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According to a recent Forbes article, successful businesses will share “a deliberate choice in favor of leveraging Open Source technologies at the heart of their modern Data Architecture” in the near future. The article highlights how large companies like GitHub, Ford Motor Company, Macy’s, Progressive Insurance, and Netflix have all leveraged Open Source code and data technology to propel their businesses forward. However, Open Source data technology does not come without hidden and often overlooked costs — especially when it’s used in products. While upfront costs for Open Source Analytics may be less expensive or entirely free, there are still other costs to consider.
Below are the real costs associated with using Open Source Analytics in your products and across your organization.
Even if your organization has a full, well-equipped, and highly-skilled development team, team members will still have to spend a great deal of their time researching and gathering requirements for the Open Source code they’ll be using as they compare it to your organization’s current systems, integrations, and software. Each organization has its own customization requirements and different data infrastructures in place. For this reason alone, sometimes almost half of the development team’s project time will be spent on researching and evaluating different components of the Open Source code to see how it will fit with what their organization already uses.
That’s a lot of work that has already been completed and replicated by a variety of commercial vendors who are accustomed to working with a vast array of clients in various industries and have thoroughly tested and validated their solutions. Overhead costs for developing a platform or product that uses Open Source Analytics will end up being much greater than overhead costs for a commercial vendor, especially for organizations that already have a lot of systems and technologies in use.
Architecting (and Re-Architecting) Costs
As developers work to fit Open Source Analytics into their existing infrastructures, they may constantly be changing and rebuilding code and systems as their organization’s goals and requirements change. Developers will need to be prepared to make a variety of changes on an ongoing basis. For example, if an Open Source Analytics component they want to use to bind data doesn’t work with the component they want to use for charting, then they’ll most likely have to go back to the beginning and rebuild the entire architecture.
Additionally, every time free Data Visualization tools or other free BI components have an update, they’ll need to rework integration efforts across the board to ensure everything still works together. This is a long-term cost to seriously consider when new integrations and features are being innovated at rapid rates.
Expenses for Vendors with Enhanced and Additional Capabilities
Chances are that Open Source options will not offer all the specialized and customized capabilities that every organization needs in order to stay competitive in their marketplace. Therefore, at some point, organizations may need to look to Embedded Analytics commercial vendors for options like advanced features, customized integrations, and enhanced security. The need for more robust and secure capabilities is especially heightened when analytics are being implemented in a product, as the product will always need to have the best and most innovative and optimized Data Analytics capabilities to maintain its advantage over its competition.
Data Analytics platforms will need to have features like secure and restricted user access, encryption, multiple data source integrations, as well as easy-to-use interfaces. Otherwise, an organization may ultimately lose out on profits and revenue, due to owning routines and outdated Data Analytics capabilities and architectures that are either comparable or inferior to their competition.
Expenditures for Analyses and Analysts
A lot of Open Source Analytics tools aren’t intuitive or easy to use for most users, so they require a staff of specialists. Knowledgeable data analysts need to be on staff, as well as a staff of dedicated developers. This will continue to increase overhead costs. More data analysts will be needed to run reports and present data to executives and others across the organization with Open Source tools because the tools don’t often have a friendly user interface or dashboards that are easy to navigate.
Analysts will also be responsible for providing insight from the data, as well as running more administrative tasks to collect, manipulate, and present data. They’ll be required to make more manual queries and create more visual graphics. Most commercial vendors, on the other hand, have easier-to-use interactive features and dashboards that automatically query and visualize data, so data analysts can focus on delivering data insights instead of completing more mundane, administrative tasks.
Ongoing Support and Maintenance Costs
Most Open Source Analytics tools only offer discussion forums and manuals for continued support when issues arise and do not offer support for customized products that use their source code. Commercial options, though, often include support as a part of their offerings and are routinely tested and validated for accuracy, reliability, and flexibility. Additional support can often be acquired for a fee for Open Source options. But sometimes Open Source code creators may suddenly vanish or stop supporting their creation altogether, which can lead to outdated or bug-ridden technologies for your organization. And sometimes, if one component of the Open Source solution breaks or needs to be updated, it has the potential to throw off the entire architecture of the application or product you’ve used it to build, leaving your application or product useless.
In addition, Open Source Analytics solutions are not easily scalable. As organizations continue to acquire more users and analyze more data, Open Source solutions will not be able to keep up with the demands of their organization or product. Therefore, your organization will have to spend time and money on rebuilding the architecture for a new deployment each time its staff or operations grow.
Privacy and Data Compliance Regulations Fees and Costs
As Data Analytics continues to permeate various industries, privacy and data compliance regulations are becoming more prevalent and vigorous to keep consumers’ and companies’ data safe and secure. Keeping data secure, private, and regulated will be a major concern for all organizations, especially for those that don’t want to pay hefty fines. It’s also a lot harder to keep data private and well-regulated in accordance with outside standards when it’s analyzed and stored with Open Source technology that’s built by internal teams.
Ever since the banking crisis of 2008, and many subsequent data breaches, organizations (especially those in regulated industries, like finance) will increasingly be held accountable for compliance with data quality and accuracy standards. Government authorities will eventually require organizations to have documented governance programs and the use of tested, validated software to generate critical analytical models and algorithms. As a case in point, the General Data Protection Regulation will go into full effect in the EU in 2018, but will have ramifications and implications felt worldwide.
Organizations with Open Source Analytics tools may be violating these regulations without intending to do so, but will still be responsible for any fines or repercussions. Meanwhile, commercial options will account for all relevant privacy and data compliance regulations.
Your brand’s reputation, alone, is worth considering in this instance. If your organization experiences a data breach, due to unregulated Open Source code, where vital customer financial or personal data is stolen, it will lose the trust of consumers and partners for a prolonged time. They will have a hard time trusting your organization again and will be a lot less likely to buy or use your products in the future.
Costs to Back Out if You Choose the Wrong Analytics
While this cost can be associated with any type of analytics solution whether it’s Open Source or not, the costs and risks are much higher with Open Source options. Unfortunately, you’ll never truly know if an Open Source Analytics solution will work with your existing architectures until you have already attempted to implement it or once it has already been implemented. Not only will you have to rebuild everything from scratch if the Open Source solution doesn’t fit with your existing data architectures and platforms, you’ll still have to pay all your overhead costs which are already higher, may lose or corrupt a large amount of data in the process, and may end up having to purchase a commercial solution anyway. With commercial embedded analytics solutions, you’ll know system requirements, levels of customization, capabilities, features, etc., from the very beginning. You won’t have to implement everything in order to know what you’ll be getting and paying for in the long run.
Furthermore, you may discover that a few years down the road, your Open Source Analytics tools don’t work well with new integrations and capabilities your organization needs in order to remain competitive as it continues to scale and build new products and services. So, even if it fits the needs of your organization right now, it may not sometime in the not-so-distant future.
Ultimately, using Open Source Analytics in your products may save your organization a lot of money up front, but it will not save money in the long run. Eventually, you’ll have to rebuild or redevelop something with Open Source options, and the higher security and compliance risks alone may not be worth it.
Analytics Market. Web Analytics Software Review: Should You Use Commercial or Open Source. Last Accessed 12/27/17.
Crowd Source Testing. Pros and Cons of Open Source Software. Last Accessed 12/27/17.
Forbes. Open Source Software: The Hidden Cost of Free. Last Accessed 12/27/17.
InfoSys. Big Data Analytics Enters a World of Open Source Possibilities. Last Accessed 12/27/17.
Risk Span. Data Analytics: Advantages and Disadvantages of Open Source Data Modeling Tools. Last Accessed 12/27/17.
SAS and International Institute for Analytics. Eyes Wide Open: Open Source Analytics Software Research Brief. Last Accessed 12/27/17.