At EK, we help organizations across multiple industries and geographic locations tackle a wide range of challenges in dealing with the data, information, and knowledge that supports their strategic goals. Data management teams within these organizations are rising to the challenge posed by the proliferation, commoditization, governance, and protection of data. Delivering data to the right audiences now provides enterprises with competitive advantages to meet the expectations of their stakeholders. However, recent trends point to organizations demanding more from their data management functions, and executive leaders now ask these functions to maximize the extracted value from its collective knowledge, regardless of the state, location, or format. 

There are many reasons for this, but perhaps the most common one is the confluence of two important factors:

1. The recognition that knowledge and data are deeply interconnected, and knowledge is similarly a valuable resource within the enterprise that can provide benefits to the organization for improved efficiencies, better customer experience, enhanced employee morale, and strengthened relationships with business partners.
2. There is rarely a dedicated knowledge management (KM) function within organizations – or the people with the necessary skills and influence to drive knowledge-driven transformations. 

This article identifies five considerations to look out for when extending a data strategy to consider a knowledge management strategy.

The Difference Between Data and Knowledge

When talking about the relationship between data and knowledge at EK, we tend to use the following characteristics to differentiate between the two:

• Structured data is easy for systems and machines to read and process; however, it is generally difficult for human users to understand without underlying context.
• Unstructured data follows no consistent format for its organization and categorization. While generally easier for human users to read and understand, unstructured data has been traditionally more difficult for machines to use and process.
• Knowledge is generally highly internalized and gained through professional, educational, and personal experience. Although knowledge is typically difficult to fully record and capture, it enables people to make decisions and take action.

Both data and knowledge are generated through the organization’s business activities, and in order to provide value, they need to be adequately captured, managed, enhanced, and made available to a wide variety of stakeholders. 

The Need for Adopting a Knowledge Management Strategy

One could argue that all organizations do KM to different degrees of maturity. Teams and individuals often curate and organize their documentation, keep notes on their desktops, and build their personal networks to tap into experts and their knowledge. However, knowledge within their organization remains tribal. There is no effective way to share knowledge, information, and date efficiently and at scale. Access to institutional knowledge comes at a price, and its costs are often hidden and larger than leaders realize.

A knowledge management strategy breaks these patterns by providing a deliberate and systematic approach to capturing, sharing, enhancing, finding, and acting on institutional knowledge. This approach aligns with organizational priorities and enables the realization of strategic goals – often creating synergies with data management programs and strategies.

Key Considerations When Extending to a Knowledge Management Strategy

1. Prioritize employee needs over strict definitions and boundaries between data and knowledge. Organizations have traditionally run into issues by drawing arbitrary lines between data, information, and knowledge. At the end of the day, people don’t really care about the differences between these concepts: they want answers, they want to overcome obstacles from getting their work done, and they want solutions. More often than not, these come from a combination of data, information and knowledge. 
2. Leverage enterprise taxonomies and ontologies as a bridge. You can think of a taxonomy as a unifying structure to consistently describe and tag the information and data resources within your organization, enabling their discoverability and understandability. Ontologies go a step further to model the relationships between different types of resources, adding a layer that brings your business concepts, data, and logic together. Both taxonomies and ontologies should exist at the enterprise level, as opposed to locally at the unit- or department-level, in order to bind business data and knowledge across the entire organization. 
3. Encourage connections between people. Enabling conversations to occur about and around your data is an incredible opportunity for new knowledge to emerge. If you are able to capture and harness it, then you can create new organizational capabilities by breaking down traditional organizational silos.
4. Create learning loops and knowledge flows. By virtue of delivering data to people and empowering decision-making, you will create new knowledge and insights. Creating approaches to capture this knowledge and embed it into existing business processes and tools for learning and continuous improvement is an excellent knowledge management practice. 
5. Governance is never glamorous, but critical. Your decisions and actions are only as good as the quality of the data and information used as evidence to make them. Strong governance is critical in ensuring that your data, information, and knowledge resources are reliable, up-to-date, and succinct. You want to prevent your users from second-guessing the resources you are delivering, and feeling the need to reach out to their networks to validate them. 
6. Identify partners and champions. More likely than not, as you embark on this journey, you will encounter different groups throughout the organization trying to solve KM issues: finding the information they need to do their work, upskilling newcomers, retaining knowledge from departing individuals, or profiting from their lessons learned. These folks can become invaluable partners in supporting a knowledge-driven transformation for your organization. 
7. Go for quick wins. As you are jumping into the execution of any strategy, we find it helpful to achieve fast, measurable, and relatable results to a variety of stakeholders. Focus on solving a visible, impactful, and easily-achieved challenge to gain goodwill and support to continue your work. 

Closing Thoughts

Leaders in the data management space are called upon to transcend their traditional roles and embrace the strategic challenges of KM. By finding synergies and aligning data and knowledge programs to the wider organizational objectives, KM becomes an unexpected, yet vital, component in the journey towards success.

If you need help aligning your data management and knowledge management strategies, contact us!