3 Reasons to Consider Open Source For Data Analytics

Open source software has exploded in recent years. This transparent, innovative community of software developers has found massive success in an industry traditionally dominated by large, established enterprises. Many companies have even built entire fortunes around open source technologies.

Recognizing the success of open source, those big enterprises are cashing in on the new age, hoping to join the movement before they get left behind. TechCrunch recently reported that Red Hat was newly acquired for by tech giant, IBM, for a handsome $32 billion. Elastic, MongoDB, and Mulesoft are all valued at over $4 billion.

If they are not acquiring, they are integrating. Businesses of all sizes are incorporating open source technologies into their IT infrastructures and advanced software products. A recent Red Hat survey found that 68% of respondent companies increased their use of open source software while 59% plan to increase their usage. Even more striking, only 1% of 950 enterprises surveyed dismissed the importance of open-source software.

Simultaneously, data analytics has risen to the forefront of tech. Businesses are investing billions of dollars in extracting valuable insights from massive amounts of collected data. More than 150 zettabytes (150 trillion gigabytes) of data will need analysis by 2025. The flood of data is forcing leaders to seek new tools, methods, and talent to cater to the unique requirements of big data.

The need for data analytics expanded so rapidly that traditional enterprise solutions could not adapt fast enough, opening the door for the open source community to develop their own solutions. Many of those open source unicorns mentioned above come from the data analytics space, and many more of them are on the way.

Should you consider open source software in your data strategy? We think so.

Why You Should Consider Open Source For Your Data Strategy

1 | Adaptability

With instant access to data and constant connectivity, the pace of business increases exponentially every day. Companies must be able to adapt and innovate to hold a competitive advantage and even survive. That's why the software industry has adopted concepts like DevOps and Agile Development.

Traditional enterprise data solutions often require massive infrastructure overhauls and long implementation periods. Often a single vendor provides all of the necessary updates, fixes, and documentation for many businesses. Therefore, the big, enterprise solution can only change as fast as the vendor even if the customer calls for immediate change.

Data strategies are unique to each organization and require customization. Cookie-cutter data strategies don't exist. Data changes overtime requiring frequent changes to models and data-driven products. Your tech stack needs to be able to move and adapt with the data. Open source solutions can provide your analytic teams with innovative solutions that adapt to your organization's unique data needs.

In contrast to big IT solutions, open source solutions are often created, tweaked, and monitored by multiple developers enhancing the speed and adaptability of these solutions. You know the phrase two brains are better than one? How about hundreds of developers' brains. The transparency of open source allows for better innovation, more often. The Red Hat survey noted “access to innovation” as one of the primary reasons for adopting open source technology.

2 | Accessibility

The transparency, low-cost nature of open source makes it highly accessible to businesses of all industries and sizes. As more companies seek ways to leverage their data entirely, access to essential technologies is crucial. It's no secret that enterprise solutions can be an expensive investment. However, there are also professional services fees, upgrade fees, and the hidden cost of adapting existing systems to fit the enterprise solution requirements.

The top reason businesses adopt open source software is affordability. 33% of enterprise users count it's lower total cost of ownership (TCO) as open-source's chief benefit. Implementing a successful data strategy has enormous upside, but the investment of enterprise solution may be too much. While several open source companies offer professional services, your developers have the option to configure the solution themselves since all documentation is accessible. The low-cost nature of open source software allows you to immediately execute your data strategy without going bankrupt.

Access to documentation also allows your developers to alter or customize the solution to fit your organization's changing needs. As we said before, data is continuously changing. You want a solution that allows you to adapt with the changes, not a solution that impedes progress.

3 | User-Driven

As companies become more user-centric, their data strategy should reflect this initiative. Luckily, data analytics is highly user-driven. Data scientists are developing predictive models to determine the right products for the right customers delivered at the right time. BI analysts are analyzing social and sales data to identify which campaigns resonate best with their potential customers. Do you see the trend? Companies are using data to drive a more valuable experience for the customer because they know their efforts will produce more value in return.

Similarly, the open source community must take a user-driven approach to development. Monetary gains are not the primary driver of open source technologies. This is partially why it is so accessible. In the open source community developers run the show, not the C Suite. The primary driver behind the technology must be the users, not the bottom-line.

Since open source technologies are not selected by price, solutions are evaluated on performance. Developers must focus on user experience, reliability, and performance to garner attention for their work. The highest-performing innovations will quickly rise to the top.

As data becomes further embedded in business strategy, business leaders need to consider how their tech stack will impact the success of their data strategy. An adaptable, accessible, user-driven tech stack is crucial to leveraging the full benefits of big data. Open source technologies can help you meet these needs and deliver an experience that customers will love without bankrupting your budget.