Open Source: Innovating Data Science with Minimum Dependency

Organizations should adopt open source software to enhance agility and security.

Open-source data architecture is no more unfamiliar for organizations since it is being deployed in various data science projects. Data science has become an asset to industries considering its importance in advanced data analytics and data-driven business intelligence. The rapid digital transformation across the globe accelerated the adoption rate of disruptive technologies and automation.

Data is the digital currency today and WE Forum says that by 2025, it's estimated that 463 exabytes of data will be created each day globally. With such huge amounts of data, businesses find it difficult to process and analyze them on a continuous basis. This is where open-source steps in as it can navigate and manage these data to provide insights.

Open-source technology is leveraged considering its feasibility and cost-efficiency in real-time data analysis and data storage. Data scientists can build data frameworks with open-source tools to streamline their workflows and make the most out of available datasets. Linux. Python, PHP are some open source software examples.

Being a publicly accessible and modifiable framework with open-source code, it is reckoned better than other proprietary softwares. Open-source software has great advantages in the field of data science.

Eliminating Vendor Lock-Ins and Adding Flexibility

When it comes to expanding an application or modifying them, companies often find it difficult with their dependency on proprietary vendor services. In a state of vendor lock-in, companies become fully dependent on the vendors and providers, and even switching to another software will demand a huge cost by the vendor. With proprietary software, there remains the disadvantage of non-scalability and often recurring maintenance issues. Open source will eliminate the vendor lock-in situation by providing a flexible platform that is open to constant innovations and updates. Open source infrastructure enables customization without costing you any extra penny. This technology is known for its interoperability with various data stacks and its compliance to disruptive platforms like the cloud.

Enhanced Security and Low Costs

Leveraging open source can ensure better data security and privacy since they are a prevailing issue in the digital world. Data vulnerability increases once they are fed on to online platforms through the internet or into the hands of third parties. With open-source, it is possible to govern the data and decide whom or where to share it with. Imagine being in full control of your data stack. Isn't it liberating?

Since open source is not controlled by any outsiders, it is possible to get expert opinions on the security of your data science projects. This helps in identifying data branches and resolving them with a better vision.

Open source projects will cost you less compared to proprietary solutions. Enterprises can begin with less expensive tools and then expand according to the needs and this will save financial wastage.

Enable Data Democratization

Data science and data scientists are both in great demand today. However, the limited supply of data scientists creates a ruckus in organizations since data science is mostly centralized into a particularly skilled group. Democratizing data science is important to enhance the data knowledge among other employees of the organization. This will further enable the employees to take up basic data science tasks and lift the workload off of data scientists. Upskilling your workforce is necessary to carry out data science democratization and an open-source framework will support this. Data scientists can create open-source frameworks that allow the whole organization to contribute without any knowledge barriers.