Fueling business analytics with Open- Source Data

In today’s global economy heterogeneous collections of Big Data is one of the most valuable assets of an organization. This data is typically collected from user interaction with web-based services which an organization has to offer. For others, it might be a collection of sensory data collected from machines and vehicles. This data is then curated in the cloud where it can be utilized by artificial intelligence modeling, generating valuable business analytics.

Ultimately, the more comprehensive the ingress of data is, the more intricate an organization’s analytics modeling can become.

What is Open-Source Data?

Data is “open” if anyone can freely use, reuse and redistribute it for any purpose, for any purpose imaginable, without any restrictions. Although many websites publish various volumes of information, it might not be considered open-source data. The key lies in the ability to re-use the data. Web content that has been developed in such a way to facilitate abstraction and reuse can be considered open-source data. The available data is essentially free to utilize by anyone.

Benefits of Open-Source Data

First and foremost, having free data at your disposal as an organization seems like a conceptual misnomer. Especially in the fast-paced data-driven economy of today. It, however, is not. Take the open-source data repository which is hosted by Google, for example. They provide links to vast collections of unique data sets which have been collected from websites all over the internet.

Organizations can leverage these open-source datasets to enrich their big data repository, reaping the benefit from the diligent work of others.

With certain datasets like governmental statistics, organizations can be certain of data being updated regularly. This allows organizations to keep their data fairly relevant.

Since such a wide array of data sets exist, the consumption of Open Source Data allows organizations to synthesize deeper insights into various statistical areas.

Having access to information that is up to date drives innovation within an organization. The possibilities are endless.

How can Open-Source Data be leveraged?

Open-source data can be abstracted and stored into private cloud repositories, that can be accessed as and when needed by artificial intelligence analytics engines. Open-source data would then act as auxiliary data sources, enriching organizational Big Data repositories.

Additionally using government- and open economic statistics, such as employment rates or growing economies, for example, organizations could predict future market expansions.

Product design and innovation are typically driven by fresh and novel data. Open-source data typically allows organizations to create models for preliminary product generation and placement. Taking data collected about the average temperature of IoT devices, for example, research and design teams can build baselines for new designs.

Are there downsides to Open-Source Data?

The main concern we have surrounding open-source data is the potential for misinformation. Although the data is free and in the public domain, organizations need to be aware that they would have no control over the quality of the open-source data they ingest. Open-source data should therefore be utilized sparingly when it comes to crucial business outcomes.

Organizations might also find that their open-source data collections present overlapping information. This convergence of information might lead to the anonymity of data actors being removed. This is called the mosaic effect. However, considering regulatory compliance and how this could put an organization in a compromising position, care should be taken when consuming various open-source data sources.

Conclusion

Overall, open-source data can be a very useful source of well-researched statistical information. Organizations need to be meticulous when choosing open-source data sources. The risk of being overzealous and overpopulating cloud data warehouses with useless information does exist. Business intelligence which is driven by artificial intelligence data modeling requires legible, multi-structured, data that can be efficiently grouped and sorted. An organization will not benefit from terabytes of incoherent data.

There is a future for open-source data and its validity is undeniable. Correctly leveraged open-source data can be a rich asset for organizations.

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