Data Science Knowledge Management

Knowledge management (KM) is the process of identifying, organizing, storing and disseminating information within an organization. When knowledge is not easily accessible within an organization, it can be incredibly costly to a business as valuable time is spent seeking out relevant information versus completing outcome-focused tasks.

A knowledge management system (KMS) harnesses the collective knowledge of the organization, leading to better operational efficiencies. These systems are supported by the use of a knowledge base. They are usually critical to successful knowledge management, providing a centralized place to store information and access it readily.

Companies with a knowledge management strategy achieve business outcomes more quickly as increased organizational learning and collaboration among team members facilitates faster decision-making across the business. It also streamlines more organizational processes, such as training and on-boarding, leading to reports of higher employee satisfaction and retention.

From right tools and strategies, knowledge management practices have seen success in specific applications, such as: Enable every employee to have access to accurate answers and critical information. Access to highly relevant answers at the right time, for the right person, allows workforces to spend less time looking for information and more time on activities that drive business. Customers repeatedly say they’d prefer to find an answer themselves, rather than pick up the phone to call support.

When done well, a knowledge management system helps businesses decrease customer support costs and increase customer satisfaction. Knowledge management systems help to address the huge learning curve for new hires. Instead of overwhelming new hires with a ‘data dump’ in their first weeks, continually support them with knowledge tools that will give them useful information at any time.

While knowledge management solutions can be helpful in facilitating knowledge transfer across teams and individuals, they also depend on user adoption to generate positive outcomes. As a result, organizations should not minimize the value of human elements that enable success around knowledge management. Management practices will affect the type of organization that executives lead. Managers can build learning organizations by rewarding and encouraging knowledge sharing behaviors across their teams. Centers of excellence in specific disciplines provide employees with a forum to ask questions, facilitating learning and knowledge transfer. This type of leadership sets the groundwork for teams to trust each other and communicate more openly to achieve business outcomes. In this way, organizations increase the number of subject matter experts in a given area of the company, reducing dependencies on specific individuals to execute certain tasks.

The above describes an organization struggling with knowledge management the identification of knowledge that enables the data scientists of the organization to perform their job effectively, keep that knowledge updated, and spread it across the community despite the members of that community being distributed across the organization. Many companies would respond to this challenge by attempting to encode the above knowledge in an internal wiki, such as Confluence, and this organization was no different. But what typically occurs with such static knowledge stores is that they quickly become knowledge graveyards; community members lack incentives to update their pages as conditions evolve.

How rapid the feedback of data science techniques evolves, the change of tooling place whereby it makes small sense to evaluate what works best when once and never again reevaluate that choice. Data Science in organization requires to constantly be undergoing a process of discovery, evaluation and integration.