Why We Built Noogata

Delivering meaningful results from AI and data at enterprises is hard. It requires a lot of hard data science and engineering skills just to get started. But does it have to be that way? At Noogata, we don’t think so. We believe that, with a new approach, we can make AI and data more accessible and more practical for enterprises looking to achieve fast results.

We know that enterprises need to roll out AI across all business functions – it is the next evolution of business intelligence. But the internal data science team can never support the scale that this requires both in terms of their skills and capacity. This needs a new approach, which we are taking. I’d like to share here why we believe the market is ready for a new way to deliver on the promise of AI at scale – and the thinking that went into developing our approach.

Introducing Noogata and enterprise data science

We call what we do enterprise data science. Fundamentally, we are democratizing AI and bringing its capabilities into the hands of those who can most benefit from it. Our approach is easily customizable and simply integrated into internal systems. Enterprise data science leverages existing best-in-class models and is based on standard datasets across a given industry.

Enterprise data science lets companies comprehensively inject AI across their business for the huge number of use cases that exist. It is about integrating data and augmenting existing workflows and systems with additional AI intelligence. It is about easily customizing a solution’s pipeline to fit the unique enterprise dataset, without rethinking every aspect of the modeling inside. The output of the models — predictions and recommendations — can be easily streamlined to the business users, wherever they make their decisions. And finally, the entire end-to-end process can be done rapidly and across many of the fundamental use-cases across any domain. These could be anything from keyword recommendations for eCommerce teams, to yield  and demand forecasting for operations teams, cross-sell opportunities and churn risk for sales. All available as data or integrated into the team’s work systems (CRM, Service Desk, BI dashboards, and many more)

Ready, set, scale

At Noogata, we believe the time is right to deliver on the promise of AI at scale. The building blocks are in place for this to happen.

For a start, the right AI models are already out here. For many use-cases, developing new models is not the barrier. Models for demand forecasting, personalization, clustering, or text analysis are well understood and already constructed for a wide variety of these use-cases. The challenge is how to apply the best-in-class models in a simple and practical way.

Secondly, enterprise data is coming. Data has become more organized and more easily available at the enterprise level. Advances in-cloud-based data warehouse and data lake solutions (BigQuery, RedShift, Synapse, Snowflake, Databricks), new generation ETL/ELT tools (Fivetran, Airbyte, dbt), self-serve analytics and others across the stack are making it easier for enterprise data teams to build, manage and become the organization’s data custodians, making the data available for use and leverage across the organization.

Finally, we believe the no-code paradigm offers the right approach to scaling AI. A good no-code framework allows non-technical people to build without coding, creating end-to-end solutions rapidly without developing. This promising transformation is already taking the enterprise software industry by storm.

Our own no-code AI platform is composed of libraries of modular AI blocks that perform specific functions around data and are combined to create full data pipelines. Blocks are built specific to a use-case and business context, with well-defined data inputs and outputs. They take care of the entire data journey from collecting, enriching, and modelling to providing recommendations and predictions for teams across marketing, sales, finance and operations. Data teams can easily combine and expand these blocks, leveraging their modularity to handle the more unique use cases of the enterprise.

No-code AI enables enterprises to move from being AI laboratories with POCs to AI factories with actual production because:

• it is particularly good at scaling integration issues
• it allows for customization and modularity without development
• it allows enterprises to build full end-to-end action-focused experiences for the business-user from gathering data to converting outputs of models into the organizational flow (smart automation of existing processes or dashboards)
• and it allows users to iterate super-fast but also scale once they have something they like.

Our mission is to help enterprises achieve meaningful, reliable, and thoroughly scalable impact from data and AI across all relevant business units and functions in the fastest and most practical way possible. We are passionate about delivering on this mission and will continue to share our thoughts and progress along our journey!