Today, almost every company is trying to be data-driven in some sense or the other. Businesses across all the major verticals such as healthcare, telecommunications, banking, insurance, retail, education, etc. make use of data to better understand their customers, optimize their business processes and, ultimately, maximize their profits.
This is a guest post sponsored by our friends at RudderStack.
When it comes to using data for analytics, companies face two major challenges:
Data tracking: Tracking the required data from a multitude of sources in order to get insights out of it. As an example, tracking customer activity data such as logins, signups, purchases, and even clicks such as bookmarks from platforms such as mobile apps and websites becomes an issue for many eCommerce businesses.
Building a link between the Data and Business Intelligence: Once data is acquired, transforming it and making it compatible for a BI tool can often prove to be a substantial challenge.
A well-designed data analytics stack comes is essential in combating these challenges. It will ensure you’re well-placed to use the data at your disposal in more intelligent ways. It will help you drive more value.
What does a data analytics stack do?
A data analytics stack is a combination of tools which when put together, allows you to bring together all of your data in one platform, and use it to get actionable insights that help in better decision-making.
As seen in the diagram above illustrates, a data analytics stack is built upon three fundamental steps:
- Data Integration: This step involves collecting and blending data from multiple sources and transforming them in a compatible format, for storage. The sources could be as varied as a database (e.g. MySQL), an organization’s log files, or event data such as clicks, logins, bookmarks, etc from mobile apps or websites. A data analytics stack allows you to use all of such data together and use it to perform meaningful analytics.
- Data Warehousing: This next step involves storing the data for the purpose of analytics. As the complexity of data grows, it is feasible to consolidate all the data in a single data warehouse. Some of the popular modern data warehouses include Amazon’s Redshift, Google BigQuery and platforms such as Snowflake and MarkLogic.
- Data Analytics: In this final step, we use a visualization tool to load the data from the warehouse and use it to extract meaningful insights and patterns from the data, in the form of charts, graphs and reports.
Choosing a data analytics stack – proprietary or open-source?
When it comes to choosing a data analytics stack, businesses are often left with two choices – buy it or build it. On one hand, there are proprietary tools such as Google Analytics, Amplitude, Mixpanel, etc. – where the vendors alone are responsible for their configuration and management to suit your needs. With the best in class features and services that come along with the tools, your primary focus can just be project management, rather than technology management.
While using proprietary tools have their advantages, there are also some major cons to them that revolve mainly around cost, data sharing, privacy concerns, and more. As a result, businesses today are increasingly exploring open-source alternatives to build their data analytics stack.
The advantages of open-source analytics tools
Let’s now look at the 5 main advantages that open-source tools have over these proprietary tools.
Open source analytics tools are cost-effective
Proprietary analytics products can cost hundreds of thousands of dollars beyond their free tier. For small to medium-sized businesses, the return on investment does not often justify these costs.
Open-source tools are free to use and even their enterprise versions are reasonably priced compared to their proprietary counterparts. So, with lower up-front costs, reasonable expenses for training, maintenance and support, and no cost for licensing open-source analytics tools are much more affordable. More importantly, they’re better valued for money.
Open source analytics tools provide flexibility
Proprietary SaaS analytics products will invariably set restrictions on the ways in which they can be used. This is especially the case with the trial or the lite versions of the tools, which are free. For example, full SQL is not supported by some tools. This makes it hard to combine and query external data alongside internal data.
You’ll also often find that warehouse dumps provide no support either. And when they do, they’ll probably cost more and still have limited functionality. Data dumps from Google Analytics, for instance, can only be loaded into Google BigQuery. Also, these dumps are time-delayed. That means the loading process can be very slow..
With open-source software, you get complete flexibility: from the way you use your tools, how you combine to build your stack, and even how you use your data.
If your requirements change – which, let’s face it, they probably will – you can make the necessary changes without paying extra for customized solutions.
Avoid vendor lock-in
Vendor lock-in, also known as proprietary lock-in, is essentially a state where a customer becomes completely dependent on the vendor for their products and services. The customer is unable to switch to another vendor without paying a significant switching cost.
Some organizations spend a considerable amount of money on proprietary tools and services that they heavily rely on. If these tools aren’t updated and properly maintained, the organization using it is putting itself at a real competitive disadvantage.
This is almost never the case with open-source tools. Constant innovation and change is the norm. Even if the individual or the organization handling the tool moves on, the community can take over the project and maintain it. With open-source, you can rest assured that your tools will always be up-to-date without heavy reliance on anyone.
Improved data security and privacy
Privacy has become a talking point in many data-related discussions of late. This is thanks, in part, to data protection laws such as the GDPR and CCPA coming into force. High-profile data leaks have also kept the issue high on the agenda.
Open-source stack analytics running inside your cloud or on-prem environment gives complete control of your data. This lets you decide which data is to be used when, and how. It lets you dictate how third parties can access and use your data, if at all.
Open-source is the present
It’s hard to counter the fact that open-source is now mainstream. Companies like Microsoft, Apple, and IBM are now not only actively participating in the open-source community, they’re also contributing to it.
Open-source puts you on the front foot when it comes to innovation. With it, you’ll be able to leverage the power of a vibrant developer community to develop better products in more efficient ways.
How RudderStack helps you build an ideal open-source data analytics stack
RudderStack is a completely open-source, enterprise-ready platform to simplify data management in the most secure and reliable way. It works as a perfect data integration platform by routing your event data from data sources such as websites, mobile apps and servers, to multiple destinations of your choice – thus helping you save time and effort.
RudderStack integrates effortlessly with a multitude of destinations such as Google Analytics, Amplitude, MixPanel, Salesforce, HubSpot, Facebook Ads, and more, as well as popular data warehouses such as Amazon Redshift or S3. If performing efficient clickstream analytics is your goal, RudderStack offers you the perfect data pipeline to collect and route your data securely.