IRI - Total Data Management

Struggling to build test data in the exact formats you need? Watch how IRI RowGen makes it easy to create custom, compliant datasets in seconds 👇 📌 Learn how RowGen can simplify your test data process: https://lnkd.in/gNagbaTW #IRI_RowGen #DataGeneration #TestData #DataSynthesis #CustomData #DataManagement #SyntheticData #DataQuality #DataTesting

Turning raw data into data AI can actually use shouldn’t take hours. Most teams still struggle to move data from ingestion to analytics. Logs sit in silos, structured and semi-structured data need manual prep, and by the time it reaches KNIME, it’s no longer fresh. That delay limits model accuracy and slows every experiment that depends on timely data. But what if you could handle everything, from data integration, cleansing, masking, and reformatting, before your data even enters KNIME? With the same IRI Workbench IDE built on Eclipse, you can: ✅ User Voracity to pre-integrate, cleanse and mask large volumes of data quickly  ✅ Pass the Voracity-wrangled and sanitized subsets directly into KNIME nodes ✅ Maintain governance and reproducibility across every data source No switching UIs. No waiting on pipelines. Just one continuous flow from raw data sources into data fed into KNIME ML/AI can actually use, all in the same pane of glass. That’s what happens when IRI Voracity powers KNIME. 📌 Learn how to accelerate data preparation and modeling in one place, one workflow: https://lnkd.in/d8Kvrj-r #IRI_Voracity #KNIME #AI #DataScience #MachineLearning #ETL #DataIntegration #DataWrangling #DataOps #BigData #DataEngineering #DataManagement

Stop treating PII protection like a box to tick. Every data breach costs more than fines. It damages trust. It slows innovation. It stalls analytics projects and derails AI initiatives. Across sprawling data lakes and pipelines feeding machine learning models, sensitive data hides everywhere: in raw logs, CSVs, JSON files, and unstructured repositories. And the larger the lake, the harder it is to know what’s inside. That’s why protection can’t be an afterthought. Big data only creates value when it’s secured and governed first. Otherwise, you’re just scaling risk. Teams that automate the discovery and masking of PII across structured, semi-structured, and unstructured data build trust in their analytics and training pipelines while maintaining speed. The companies that get this right aren’t just protecting information. They’re protecting their future. 📌 Learn how to find and secure sensitive data automatically before it reaches your analytics or AI layer: https://lnkd.in/dGFAtUpU #BigData #DataLakes #AI #DataSecurity #DataGovernance #DataPrivacy #Compliance #PII #DataMasking #MachineLearning

Spending hours manually defining data classes across hundreds of database tables? The new AI Schema Data Classification wizard in IRI Workbench can scan your relational schema and automate the discovery and masking rule assignment process. With this new wizard in the IRI Voracity platform, and also supported with an IRI FieldShield or DarkShield RDB masking license, you’ll get ✅ Faster setup of data classes and masking rules ✅ Facilitated schema-wide data class search results ✅ Consistent, deterministically masked results in target schema How much time could you save by letting AI handle these first steps for you? Learn more in: https://lnkd.in/eErNRyjB #IRI_Voracity #IRI_DarkShield #DataSecurity #DataMasking #AI #RDBMS #DataPrivacy #Compliance #PII #DataClassification

If you store PII in Oracle E-Business Suite and need to locate and mask it to nullify a ransomware or any data breach incident, use IRI DarkShield. DarkShield supports OEBS schemas with their complex constraints. Learn more at: https://lnkd.in/enTVqUg #DataSecurity #CL0P #Encryption #DataMasking

Harvard University has reported a 1.3TB data breach from their Oracle E-Business Suite platform, linked the same since-patched Zero Day flaw exploited by the CL0P ransomware group. 📖: https://lnkd.in/dCyZgMxC #Ransomware #OEBS #DataBreach #PII #InfoSecurity #Harvard #News

Data masking is NOT just encryption. Encrypted data may still be sensitive and reversible. Data masking is NOT just for testing. While test environments benefit from masked data, production data at rest often requires masking for compliance and breach prevention. Data masking is NOT the same as tokenization. Tokenization substitutes values based on a lookup vault. Masking replaces or obscures data with logic-based or randomized functions. And data masking it’s definitely NOT just erasing or redacting text. Effective masking can involve one or more functions proven to de-identify data in consistent (or inconsistent) and/or reversible (or irreversible) ways. Data masking can involve encryption, pseudonymization, shuffling, fabrication, generalization, and other functions depending on the data class involved and your test and compliance needs. When done right, static data masking delivers: ✅ Persistent protection ✅ Compliance-ready anonymization ✅ Built-in support for referential integrity ✅ Flexibility across data source formats and target silos One of the first data masking tools on the market, IRI FieldShield supports all of this, from format-preserving encryption to pseudonymization and more, while giving you full control over how and where masking happens. 📌 Learn more: https://lnkd.in/eTaT_mUJ #IRI_FieldShield #StaticDataMasking #DataMasking #PIIProtection #Compliance #HIPAA #DataPrivacy #DataSecurity #DataSecurity

Most data integration (ETL) architects still waste time transforming data in separate steps. Filter. Sort. Join. Aggregate. Then pass the results to another tool for cleansing, masking, or reporting. Meanwhile, every extra I/O pass slows performance and piles on complexity. And when Python or Perl scripts get involved, the code is hard to maintain and impossible to scale. Therefore, the real challenge isn’t just mapping values from input to output. It’s remapping and reformatting with full control, while handling compliance and data quality at the same time. Instead, you can actually do all of these things at once: 1️⃣ Align, resize, trim, and type-convert fields  2️⃣ Transform with joins, filters, aggregates, and calculations   3️⃣ Validate, cleanse, and enrich (improve data quality) 4️⃣ Apply consistent, deterministic data masking rules to preserve RI 5️⃣ Reformat RDB, COBOL, CSV, XML, Excel, JSON, LDIF, Excel and ASN.1 data 6️⃣ Produce multiple report, file, and (pre-sorted!) DB load targets The result is faster jobs, fewer moving parts, and simple auto-generated job scripts that scale with volume. All of this happens in one platform: IRI Voracity, powered by the IRI CoSort engine. 📌 Learn more about remapping and reformatting data at scale: https://lnkd.in/esvasgyT #IRI_Voracity #DataTransformation #DataWrangling #ETL #DataEngineering #DataQuality #DataOps #DataIntegration #DataManagement #DataRemapping #DataReformatting

Your VIP data shouldn’t be the house’s biggest gamble 🎲 Casinos run on AS/400 (IBM iSeries) systems packed with player names, loyalty IDs, and win/loss histories. But when that data flows to marketing vendors, reports, or regulators, you risk exposing your high rollers. One breach doesn’t just cost fines. It costs trust that no loyalty program can buy back. Here’s how to keep player data safe without breaking analytics: 1️⃣ Scan every table and export. Find PHI/PII across DB2/400 tables, game logs, and directory names. 2️⃣ Use deterministic masking. Keep loyalty numbers consistent so segmentation and comps still work. 3️⃣ Maintain referential integrity. Masked data still lines up across multiple systems. 4️⃣ Create audit logs. Prove to regulators what was masked, where, and when. Few data masking tools can do all this for DB2 data hosted on IBM iSeries hardware. Fewer still do it affordably, without breaking reports or forcing a full system migration. That’s why IRI built a solution that does exactly this: direct DB2 on i connectivity, deterministic masking, reusable jobs, and complete audit trails in human- and machine-readable formats. That solution is IRI DarkShield. 📌 Learn more at: https://lnkd.in/eviA6k97 #IRI_DarkShield #CasinoTech #AS400 #DB2400 #DataMasking #PlayerData #GamingIndustry #DataPrivacy #Compliance #HighRoller #Casino #DataSecurity #BigData

The Brightstar Lottery Group breach that exposed the PII of >100K people reportedly involved data of a complex and unstructured nature .... exactly what the IRI DarkShield data discovery and masking tool protects. Learn more at https://lnkd.in/eUNWKeHW #databreach #unstructureddata #informationsecurity