Principles We Live By: Data Democratization and Data Standardization

Principles We Live By: Data Democratization and Data Standardization

Data is the most important raw material of AI-driven intelligence. The more data we access, the more robust deep learning modules we build. In the last decade, the value of data multiplied and surpassed oil as the most valuable resource. The popular premise is that data in the 21st-century digital economy is what oil was in the 19th-century industrial economy. The ones who acquire the most have a greater advantage in shaping the world economy and the course of the near future.

The growing complexity of our times means much more data is actually in need to make a solid decision and lead innovation in the right path. However, access to big data sets remains limited to few to make the data distribution issue a problematic matter. Big tech has easy access to big data sets thanks to their politics and the millions of users using their product. But small size AI-labs and start-ups in the beginning stage still have to fight for their way to access data sets.

As Algomedicus, we stand by principles of data democratization and data standardization.

Data democratization means that data should be accessible to anyone who looks for it regardless of their technical knowledge and influence sphere. Democratization fo data is crucial to equalize the infrastructure strength of big and small size tech companies. For societal improvement, it is the only way to raise informed and aware generations. In business life, data democratization enables self-service analytics and paves the way for data-literate society to emerge. Data analytics is a new language of the digital world, and the ones who become literate sooner will belong to the digital community quicker.

Related to the problem of data democratization is the problem of not knowing whether or not you can trust it. Some sets are compiled in a different format and need to be tailored to group well with other collections. We pay the utmost attention in our work to standardize data and make all modules have consistent algorithms. Data standardization enhances the technological infrastructure of the services and improves the performance of the machine learning system. In B2B sales, data standardization allows companies to communicate better. Workable data standards should be established in all AI-labs to make the best of their commodity.

Clive Humby, a UK mathemetician and a pioneer in data science, is the first to mention that data is like oil as a resource. He said, “Data is the new oil. It’s valuable, but if unrefined it cannot really be used. It has to be changed into gas, plastic, chemicals, etc. to create a valuable entity that drives profitable activity; so must data be broken down, analyzed for it to have value.”

The uneven distribution of oil resources was the most critical reason for the systematic shortcomings of the world economy. We now have a new chance to maintain even distribution of resources and allow everyone to lead innovation. Once refined, data will enable new kinds of knowledge to be extracted. For this distribution to flow in the right direction, we have to get our principles straight and operate ethically and carefully. Data democratization and standardization are the principles needed not to make the mistakes of 19th-century politics in the digital age.