Data-Centric AI: What It Means and Why It Matters
AI is far from being completely autonomous. Its basic implementations rely heavily on algorithms, even to a fault. AI takes large amounts of data, crunches numbers behind closed doors, and then generates predictable, formulaic, and even sometimes, erroneous results.
Without reliable data, AI applications can’t generate reliable results. This is where data-centric AI (DCAI) steps in and changes the game.
What Is Data-Centric AI?
DCAI is a step up from traditional, model-centric AI. It revolves around using clean, quality data to build AI systems that run automations, make predictions, and “learn.”
Why Is It Important to Have Good Quality Data?
Remember, an AI system requires two core components to work: a model and data.
Most projects focus on building the algorithms, code, and processes; all of which constitute the model. However, the data aspect of things doesn’t get nearly as much attention, despite its sensitive and crucial impact on AI systems.
To inspect data quality, developers look at three important factors:
- Data accuracy and alignment with quality goals
- Relevance of data for the model
A lot of developers isolate their focus on data quality within the sourcing stage, which is the first pre-processing stage of the AI life cycle. This stage is critical for getting a working AI model up and running.
Successful developers know that AI models, particularly machine learning (ML) models, require the constant availability of quality data. DCAI supports this with a framework that involves consistent labeling, feature engineering, data augmentation, and ongoing error analysis.
DCAI ensures models get not just more data, but better, more accurate, and “cleaner” data to optimize performance.
How Does Data-Centric AI Work?
In general, DCAI development is done using the following steps:
- Using consistent labeling for data sets and pieces of data
- Using consensus labeling to fix discrepancies and clean up “noisy data”
- Establish clear-cut labeling instructions
- Augmenting data sets
- Feature engineering (improve model training by refining raw data)
- Error analysis (find data subsets to improve)
- Integration of domain experts to further enhance data quality
DCAI is best used with a centralized data hub, which serves as a “single source of truth” for large organizations. This provides teams with a single platform for continuous improvement, which maximizes the efficiency and impact of optimizations.
DCAI systems also normally leverage other AI technologies like ML, big data analytics, and natural language processing (NLP). These components maximize the potential of global AI models in business decision-making, production, marketing personalization, customer success, customer support, analytics, localization, and other high-level activities.
How Data Can Power Global AI
Data-centric AI offers huge benefits for companies looking to expand on a global scale.
Accelerate Localization & Translation Efforts
AI-powered language services allowed organizations to penetrate foreign markets at a never-before-seen pace.
DCAI can enhance existing translation solutions with cleaner and more efficient data. And with DCAI’s data augmentation and error analysis components, product translations, even in technical spaces like life sciences, construction, and education, will continue to get cleaner and more reliable over time.
AI models, especially with the introduction of DCAI systems, can also power accurate image translation tools. These are capable of translating text found in images that serve multilingual audiences, like product inserts, user manuals, product renders, and more.
Streamline Data Privacy Compliance
According to Gartner, 40% of privacy compliance solutions will use AI technology. A good example is the use of AI in following data privacy protocols in healthcare translations.
Using NLP, sensitive patient information can be automatically anonymized, protected, and in line with the Health Insurance Portability and Accountability Act. DCAI-powered services, with the help of customized neural network models, can automatically filter non-inclusive or offensive language that may slip through traditional translation services.
Enhance Inspection Systems
DCAI in a production environment and iterative workflows are a huge timesaver.
Companies in the medical and manufacturing industries can use DCAI systems to accelerate the inspection process, teaching the model to better spot and label product defects. Rather than training an AI system with inconsistent data and generating skewed results, a DCAI strategy ensures data is uniform, relevant, and clean.
Improve Customer Experience Through Personalization at Scale
The benefits of DCAI eventually make their way to consumers through improved product and service quality. However, that’s not the only way AI affects the customer experience.
To create personalized marketing experiences tailored to each customer, AI solutions can rapidly sift through truckloads of data, including buyer intent, online behavior, and previous brand interactions.
Sales teams can also take advantage of DCAI systems to identify high-priority prospects, including global accounts that are ready to close a deal. Customer intelligence platforms can use AI to sort prospects according to their recent activities, technology stack, and bits of firmographic data (cleaned up and optimized by DCAI).
You can also add AI-enabled (multilingual) chatbots to the mix, using conversational AI, in customer support and to further enhance CX.
Gather More Quality Data on Autopilot
AI requires massive amounts of high-quality data to function reliably (and global AI requires multilingual data). High-quality data, on the other hand, requires data to be collected quickly and efficiently en masse.
A data-centric approach supercharges this cycle with reliable data from the very beginning. It can provide AI models with the quantity and quality of data needed to shoot for maximum accuracy.
DCAI also catches labeling issues and inconsistencies early to prevent confusion and failure down the line. Other ways data-centric AI can empower global marketing include:
- Customer success
- AI content assistants
- AI-powered risk management
Use Data-Centric AI to Connect With Global Audiences
In a world where further breakthroughs in AI models get diminishing returns, organizations need to shift their focus to the other side of the equation: the data.
Execution-wise, it’s as simple as finding the best DCAI vendor that fits your global AI and business goals.