Creating a defined data centric space with AI and data analytics

Written by: Saikat Chakrabarty, Senior Manager – Engineering, Mihup

AI and Data analytics are undoubtedly the future of digital technology. AI is critical for automation and data analytics is the key to evolution of inputs that make the former better. Historically, AI development efforts centred on the Model-Centric approach, primarily due to the fact that AI technology development was closely related to academic research. However, now we have moved into an era where billions of smart machines with capability of listening to voice commands and responding to questions, are active all over the world, and the numbers are going to grow exponentially in the years to come. This is where AI and data analytics integration is going to level up significantly, and transition from a model-defined to data-centric approach.

Model-Centric AI Approach

In the conventional model-based scenario, regular tests and training is done to enhance the AI model’s performance and capabilities. It is a continuous and long process of training wherein new possibilities, scenarios and options are added everyday to eventually create a better version of the technology.

Data-Centric AI Approach

Herein, the focus is on the accuracy and consistency of the data presented to the system. This data is analysed and consumed by the AI system. This model thrives on consistency of data supplied. It is becoming increasingly important in view of the rapid surge in number, diversity and applications of the AI tools.

The important thing here is to not only focus on generating more data through a multitude of channels, but also to ensure good data quality. By using a data-centric AI approach, one of the biggest benefits a company gets is that it is able to unite the diversity of data silos, and compile the entire information into a unified, simpler and better visible data platform. This unification of different data makes it faster to access and analyse the data for real-time usage. The need for manual upload of data into warehouses with predefined schema is eliminated. Also, as time progresses, more data is generated and the overall, data collection becomes more valuable for the business.

Large data compilations provide an opportunity to look into the historical trends, business performance and to predict as well as identify future trends and patters. Real-time data for AI applications proves to be valuable in determining the response to be provided. It allows decision making in a way that’s more effective for the users, reduces risk and costs.

In conventional settings, compiling data as well as deploying human resources dedicated to analysing it, can prove to be costly. However, the trend is now to move away from such capex heavy processes and to move towards cloud-based, pay-for-use systems.

This kind of smart AI and data analytics integration can do wonders for verticals such as sales and customer service. Teams are able to understand their customers better through AI-driven holistic analysis of data. It gives them the ability to assess different outcomes, consumer sentiments and preferences. Thus, the sales or customer service representatives are empowered with better knowledge, process adherence, and understanding of the customer’s expectations. In contact centres, such data-driven operations lead to faster response, shorter call handling durations, reduced frequency of repeat calls, and optimisation of operational costs.

Even in other aspects of operations, this unification of data and analytics leads to superior collaboration and overall business efficiency. The data science engineers are able to connect different silos and build pipelines for continuous and consistent flow across the network. Efficient data labelling is done to identify and process high quality data at significantly high volumes than possible manually. This also leads to better ability of training machine learning models through usage of insights extracted from the same datasets. Essentially, the entire AI lifecycle is enhanced and expedited through usage of data-centric approach.

Usage of such an approach is enabling modern technology startups to create better AI and analytics-powered solutions in areas such as automation, in-vehicle smart assistants, contact centre voice AI, as well as numerous other such applications. With proliferation of such techniques and the ability of machines to comprehend natural and native languages and dialects, we will soon see an era of AI technology reminiscent of the science-fiction of yesteryears!

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