Compared to Trillium Software, Data Ladder offers best-in-class matching – a solution that beats IBM, SAS and every major player in the space in terms speed, accuracy, and ease-of-use. As seen in 15+ independent studies.
With Data Ladder’s world-class fuzzy matching software, you can visually score matches, assign weights, and group non-exact matches using advanced deterministic and probabilistic matching techniques, further improved with proprietary fuzzy matching algorithms not found in solutions like Trillium Software.
Data cleansing and fuzzy logic can be complicated. Use our built-in libraries, proprietary matching capabilities, and sophisticated pattern recognition features to clean and standardize your data at scale. Our world-class visual interface further minimizes the number of clicks needed to complete a data cleansing and matching project, fully customizable according to your unique data quality project needs – both for real-time matching and batch-oriented. Users can see their data as it changes with your data cleansing settings with our instant data preview feature.
Prevent bad data from entering your systems with a powerful data quality firewall for perimeter protection across third-party and custom applications. The DataMatch Enterprise API splits and cases names and addresses, generates match keys for phonetic matching, and uses probabilistic language models so you can get the most accurate matches – at unprecedented speeds. All matching records are graded to help you refine the process with the human touch as and when needed. Get started with preventative data quality to match within or across systems and applications in real-time.
From spelling errors to redundancies, the platform works through many of the common issues found in large amounts of data. Missing letters: “Hammer” or “Hamer” Variations: “Vinne Smith” or “Vinny Smith” Extraneous letters: “Folder” or “Foldwer” Incomplete words: “Cleaners” or “leaners” Incorrect fielding in fielded data sets: Larry Jones for Jones Larry Incorrect or missing punctuation: “World-class data” for “World class data”
Developing a deeper understanding of your data at the start of a project empowers users to make smarter, more informed decisions and prevent costly mistakes. Determine what data needs to be cleansed and standardized and what may be used as match criteria with our ‘Quick Profile’ feature and get the most out of your data. Findings are saved for future reference in a DataMatch Project.
Check the validity and deliverability of a physical mailing address to standardize and enrich your address lists for mailings. Correct addresses automatically, add missing information (such as a zip code or a suffix), and compare against a list of valid addresses to verify it. Once it is verified, you can enhance each matching address with geocoding information automatically and add ZIP+4 level latitude and longitude values for the best in mapping precision – while improving deliverability with LACSLink® to convert rural addresses to street style addresses automatically.
The ultimate goal of a CRM is to maintain a “Single Customer View (SCV)”, but the 360° customer view is rarely achieved because of the reasons outlined above. Start with CRM data cleansing with a thorough investigation of customer data assets, and an in-depth assessment of accuracy and impact the current data has on your business. Once you know your data, decide on the techniques you will use to cleanse, standardize, and match lists in your CRM or silos and peripheral systems that you want to consolidate to build your Single Customer View.