Are you finding it difficult to extract insights from your data using AI and ML?
On average, 85% of AI and machine learning projects fail to deliver value, and 50% of ML deployments take over 7 months from start to finish. —Gartner
Traditional Data Science
The Devron Difference
Traditional data science requires you to centralize your data before analysis.
But, enterprise data isn’t simply in one place today — it’s distributed across different data silos, departments, systems, jurisdictions, and privacy tiers. Centralizing this highly distributed data results in lengthy and costly delays.
Gartner predicts that by 2025, 75% of enterprise-generated data will be “created and processed outside a traditional centralized data center or cloud.”
Devron is a federated machine learning and data science platform that enables teams to build & train models on disparate, private data.
Instead of bringing the data to the analytics, Devron brings the analytics to the data — training algorithms where the data resides.
Compliance & Legal
Data science often starts with gaining approval to access, duplicate, and move data.
This process can be lengthy, lasting weeks or even months, and can cost thousands of dollars in legal overhead. Especially if the data contains sensitive information subject to different privacy rules and regulations.
According to Gartner, an estimated 75% of the global population will be covered by data privacy laws by 2024.
Streamline the data approvals process and eliminate costly legal overhead.
By leaving the data where it is and keeping the raw data private, you only need the approval to grant your model read-only access to the data in situ. As a result, you can safely extract hidden innovation within disparate, private datasets, while maintaining compliance with regulations, such as HIPAA, GDPR, and CCPA.
Cut compliance & legal overhead by up to 85% by drastically reducing the complexity of data access and approvals and eliminating the need for deidentification software.
Data Preparation
Once you’ve cleared the compliance and legal hurdles, you still have to duplicate, centralize, and clean the data before the real “data science” begins.
In total, data preparation can account for nearly 80% of a data scientist’s job, meaning they only have the remaining 20% to spend on data analysis.
ETL without the “E” or the “L”.
If you didn't have to move your data, why would you? Since there is no longer a need to extract or load data into another system, Devron reduces data engineering overhead, costs for various ETL/data transfer tools, inter-cloud data transfer expenses, and data lineage and data currency issues.
Reduce ETL costs by up to 30% by eliminating the need to duplicate and move data.
Infrastructure Costs
Centralization requires additional infrastructure costs associated with duplicating and storing your data in multiple locations (both in its source system and in one or more centralized stores or local machines).
This can be very expensive and risky, especially for industries such as professional services or financial services, when you’re analyzing data that isn’t your own.
Eliminate the need for additional or duplicative infrastructure by leaving your data where it is.
Devron is platform agnostic and seamlessly integrates with your cloud data lakes, warehouses, and data sources to enable decentralized machine learning.
Cybersecurity & Privacy Leakage
Duplicating and centralizing your valuable and confidential data provides a larger attack surface for bad actors—increasing your risk of cyberattack and privacy leakage.
This can impact legal costs and add franchise and customer trust risk.
In 2022, an IBM Security report found that data breaches costed an average of $4.35 million.
Lower cybersecurity risk and privacy leakage concerns.
Devron’s decentralized architecture significantly reduces your cybersecurity risk. In addition, Devron employs privacy-enhancing technologies, like secure multiparty computation, differential privacy, and encryption to ensure model learnings can never be reverse-engineered to reveal the source information.
Data Science Talent
Data scientists continue to be entangled or directly responsible for a lot of this data work.
Ultimately, this contributes to emerging retention issues.
According to a survey of data scientists, 76% say cleaning and organizing data is the least enjoyable part of their job.
Slash time wasted on unnecessary data access and movement tasks.
Using Devron’s platform enables data science teams to spend more time on value-added tasks, like model building and analysis, instead of data access and movement. This means better, faster results—and more efficient use of precious human resources.
Speed up your time to insight by shortening the length of data science projects by up to 30% — cutting two months off your timeline on average.
With Devron, companies can cut the total time and cost of their ML projects by up to 30%.