The Rise of Small Data and Private AI Cloud Solutions
Despite the hype surrounding Big Data, small data is proving to be just as powerful.
Many industries are embracing AI and machine learning to increase efficiency and reduce manual processes.
Big Data vs. Small Data
"Big data" refers to all of the data collected by the system through transactions, internet activity, emails, and other sources.
Machine learning models are trained with large amounts of data to store in a dataset. The more data these models receive, the better they will perform.
Traditionally, this has required millions of records to get good results.
For certain types of machine learning such as Natural Language Processing (NLP) algorithms have improved and less data is required to get accurate results.
Small datasets may just require a few dozen or hundreds of records rather than millions, but the information will be relevant to the company and it will be their private data.
Developing a machine learning system and feeding it a small dataset will yield contextual results that are easy to manage and valuable to the company.
We believe small data is superior to big data, for two reasons: privacy and better results.
Many big tech companies offer API solutions for companies to use their AI technology as a service. There are a huge number of tools available on-demand.
For example, in the pharma industry, Natural Language Processing can be used to discover general medical terms such as dosage or symptoms.
However, all this data is being sent to another company’s data model.
Many regulated businesses such as pharmaceuticals and financial services maintain their AI and machine learning data in a private cloud.
When using a private AI system, the data is not shared with anyone outside the organization and the AI actions are unique based on specific company data.
Getting insights from your own data, even if your dataset is relatively small, is much more valuable for things like recommendations.
For example, life science companies can use AI to maintain private datasets which are used to classify responses to regulators' letters, categorize documents into groups, and rapidly discover approved product claims for use in commercial material. It works better when it’s your own data.
These use cases are more valuable to a company because they can assist them with their specific responsibilities and keep their data safe.
While vendor API solutions may be faster to implement, they are expensive at scale.
It is more cost-effective in the long term to develop an in-house solution using open source frameworks and algorithms.
The machine learning recommendation results are nearly identical at a fraction of the cost.
Developing an AI solution with small datasets is less expensive, keeps your data private and results in more valuable recommendations for the company.
At Papercurve, we decided to build our own in-house AI solution.
We believe in reducing manual processes and getting insights from your data while keeping information private.
To make a compelling business case, the solution also needs to be affordable.
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