What are Knowledge Management solutions?

Peter Drucker, famous for declaring, "Information only becomes knowledge in the hands of someone who knows what to do with it," coined the term "knowledge management" in the 1950s. 

Knowledge management has since become a specialized emphasis area for larger companies.

There have been numerous discussions about how knowledge management solutions can be implemented since the subject was created in 1991. 

One of the most popular is using a knowledge management software solution to support customer service and technical support operations such as contact/call centers, shared service centers, web self-service functions, and help desks, including ITSM (IT Service Management) operations approaches in businesses. 

Evolution of Knowledge Management solutions

Many attempts at internal systems have been made, ranging from internal "shared drives" to massive databases with intricate hyperlinks and meta-data. 

The accumulation of big data has made it increasingly difficult to find the exact information needed without a lengthy search or an intimate understanding of where the data is stored. 

In addition to offering an internal content management function and reports to assess knowledge base usage and knowledge gaps, the best software for Knowledge Management simply indexes a wide range of information resources before filtering and prioritizing relevant knowledge.

#1 - Central repository (Knowledge base systems)

Many information sources are accessible from a single location.

The system indexes all required content from all relevant sources without requiring any information to be moved to a single location and uses a natural language search function to allow users to quickly and easily retrieve the information.

This enables quick and easy solution deployment without the need to reformat or repurpose vast amounts of legacy data.

#2 - Guiding customers to answers (Content relationship Management systems) 

Users frequently know how to articulate their problem but are unsure how the answer will be phrased or communicated. 

Staff, partners, and customers can describe the issue, pain, or query in their own words and enter the term immediately into a search using the best software for knowledge management. 

The ai knowledge base solution will find and display solutions known to handle similar issues in order of importance.

#3 - NLP (AI – powered solutions)

Users can utilize natural language search instead of typing in keywords to ask inquiries. 

Documents are frequently prepared in informal language that differs from the language used to ask questions. 

Natural language search functionality is currently a significant element of the best software for knowledge management.

It allows the system to grasp the context of the query rather than just the keywords required for a successful search result.

This is especially significant in industries that employ industry jargon, such as finance, where a direct debit is commonly referred to as DD. 

Natural language capabilities can permit the use of the organization's common words.

#4 - Self-learning (Decision support systems) 

A self-learning capacity in a knowledge management solution captures the continually changing flow of information. 

This keeps the index up to date with the actual phrase used in inquiries. 

New content can be added to the content repositories that are already in use after the initial implementation.

Self-learning also includes users determining the quality of the solutions offered, so the most useful options are presented first.

#5 - Single source of truth (Document management systems) 

A knowledge management solution can also help with a push strategy, which allows specific content to be 'pushed' to a specified user group. 

This guarantees that new information reaches the right individuals at the right time.

The system keeps track of who read the material and when.

This allows administrative users to see who needs to be updated on new information.

The system may also manage user profiles across an organization, allowing each user profile to have specific access to pertinent information. 

This allows knowledge to be transmitted both within and outside of the company while maintaining control.

#6 - Identifying and closing knowledge gaps (Learning Management Systems)

A new age knowledge management solution can also identify knowledge gaps and refer unanswered concerns and queries to content specialists unlike old DMS like Sharepoint, who can react to the inquiry by updating the system with new information. 

This eliminates the need to elevate the same investigation to relevant experts numerous times. 

Tracking and responding to knowledge gaps also removes the guesswork involved in determining where knowledge gaps may exist before developing new solutions. 

#7 - Social communication systems

Knowledge is created and shared whenever your teams interact with one another.

It's critical, then, to analyze how your organization's communication and collaboration technologies relate to your knowledge management goals.

You want to know, in particular, that:

· In the first place, your teams can easily share knowledge and information.

· This information can be shared in a variety of ways.

· At all times, engagements are recorded, and knowledge is saved.

#8 – Decision support systems 

Decision support systems are technologies that assist people in making informed business decisions by analyzing large amounts of data. 

While DSSs can be used to collect and handle any form of data, the most frequent data types involved are:

· Performance in marketing and sales

· Services of assistance

· Internal operations

The idea is to use data to find the paths that will lead to the maximum development for your company, whether that growth is financial, productivity, or something else.

Conclusion

When implemented correctly, a knowledge management system can help your company raise customer happiness, lower customer care expenses, and boost overall customer success ROI.

While the tactical features of knowledge management systems may differ, the goal remains the same: to educate your consumers to effectively use and interact with your products or services.

You may accomplish this by using a combination of knowledge base FAQs, tutorials, academies, how-to articles, and forums.

Any aspect of a knowledge management system should contribute to addressing and educating consumers and gathering information about your products or services.

Data analytics and knowledge management (KM) are two integral elements of modern business. We depend on big data to inform our business practices and customer needs, and we need KM to cultivate an environment of transparency and insights. By using the best of evolving data strategies, you can streamline knowledge management to produce actionable information that improves business efficiency.

However, managing knowledge with data effectively isn’t always simple. The more data analysis strategies advance, the more complicated they can get. Start by exploring these changes and their impacts, then integrate these evolved strategies into your KM system.

Evolving Data Strategies and Their Impact

Knowledge management never stays the same. The amount of information you need to juggle and make visible to your organization is influenced by rapidly changing digital platforms and data sources. Without a comprehensive understanding of the latest data trends, you’ll run into problems deciphering solutions for operational efficiency.

This is especially the case when dealing with an overabundance of data. These days, massive amounts of data are collected and cycled through data processing software or stored as raw data on internal networks, never to be utilized to their full potential. To maximize the effectiveness of the data you assemble, you need to apply modern data management strategies.

Data management, as opposed to knowledge management, focuses specifically on the administrative challenge of organizing and controlling data resources. This is only one aspect of a larger KM strategy, but an essential one since most of the knowledge you store will likely revolve around data utilization. Evolving strategies in data storage can complicate matters.

Here are some of the modern data developments that are impacting the world of KM:

●      Data storage is moving to cloud systems and even blockchain technology.

●      Visualization of data through augmented and virtual reality (AR and VR).

●      Artificial intelligence tools for data monitoring, storage, and safekeeping.

These evolutions in data management all carry significant implications for any business’s knowledge approach. For example, AI is streamlining KM by enabling cognitive computing functions that explore huge data sets and connect patterns through powerful deep learning and neural network functions. The result is a living knowledge system that can improve itself.

As data processing methods like these improve, so too will the benefits that knowledge managers can bring to their business. But integrating new data strategies will take work.

Integrating New Data Strategies in Knowledge Management

You can build in the effective use of evolving data strategies into your own KM. The process involves striking a balance between your architecture, analytics, and communication tools, but by streamlining your process with modern features you’ll set yourself and your users up for greater success.

Start with your information architecture and its role in your analytics process. With the right data structure, you can integrate new tools easily and successfully. From there, it’s a matter of getting used to new systems across your organization.

Here are a few tips to help you integrate new data strategies in your KM system:

   1. Centralize your knowledge base on a cloud service. There are good reasons cloud-based data management services are gaining in popularity. With all your information in a single place, you can more easily apply the data service innovations of the modern era.

   2. Find the right knowledge tools. All kinds of comprehensive knowledge base software are out there. Modern offerings include AI-built hubs of information, containing graphics, personalized knowledge article recommendations, and more. Exploring these tools can be an effective way to streamline your own KM.

   3. Implement new visualization experiences. Understanding business data and procedure instructions can be difficult. It helps to have visualization tools. These days, AR and VR technologies are fueling new ways of looking at data and transforming the workplace as a result. Look for ways you can integrate these experiences into your own KM.

   4. Use AI. Far from being the frightening, job-ending technology you might imagine, AI can actually help streamline KM by allowing users to find information through voice, visual, and data searches. The evolution of AI features like Natural Language Processing (NLP) means your users can navigate knowledge with unprecedented convenience.

   5. Involve your whole team. Managing knowledge is a big job. The more voices and perspectives you bring into the process, the better you’ll be able to bring in evolving tech to streamline the experience. Engage your team with discussions and brainstorming sessions to help implement an effective plan.

Streamlining KM through evolving data strategies can make for a simpler and more impactful experience. Such a system can bridge understanding and connect users with better results, building a more transparent and effective business. However, you’ll need a comprehensive view of the tools and features available to you.

After thorough research and a team-generated solution, you’ll be able to apply new data tech to create all new experiences for your knowledge-seeking users. From cloud services to AI functionality, these tools in KM mean better communication, more transparent business, and more actionable insights when it comes to improving your processes.

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Knowledge management as applied in modern organizations is both a simple concept and a wide-ranging one. Definitionally, it can be boiled down to the effort to manage information and resources to improve the organization’s efficiency. But this effort can of course be applied to any number of specific issues or aspects of an organization.

In this piece, we’re going to explore the use of knowledge management for the navigation of an overabundance of information within a business.

Can There Be Too Much Data?

Any discussion on this topic needs to start with the logical question, which is whether or not there’s any such thing as an overabundance of information. Or, putting it another way, is there such a thing as having too much data for a modern company?

A lot of people might be inclined to impulsively answer in the negative. In our increasingly digital world, we’re perpetually inundated with discussions about the importance of data in business. We understand that robust data operations yield invaluable insights, and we hardly think to question whether there might be an upper limit on how much information is actually useful. The general perception is that the more data a company can produce, the deeper its insight will be — and the deeper the insight, the greater the opportunity to enhance efficiency and improve operations.

There’s a certain logical sense to this, but the truth is that there is such a thing as generating too much information, to the point that it becomes an inefficiency unto itself. As one discussion on data saturation put it, it’s actually a problem that is “everywhere” in modern business. This is because “the rapid rise in our ability to collect data hasn’t been matched by our ability to support, filter, and manage the data.” These comments were made with specific regard to marketing departments, but they do a nice job of summing up the problem more broadly. Companies that focus too much on gathering data in large quantities wind up with more than they know how to make sense of, and thus struggle to find meaningful insights.

How Knowledge Management Can Help

The most fundamental way to think about how knowledge management can be applied to this problem is to slightly tweak the definition provided above. As stated, KM is typically about the effort to manage information to improve efficiency. In this case, however, we might think of it more as the effort to make the management of information more efficient in the first place, so as to improve the quality of business insights. It’s a subtle distinction, but one that establishes a helpful way of thinking: a focus on turning the scattershot collection of information into a more targeted and productive effort.

This is something that’s easier to theorize about than to put into practice. It is actually easier, at this point, for organizations to simply cast a wide net and gather all possible information that might pertain to company performance — from internet activity, social media, internal performance, and so on. Where KM comes into play, however, is actually in narrowing that collection process to focus only on what is relevant, pertinent, and ultimately useful. It is essentially a filtration process that narrows the parameters of data collection in ways aimed at generating only the most helpful information, and avoiding excess clutter.

Naturally this is not an exact or flawless process. Some excess data without particular utility will trickle through. But by making the actual collection process more efficient, an organization can effectively apply KM as a solution to this problem.

How to Implement Knowledge Management

As mentioned, this sort of solution is easier to develop as a theory than to implement as practice. However, there are simple and strategic ways to go about the application of KM.

One is to turn to an employee or team specifically trained to handle data-related needs. For some companies, this might mean hiring data and/or analytics experts from the outside. Others, however, may find it more efficient to train internal administrators for the task at hand. Today, this sort of training is accessible via online business administration degree programs that make it easier for working professionals to study and learn new skills without having to quit their jobs. These programs prepare students for a number of different business tasks, but operations management, data research analysis, and marketing coordination are among them. Any of these specialties can help a company employee to gain expertise in data-related practices, and thus prepare said employee to direct a KM effort.

The other, similar but perhaps simpler option is to establish and train people in what are sometimes referred to as gatekeeper roles. Beyond the general definition that inspires the term, the gatekeeper concept is one more commonly associated with product development. Basically, the idea is that someone in a gatekeeper role controls the flow of information between stakeholders and project development teams, so that there isn’t excessive information or pressure moving one way or the other. And the same concept can be applied to data operations as a form of KM. Essentially, an organization can train gatekeepers to recognize what is pertinent and what is not in data collection, and thus — with relative ease — cut down on the clutter. This in turn makes an entire data operation more efficient.

In the end, the effort can be more complicated than how it is presented here. Particularly where larger organizations are concerned, data operations tend to be vast and multi-faceted. Applying KM across the board takes a thorough, effective strategy. The foundation for this strategy, however, is understanding the problem and the ways in which knowledge management can be implemented to solve it.

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Article specially written for kminstitute.org

One of the biggest challenges faced by most organizations is organizing, finding, and marking information in the knowledge repository in such a way that it is easily accessible as and when needed by the employees. The classical approach used widely is indexing documents to help users in deciding documents relevancy and retrieval. Classical methods comprise classification systems (taxonomies), thesaurus, and controlled keywords (nomenclatures) [Aitchison et al. 2004; Cleveland and Cleveland 2001; Lancaster 2003; Stock and Stock 2008].

Folksonomy is a most recent knowledge management (km) tool of web 2.0 for searching, accessing, and labelling information by the content creator and the user in a way that makes sense to them. Folksonomies include novel social dimensions of tagging [Mathes 2004; Smith 2004]. It is a new model for content indexing based on collaborative tagging with user generated keywords that broaden the spectrum of knowledge interpretation methods. Folksonomy is a valuable addition to the traditional KM methodologies since it facilitates tagging input from the end user, promote the use of active language, and most importantly allows community navigation of an organization’s knowledge base in new ways.

With the introduction of folksonomy end user is no more a passive user but an active contributor to the indexing and retrieval of content. These tags are written in common language rather than the pre-conceived formal list based on the user’s understanding of the content. The tags created by the end-users are searchable for everyone beside the interpreter-created controlled terms and the author-created text words and references (Stock, 2007). Keywords are no longer keywords now, but tags and the collection of tags used to classify content on any different platform forms a Folksonomy. This makes the content scalable and easy to find and use.

The purpose of knowledge management is to encourage collaboration for knowledge sharing and innovation by making internal knowledge available for one and all anytime and anywhere in a structured manner. There are definitely major issues in relying solely on folksonomy in the context of knowledge management. The lack of semantics connections, spelling variation, tags ambiguity, use of acronyms are some of the issues that create problems for documents only tagged with folksonomy. Using parallel indexing strategy on the other hand can create more confusion.

The key is to integrating folksonomy with traditional tagging methodology like taxonomy to knowledge discovery and sharing efficient and easier. It is the only way forward for KM 2.0 to be sustainable and successful in organization wide setting.

Coming up in next article difference between taxonomy and folksonomy... Stay tuned!

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Imagine that you are in a theater listening to an orchestra. Do you notice that all the musicians refer to the same set of music sheets to ensure that they play their instruments in sync? Just like an orchestra performance, organizations also require aligning various components so that there is a harmonious content management performance. This blog describes the elements that they both have in common.  

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