Creating a knowledge management system for market and competitive intelligence across a large enterprise can be an overwhelmingly complex process, especially if you’re trying to build the system from scratch. On the other hand, if you opt for a SaaS-based solution like Northern Light SinglePoint™, the complexity and time to go-live may be significantly reduced.
Regardless of your approach, however, there are five core challenges to address when creating an enterprise knowledge management system, and all five pertain in some way to content. The system must enable users and administrators to:
- Aggregate content – what types and sources of content (internal and/or external) will be included in the knowledge management system?
- Find content – how will the knowledge management system enable users to find the documents most relevant to the research task at hand?
- Access content – how readily will users be able to access the documents they need, in full compliance with contractual and legal parameters?
- Analyze content – what automated capabilities will the knowledge management system offer to help users extract and synthesize information contained in the documents and transform it into insights?
- Share content – how will documents (and the information and insights contained within them) be shared with decision-makers throughout the organization?
Challenge #1: Aggregate content
Documents generated internally within an organization – primary market research reports, field intelligence about customers and prospects, and product plans, for example – are a major source of content for many enterprise knowledge management systems. While aggregating internal documents can present challenges – for instance, Microsoft® SharePoint® sites are notorious for creating workgroup or departmental content silos, and documents stored on individual employees’ laptop computers are of little use to anyone other than the laptop owner – aggregating content licensed from external sources is far more complex. Critical strategic research content discussing the outside world often originates in the outside world, at third-parties such as industry research firms, journal publishers, news sources, conference presentations, web sources and social media. Web content is voluminous, so curating it is the key to usability in a knowledge management application. Aggregating licensed external content requires a complicated set of skills and activities, including content industry awareness and experience, the ability to use any aggregation technique (API, FTP, RSS, or crawl, as may be specified by each content publisher), licensing and copyright compliance, normalizing disparate metadata, and search and machine learning across multiple sources.
Challenge #2: Find content
A user’s ability to find meaningful content within their organization’s knowledge management system is largely a function of how documents have been indexed, and the search engine within the system. When all documents are deeply and consistently tagged according to relevant industry-specific taxonomies, text analytics can be applied, enabling users to discover relationships between the concept areas revealed in search results and uncover relevant business issues hidden in content, and identify threats and opportunities.
Challenge #3: Access content
Especially when external licensed content is a significant component of a knowledge management system, defining protocols and rights for users to access documents can be extremely complex. Enterprise systems like Microsoft SharePoint have no built-in mechanisms for enforcing content licensing rights. At the other end of the spectrum, Web content is unruly, misbehaved, messy, scattered, badly formatted, when formatted at all. Despite being easily accessible without payment of a subscription fee, the content found on webpages is almost always copyrighted. Organizations, users, IT departments and even some news aggregators confuse “free” and “openly-accessible” with “copyright-free”, which can lead to legal and financial risk, and even penalties. A thorough understanding of “fair use” is essential. You cannot redistribute copyrighted material, even internally, at your company. Therefore, copyright compliance must be built into your knowledge management solution.
Challenge #4: Analyze content
Applying machine learning to enterprise applications became feasible in 2016 when Google open-sourced its key machine learning algorithms. Today, in knowledge management systems, machine learning enables the automated generation of document summaries based on an analysis of the important ideas in a document. A researcher can glean the important insights without having to download and read a document, resulting in much faster business research with a corresponding productivity gain. A second significant knowledge management application of machine learning is the ability to automatically refine a user’s search query; think of it as a “More Like This” button. In market and competitive intelligence research, users typically search on vague general terms; they under-specify queries by not expressing what they are actually looking for. Machine learning-based “More Like This” functionality changes the game by letting the user scan the initial (much-too-general) search result and, once they have found an on-point hit, invoke the machine’s intelligence with a single mouse click to rewrite the query based on an in-depth semantic analysis of the on-point document, which produces an entire search result of highly relevant material. An extension of “More Like This” is the automatically generated “Recommended Reading List” specific to the interests of each user, analogous to what shoppers on Amazon see when the system suggests other products they may be interested in, based on their past purchases.
Challenge #5: Share content
The best knowledge management systems are those that enable content to find users, rather than making users seek out content. This requires an AI-enabled insight distribution ecosystem – an infrastructure that directs relevant content and insights to the individuals who need it, in a timely manner, via whatever medium or mechanism those individuals prefer, automatically. Options may include strategic dashboards, search results, newsletters, machine learning-driven recommendations and insights reports, email alerts, RSS, and more. The goal of an insight distribution ecosystem, of course, is to maximize consumption of content by those who can put it to best use, which improves the quality of decision-making and enhances the value of the organization’s investment in content and in the KM infrastructure itself.
Addressing these five “content keys” will propel your organization down the path to creating an effective knowledge management system for enterprise market and competitive intelligence. While technology skills are required for implementation, building a useful and usable enterprise knowledge management system clearly is much more than an IT project.