On May 18th at the SIKM Community Leadership Call and again on May 27th at the KMC – DC, I had the pleasure of presenting a webinar on AI and Big Data in Knowledge Management. At both sessions the interaction, questions and exchange of ideas made for great sessions. This blog post provides some details of the presentation. I would encourage you to listen to either of the webinars by accessing the links provided above.
A goal of knowledge management is to capture and share knowledge wherever it resides in the organization. Leveraging the corporate collective know-how will improve decision making and innovation where it is needed. Organizations do not make decisions just based on one factor; the total picture is what should drive decisions. KM enables organizations to take the total picture Big Data provides, and along with leveraging tools that provide processing speed to break up the data into subsets for analysis will empower organizations to make decisions on the vast amount and variety of data and information being provided.
The emerging challenge for organizations is to derive meaningful insights from available data and re-apply it intelligently. Knowledge management plays a crucial role in efficiently managing this data and delivering it to the end users to aid in the decision-making process. Knowledge management plays a crucial role in efficiently managing this data and delivering it to the end users to aid in the decision-making process. AI through predictive analytics and Knowledge Flow Optimization will provide a Dynamic, Accurate and Personal delivery of Knowledge. AI will enable Analyzing and synthesizing data to derive meaningful knowledge.
Extracting Knowledge: Organizing & Optimizing with Information Architecture
Information Architecture (IA) provides the methods and tools for organizing, labeling, building relationships (through associations), and describing (through metadata) your unstructured content adding this source to your overall analysis. In addition, information architecture enables AI, in particular machine learning (ML) to rapidly explore and analyze any combination of structured and unstructured sources. ML requires information architecture to exploit relationships and synergies between information, aligning unstructured and structured data. This infrastructure enables organizations to make decisions utilizing the full spectrum of your big data sources.
To facilitate the inclusion of unstructured data (content) the metadata schema must be utilized (it is developed as a part of the information architecture). Having a sound Information Architecture will enable a consistent structure to big data in order for this data to provide value to the organization. Interacting with stored knowledge in repositories and connecting this explicit knowledge to the tacit knowledge holders brings together a holistic view of knowledge.
Tacit knowledge holders include the knowledge workers who are experienced with executing certain tasks, developing a solution, working in a specific industry, practice area or company while leveraging the stored knowledge.
IA effects ML/Data Analytics through Content (unstructured data) Curation. Content Curation is an essential step to enabling analytics to be effective. Curating the content, adding the necessary metadata (descriptive, structural, administrative), and categorization elements is enabled by applying IA. Content curation provides the methodological and technological data management support to address data quality issues, while maximizing the usability of the data.
AI plays an important part to KM by elevating how the delivery of knowledge occurs to the people who need it. AI is used to scale the volume and effectiveness of knowledge distribution. AI provides KM with the ability to deliver knowledge that is:
Dynamic knowledge is constantly updated adhering to your organization’s content (information and knowledge) lifecycle management processes. This also includes the experts who can provide insights about the knowledge. The Dynamic component of knowledge reflects your organization’s brand, tone and evolves over time.
The Accurate component of knowledge is identified as the authoritative source and authoritative voice for that subject matter. This knowledge is accepted by your organization as the “source of truth”.
The Personalized component of knowledge answers the questions that the users of your knowledge are seeking. Personalized knowledge is tailored to what the individual need to make a decision and presented in a way that is tailored to the device and applications the users are leveraging to access answers to their questions. Personalized knowledge is facilitated by how knowledge flows throughout your organization.
Applying AI to KM enables predicting trending knowledge areas/topics that your knowledge worker’s needs. AI for KM typically leverages supervised learning algorithms that will learn over time. Supervised learning allows the algorithm to make inferences from curated labeled data.