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KM: looking to the future

This article appears in the issue January/February 2018 [Volume 27, Issue 1]
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Mature areas of knowledge management such as enterprise search, collaboration, business process management (BPM), business intelligence (BI) and enterprise content management (ECM) have reached a critical mass. Simply put, organizations need to capture, store, share and analyze information to carry out their business activities, and those requirements will continue to drive the market. To pick just one, the ECM market is estimated to be anywhere from a low of $5 billion to $28 billion (depending on what is included), with projections of up to $66 billion within the next three years.

Other sectors are equally vibrant. Ongoing mandates for compliance and records management will continue to sustain the demand for KM-related technologies. Automating processes through BPM is saving companies time and money. Virtually every technology that is encompassed by knowledge management is vital to an organization’s digital transformation in one way or another. Those well-established and reliable tools can be expected to find broad and consistent use as organizations make their way through this process.

Meanwhile, new approaches will extend the capabilities of many of those established technologies. For example, artificial intelligence (AI) is being embedded in an increasing number of applications. New tools and techniques for validating information are emerging as concerns grow about ensuring that the increasing volume of information is accurate and verifiable. Some interesting solutions such as customer data platforms are enabling marketers to make better use of information that is scattered throughout the enterprise and beyond. But with all the advances in technology, what tops the concerns of CEOs is talent management—attracting and retaining workers for the knowledge age. Knowledge management can help that challenge as well.

AI reinvents itself

Artificial intelligence has been around for decades, but has undergone a resurgence as computing power has increased and techniques for embedding knowledge in many different processes have improved. Cognitive computing, machine learning and natural language processing form a cluster of technologies and techniques that are enabling advances in many applications of knowledge management. They are helping software solutions advance both in the areas where computers have the edge, such as processing large volumes of data, and in areas where the skills of humans excel.

Search software, for example, is increasingly using machine learning and natural language processing to extract insights from both structured and unstructured information. Machine learning allows the software to continuously improve relevancy, and the natural language processing lets humans ask questions and receive answers in ways that make the information more meaningful.

Virtual assistants, including chatbots, are being used for email filtering, tech support, customer service and booking appointments. When they are used to handle initial contact from customers, virtual assistants can resolve basic issues in specific categories. They can escalate those issues that are beyond their areas of expertise. Facebook, Google and Microsoft now allow chatbot developers to access their messaging platforms. Those intelligent assistants require well-structured information that has an associated ontology that defines relationships among different data elements.

Although experts agree that AI applications are not close to the point where they can simulate human behavior across a broad range of topics and actions, they can be a first line of defense if the domain is relatively narrow and the number or issues is finite. Over time, more and more intelligence will be embedded in routine interactions.

And nothing but the truth

Big data’s “volume, velocity and variety” are often spoken of but the fourth one may soon get more attention. That’s “veracity.” Whether people are receiving information from an email, a database or a newsfeed, they are increasingly concerned about its reliability. Is the email a legitimate message from their bank or a phishing expedition? Is the newsflash a real alert or fake news?

Users of knowledge management solutions are in a good position to aggregate and analyze information that originates from many sources to address such concerns. Business intelligence companies have been doing that for a long time—for example, detecting fraud based on divergence from expected norms. Through the use of text analytics, big data can go beyond that to reveal a great deal about the reliability of both structured and unstructured information.

Knowledge management can also offer an improved approach to validating information security, a top concern in government and industry. Effective cybersecurity requires multiple security software solutions (see “Information Security: It Takes an Ecosystem,”). In combination with other data, an earlier or more accurate picture of imminent threats could be developed. Although it is not widely done yet, some researchers are exploring issues relating to integrating external knowledge with cybersecurity data warehouses. The concepts and practices of knowledge management can facilitate those efforts.

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