Ovum SWOT Assessment: Why consider Onedot Platform?

Onedot started catering to the needs of an emerging market, data prep-as-a-service instead of self-service data preparation. This means that customers do not have to set up, configure or operate a technical tool, and on top, rely on comprehensive coaching from the software provider. Customers can get data processed easily by Onedot and give feedback to any processing results.

Ovum already put Onedot on the radar in early 2017 and now run a complete SWOT (strengths, weaknesses, opportunities, threats) assessment in August 2018. The Ovum assessment revealed that the data preparation problem is particularly pronounced in commerce industry. Businesses need to accelerate time to results and automate product information management (PIM).

Headquartered in London, Ovum provides expert analysis and insights into the technology, media and telecommunications industries. Ovum’s independent 180 analysts are located in 23 offices across six continents and offer worldwide expert analysis and strategic insights. These reports are recognised as a respected source of guidance for technology business leaders, CIOs, service providers, vendors and regulators looking for comprehensive and accurate market data. Thousands of clients worldwide access through Ovum comprehensive workflow tools, forecasts, surveys, market assessments, technology audits, and opinions.

The Ovum conducted Onedot SWOT assessment explored key aspects of the data preparation business. Ovum describes Onedot's specialisation in preparing complex data like this: “The company has developed numerous proprietary machine learning algorithms that train on customer-provided data and learn from customer feedback.”Ovum assessed that:

- Onedot is ideal for organisations that have time-consuming and complex data prep challenges

- Onedot is focused largely on solving the data quality and standardisation challenges associated with product data

- Onedot’s proprietary machine learning algorithms require fewer data and fewer training iterations than competing models.