Data can be inconsistent, redundant, incomplete, and difficult to match, standardize, validate, and process.
The challenge is that it can be difficult to know if any of these or other data quality issues exist within your various data assets, and if so, to what extent and what can be done about them. If there are data content exceptions and quality issues, they could be affecting analytics, reporting, data-driven processes, customer communication, data science initiatives, trained ML models, and a whole lot more - not to mention missed opportunities because your data assets are a shell of what they could be. That's not good and results in a poor ROI with current data investments.
You might also be trying to utilize data in new ways, which could be proving difficult. For example, creating new datasets by combining multiple disparate sources, ETL/ELT requirements, data preparation for use in another application, and so on.
This is why we provide data quality and data usability services. We will analyze a sample of your data (perhaps a single dataset, a few columns, multiple databases - you choose whatever makes sense) and then let you know what we find. If we don't find any (unlikely) data quality or data usability issues, or we do find some issues of minimal consequence in your opinion (possible), then you can rest assured your data is in pretty good shape. If we do find any significant issues, we will share them with you and then outline how we can help you with these challenges. At that point, it will be up to you to choose whether or not to work with us. Fill out the form and will be in touch.
So if you need a data partner to help with your data processing challenges, including multi-pass data matching, data engineering, data standardization or database building, let us put our decades of data analysis/processing experience and innovation to use on your behalf. Let's do an assessment right away!
Some examples of what we can do to help:
✔ Matching data across different and
heterogeneous
sources
✔ Eliminate data redundancy and duplicate data
✔ Leverage Generative AI technology for maximum results
✔ Identify inconsistently represented data content
✔ Standardize data for accurate analysis
✔ Match data within internal sources
✔ Match data from external sources
✔ Match data across multiple sources
✔ Verify data for accuracy
✔ Consolidate duplicate customer or prospect information
✔ Re-engineer data per requirements
✔ Database building from multiple sources
✔ Data quality analysis
✔ Enrich data from additional sources
✔ Prepare data for use in ML model training
✔ Prepare data for use in various data-centric applications
Inconsistent data provides inaccurate analysis and redundant data can result in substantial costs ands missed opportunities. And that's just the beginning. We can help transform and re-engineer data using our experience combined with our proprietary tools, contextual Machine Learning-based processes, and advanced data processing capabilities. These innovative data tools, built on decades of experience and at the heart of our capabilities, are not available with anyone else.
We do not outsource. We do all of our own work.
Contact us using the form to learn more and to discuss your requirements. Below are some samples of the types of challenges we can help resolve.
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Bill JamesonSample Address Matches:
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