Filling the Gaps with Knowledge Discovery Frameworks

To the ever-growing list of AI-adjacent technology increasingly used in business operations, you can add knowledge discovery frameworks. These high-tech systems sit in a sweet spot between automation, data analysis, artificial intelligence and service software. That can mean significant savings for enterprises, as one estimate puts the annual cost of poor knowledge sharing for Fortune 500 companies at $31.5 billion annually.

What Are Knowledge Discovery Frameworks?

“Knowledge discovery frameworks are a system that defines how data can be mined and processed in such a way as to deliver meaningful, actionable and business-oriented outcomes,” said Tej Redkar, chief product officer of LogicMonitor, a fully automated and cloud-based infrastructure monitoring platform for enterprise IT.

The frameworks take a platform-centric approach to generating insights from multi-variant data sources including technology, process and people data, said Sree Subramaniam, director of product management and IT operations manager for Business Units at ServiceNow, which creates digital workflow platforms.

As a result, business operations can continue to run smoothly as the framework handles the friction of missing knowledge and broken processes, said Tim Porter, CEO of Kare Knowledgeware, a knowledge management and self-service automation platform. “They span a wide range of tools, from simple search-based tools and software such as elastic search and higher up the stack – Algolia or SharePoint – through to more sophisticated systems that retrieve information through conversation, question answering or automation,” Porter said of the frameworks.

How Long Have They Been Around?

Knowledge discovery frameworks are both currently in use and ever evolving, as would be expected of technology related to artificial intelligence and data analysis. The frameworks themselves are shifting as a trend, said Colin Stauffer, director of data and development services at Resultant, a consulting firm focused on data analytics and digital transformation.

“The biggest trend is the movement from traditional knowledge discovery frameworks, which lead with science and technology, to actionable knowledge discovery [AKD] frameworks, which pair the deeply technical with the strategic priorities of the organization,” Stauffer said. Enterprises often start with the tech without knowing why it’s important to their business operations, but with AKD frameworks, a clear understanding of business needs is the starting point.

Why Are People Paying Attention to Them Now?

Through knowledge discovery frameworks, customers can access intelligence and insights from varied and unexpected data points, Subramaniam said. As a result, workflows and frameworks can be created that don’t just keep business as usual going but can do so during unusual circumstances such as a power outage or security threat.

“For example, understanding that an application is business-critical and its availability to users is a key business requirement,” Redkar said. Knowledge discovery frameworks provide a new path to gaining that understanding – the framework can help collect an application’s metrics and interactions, determining its degradation or availability, then tie that information together with the business metrics required to keep the application running smoothly.

As enterprises face an increasing number of anomalous and hard-to-predict challenges due to cyberattacks, climate change-related weather events and global circumstances, this capability is increasingly important. “The power of coherently implementing them shouldn’t be underestimated,” Porter said of the platforms.

Watching the AIOps space is vital right now and into 2022, Redkar said. “IT leaders should evaluate solutions that help their organizations leverage these knowledge discovery frameworks,” he said, “so they can better optimize and automate the services they deliver to their business and customers.”

Who Benefits from Them?

“Knowledge discovery frameworks should be front and center in a business’s day-to-day activities without users being conscious of them,” Porter said. “Today, they are business-critical.” That said, their execution must be well-thought-out because every business’s needs are different. Think about which frameworks can best store your information across the needs of different business operations, he said, and what your business’s compliance requirements are, among other important initial questions.

The data sources that can be accessed and analyzed via knowledge discovery frameworks are varied. Some examples include machine data, data mined from large config files and databases, data generated due to a service outage and user behavior data. Ultimately, knowledge discovery frameworks can help enterprises determine centers of knowledge and the business actions they should take, based on data collected from their different technical and business systems, Redkar said.

In short, the frameworks can be applied to a number of industries that could benefit from sophisticated data analysis and that rely on data generated from a variety of sources.

“In the era of digital transformation, where the velocity of code deployments is going at lightspeed, enterprise customers require data intelligence platforms which can enable them to analyze multi-variant data sources in real time and use predictive modeling algorithms to effectively deliver proactive AI-powered service operations,” Subramaniam said.

Where Can You Get Them?

Platforms such as Onna, Kare Knowledgeware and Viva are just a few examples in this space. Knowledge discovery frameworks can, as Porter said, work with a variety of tools from different sources. Knowing which are best for your business will involve an assessment of your current tools and needs, as well as a look at where knowledge and/or processes are currently lacking.