Insight engines: What really matters

From classifying documents, linking and preparing information from in-house and external data sources intelligently, to displaying assets and complex processes – the demands on today’s intelligent knowledge management solutions are immense. Using methods derived from artificial intelligence also empowers users to answer specific questions at the touch of a button, speeding up decision-making processes.

"Businesses today are no longer faced with the question of whether they need a modern knowledge management solution," explains Daniel Fallmann, founder and CEO of Mindbreeze. "Based on countless conversations we’ve had with corporate decision-makers, we know that virtually every business is utilizing multiple processes that can achieve dramatic gains in efficiency with the help of an insight engine. Instead, executives are faced with the challenge of finding an adequate solution that can generate long-term value across a range of business cases."

Analyst firm Gartner lists a number of criteria in its Magic Quadrant that define an insight engine as such. These include the ability to incorporate major data sources, support for data enrichment, delivering the results to multiple touchpoints, assessing and reconciling relevance, embedding security features, and offering flexibility in query input.

Gartner’s definition holds true for a lot of solutions, but when it comes to finding a suitable match during the evaluation process, what should companies specifically be on the lookout for? The experts at Mindbreeze have compiled a number of aspects that are particularly crucial for the corporate world to guide the decision-making process:

1. Test the products with in-house corporate data
   At the outset, the focus should be on clarifying how flexible the solution really is. Is it applicable for a variety of application scenarios? Example: Highly flexible solutions enable process improvements in virtually every department, including customer service, sales, marketing, research and development, as well as production and maintenance – and this is exactly where the next question comes in: Can the solution be rolled out to other business cases rapidly and easily to improve the ROI even further?
    
2. Data analysis – where the data is actually located
   Decisions about the system architecture and how to implement it will set the course that determines how the solution will be used later on. Not every vendor's solution covers every method of implementation. With this in mind, it is wise to clarify the following fundamental questions upfront: Which solution matches your company’s existing IT environment? And which solution aligns with your long-term IT strategy? When considering the answers, it is worth bearing in mind that CIOs often pursue varying strategies for core systems in line with the business model and the compliance requirements, such as a cloud-first strategy, a hybrid strategy, or hosting on-premises.
    
3. Personalization and the customer journey
   When it comes to personalization the key is understanding how user-friendly the solution is from the perspective of each department. This is where search app designers come in: Can the solution be readily tailored to specific business cases and, beyond that, to the requirements of individual users? Another important decision has to do with the topic of no-code: Users should have the option of customizing user interfaces themselves wherever appropriate, without any special intervention on the part of IT.
    
4. Efficient dialog is the foundation
   Providers should also design the interface to maximize user-friendliness. Using natural language processing (NLP) and natural language question answering (NLQA), users can ask questions in natural language, enabling them to search for answers without having to rack their brains as they enter them. Based on how the results are broken down, the solution should be capable of displaying results graphically, using tools such as 360-degree views and network diagrams.