Transformation Through Insights
Organizations today can be so overwhelmed by data that it’s hard for them to know how to access or use it. Activating it can mean committing to major infrastructure upgrades or implementing cultural changes that will allow them to move out of siloed ways of working. Making that decision is not easy, but for those who can see the huge benefits of modernization, an untapped world of data is waiting to transform their business.
Based on our analysis of the report, Gartner acknowledges the impact that insight engine implementation can have and advises, in the Context section of the Magic Quadrant, that “they should never be treated as discrete, insular applications. Rather, they tap into, enhance, and extend a wide variety of other data types, sources, and systems.”
Per our understanding, the report points out that insight engines are “integral to an organization’s digital ecosystem, which is complex, extensive, and interconnected.” At Squirro we recognize this, and we have worked hard to make the onboarding process as frictionless as possible by creating tools that can be easily integrated by enterprises with any data source via pre-existing connectors. Our solutions are fast to deploy and can be used independently or integrated with enterprise software. We deliver out-of-the-box apps, and we have built-in an automatically generated 360˚ client cockpit that helps to improve decision-making. In other words, we have made it as easy as possible to use our insight engine.
Based on our understanding of the report, one key trend that was highlighted by Gartner in its Market Overview was natural language and conversational interfaces. The analyst company has seen “an evolution in the AI techniques and modalities supported by insight engines, which now often offer a broader range of natural language-related technologies such as conversational AI.”
This is something we have been incorporating into our own solutions, using natural language to fit more conversational searches. We have found that connecting various data sources, internal and external, and analyzing the contents with NLP derives considerable value, particularly among our banking and finance customers.
Looking towards the future, Gartner says that “as businesses evolve to become digital and to generate more structured and unstructured content, the need for insight engine technology to surface relevant facts, content and knowledge to stakeholders is critical.” This is true, but the key to optimizing the technology is ensuring it can be used by data scientists, business analysts, data analysts and machine learning engineers. It is these users that will shape the landscape for multiple different sectors from banking and insurance to IT services and healthcare.
To that end, we have developed our new solution, AI Studio. This allows those analysts, engineers and scientists to rapidly develop artificial intelligence and ML models through a user-friendly and visual interface. It is also structured in four steps, so it can support the full AI lifecycle.
Like so many other companies, we have experienced a year of tumultuous change, but we have made significant progress and for us, being named among respected peers in the Magic Quadrant is the icing on the cake. We won’t rest on our laurels, however, because when it comes to insight engines and the need to extract maximum value from data, there is still a lot of work to do.
*Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.