Collaborative Problem Solving, Reimagined
Hello readers, welcome to our first company blog on Medium!
Collaborative problem solving, reimagined. That’s what best describes the motivation and activities of our startup smartQED. To understand what we do and why, let’s start from the beginning. First, we need to examine how people solve problems today.
Complex problems arise in many domains, such as in Enterprise IT systems, electro-mechanical equipments, aircrafts and automobiles, financial systems and others. Solving such problems often needs the involvement of multiple subject matter experts (SMEs), who might be working from various locations. Given the prevalence of global teams that keep IT systems running smoothly 24x7, teams are quite likely to be distributed. This is also most likely to be the case currently as a result of the coronavirus pandemic.
So, how do operations teams in IT and other domains investigate and solve problems today? There are four essential steps:
Recording of the observations, conclusions and solutions for the problem is generally done using text-based notes in incident tickets, chats or emails for team collaboration and/or in knowledge articles for sharing & reuse. In future if a similar problem occurs, often the first step is to search for earlier documented incidents and knowledge articles and see how the problem had been solved then.
Locating and reading text-based tickets and articles through keyword-based searches is highly inefficient, as the entire information has to be read and understood in order to establish relevance. Furthermore, search keywords are highly person-dependent, and the tickets or articles have to first found & then applied in order to be of any use. It is particularly difficult for new or junior members in the team, who are overloaded with too much information to read and remember.
Now, our motto at smartQED is:
To do that, we are convinced that unstructured text in chats, notes, emails and knowledge articles is not the best way to communicate or collaborate while solving problems, or for learning. This observation comes from our many years of Enterprise IT work, where we have faced these issues first hand.
To address this effectively in smartQED, we have introduced the concept of visual Investigation Maps™, which can depict a hierarchy of potential causes for a problem, and enable teams to record various pieces of information concurrently in the context of a cause. For example, evidence (e.g., the symptoms or lack thereof) for determining the fault outcome of a potential cause can be attached to it for evidence-based analysis and decision making.
Additionally, our powerful Machine Learning algorithms can automatically analyze the Investigation Maps of problems solved earlier to make recommendations for future problems, based on their observed symptoms.
With its highly concurrent visual Investigation Maps and precise ML-powered recommendations that provide good starting points, smartQED streamlines and greatly speeds up problem-solving by teams. Benefits are most significant when problems are complex, with many potential causes, and investigation teams are distributed across different locations and time zones.
How can our users best leverage our collaborative Investigation Maps that continuously self-learn, and reap their immediate and longer-term benefits? This will be the subject of our next article here… stay tuned!
If you are interested in learning more please visit our website https://smartqed.ai or try our live app at https://app.smartqed.ai
Let’s all be safe and win together!
