Angoss by sennchi

Predictive Analytics And Machine Learning Solutions

Angoss is ready to be your primary solution. Angoss KnowledgeseeKer is a must-have for data science teams that wish to use beautiful and comprehensive visual tools to build decision and strategy trees. that’s what Angoss is known for, but its portfolio is actually very well-
rounded: it also offers KnowledgestUDio for building models, insightoptiMiZer for numerical optimization, and KnowledgeMAnAGer for model management. Angoss recently added a coding environment that allows data scientists to use programming languages including r, python, and the language of sAs. it also has some integration with Hadoop and Apache spark. Angoss has impressive adoption for a small company, but its key challenge is to create market awareness in an increasingly crowded field of data science startups.

Angoss , which is based in Toronto, Canada, was acquired by Datawatch in January 2018. It still appears as Angoss in this document due to the acquisition's lateness, relative to the Magic Quadrant process, and uncertain impact. This evaluation covers the following products: KnowledgeSEEKER, the company's most basic offering, aimed at citizen data scientists in a desktop context; KnowledgeSTUDIO, which includes many more models and capabilities than KnowledgeSEEKER; and the newly launched KnowledgeENTERPRISE, a flagship product that includes the full range of capabilities.

Angoss has lengthy experience with banking customers. This underpins its ability to deliver to the banking sector and other sectors with similar data and analytical needs, such as insurance, transportation and utilities. Angoss has loyal customers, but remains a Niche Player as it is still perceived as a vendor for desktop environments. It recently added a series of enterprise functionalities to the platform (namely model management, cloud and open-source functionalities), but too recently for evaluation in this Magic Quadrant. Angoss focuses on nonindustrial clients and use cases.

STRENGTHS

• **Ease of use and well-rounded functionality: **The user-friendliness and ease of use of the company's core products, KnowledgeSEEKER and KnowledgeSTUDIO, earned Angoss solid scores for most of the critical capabilities assessed.

• **Some strong features: **Angoss has significantly improved its open-source support by allowing users to integrate some cutting-edge capabilities into its visual composition framework (Spark ML, TensorFlow and H2O, for example). In terms of deployment, Angoss has better-than-average capabilities to export models into SAS, SQL, Predictive Model Markup Language (PMML) and, partially, into Java.

• **Integration of other advanced analytics features: **Angoss has a good breadth of tightly integrated optimization capabilities (linear and nonlinear constrained optimization, for example) and text analytics (via an OEM relationship with Lexalytics). In addition, Angoss enables model training to be conducted seamlessly either on-premises or in the cloud.

CAUTIONS

• **Market traction: **With over 20 years' experience, Angoss is an industry veteran, but its market traction should be stronger than it is. With just over 300 loyal clients, the company's user community remains modest. Angoss' reputation is still that of an easy-to-use desktop tool vendor, even though many customers use Angoss' server environment. Without significant marketing and sales efforts, it remains to be seen whether the company can materially accelerate adoption of its technology.

• **Customer concerns: **Our survey of reference customers shows that overall satisfaction with Angoss' platform is moderate, but some reference customers had concerns about aspects of product stability, flexibility and data access capability. We expect the company to address these concerns in upcoming releases.

• **Innovation speed: **Angoss is lagging behind in a few important respects: for example, it has yet to embrace the trend for automating core machine-learning processing, and it has limited traction in the AI service market. Its upcoming model factory capabilities could help remedy these shortcomings