Institutional Markets: Eliminating the Data Headache of an Untapped Goldmine
Hospitals, libraries, and schools…what do all these entities have in common?
Besides providing important services for our nation, these non-commercial organizations represent approximately 1/3 of the U.S. economy, and nearly $4 trillion of the GDP, according to MCH Strategic Data. This is an important fact for business to business marketers, who may want to consider the untapped potential of this huge market. In fact, these institutions actually have more buying power than most commercial businesses due to size and scope, and have been growing faster than many businesses for the last 50 years.
From a data quality perspective, this is critical information for marketers who want to work with this large segment. Unfortunately, many databases don’t treat these institutions as the large potential revenue generators that they are due to the quality of the information provided, often leading to very poor, inaccurate data!
While these institutions can be a great source of business for an organization, treating these non-commercial entities like businesses in databases creates huge problems with data quality for several reasons:
- The SIC system used to classify businesses is out of date and doesn’t work appropriately for institutions
- Many institutions share the same physical addresses and may have similar names
- Many typical business attributes do not work for institutions
From irrelevant records and duplicates to typographical and spelling errors, having poor,inaccurate data on this large group of prospects can be very unsettling from a data quality perspective. Institutions represent a large group of potential revenue, and it is important to have this data cleaned and appropriately segmented for use.
Using the appropriate attributes can help clean up some of the data. For example,using “number of employees” as a business attribute may be misleading for an institution such as a church, where a majority of the employees are actually volunteers.
Another issue arises with name similarity. Many institutions have similar names due to the fact they are publicly funded and may use their city as part of the name. This can be a challenge indata matching.
Data Ladder can help your organization sort through the data. From data cleansing to data matching services, we work with all types of data and can provide the right services make your databases work for you. Contact us for a consultation.
Why Data Quality and Data Cleansing Projects Fail
Data Quality and Cleansing initiatives are essential to improving overall operational and IT effectiveness. However many efforts do not get off the ground and get stalled before they really start. Read more
A Healthcare and Education Case Study
Hello Friends,
Please see the attached case study about how West Virginia University used DataMatch to save thousands of work hours and improve patient care.
Case Study
Title-Tracking Patient Records Across Multiple Databases
Industry: Health Care, Education
Situation: West Virginia University was tasked with assessing the long term impacts of certain medical conditions over time. Specifically if previous conditions affected long term health and patient care. The difficulty was that the databases records identifying the medical condition were in a separate system from the current health records provided by the state. Linking these records manually, which number in the hundreds of thousands, was a very time consuming process and threatened to derail research activities.
Solution: Using Datamatch, Data Ladder’s flagship product for data cleansing, WVU was able to clean records from multiple system and create a unified view of the patient over time. With the best in class data cleansing and matching routines, along with the included customized training, WVU was able to see unified results quickly and easily.
Results: With a unified view research was able to resume at a much quicker and more efficient pace. The cleansed data has been used and referenced in several medical journals, with the hope of improving patient care effectively and efficiently. With this success WVU is expanding the use of DataMatch across several other functions within the university.
Data Ladder LLC, a Provider of Simple and Affordable Data Cleansing Software, Announces the Release of DataMatch 2011
Data Ladder LLC, a provider of simple and affordable data cleansing software, announces the release of DataMatch 2011. With ease of use design changes, additional cleansing functionality, and best in class fast fuzzy matching algorithms, DataMatch 2011 brings the capability of a strong data cleansing software to the desktop. DataMatch 2011 provides key capabilities to keep your databases and lists clean and free of duplicates, saving time and money for business professionals.
Free Trial Available at http://www.dataladder.com/download.php
“The Data Warehousing Institute (TDWI) estimated that inaccurate customer data costs businesses (over) $611 billion a year in postage, printing, and staff overhead. Frighteningly, the real cost of bad data is higher. Data problems can alienate customers, create revenue and cost leaks, undermine process efficiency, delay expensive projects, and expose an organization to compliance risks. In short, bad data can make it hard for the business to achieve its financial and strategic goals.”
Key New Features:
Intuitive wizard sets data cleansing options based on 4 questions, drastically reducing data cleansing setup time and confusion. Additionally Data Ladder’s world class customer support is available to walk every customer through their own data cleansing project.
A simplified user interface allowing visibility on how each data cleansing step has changed the data set, ability to undo changes, and the ability to save your work as a project that can be reused and scheduled.
Visually appealing data profiling and match results reports that can be exported to Excel and PDF.
Improved proprietary matching algorithms providing best in class matching accuracy and speed.
Data Profiling: including automated field type identification and extraction (Address, First Name, etc.)
Multiple Match Definitions: Automatically identifies multiple match definitions within your data set. Examples: Simultaneously identify and group all records with matching emails, at the same time identify and group all records with similar person and street names in the same zip code.
Multiple deduplication options
Survivorship rules for defining what record or field should remain in the cleansed data set. Example: Keep all records in a matched group with the newest date, and merge all non null emails. Record level traceability of all changes.
Miscellaneous new features; column merge, regular expression builder, etc.
With additional features and knowledgeable customer support, Data Ladder’s DataMatch 2011 provides a simple, world class data quality suite to the business user at an affordable price. For more information please visit http://www.DataLadder.com.
About Data Ladder
Data Ladder is a data quality and cleansing company dedicated to helping you “Get the Most Out of Your Data” through Data Matching, Profiling, Deduplication, and Enrichment.
We strive to keep things simple and understandable in our product offerings to give our customers the best solution and customer service at an excellent price.
Our products are in use across the Fortune 500 and we are proud of our reputation of listening to our customers and rapidly improving our products.
Every data cleaning situation is unique
A quick post today.
One often overlooked fact is that every data cleansing situation is very unique.
Take a simple customer deduplication (removal of duplicates) exercise. At first glance it is a very simple problem. Identify the duplicates, and remove them. However once you get into the details you realize there are several items worth considering.
1. How do you identify a duplicate? Is it the company name? Contact name? Address? Maybe you deal with 2 completely different offices that are the same customer (IBM in Australia and in the UK for instance)
2. Do you want to remove all information about a duplicate contact? There may be important contact information or customer notes associated with the record.
3. Have all affected stakeholders in your organization been made aware that the cleanup was occurring? There may be individuals and departments inside your own organization who should be notified to insure no unintended consequences occur.
4. Are there any new standards you’d like to apply? Capitalizing street suffixes, separating full name fields to a First and Last name field, etc.
Note that Data Ladder is here to walk you through these issues which is why we give free personalized WebEx demonstrations addressing your specific data cleansing activity.
Any other big questions that I missed? Feel free to comment below. We welcome and thank you for taking part in the conversation.
