direct Marketing Association “Do Not Call” databases – again in real time – to verify whether a new lead is a candidate for a follow-up inside sales call.

Data is delivered to the application or web page as a web service. This enables the data service provider and consumer to be geographically and organizationally separated from each other; all that is needed is an Internet connection.

In terms of security, data is not stored or used outside of the company’s account. All information is kept strictly confidential with no data sharing or access to other customer’s records.

This brings with it a fundamental benefit to implementing DaaS: The application operator doesn’t need to install and manage supporting infrastructure. Instead, the application developer places calls to a programmatic interface provided by the data service being operated out of the cloud. More often than not, companies are able to get a DaaS solution installed, up and running in one day.

Business Model

As a business model, DaaS is typically priced in one of two ways:

• By usage. For instance, the consumer could be charged a few pennies per record verified. This model has benefit especially to small and medium-sized businesses that prefer only to be charged by the record or more commonly referred to as a “hit”.
• By subscription. This approach often benefits larger companies that need tens of millions of records verified in any given month. For these companies, a consistent operating expense is easier to budget.
  

DaaS Implemented as a New Best Practice

With the emergence of DaaS, some new best practices have emerged to improve data quality.

Best Practice #1: Verify at Point of Entry

One best practice for companies collecting data about their customers is to verify at the point of entry, while the customer is still engaged. Whether the customer is entering data into a web form, giving information over the phone to a call center operator, or providing data to a clerk at a point of sale, it’s more efficient to verify the data in real time.

This highlights one of the primary benefits of DaaS: It works in real time, so it can be installed at point of entry. After all, it’s at this point that the customer can be asked directly to re-enter data should the data verification service show a piece of information to be invalid.

A notable example of this need in action occurs in the emergency room at patient registration. In this high-stress environment with high patient volume, hospital administrators have shared that up to 50% of the people who register give either a bad address or bad phone number, whether intentionally or unintentionally. Additionally, registration clerks aren’t infallible either.

Faulty patient registration has at least two negative consequences:

• The patient can’t easily be billed. This results an increase in back-end work to attempt to locate the patient and an increase in unpaid charges.
  
• The medical team may be unable to contact a patient post-care. For instance, test results available after discharge from the hospital may indicate a change in course for prescribed medications. If the patient can’t be found, there could be negative medical consequences.
  

Again, in terms of security, a DaaS supplier like StrikeIron keeps patient data, strictly confidential. No data is ever shared for additional use. In addition, access to patient information remains with the hospital or healthcare system allowing them to remain in compliance with all HIPAA regulations.

Best Practice #2: Cleanse During Multiple Database Merges

Many companies are dealing with the legacy of having multiple databases internally. Manufacturing has its own ERP database. Sales has its own CRM database. Many of these databases have some level of customer information, whether shipping addresses used by manufacturing or phone numbers used by sales and support.

As a result, many of these companies are embarking on Master Data Management (MDM) projects. These can entail consolidating six, seven or even a dozen disparate databases into one single centralized repository of customer records. While implementing MDM projects, another best practice is to validate the data during consolidation.

A large retailer had 25 million customer records spread across its multiple databases. Yet management believed it had only 13 million actual customers. In planning a catalog campaign, an obvious question emerged: Which 13 million were accurate?

Imagine sending 10 million catalogs and 10% of the addresses were incorrect. That’s 1 million catalogs that are not going to reach their intended destination. If each catalog costs between $2 and $5 to print and send, that’s $2 million to $5 million gone because of faulty information.

DaaS Used for Outbound Communications

Data-as-a-Service not only can be used to cleanse incoming and existing data, but it can also be used to communicate with customers.

For instance, a cable company wanted to solve the problem of customers sitting at home for hours waiting for the cable guy. The company now gives its customers who call into its support organization the opportunity to receive an SMS text message 15 minutes before the technician arrives.

To enable this, the cable company bolts into its CRM application an SMS text messaging, on-demand service offered by StrikeIron. When a customer calls in and requests a technician visit, the call center operator asks if the customer would like to receive the text message service. If the customer opts in, the operator simply ticks a box in the CRM platform that triggers the text message when the technician leaves his previous call.

Stepping Into the Cloud

The Internet has created a problem and the solution at the same time. The problem concerns companies now being able to collect more information about their customers than ever before. The solution includes deploying on-demand web services via Data-as-a-Service that can provide data quality management.

Likewise, cloud computing has piqued the interest of the IT industry. However, many companies aren’t quite ready to make the full move to the cloud. For some, security concerns remain obstacles. Others are not ready to abandon large investments in existing infrastructure and proprietary applications for deployment on someone else’s cloud.

That said, these companies are still interested in cloud services and are looking for opportunities to leverage the cloud in their day-to-day business. Data-as-a-Service represents an easy way to access a cloud service without a big infrastructure change. Improving data quality can be an ideal first step, as one could argue maintaining clean customer records should be a strategic initiative for all companies.