Why Should Data Analytics Be Open Source?

0
32

With its adoption flexibility and reliance on a community of dispersed individuals, open-source data analytics is becoming an even more popular approach among cash-strapped companies, nonprofits, and governments.

Perhaps the most famous software in this category is the Linux OS. Other common examples in the data industry range from database management software like  PostgreSQL and MySQL to tools like Trusted Analytics Platform (TAP) and Matomo.

Multiple potent incentives pull organizations to open source. Here are the primary ones:

Cost Effectiveness

Proprietary data analytics solutions may cost hundreds of thousands of dollars. The minimal return on investment doesn’t justify the costs for small to medium-sized entities.

On the other hand, open-source analytics solutions are free to use. Even if you go for their enterprise versions, you’ll find them reasonably affordable than their proprietary peers. So thanks to their lower up-front costs, support and maintenance, reasonable training expenses, and no licensing fee, open source tools are reasonably priced. Moreover, they offer better value for money.

Added Operational Flexibility

SaaS-based proprietary solutions will invariably restrict how they’re used, especially in free lite or trial versions of tools. For instance, some software solutions don’t support SQL, making it difficult to query and combine external and internal data.

Also, warehouse dumps don’t provide support. You’ll have to pay more when they do, and the functionality will still be limited. Google Analytics data dump, for instance, only loads into Google BigQuery and is time-delayed.

On the other hand, open-source databases offer complete flexibility from tool usage and stack building to data utilization. If your requirements change, you can adjust appropriately without the additional cost of customized solutions.

Access to The Appropriate Tools and Talent

The barrier to entry is lower in open-source software than in licensed tools. Organizations can encounter fewer experimenting constraints and will likely find freely available programming languages, talent, and data science tools.

Python programming language is a great example, offering extensive and versatile data manipulation capabilities. Moreover, most machine learning and data science frameworks like SciKit-Learn, PyTorch, and TensorFlow are open-source and built directly on the programming language.

Finally, machine learning libraries like XGBoost came into being as a university research project. For these learning institutions, the positive impacts of open-source tools are overwhelming.

Overcome Vendor Lock-In

Also referred to as proprietary lock-in, this is a state where a client becomes entirely dependent on the vendor’s services and products. To switch to another vendor, they must pay a high moving cost.

Some companies spend considerably on the proprietary services and tools they rely on. Without proper maintenance and regular updates, you can lose your competitive edge.

But this seldom happens with open-source software, where constant change and innovation are the norms. Even if the party handling the solution moves on, the community can pick the project and keep it running. Thus, you can be confident that the tools you use are up-to-date.

Enhanced Data Privacy and Security

Data privacy is among the major talking points in almost every data-related discussion. This development can be partly attributed to the enforcement of data protection laws like CCPA and GDPR. Furthermore, high-profile data breaches and thefts have kept the topic high on the agenda.

You enjoy complete data control when open-source stack analytics runs within your on-premise or cloud environment. As a result, you can decide which information is available for use when, how, and by whom. Moreover, you can regulate how third parties access and utilize your valuable data.

Investment in open-source tools is only likely to increase. Businesses, government agencies, and nonprofits will continue to move away from the notion that software must be highly customized. Instead, many will seek robust solutions with added flexibility and cost-effectiveness.