CEO of Edge Platforms, EdgeVerve Systems Limited.
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Successful and innovative enterprises are well connected. They are notably good at preparing and harnessing external data. Artificial intelligence (AI) can enhance sources, processes and workflows, making a well-run enterprise stronger, quicker and more competitive.
Being able to access and use data from customers, suppliers and other stakeholders is a good indicator of an organization’s capacity to make the right decisions. An externally informed mindset, according to the authors of a McKinsey report on innovative companies, is less vulnerable to biases and internal politics and enables rapid course-correction of strategies, R&D priorities and other initiatives.
Applied smartly, information can improve decision-making and erode inefficiencies. The right kind of data infrastructure is what enables a company “to break down (or at least perforate) silos,” as McKinsey puts it. What you need are integrated data connections, more structured data, and a platform or fabric that can unify workflows, tasks and analytics. All can benefit from AI.
Connectors And APIs
Data integration is a complex equation. To start with, enterprises use myriad application programming interfaces (APIs), typically paired with connectors, to link with data sources. Managing these sets is a challenge. One way we do so is through crowdsourcing, enabling the reuse and adaptation of capabilities.
Many of our clients are already familiar with the task-mining capabilities of robotic process automation (RPA) and AI/machine learning (ML) algorithms. But you also can use AI to build and manage your API infrastructure.
An emerging use case for generative AI (GenAI) is developing, optimizing and protecting APIs. (See, for instance, this Google Cloud session.) These kinds of deployments can, in turn, trigger a virtuous cycle: simplifying existing stacks of APIs, which make it easier to adopt more AI. The other prerequisite to using data is making sure that it’s in good order.
Leveraging More Structured Data
A connected enterprise deals with two types of data. There is machine-generated structured (and semi-structured) data, which may come via API-driven connectors with customer relationship management (CRM) solutions, enterprise source planning (ERP) platforms and other core record-keeping systems. Then there is also a much larger volume of unstructured data that is mostly human-generated.
Each day, a massive volume of enquiries, complaints, claims, reports, statements, log files, correspondence and more flow into or are generated by large companies. Altogether, this influx may account for as much as 90% of all enterprise data, according to one IDC report.
Leveraging this raw data in a timely manner requires mining for information (often buried in silos) and structuring it into usable formats. This has been a big challenge for enterprise data managers. In that regard, natural language processing (NLP) and AI are timely arrivals. These automated tools can transform a body of text through several techniques, including:
• Rapid Entity Recognition: Identifies, sorts and ranks named pieces of information.
• Part Of Speech Tagging: Assigns a grammatical category to each word.
• Semantic Analysis: Interprets correct context of words with multiple meanings.
• Keyword Extraction: Pulls representative words that express key aspects of content.
• Frequency Matrices: Represents the co-occurrence of linguistic units.
• Sentiment Analysis: Assesses text for positive, negative or neutral sentiment.
Toward Unified Data Fabrics
With RPA, automation scripts and APIs, you can create data pipelines. One outcome is straight-through processing, which can benefit time-sensitive and high-volume processes in applications from finance to healthcare. But new tasks associated with enhanced AI, which could also boost existing core data processes, involves more components and preparation. Such as:
• Data dictionaries to identify the structured data that begins GenAI model training and the less structured data that adds context.
• Data catalogs to provide broader and deeper intelligence.
• New data architectures (e.g., data lakehouses) to create better storage solutions and formats.
• Manual processing to establish quality control.
• Data engineering to transform data into features, which boost a system’s predictive power.
To integrate data assets, pipelines and these other components, organizations are turning to data fabrics. Here again, AI can itself help, for instance, by generating code, building models or visualizing results. Whether on your own or working with a provider, the goal should be a common platform that brings together task, work, process, document and insights from underlying and partnered systems.
Applications And Summary
The race is on. Companies that efficiently connect with all data sources, make unstructured data more usable and unify their data infrastructure have advantages, such as fewer silos, more flexibility and greater intelligence. Advanced AI can help deliver those gains.
On top of the three cases discussed above, AI is expanding the scope of what is possible. If you’re exploring advanced AI, start by refining your target. Are you trying to better serve customers? Supercharge existing ERP and CRM systems? Build new apps? Optimize the supply chain? Especially with GenAI, the sky seems to be the limit.
To hit those or other goals, you may need to clean up your connectors, harvest more structured data and begin building an enhanced data fabric. Within a well-connected enterprise, AI can enable those gains and supply your workforce with real-time, relevant data and contextual insights, key drivers for innovation, agility and competitive strength.
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