Actual data doesn’t always generate business value or create competitive advantage. What companies do with their data is what really moves the needle. Organizations need the power of a state-of-the-art knowledge management solution at their disposal – one that incorporates a broad range of forward-looking technologies – otherwise, they can’t exploit the value of their data profitably.
Data has been called the “oil of the future.” Data is a lot like petroleum because it only has value when it’s processed. Data becomes valuable when it is converted into information and then knowledge that is available not just to specialists but it can be easily shared across the entire organization.
As complexity increases and mountains of data grow, it’s becoming more and more difficult to accomplish that mission. We end up with what seems like a paradox – companies don’t know what they know.
Knowledge management specialists have been working for years to develop technology that would overcome this challenge. This technology was at first known primarily as “enterprise search”. Even if today, due to the significant technological progress and the fields of application, one often speaks of “insight engines”, “cognitive search” or “AI search”, they all pursue the same objective: providing users with the information they need – at the right time, in the right place/business case, and with pinpoint precision – enabling users to perform specific tasks.
The most relevant information varies for each user. It could be a customer’s address, the technical specifications for a component, or the sales figures for a product in a particular region.
In this trend analysis, we present our readers with an overview of the methods and means organizations will need to achieve competitive advantage and data profitability in 2021.
1. Gleaning information from user behavior
One of the key prerequisites for forward-looking knowledge management is the capacity to extract data from the typically hundreds and thousands of data silos scattered throughout a company and link them together to create meaningful insights. For this purpose, “connectors” have long been used to retrieve data from siloed applications and move it to a kind of metalevel, where it can be correlated and contextualized.
Today’s systems are already doing an excellent job of clearing away silos. However, if you only provide information in raw, unfiltered form, users will rummage around through mountains of data and drown in an ocean of unnecessary search results. This makes it impossible to find the answers they need to complete their tasks.
That’s why knowledge management providers are now turning their focus toward analyzing factors that are found specifically in the way information is used. In technical parlance, this is known as the behavioral model for information retrieval system design. These factors include the importance of activities, past actions taken in connection with a particular piece of information, specific search behavior, and even the emotions that users associate with information – a topic that is very strongly related to customer experience or the “experience economy.”
On the basis of behavioral analysis, today’s knowledge management systems can deliver exactly the information users need without overtaxing them. Or, to put it another way, the system personalizes the relevance of a given piece of information.
2. AI data analysis and search provides real answers to natural questions
This group of topics includes technologies that enable natural human-machine interaction, such as natural language processing (NLP), natural language question answering (NLQA), and semantic content processing.
While these factors remain crucial for acceptance and efficiency and will continue to be optimized in 2021, they are joined by the topic of “X analytics,” which in simple terms involves integrating the world of human perception into the process of information and knowledge acquisition – a method that will gain increasing momentum. According to Gartner, this includes analyzing video and image content to tackle challenges like optimizing the supply chain or providing support for medical diagnoses. Vibration and audio data analysis can be used to boost the efficiency of predictive maintenance – an example that merely scratches the surface of the plethora of other potential applications.
To put it plainly, the factors outlined above aim to digitalize the human experience, and transform knowledge management systems into real, effective partners in the workplace.
3. Weak supervision: AI without the growing pains
Until now, artificial intelligence has been plagued by a serious shortcoming: Getting an AI system up and running in a company required massive sets of training data. Gathering data together, preparing, and cleansing the data entailed a considerable amount of manual labor – and the more data there was, the more complex that task became.
The “weak supervision” method takes a new approach: AI learns on its own using data sets that already exist in the company, and which traditionally had to be entered manually and possibly also classified.
Once the process of machine learning or deep learning is under way, the accuracy improves continuously thanks to the feedback effect. After two or three months of operation, the system can be switched over entirely to fully automated processing or so-called dark processing – and it can all be done with a drastically reduced training phase or even with no training at all.
At the same time, and as sort of an extension to dark processing, knowledge management solution vendors are placing greater emphasis on the trend of explainable AI (XAI) in an effort to make certain decisions made by artificial intelligence traceable and comprehensible. Given the fact that this can only be achieved to a limited extent using highly complex models, the journey in 2021 will head squarely in the direction of simplification.
4. The 360-degree view goes mainstream
Instead of having to collect the data and information you need on your own to accomplish a particular task, holistic information processing is the new standard. This applies every bit as much to the service mechanic as it does to the CEO who relies on a 360-degree view to make sound decisions quickly.
The merging of the worlds of business intelligence (BI) and enterprise search is an outward indication of this development, and we’re seeing more and more vendors entering into partnerships or acquiring the other party.
Illustration: 360 Degree View
5. Distributed cloud and hybrid cloud: The cloud comes to the user
Gartner reports that cloud services are increasingly spread across multiple locations, although operation, management, and development remain the responsibility of the public cloud provider. Market researchers describe this scenario as “distributed cloud.”
Alongside the overall hybrid cloud trend, distributed cloud computing is designed to be used wherever low latency, low data costs, or laws dictating that data must remain in a specific geographic area prevail. This also means that organizations continue to reap the benefits of the public cloud rather than managing their own private cloud, which can be both costly and complex.
This will be relevant to the knowledge management of the future to the extent that appropriate solutions are available as appliances in a company’s own data center and provide their services to employees around the world from there. At the same time, smart services are also available as dedicated cloud solutions also known as Software as a Service (SaaS). This means that the advantages of distributed cloud solutions outlined above are significant in both scenarios.
6. Hyperautomation: AI for business process transformation
As Gartner puts it, hyperautomation is nothing more than the idea that everything that can be automated in an organization should be automated. The underlying aim is not to slash the number of employees, but rather to liberate them from tedious work routines – “busy work”– so that their activities can be more fully integrated into the company’s value creation processes.
The term “hyperautomation” also implies optimizing or upgrading traditional business processes with the goal of enhancing flexibility and agility. The global pandemic has clearly underscored how essential these capabilities are. In 2021, AI will support the transformation of business processes to an even greater degree.
At the same time, this development is also a means of discovering and realizing new business areas. However, that can only be achieved when companies have a solid foundation in knowledge management.
The bottom line: Connecting the dots
Knowledge, which companies tend to have in ample supply, can only generate value if it is available at the right time, in the right place, and in the right form – that is, as concisely as possible – for every employee who needs it to carry out their work. The motto “connecting the dots” is thus the foundation for the knowledge management of the future, which unites a multitude of advanced technologies under a single umbrella and makes them available to users in a format that is as natural and as seamlessly integrated as possible.