Your business—like all 21st century businesses—runs on data. But your data is failing you. Even in the best of times, you’re not seeing the full potential of your data in your decision-making.
Why can’t you use all of your data?
- Data is in silos. Different enterprise groups hold data in different information systems. Data silos exist for personal, business, technical, and sometimes arbitrary reasons and impede work and decision making. Some systems are redundant, and some are conflicting, but they all create barriers to sharing and leveraging hard-earned data.
- Data can’t be shared, or shared effectively. Data ownership issues that arise internally are often magnified externally with customers, suppliers, and collaborators, where additional issues of data security and privacy are layered on.
- Legacy data systems weigh down your data infrastructure, making all of these problems worse. Waiting and hoping for everything to someday be on a single source of truth is not an effective IT or business strategy.
- Data is corrupted. Errors, sparse data, and misinterpretations undermine confidence in the analysis. High-consequence deployments fail even with expensive and continuous manual work by skilled developers.
- Data quality, fidelity, and provenance is lost. Data Transformations (ETL, integration, warehousing, etc.) can forever lose data lineage/provenance and other important metadata. The intended meaning of the data is lost over time as specialists and knowledge fade away without being formalized. Continuing application upgrades and migrations are even harder as people depart, and even success criteria may no longer be apparent to the stakeholders, reducing business value.
The business consequences are staggering. Data-dependent projects fail more than 50% of the time. Even when successful, they are slow, expensive, and the results are inherently untrustworthy–compromising decision-making. Poor data quality threatens labor and capital goods planning (garbage in, garbage out?). The heart of any data-dependent business must be data transformations that are 100% dependable – guaranteed.
With groundbreaking Mathematics and AI out of MIT, the Conexus platform (“Conexus CQL”) is unlike anything else you have seen. Conexus CQL can reveal new applications of your data (enabling action based on that data) while reducing your IT budget.
Never before has it been possible to analyze and share data across different sources and platforms that previously couldn’t “speak to one another”—while using AI to avoid the errors and corruption that used to be inevitable.
Conexus CQL enables unprecedented control, integration, migration, and querying of your data while:
- Preserving data quality. Protecting data assets against errors, corruption, and losses. Application data processing is expensive. Preserving data against degradation is a big part of costs at each stage of data processing, Conexus CQL acts to efficiently protect assets and enable analysis.
- Avoiding failure through artificial intelligence. Conexus CQL contains an automated theorem prover (an AI) that guarantees the correctness of Conexus CQL programs and ensures they never endanger data integrity. Such errors are detected at compile time, when they are easiest to fix.
- Conexus CQL changes IT budgets by reducing rework, late projects, and data failures. Conxus CQL’s higher-level abstractions save developer time and enable complex processes that are not possible in other technology.
Before Conexus CQL, no solution allowed you to migrate, transform, and integrate your data across platforms with zero degradation, perfect provenance, and guarantees against runtime failure.
Conexus CQL is the only tool that offers true interoperability—visibility of all your data across platforms and the ability to work with that data interchangeably.