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.