Companies from all sectors, not least investment management, are finding immense value and insight from the systematic analysis of data and are recognising the competitive risks of falling behind in this area.
The challenge is that while data analysis requires datasets which are standardised, organised, inter-linked, computationally searchable and as free from error as possible, most data in the world is not like that.
Most data is unstructured: in text documents from news articles and company reports to emails, blogs and social media posts; or in other formats like images, videos and so on. Of the data that is structured, much is riddled with outliers or gaps; and it’s always a challenge to link different datasets together accurately.
These sources and processes are extremely hard to deal with in a purely computational way. As a result, unique investment insights remain undiscovered, operations teams get swamped with data maintenance, and time is wasted by specialist investment professionals on what are often trivial data wrangling tasks.
Hivemind provides software to help companies deal with data quality, data mapping and unstructured data sources in a systematic, flexible and practical way.
Seemingly intractable unstructured data problems can be broken down into chains of simple tasks to be completed either through automated methods, or distributed to appropriate groups of human contributors. The results are aggregated – using independent samples to assure data quality – to produce a structured dataset ready for analysis.
We believe a combined man and machine approach is the most efficient way to turn messy unstructured data into valuable structured data assets that can drive new investment insights.
Hivemind can transform a number of important areas of a modern investment management organisation. Our clients use Hivemind to:
efficiently collect new and unique research data from primary sources;
build systematic data cleaning processes;
lift manual data tasks out of operations teams;
map datasets together to make them more useful;
create high quality training data for machine learning applications;
aggregate expert opinion to predict the likelihood of future outcomes.
Across all use cases, Hivemind cuts costs by minimising the time spent by specialist staff on tedious, repetitive data tasks. By splitting the work into simple tasks and routing them flexibly, we ensure automated methods can be used wherever possible while tasks requiring human judgement are sent to people with the appropriate level of skill.
If you would like to see a demo, discuss a trial of our software, or you are just curious if Hivemind can help your organisation, please get in touch via hvmd.io/contact.