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- The SIC system used to classify businesses is out of date and doesn’t work appropriately for institutions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
The Biggest Issue with Data Quality
Many realize that data quality is an issue and wish to address it somehow. However many organizations struggle with the same issue: The need for a simple plan.
A Simple Plan to show success in one week
Data quality initiatives can be complex and intimidating. Most vendors offer only large scale, expensive, and time consuming solutions that are not guaranteed successes based on previous engagements. Some organizations develop initiative fatigue after a few months of constructing elaborate schemas and structures with an outside vendor. Even the vendor screening process can be a barrier to starting a data quality initiatives.
While each plan may differ, we find the following quick start plan gets things moving immediately and maintains the morale of the team with fast successes and demonstrated ROI.
Step 1: Where is the biggest pain?
Maybe it was a dissatisfied customer, or an awareness that marketing campaigns are duplicating effort, or many separate systems/spreadsheet/lists causing frustration. Chance are there is one large issue that prompted the search for a data quality solution. This can be the rallying point for the organization.
Be careful not to complicate matters with too many technical terms or adding to the scope of the project at this point.
Step 2: Someone to talk to.
Most data quality problems are similar and chances are someone has encountered your problem before. A quick discussion can save a lot of time, help avoid mistakes, and motivate the team by showing that success is very likely. Due to the nature of our software we have encountered many different data quality issues on all different types of data. Feel free to contact us and we’ll schedule a personalized WebEx discussing your specific problem and how we can help. Contact info: Sales@DataLadder.com Telephone: 866-557-8102
Step 3: Simple, affordable, and customizable solution
Intuitive easy to learn solutions are paramount. Our DataMatch software comes with walkthroughs, video demonstrations, and a customized WebEx session. Users are up and running in an hour. It is also important to have a solution that is customizable as no 2 data quality situations are exactly the same. May be you want to remove duplicates from a customer list. But what you think of as a duplicate may change (same address, same phone number, or maybe the company name)
Step 4: Celebrate your success
Once the data set has been cleaned, let everyone know about your success. This improves morale and demonstrates the ability to address data quality issues simply, quickly, and affordability. Now is the time to determine what the next issue is on the data quality agenda, and discuss ways to keep data quality high at the start of each process.
Please let us know your thoughts on the simple plan, and any other topics you would like addressed in this blog.
