Acceptability for AI and its adoption is increasing. Historically, AI was largely leveraged by technology companies. In today’s fast-paced business environment, organisations are under constant pressure to stay ahead of the competition and meet the changing needs of their customers. To succeed, they must be able to leverage their data resources quickly and effectively. Hence, they are leaning on AI technology to build their business applications. Any AI application must have two essential components: model and data. Most AI solutions until now have concentrated on the Model Centric AI approach through the improvement of model architecture, algorithm design, and feature engineering. Model-centric AI systems rely on the availability of a wealth of data to train the model on every conceivable scenario. Model centric AI assumes availability of vast volumes of data with limited variability of input and output data. These rule-based or model-centric solutions have advanced to the point that businesses are beginning to recognise the limitations on the data side, giving rise to a data-centric AI adoption.

Constraints due to Input data volume and quality:

AI adoption is spreading into other industries like financial services, insurance, manufacturing, and others. While technology companies had access to large volume of data, most industries do not have the luxury of data. AI systems need to deal with such constraints and yet be effective. For the AI solution to be relevant, it must be adaptive to deal with data volume constraints. 

Increasingly, the focus is shifting from Big Data to Good Data.

Ability to deal with input data variability:

Data scientists and analysts concur that data drives AI success. Training a model for data is often merely seen as a one-time event. Real-world data is often noisy and can contain multiple sources of variability such as outliers, irrelevant features, and changing distribution over time. Model Centric AI systems anticipate the data to adhere to certain templates employed during training. Business operations take place in a very complicated environment, with minimal control over the data input. Businesses operate using a network model, interacting with a variety of partners, the government, regulators, and more. The input data cannot be prescribed. It becomes expensive and time-consuming to train the data models with new input data utilising human resources. A bias that develops during manual labelling frequently causes the AI model to fail. The inconsistent quality of the input data is another issue. The iterative loop of model tuning may cause it to lose priority over time. Inadequate control may result in potential issues with model robustness owing to data drift when there isn’t a critical eye on the data being deployed.

Client expectations on output data flexibility:

Customers now anticipate predictability and convenience when interacting with brands in today’s digital environment. They also expect these brands to be aware of and responsive to their needs. Businesses need the ability to change their output data in line with clients changing needs. Since consumer behaviour is dynamic and ever-changing, the data must also be continually improved to retain model efficacy. A data-centric AI approach facilitates growth when highly predictive models hold the key to a first-rate client experience and adaptability for them.

Data-centric AI approaches can help address inconsistent quality of the input data in several ways:

• Data cleaning and pre-processing: A well designed data centric AI solution can automate the process of data cleaning and pre-processing to handle missing or inconsistent values and outliers.

• Data quality monitoring: AI models can be trained to identify and flag data points with poor quality, allowing organisations to identify and correct data quality issues in real time. This can be further enhanced with a human in the loop approach. 

• Data augmentation: Data-centric AI can automatically generate synthetic data to augment the existing data, making the model more robust to data variability and quality issues.

• Data imputation: AI algorithms can be used to predict missing values in the data, improving the quality and consistency of the input data.

• Data standardisation: AI algorithms can automatically standardise the input data to reduce the impact of scale on the model, making it more robust to variability in the input data.

“Data is food for AI. A data-centric approach allows people in manufacturing, hospitals, farms, to customise the data, making it more feasible for someone without technical training in AI to feed it into an open-source model.” —Andrew Ng, founder and CEO of Landing AI. 

In summary, while data is key to the success of an AI solution, organisations may have little control over it and need to build systems that are adaptive. Organisations operate in a very dynamic business environment. In many industries, the conventional approach of training the AI solution with large training data may not be feasible. Moreover, businesses need to have the ability to deal with input quality issues and be able to improve the quality, considering the limited volume of data. Thirdly, given the complex business environment, business applications need to be adaptable to handle variability in inputs without having to rely on the retraining of the model. Data-centric AI helps build such adaptive business applications.

This article is written by a member of the AIM Leaders Council. AIM Leaders Council is an invitation-only forum of senior executives in the Data Science and Analytics industry. To check if you are eligible for a membership, please fill out the form here

The post Council Post: Building Adaptive Business Applications using Data centric AI appeared first on Analytics India Magazine.