Do we have Bricks or Buildings
Organisations store a lot of business data. Depending on size it is estimated that the average is between 150 and 350 terabytes of data for a mid-sized company. That's a lot you say! Well not really, Big Data is really big and it's estimated that we create 330 million terabytes of big data a day. It's made up of many types of data structured and unstructured like logs, analytics, and all types of data collected in real-time. It's held in a large pool or lake of data and has specialised systems that troll it for useful insights. But this is not about Big Data it is about a business's knowledge.
Data is Not Knowledge
Data is not knowledge and only becomes knowledge when it is processed, organised, interpreted, and connected in a way that provides meaningful context and insights.
So, data is like a pile of bricks, it’s just a pile of bricks until you build something.
The transformation of data to knowledge is not magic it typically follows several steps:
Data Collection where data in its raw form can be numbers, words, measurements, or observations that have not yet been organised or analysed. At this stage, data alone does not provide much understanding beyond its explicit content.
Information Processing when data is processed and structured, it becomes information. This process includes organising, structuring, and contextualising the data, such as putting sales figures into a spreadsheet or summarising survey results in a report. At this point, the data has been cleaned and formatted but might still lack deeper insights or implications.
Knowledge Creation, data is transformed and knowledge emerges when information is analysed and structured to draw conclusions, make predictions, or uncover patterns. This step involves applying understanding, experience, and content awareness to translate the information. For instance, understanding why sales peaked in a particular quarter by correlating it with marketing campaigns, economic conditions, and consumer behaviour changes.
Knowledge Application, this is when the business uses the knowledge to make informed decisions or to solve problems effectively. Wisdom implies a deeper understanding that allows for the anticipation of outcomes and application in different contexts.
This transformation is crucial in organisations and is the foundational idea behind implementing effective Knowledge Management Systems.
Knowledge Management Systems (KMS)
A Knowledge Management System (KMS) is crucial for any organisation seeking to preserve, manage, and utilise its intellectual assets systematically. Implementing a KMS involves developing a comprehensive plan that integrates technology, personnel, and process components to facilitate the seamless sharing and management of knowledge. This article outlines the critical elements of a KMS plan, explores relevant technologies, identifies new organisational roles, and emphasises the importance of data taxonomy and metadata in the effectiveness of a KMS.
Knowledge Management System Plan
Many organisation fail to capitalise on the value that they have maintained in the data that has been collected. It can disappear, get duplicated, get corrupted, be ignored. In order to turn all this business data in to knowledge you need to plan. A successful KMS plan will need to look at the what is there now and build a strategy to make it usable for the business. The components of the plan are based around a Knowledge Audit within the business.
Needs Assessment and Knowledge Audit
Understanding the specific knowledge management needs of the organisation and what exists now. How these will be impacted but the adoption of a KMS.
Strategy Development
Defining a strategy for the core functions and the objectives, scope, and goals of the KMS.
Technology Infrastructure
Selecting and implementing the appropriate technologies to support the KMS.
Roles and Responsibilities
Establishing new roles and defining who is ultimately responsible for the KMS.
Establish Business Policies
The KMS needs to be supported by key and enforceable policies in the business. For example a Knowledge Management Policy, Meta Content Policy, Document Retention Policy, Security and Privacy Policy.
Data Taxonomy and Metadata Structure
Designing how knowledge is categorised and tagged.
Training and Support
Developing comprehensive training and ongoing support for users.
Evaluation and Adaptation
Setting up mechanisms for ongoing evaluation and adaptation of the KMS.
Technological Infrastructure
The plan will also look at the technologies that are available within the business and also what is missing. Part of the plan will define the required tools, for example:
Content Management Systems (CMS), such as SharePoint, which allows for the creation, management, and storage of digital content.
Collaboration Tools for example Microsoft 365 offers tools like Teams and OneDrive that facilitate collaboration and knowledge sharing.
Search and Retrieval Technologies that are enhanced by good data taxonomy and metadata to ensure efficient searchability and access to information.
Security and Privacy Tools to protect sensitive information and comply with data governance standards.
New Roles and Responsibilities
There may already be resources and people in the business that can plan and implement the KMS. But it often required the definition and deployment of new roles and responsibilities.
Corporate Knowledge Manager who oversees the KMS operations and strategies. In a large enterprise this role may be as a Chief Knowledge Officer at board level. Diverse organisation may need Business Knowledge Managers at geographical or business group levels.
Content Curators who manages the creation, acquisition, and maintenance of knowledge content.
Taxonomy and Metadata Specialists who designs and maintains the data categorisation and tagging systems.
KMS Trainers who develops and delivers training programs for the KMS users.
The Chief Knowledge Officer (CKO), Chief Information Officer (CIO) or Chief Technology Officer (CTO) typically holds ultimate responsibility for the KMS.
Importance of Data Taxonomy and Metadata
Effective data taxonomy and metadata are critical as they enhance the searchability and retrievability of information, crucial for global organisations where knowledge is dispersed across various regions and departments.
Data Taxonomy, organise information into logical categories this will often follow a model similar to a relational database. The taxonomy maps the relationships and sets privilege levels.
Metadata, descriptive information about knowledge assets (e.g., author, creation date). Data is not Knowledge if it cannot be found. Metadata is used by search engines to find knowledge. The metadata is structured within the taxonomy so that it relates logically to the organisation.
Knowledge Management Systems
Once you have a plan you need to implement that plan and there are numerous methods to do just that. The choice depends on the skill sets in the organisation and budget.
Microsoft 365 and SharePoint, both online or on premise as already mentioned earlier.
Confluence from Atlassian, a collaboration software that specialises in project and content management.
Alfresco, an open-source knowledge and content management system known for its robust document management capabilities.
Google Workspace, offers tools for collaboration and content management, with strong integration features.
Salesforce, offers a comprehensive cloud-based platform.
And there are many others available.
Implementation Timeline and Costs
Indicative costs and time to plan and implement a KMS can vary greatly. For a medium-sized global organisation, implementing a KMS using Microsoft 365 and SharePoint could typically range from 12 to 18 months. Costs vary depending on specific requirements but can range from $50,000.00 to well over $500,000.00, including software, hardware, training, and personnel costs.
Training and Support
A comprehensive training plan is vital for KMS adoption and should include:
Initial Training Sessions:Â To familiarise users with the system.
Ongoing Support:Â Regular updates and helpdesk support.
Feedback Mechanisms:Â To continually adapt training based on user feedback and evolving needs.
Enhancement by Artificial Intelligence
Artificial Intelligence (AI) has been incorporated into many areas of business technology and that includes Knowledge Management. It is early days, but we can see AI can enhancing a KMS in numerous ways such as:
Automating Content Categorisation, using machine learning to create metadata, tag and organise content.
Personalising User Experience, recommending relevant content to users based on their behaviour and preferences.
Enhancing Search Capabilities, implementing advanced algorithms for more accurate and efficient search results.
Knowledge brings Key Benefits and Savings
Improved Productivity through streamlined access to information reduces time spent searching for data.
Enhanced Decision-Making is enabled by better knowledge sharing which leads to informed decisions.
Competitive Advantage the use of efficient knowledge management supports innovation and agility in the organisation.
By planning and building a Knowledge Management System data becomes more valuable. Organisations can significantly improve their operational efficiency and competitive edge by using the knowledge available to the business. The right plan can position a business well for future advancements in technology such as AI integration and advanced learning systems.
Author: John Debrincat FACS, MAICD