Customer Data Platform (CDP) vs Master Data Management (MDM)

Melissa AU Team | Customer Data Platform, Master Data Management | , , , , ,

There’s no doubt about it – the key to retaining customers isn’t about fighting price wars, it’s offering them the best experience possible. Customers like being wooed and to do this, the right way, businesses need to understand their customers. This is why customer data is so important. You need to know what is in your customer’s shopping cart to send them reminder emails, you need to understand their demographic profile to personalize their landing page, and so on.

This is where Master Data Management (MDM) and Customer Data Platform (CDP) come in. While both handle data, there are a few intrinsic differences between them. Let’s take a look at what these applications are and the difference between them.

What are Master Data Management (MDM) applications?

MDM is a data-led, technology-enabled discipline aimed at creating a single, reliable data source for all operational and analytical systems. It is typically driven by IT to ensure accuracy, uniformity and semantic consistency of the enterprise’s master data assets. To do this, it pools the data into master data files and tracks relevant data points throughout the organization.

MDMs merge data by comparing multiple data points such as names, addresses, phone numbers, etc. Many MDM products are a part of larger data handling solutions.

What is a Customer Data Platform (CDP)?

As the name suggests, a CDP caters exclusively to customer data. It can be described as a packaged software that connects to all customer-related systems and allows business users to manage and manipulate the collected data. CDP empowers marketing teams to provide personalized customer experiences. It links data by comparing a single data point such as IP addresses or email accounts to identify prospects.

Master Data Management vs Customer Data Platform

Let’s look at some of the critical differences and approaches between MDM and CDP.

Data governance

Both CDPs and MDMs can handle vast amounts of data. However, as all businesses know, when it comes to data, quality matters more than quantity. A CDP cannot differentiate between good and bad data. Once the data enters the system, it does not have rules for how to deal with poor data. Thus, the value of the data held is significantly lowered.

On the other hand, MDMs are designed to create unified data views. The system collects data from multiple sources and validates it to create a reliable data source for the organization. Often, the data from MDMs is used for CDPs.

Data integration

CDPs are designed with the marketing team in mind. It pulls structured and unstructured information from various marketing applications and helps the marketing team assess what or why a customer is doing something and thus, what is the next step they need to take to woo the customer. The system is not capable of further integrations.

On the other hand, MDMs are designed to create a single source of truth. They can collect and send information to various enterprise applications and are not limited only to marketing. It can be used for business analytics, to drive data-based decisions, etc.

Adding context to data

For any kind of data to be valuable, it must be with context. CDPs collect and compare data but they have limited abilities when it comes to the hierarchical management of customer data. MDMs are much better suited to do this.

MDMs create links that help businesses understand aspects such as which 2 customers are related, which customers are also suppliers, etc. Understanding how customers interact with each other and the different parts of a business provide valuable operational intelligence.

Single vs Multi-Domain

Data is complex and typically the various domains are constantly interacting with each other. CDP offers a great perspective on customer data in a domain but this is an isolated view of the customer. Users can apply varied rules to match data for different purposes and each application can work with their own IDs to unify the data within the CDP.

If you’re looking for a multi-domain view, MDMs offer better functionality. By organizing data with cross-domain relationships in mind, it allows a business to see all the different factors affecting it. For example, you can see the type of products being sold to a segment of customers and the modes of purchase to improve promotions and target customers with higher accuracy. In terms of matching data sources, MDMs follow more rigid rules.

What do You Need?

While MDMs are more established, CDPs are a relatively newer player in the field. You can work with both or choose one based on your end goals. If it’s just about customer data for your marketing team, A CDP will suffice. But, if you need a system that helps derive context from all the data collected and gives you a holistic view of the business, MDMs are a better solution.

Melissa Unlocks Insight and Revenue with Breakthrough AI Tools for Clinics and Research Centers

Author | Clinical Data Quality, Healthcare Data Management, Machine Learning, Machine Reasoning, Melissa Informatics, News & Events, Press Release, Product Launch | , , , , , , , , , , , ,

Cutting Edge Data Quality Transforms Real-World Clinical Data into a Future-Proofed, Research-Ready Data Goldmine for Better Patient Care

Melissa, a leading provider of global contact data quality and identity verification solutions, today announced advanced artificial intelligence (AI) solutions that combine machine reasoning, natural language processing, and machine learning to tackle one of healthcare’s biggest problems: the time and cost of data harmonization and integration.

Hospitals, clinical care, and clinical research organizations are sitting on a veritable data goldmine, based on data gathered for years from electronic medical records (EMR), electronic health records (EHR), and laboratory information management systems (LIMS). While this real-world clinical data is unusually valuable, it is also unusually complex and diverse. Melissa Informatics’ Sentient (MIS) solution is a new and unique set of clinical data quality and integration tools that quickly and easily turn diverse, dirty, and disconnected data into a clean, research-ready data resource. Melissa Informatics has laid out the company’s easy-to-follow steps to turn clinical data into a future-proofed, AI-enabled “knowledgebase” in its newest paper, “The Six-Step Guide to Turn Clinical Data into Gold,” downloadable here.

“Too often, clinical data is expensively gathered and under-valued,” said Bob Stanley, senior director, customer projects, Melissa Informatics. “When you apply machine learning and machine reasoning to access, curate, and integrate this data, it becomes ready for rewarding new uses in patient care, precision medicine research, intellectual property, and unexpected new revenue.”

Melissa Informatics further demonstrates the value of AI-enabled data quality by providing real-world use cases from world-renowned clinics including Parkinson’s Institute and Clinical Center (PICC) and PROOF Centre.

Using Melissa Informatics MIS technology, PICC transformed data such as unstructured text, XML, tables, tsv, image content and other data formats into a research quality, well-managed data resource. This helped the organization meet its technical goals including creating a new, unified “Parkinson’s Insight” data resource – as well as its business goals, including researching and publishing discoveries from that data, and engaging in revenue-generating partnerships based on the new data resource. Access the full case study here.

PROOF Centre used MIS tools to better understand and model connections between desired data sources, working under PIPEDA, HIPAA, Safe Harbor and EMEA requirements for handling of confidential patient data. MIS enabled a global platform for integrated search and reporting, analytics, and knowledge creation and sharing. This project integrated diseases, treatments, outcomes, tissue bank information, laboratory data, molecular data from blood (gene expression, proteins, metabolites), and published data. Access the full case study here.

Melissa Drives Secure Healthcare Data with Compliant, On-Premise Tools

Melissa Team | Data Cleansing, Data Integration, Data Management, Data Quality, Healthcare Data Management, HIPAA, News & Events, Product Launch, Street Route, Unison | , , , , , , , , , , , , ,

Certified for Privacy and Security, Melissa’s Smart Data Quality Suite Accelerates Patient Onboarding and Routing to Physicians

Rancho Santa Margarita, CALIF – September 26, 2018 – Melissa, a leading provider of global contact data quality and identity verification solutions, today announced identity management and verification tools purpose-built to meet healthcare customers’ data processing, support, and security needs worldwide. Certified compliant to SOC2, HIPAA, and HITECH industry regulations, Melissa’s Unison data quality platform and other data verification APIs can be deployed on-premise to ensure sensitive patient data never leaves the organization. Solutions are scalable and flexible to support all types of healthcare groups and agencies, helping providers and their end-users reduce costs, increase efficiency in managing private patient data, and improve the patient experience.

One Fortune 500 customer integrates Melissa geocoding in its healthcare administration platforms to provide distance calculations, needed in determining the nearest options for assigning primary care physicians to patients. Other uses include optimizing route planning for emergency response, enabling hospitals and first responders to calculate the most accurate distance and travel time to patients’ homes or emergency shelters.

“Our healthcare customers are empowered with secure identity and address management, and many see additional value from sophisticated tools they may not have been able to access before. For example, only viable addresses enter their systems, optimizing customer records as well as reducing costs for invalid, returned mail,” said Bud Walker, vice president, enterprise sales and strategy, Melissa. “These are business tools that are truly optimized to meet the needs of global providers. Attention to security and regulatory compliance is our priority, even while we’re able to integrate processes quickly and remain flexible to diverse data needs.”

Melissa’s healthcare customer base includes some of the largest providers worldwide, tapping into data excellence to drive specialized products and services for a broad spectrum of healthcare end-users. Click here for greater insight on Melissa’s Unison data quality platform. To connect with members of Melissa’s global intelligence team, visit www.melissa.com or call 1-800-MELISSA.

Gaining a Data Advantage: Hazard Data Enhancements Reduce Risk and Improve Service

Blog Administrator | Data Enhancement, Data Enrichment, Data Integration, Data Quality, hazard data, Hazard Hub | , , , , , , ,

Hindsight is, as we know, 20/20.  But, what if, when it came to natural hazards
and their impact on your portfolio, you didn’t have to rely on hindsight?  What if, you could understand your risks and
exposures before an event occurred and write and plan appropriately?  Wouldn’t that be better than hoping and
praying that nothing bad happened?

 

Of course it would.  And we all know that bad things still happen
no matter how much hoping and praying goes on. 
That said, how many companies truly use hazard data in their everyday
processes?  Sadly, very few.  For a large number of insurers either cost or
technology or both keeps them from making use of some of the most effective
data for pricing a policy, validating claims, and knowing your true exposure.

 

This probably isn’t news that extensive
property level data can enable a great level of risk awareness and location
intelligence which in turn reduces risk for insurers while improving customer
communications and service.  But what is
news is that this risk information is now more available and easily accessible
than ever before, enabling more insurers to take advantage of it.

 

When geospatial data first arrived it was
primarily used by the big insurers who could afford to have a geographic
information systems (GIS) team to develop or purchase the information,
integrate the information, build decision models and monitor usage.  But thanks to technologies companies can now
get a hazard answer for just about any hazard easily through Data as a Service
(DaaS).

 

With DaaS you get answers to the hazard
questions you have, when you have them. 
For example, at time of quote: What is this policy exposed to?  How far is it from a fire station?  What’s the distance to coast?  What rating territory or premium tax area is
it in?  Knowing this information at the
time of quote saves the embarrassment of an initial rate quote moving
dramatically upwards if this information is only used at time of binding. 

 

Do you understand the hazards your
portfolio is truly exposed to?  For
example, knowing if properties are in a flood, surge, fire or earthquake zone
can support a detailed plan for prospects your business can reach.  You may not insure for flood or earthquake,
but knowing the risk exposure to those hazards and your ability to educate your
prospective customer on them can separate you from your competitors.  While this has always been the case for the
use of hazard data, the difference today is that hazard data is now a real-time
asset, helping insurers make internal risk assessments as part of policy
services.

 

Hazard information also allows you to
better educate your customers.  Most
customers do not know what hazards their property, personal or commercial, is
exposed to.  A neatly formatted on-line
report provided by your company using hazard data can help your customers
better mitigate for risk.  As an example:
This address in New Smyrna Beach, FL.

Flood Risk

Covered
by FEMA digital maps. Minimal Risk of Flooding                            B

Fire Protection Class

Unprotected                                                                                                  D

Wildfire Risk

Very
High                                                               
                                       F

Drought Risk

Abnormally
Dry – Increases the risk of wildfire at this location                     C

Earthquake Damage Risk

No
Damage                                                                                                   A

Hurricane Damage

High
Property Damage                                                                                 D

Superfund Site Risk

>
2,500 Feet from Known Superfund Site                                                     A

Brownfield Site Risk

>
500 Feet from Known Brownfield Site                                                       A

Florida Sinkhole Risk

Limited
Sinkhole Risk                                                                                    B

Straight Line Wind Risk

Very
High                                                                                                      D

2″ Hail Risk

Very
High                                                                                                      D

Tornado Risk

High                                                                                                              C

Lightning Risk

Very
High                                                                                                      D

Special Wind Regions

NOAA
Hurricane Prone Wind Region: Risk varies with location                   C

Florida Wind Born Debris Zone

130
MPH Wind Speed Zone                                                                          F

Thunderstorms

Very
High                                                                                                      D


In looking at this property an insurer can
instantly tell that there will be insurability issues.  If this property were already a part of your
book of business there are any number of steps you would want to encourage this
policyholder to take.  Unprotected for
fire – fire extinguishers.  High risk of
wildfire – mitigation steps.  Wind borne
debris – shutters.  Impacts of convective
storms and hurricanes – roof ties, mitigation information, lightning rods.  On the plus side there’s no storm surge risk
and minimal risk from flood.  Knowing that
it’s unlikely that there will be a water claim.

By accessing geographic risk-sets in
real-time, natural hazard data such as wind, fire, water, or earthquake risk
can be easily associated with specific properties.  Ideally, this is coupled with property and
mortgage data enhancements, as well as location intelligence toolsets, such as
address verification, geocoding and reverse geocoding, and IP location.  Based on multi-sourced datasets, insurers
have real-time access to hundreds of different metrics on individual
residential or commercial properties. 
Hazard data scores are based on information from sources such as FEMA,
NOAA, USGS, and state and local governments, and include risk of flooding,
wildfire, lightning strikes, straight-line winds, hurricanes, tornadoes,
earthquakes, and more.  It’s a higher
level of insight for insurance professionals, making decisions based on
end-to-end property intelligence supported by precise risk scores made easier
through hazard DaaS.