ROKITT

With decades of hands-on experience in the financial services technology space, ROKITT CEO Oksana Sokolovsky and CTO Rohit Mahajan understood that data discovery and data management are huge and costly challenges for companies. As enterprise systems grow, a company is increasingly challenged to understand its data assets and how they are related. This can create significant problems, ranging from the ability to confidently execute data analysis to modernizing legacy systems to address increasingly complex regulatory issues. “We understand that enterprises are constantly changing and data is growing and becoming more distributed. We realized it’s hard to keep up with data and discover intricacies of the data relationships and that there wasn’t a single product that would do an outstanding job at solving this problem,” notes Mahajan.

This insight was the duo’s impetus to develop ROKITT ASTRA product, which automatically discovers ‘hidden’ and undocumented data elements in structured data, traces their relationships and how information flows across an entire enterprise. Using proven scalable technologies, ROKITT automates this process thus removing process inefficiencies and creating a reliable way for organization to discover and maintain its data blueprint.

ROKITT ASTRA uses machine learning algorithms to discover the implied and often unknown data relationships that are not contained in a company’s metadata. Data changes, moves and grows making it difficult for enterprises to keep metadata up to date. Discovering data relationships within a database and across databases is very time consuming for enterprises. ROKITT ASTRA was designed to address both data growth and data management challenges that data intensive companies have been plagued with for decades. “Companies can now quickly and cost-effectively improve their understanding of their data assets across their businesses, often consisting of undocumented legacy or siloed systems,” says Oksana.

ROKITT ASTRA is a self-learning system that continually improves its performance, including accepting input from SMEs within a company. It has the capability to recognize implied dependencies between values in a database. For example, one column in a table can reference data in different columns in another table.

It learns that there are two tables with columns containing related information. This can produce useful data that otherwise would not have been obvious.

With more complete information, companies can make better decisions that have greater impact on the bottom line. Additionally, “we see ROKITT ASTRA being used across SDLC—business analysts, architects and testers can utilize ROKITT ASTRA to plan and execute complex technology projects,” notes Oksana. Customers look to use ROKITT ASTRA as part of impact analysis. “Customers migrating onto the cloud or going through re-platforming efforts can leverage ROKITT ASTRA to assure proper impact analysis is completed prior to migration, to identify redundant data, to identify data dependencies and to identify operational/transactional data. Customers who are moving to data lakes or are trying to understand what is in their lake can leverage ROKITT ASTRA to figure out data relationships automatically.”

Companies can now quickly and cost-effectively improve their understanding of their data assets across their businesses, often consisting of undocumented legacy systems

Due to the many benefits of automating, discovering and connecting related pieces of data across an enterprise, ROKITT ASTRA produces a very positive ROI for customers. ROKITT also provides responsive implementation, professional services, training, and support to get customers onboarded quickly and easily. Says Sokolovsky, “Our mission is to help our clients solve their most challenging data discovery problems, and we measure our success on the high level of impact we have with our clients.”