The changing face of knowledge management: How cognitive search can help

We are all familiar with the modern-day adage that “data is king,” but all rulers need systems in place to ensure that they are on the track to success. Companies have so much data at their fingertips in today’s always-on world, but unless that information is properly contextualized, it remains ineffective, almost useless.

While the expression “360-degree view of the customer” is overused, it’s hard to overstate the benefits that come from having access to all your information in one place, in the context that serves your business needs.

Ten years ago, enterprises struggled to weed through bottomless pits of data, searching for information in unstructured environments. Valuable time that could have been allocated to more meaningful, productive tasks was wasted as companies searched for a system that would allow them to conduct such tasks more efficiently. Over the course of the past decade, digitalization created an undeniable shift to bring businesses to where they are now. As companies create more varieties of content, audio, video, and big data files have filled cloud and on-premise databases, making the process of finding relevant data even more difficult for employees. According to Interact Source, 19.8% of business time – the equivalent of one day per working week – is wasted by employees searching for information to do their job effectively. Further, IDC data shows that “the knowledge worker spends about 2.5 hours per day, or roughly 30% of the workday, searching for information.” The challenges posed by searching in data silos are only increasing as enterprises accumulate more big data.

To address this situation, companies of all sizes have attempted to provide their employees with cloud-based tools to automate standard day-to-day processes and streamline the process of storing and accessing information. However, this approach only skims the surface of addressing basic organizational inefficiencies. While these tools have helped employees back up big data sets and access them remotely, they haven’t provided a streamlined way to search for relevant information. Converting to the cloud simply changed the data’s location.

The common dilemma companies face today is an inability to mitigate these obstacles to productivity, as employees search through loads of data in multiple sources to find exactly what they need to do their jobs. More likely than not, businesses have a knowledge management strategy in place to streamline processes that will cut down on unnecessary time and cost, but still haven’t found the right solution to put in place, and that’s holding them back. 

The key to solving this time and cost consumption dilemma may be in “cognitive computing,” a revolutionary approach to enterprise search. Cognitive search or insight engines combine powerful indexing technology with advanced Natural Language Processing (NLP) capabilities and machine learning algorithms in order to build an increasingly deep corpus of knowledge from which to feed relevant information and 360° views to users in real-time.

While cognitive search can be put to work for any company, the fundamental problem comes down to putting it into action and successfully executing the implementation process. Some issues in this process might arise from company executives who are reluctant to conduct a large, company-wide digital transformation project entailing employee training and education, disruption of operations, and other organizational costs. But there are ways to make the process as seamless as possible.

Start small: Focusing on influential departments

Like any experiment, the key to identifying success is by starting with a small sample size, focusing on a specific problem area. As you evaluate your business needs, identify one or two departments that are in need of tailored solutions to streamline operational efficiency. Departments that typically experience a heavy flow of content on a day-to-day basis are easy targets to prioritize, as they are likely the areas struggling the most with productivity. For example, a mid-sized customer service team could implement a new platform to study how the software fits into their workflow, smooth out any operational issues, and then provide the case study to apply to other teams. Key performance indicators (KPIs) such as cost and time savings from this specific department will paint a more detailed picture of how the platform might perform in other departments.

Gathering insights

At the end of a trial period, knowledge management leaders must ask themselves, “What insights can I gather from employees using the solution first-hand?” Collecting these employee insights will help determine whether the project promises to make lives easier and resonate with the rest of the company. After all, cognitive search is an investment.

Evaluating longevity

Adopting new technology usually begs the question: will this platform remain relevant and impactful enough to generate a strong ROI in the future? With the advancement of knowledge management tech, who’s to say a given solution won’t be replaced in a few years? That may be the case for many platforms in today’s rapidly changing IT landscape, but AI and NLP-aided cognitive search is the technology of the future and the present.

Platforms using NLP and other forms of machine learning have begun changing knowledge management as a field—today, companies are already gaining the edge on their competitors who have yet to embrace newer search practices. Tomorrow, they will find themselves ahead of the competition as they work efficiently by continuing to retrieve relevant, contextualized information and generate significant cost and time savings that can be invested back into areas of the company that need it.

What can businesses expect in future iterations?

Industry researchers in cognitive search are evaluating ways to give users more actionable information beyond the search and find process altogether. By investigating more granular human behavior, companies on the forefront of enterprise tech are developing machine learning that can trace a user’s intent while they search for particular queries. By having more in-depth insight into these granular details of human behavior, search platforms may be able to automatically and proactively present information that will be helpful to users, resulting in even greater productivity and more significant knowledge management ROI.