The synergies of AI-integrated data ecosystems

By  Santhosh Subramaniam

With 64 percent of enterprises reportedly managing at least 1 PB of data and 41 percent dealing with 500 times that, data availability is clearly not the problem. However, managing data and extracting full value, is another matter. One study concluded that 40 percent of projects fail because of data integration challenges. A major reason for this is that traditional data management approaches are riddled with data silos, trapping information within islands to deny enterprises the holistic data view they need to automate operations, take better decisions, etc.

Data ecosystems push for integration

This is one of the primary concerns that a data ecosystem seeks to resolve. By creating a central repository for enterprise data, the data ecosystem dismantles silos to offer full visibility, enable free flow of information between systems, and encourage collaboration between teams.  Key features include pre-integrated solutions that streamline data use, unified management of data operations, a data fabric and governance framework ensuring data consistency and security, and unified access management for users.

AI and data ecosystems: two is (good) company

Data ecosystems are a pre-condition for artificial intelligence technologies, which require huge quantities of high-quality data to run successfully. But the relationship is not one-way; AI has an equally significant role in driving the performance of data ecosystems.

Let’s start with the data itself. Artificial intelligence automates tedious tasks, such as data entry and cleansing. AI algorithms improve  data quality and accuracy by identifying and fixing errors, automatically deduplicating data, and detecting anomalies with efficiency. Better data leads to better insights.  Some examples of frequent conversations with our clients on this topic are

1. Retailers use AI to automate customer data collection and product listing updates, minimizing manual data entry errors.
2. AI algorithms detect and merge duplicate product or customer records, ensuring cleaner and more reliable databases.
3. Anomaly detection in sales and inventory data helps retailers quickly spot and correct unusual or incorrect entries.
4. Automated data cleansing improves inventory management, helping avoid stockouts and overstocking.
5. High-quality, well-organized data enables better customer personalization and more accurate business insights for retail and CPG companies.

Moving on to efficiency.  AI boosts the speed and efficiency of traditional data integration processes by automating them. It also improves integration qualitatively by mapping the relationships between different data sources, spotting anomalies and recommending the best processes for data transformation. By reducing the time needed for preparing and integrating data, AI enables enterprises to perform real-time analysis and take quicker decisions.

AI’s powerful analytical and predictive capabilities enable businesses to extract key insights, such as emerging trends, customer expectations, business risks, and competitor strategies. Interpreting complex and even unstructured information better with the help of AI, organisations can add nuance to their decisions.

To quote on few real life examples from our customer discussions –

1. CPG companies leverage AI to analyze shelf images and automatically map product placement, integrating visual data with sales and supply chain systems for instant feedback
2. AI automates the integration and transformation of supply chain data—such as demand forecasts and logistics—helping companies like Walmart optimize inventory and reduce waste.
3. By automating data mapping and integration, AI enables retail and CPG brands to deliver personalized recommendations and promotions in real time, based on unified customer profiles

Importantly, AI-enabled data integration platforms can work with the hybrid environments of large organisations, combining data from cloud and on-premise systems distributed across geographies, with efficiency. Furthermore, AI ensures the organisations are compliant with the rules governing data security, privacy and exchange in all their markets of operation.

Combining with machine learning and advanced analytics, AI safeguards the data ecosystem from malicious attacks and inadvertent breaches. Real-time threat detection drives proactive, preventive security practices. AI-powered identity and access management tools ensure only authorised users can access enterprise data.

AI plus data integration provides the scalability and flexibility required to handle ever-increasing data volumes. Massive quantities of data residing in different locations can be managed without a drop in performance. What’s more, easy adaptability means that enterprises can seamlessly integrate new data types, formats and sources as their needs change.

The ecosystem advantage

AI-enabled data ecosystems are better at integrating, managing, and analysing data. They convert raw data into valuable, actionable insights to automate tasks, enable informed decisions and improve all-round performance. Last but not least, in enabling efficiency, insight, security and compliance, AI-enabled data ecosystems presents themselves as a strong driver of competitive differentiation.

(The author is  Santhosh Subramaniam, AVP & Head of Domain Consulting Group for Consumer, Retail and Logistics, Infosys, and the views exppressed in this article are his own)