Compare to 360 Science Best-in-Class Data Matching and Cleansing
Compared to 360 Science, 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 360 Science.
See How Data Ladder Compares Against 361 Science. Get Your Trial Now
See How Data Ladder Compares Against 360 Science. Get Your Trial Now
Rated Industry’s Fastest and Most Accurate Record Linkage Software
Linkage Scenario | Runtime | Linkage Quality |
|---|
Runtime | Precision | Recall | F-Measure | Deduplication 40,000 records | 12 Seconds | 95.3 | 97.1 | 96.2 |
|---|---|---|---|---|
Deduplication 400,000 records | 3 Minutes | 92.6 | 93.2 | 92.9 |
Deduplication of 400,000 records to 4 Million Records | 45 Minutes | 91.5 | 93.1 | 92.3
|
Rated Industry’s Fastest and Most Accurate Record Linkage Software
- Quick Profile: Highlight potential data quality issues like non-printable characters, missing data, mixed numbers, and letters, etc.
- Data Survivorship: Define a single ‘master’ record by choosing which duplicate survives and the field you want to merge on while capturing additional data in a separate field.
- Data Deduplication: Merge the most complete information across duplicates, overwrite data from a master to other duplicates, and delete duplicates from your data sets.
- Data Integration: Integrate virtually any data source across the enterprise (databases, CRMs, social media, file formats, email, Big Data repositories, etc.).
- Workflow Orchestration: Schedule your projects to run once, on a recurring basis, on a defined schedule, or when an imported file is updated
- Email Address Cleansing: Advanced email address cleaning feature finds errors and automatically suggests corrections.
- Instant Data Preview: See how your data changes with your data cleansing methods in real-time and create the best configuration
- Reusable Workflows: Test and configure your data cleansing workflows and save them as reusable DataMatch projects
- Cross-Column Matching: Match data across columns – useful when data entry errors put data in the wrong column.
- Standardization Libraries: Built-in libraries for nicknames (Jon=Jonathan), postal codes, street suffixes, state and city, and a best-in-class library creator for your custom needs.
- Pattern Matching: Use Regex wizard to quickly identify patterns and extract into new fields. Example: Text “3 x 4 x 6” can be extracted into: Length = 3, Width = 4, and Height = 6.
- Match Scoring: Set a match threshold (0-100%) and view your matches/duplicates – match on multiple fields/columns and see the match percentage on each field/column.
- Advanced Filtering: Manipulate your data with advanced filtering functions like wildcards, and/or, or/not statements, etc.
- Phone Number Cleansing: Automatically cleans telephone numbers to improve matching capability. Useful when matching international numbers.
- Name Standardization: Includes over 60,000 common names for standardization when matching. i.e. Danny becomes DANIEL, Abby becomes ABIGAIL, etc.
- Bulk Standardization: Identify and count unique words and values in your lists and so you can replace, delete, or extract values into new fields.
- In-Memory Processing: Process millions of records without physically importing data with our highly scalable, in-memory architecture and export only when you are satisfied.
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Why Choose Data Ladder Over 360 Science?
DATA CLEANSING DONE RIGHT
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.
DATA DISCOVERY AND PROFILING
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.
DATA QUALITY FIREWALL
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.
HANDLING DIFFICULT SEARCH PROBLEMS
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”
ADDRESS STANDARIZATION AND VERIFICATION
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.
CRM DATA CLEANSING
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.
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