How Third-Party Data Can Enhance Analytics and ML

How Third-Party Data Can Enhance Analytics and ML

What is third-party data enrichment? Discovery the importance of enriching internal and external data overtime to produce meaningful information with greater relevance. Turning third-party web traffic and social media data into valuable corporate insight is possible with the ForePaaS Platform.


External third-party data coming in from the other side of the wall (not a barrier as in Game of Thrones, but a fence marking the company’s IT assets) are now essential. Organizations will gradually use external data as naturally as they use internal data today to make informed decisions. This mix between internal operational data and external real-time data is invaluable to avoid a blinkered view of your business and open a wealth of new possibilities.

It isn’t easy to adjust or sustain predictive models without this data mix. For example, poor weather conditions can turn roads into a swamp on a construction site, which slows down truck movements and increases energy consumption. Customer reviews, school holidays, and the weather (again!) can influence box office sales in a movie theater. We could provide countless examples, but they all point towards the same challenges. These data challenges can quickly be addressed in the following ways.


How to incorporate third-party data


External real-time third-party data is intrinsically scattered, fragmented, and technically varied. However, organizations can incorporate this external real-time data while keeping costs under control and performing tests with short turnaround times. The keyword here is agility.

Quickly set up a test environment, capture data according to available methods (import, API, etc.), cleanse the data for more significant ingestion, and store in a model matching the type of data for subsequent exploitation… in a conventional environment – i.e., where no applications can be deployed without first issuing detailed specifications – stacking up these tasks soon leads to a tunnel effect. Only Analytics or Machine Learning as a service approach can shorten the timeline and quickly provide the means for detecting correlations. These approaches are driven by agile methods and highly automated modern machine learning and data platforms, of which ForePaaS is a perfect illustration. The aim is to spare users from all the complexity of assembling the stack (the required software components) to concentrate on the essentials, namely interpreting the data for business insights.


How to share valuable insight


How exactly do you determine the value of the contribution made by third-party data? The answer is simple: by ensuring that they are effectively circulated. It may be hard to keep one point of view in mind without knowing precisely what you are looking for, especially since the relevance of a given data set may only be revealed at the local scale. A mass retail company looking to use new external data to improve the performance of its different sections may struggle if it chooses to ignore feedback from its section managers.

Experience has proven that unexpected practices arise on the front lines. To be used effectively, data must circulate throughout the company, which means avoiding the belief that data decentralization is a simple concept. In practice, decentralizing data is an integral part of a Machine Learning or Analytics as a service strategy.

However, such good intentions may sometimes clash with the very nature of modern data platforms when billing is based on the number of users. That is one of the reasons why we have not chosen this model since we believe that it goes against the natural grain: involving the right users in a data project increases its value. The idea is to foster, and not hinder, such synergy.


How to turn data into a service


External data, especially open data, should not be considered a “gift.” It is a rendered service that invokes another service in return. The aim is to be transparent about where data come from and commit to the value of the resulting benefit.

An insurance company collecting healthcare data from its policy-holders is required to explain the reasons behind its project and specify which new service (for the customer) will benefit from the data, not only for regulatory reasons but to obtain consent from the customer that is worth the paper it is written on. Explaining the purpose of collecting data is also the best guarantee of getting high-quality data. The critical question in both the B2B and B2C markets is how to return third-party data as a service to the people submitting their information?

As we have seen, the key to a successful transformation is the ability to incorporate and correlate the information with other data sets and display or easily incorporate them into applications. This is the natural evolution of data applications: enriching internal and external data overtime to produce meaningful information with greater relevance. There is no need to gaze into a crystal ball to realize that it will become more and more difficult in the future to tell the difference between both types. In the age of big data, advanced analytics, and machine learning, the dividing line between different data types will become increasingly blurred.


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