Alternative data products are being driven by customers who don't have experience with the product and by data providers who don't understand their customers' KPIs.
Success in the alternative data market requires distinctive products created by experts with feedback from their customers (not the other way around). CogniSent works with alternative data providers to build a comprehensive product strategy using a four-step proven process that supports a solid business case.
At the base of every effective strategic plan is a solid understanding of the our clients' products & KPIs.
Once we have analyzed a client's products, we apply our “data alchemy” machine learning process which groups your unstructured data into industry, sector, company, and/or product clusters laying the framework for your go-to-market product strategy.
By grouping datasets, our clients can create thier own index products derived from the above segmentation.
Now in a usuable format , the data is fed into CogniSent's cognitive data analytics engine (a combination of AI, Machine Learning and a proprietary research methodology) which identifies investment themes and KPIs across 20 million Web resources and matches your data against them.
Phase I culminates with a data product that is immediately usable by data scientists.
Depending on the client & industry, some clients will exit this phase and choose to immediatley deploy their dataset as a standalone product. Others choose to enrich their data product with third-party datasets in order to include additional KPIs that round out a more comprehensive offering to their clients.
Regardles of the direction they choose, CogniSent continues to guide them through the process by leveraging our deep industry relationships and expertise in order to make sales but equally as important, to gain valuable feedback.