Data collection in the age of privacy and machine learning

Data collection has become quite indispensable in the recent years with the burgeoning usage of deep learning. Most of the deep learning architectures require large amounts of data and hence the importance of data collection has become very prominent.

Companies that newly take up Machine Learning make sure that they have sufficient proportion of labelled training data. These firms put in so much economic emphasis on data and data pipelines that generate essential features. Data is essentially required to tackle the applications being used by the firm.

Firms look for additional data sources in order to augment them with the existing data. All practitioners place the value of data above all other entities and most of the practitioners are highly reluctant to divulge the features they feed into the model although they open up about the model they use. Estimating the cost of acquiring training data is very important for enterprises that build machine learning models.

Subscription services offered by the websites have become very common these days. Existing data set has to be augmented with external data sources. Bloomberg, Nielsen, Dun & Bradstreet and a very recent addition like Planet Labs furnish such subscription services.

The demand for services like Figure Eight and Mighty AI has soared high in the recent years with the advent of data feeding techniques like deep learning. Building training data sets from the scratch is pretty costly.

Data types can be in different forms. For instance, images are specific data types and are dealt by companies like Neuromation, DataGen, and AI.Reverie. These firms make sure that training data costs are lower with the aid of tools for generating synthetic data.

There are multiple significant ways to glean the value of data. One such way is the estimation of startups that are primarily known for their data sets. There are various new firms that collect data like aerial imagery, in-game sports data, weather data and logistics data among other such entities.

New start-ups can be built by focusing on the collection of data. The existing data products will be influenced by new data sources that make their advent. Data scientists and data engineers try to measure the impact of these new data sources and consequently map out the value of data.

Data privacy has grown to be one of the most primarily talked about issues when it comes to data collection and more. Relying on external sources of data can pose various risks to the existing set of data.

The present supply of data can be cut off any time due to security breach and other such technical reasons. This is principally due to the increase in scrutiny revolving around data collection and data privacy. The loss of access to data can be detrimental to a company’s market.

Lately, the approach to data privacy has changed with increasing awareness amongst companies and general public. User expectations have shifted towards the type of data being collected by the big-wigs like Facebook and Cambridge Analytica. Customers demand better transparency and control over the data that is shared.