Why Data is One of the Biggest Barriers to AI Adoption

Why Data is One of the Biggest Barriers to AI Adoption

Find out why 69% of companies said issues with data were a top barrier to investing in AI/ML & how to overcome them.

Why Data is One of the Biggest Barriers to AI Adoption
Leslie Barthel

From intelligent chatbots to autonomous vehicles to medical discoveries, AI and ML are changing how we interact with the world and solve problems. However, even though over 90% of companies have invested in AI, only a few have realized its full potential and value.

In fact, according to Gartner, only 15% of AI projects succeed. And despite the broad investment, AI initiatives typically fail to produce results, with 70% of companies seeing little to no impact from their projects

So, why are so many AI projects failing to realize value? A new study from one of the top consulting firms may help shed some light. The survey found that 69% of Fortune 500 companies said issues with data were one of the top barriers to investing more in AI and ML. While shockingly high, the data challenge is not surprising. After all, successful AI and ML models depend entirely on accessing enough quality data. 

The good news is that 2.5 quintillion bytes of data are generated every day, creating endless fuel for more robust and generalizable models. The bad news is that most of that data is wasted, with 60-73% of all enterprise data going unused for analytics.

Regulatory Compliance 

One of the significant limitations to accessing data today is regulatory compliance. Data privacy laws cover over 65% of the world's population, restricting how critical data can be stored, moved, or used. This effectively locks away billions of valuable data points. 

Every country has different regulations (they vary from state to state in the US as well), making things even more complicated if you have to move data between multiple jurisdictions. This process will not only give you a massive headache trying to understand the nuances between the different laws, but it will also likely generate hefty legal bills.  As a result, many companies are hesitant to take on this arduous and costly task and instead forego AI/ML projects requiring them to access data in protected regions or heavily regulated processes or industries. 

Devron's platform alleviates this burden by eliminating the need to move or share data across borders. Instead, you can leave the data where it resides—compliant and secure. Devron leverages federated machine learning to bring the model to the source data, unlocking previously inaccessible datasets. As a result, you can safely extract hidden innovation within disparate data sources while maintaining compliance with regulations, such as HIPAA, GDPR, and CCPA.

Cybersecurity Risks

Traditionally, once you clear the regulatory hurdles, you must go through the antiquated centralization process. This process is time-consuming, labor-intensive, and increases your risk of a cyberattack. That's because duplicating all your valuable and (most likely) confidential data and moving it to one location creates double the attack surface for a given dataset as well as a very juicy aggregated target for bad actors. 

While COVID created a breakneck digitization process, it also led to a renaissance in hacking. In 2021, data breaches were 68% higher than the prior year, costing nearly $4.24 million on average. Not to mention the irrevocable damage a public cyberattack can cause to your brand. Therefore, security concerns will deter some companies from investing further in AI and ML as long as centralization is the norm. 

Since Devron's platform eliminates the need to duplicate, move, or centralize data before analysis, you can significantly reduce the security risk associated with AI/ML projects. In addition, Devron provides a way for your data science team to derive value from sensitive datasets without giving a broad group of people direct access to analyze the data. As a result, you can reduce the number of people within your organization that have access to personal identifiable information (PII) or protected health information (PHI) without compromising model insights. 

Third-Party Data Sharing 

Data sharing challenges don't only apply to internal teams. They also apply to third parties. Companies have many potential opportunities to share data with third parties for analysis. For example, data sharing can result in better health diagnoses and new medical discoveries in the healthcare industry. And in the financial services industry, it can create better benchmarks and even result in new product offerings. However, companies are reluctant to share their sensitive datasets with third parties due to privacy and security concerns.

Devron's platform enables third-party data-sharing without the risks. Using synthetically-generated data, Devron can provide third-party data science teams with representative data that is useful during the model set-up and data pre-processing phases. However, it doesn't expose any underlying confidential client or patient data. And, since all training takes place against the actual raw data, there is no compromise of model accuracy. As a result, companies can securely share their data for third-party analysis (or monetization) without giving away proprietary information.  

Poor Data Quality

Poor data quality is another aftereffect (and barrier) of centralization-based data science. Because data is duplicated and moved away from its source, it could be out-of-date and no longer relevant when it's finally analyzed. A great example is the 'single source of truth' data lake. It promises endless ML applications once all enterprise data lives in a single cloud-based storage center with a single analytics layer. While great in theory, multiple vendors, organizational silos, and constant improvements mean data could take days to flow down to the lake. As a result, the data lake could be holding stale data and offering insights too late.

In addition, data severely loses its value in the anonymization process. Although anonymization may successfully mask confidential information, it also significantly reduces the quality and substance of the data. As a result, it will negatively impact your model. 

Devron enables you to skip the anonymization process entirely without compromising privacy. Instead, the model trains at the source—on the raw data itself—thus improving model accuracy. However, the underlying information is never exposed. Only the updated model metadata gets sent back to Devron's Control Center.  And the platform leverages privacy-enhancing technologies to ensure the metadata cannot be reverse-engineered. As a result, you can build and train better models for sensitive datasets.

By breaking down these barriers, Devron delivers a new paradigm for data science and transforms AI/ML success. To learn more about Devron's platform and see how it can unlock access to new datasets while ensuring security, privacy, and regulatory compliance, request a demo today.