Data analysis platforms are transforming the way businesses operate by eliminating data silos and offering real-time insights.

Data analysis platforms are transforming the way businesses operate by eliminating data silos and offering real-time insights. Harnessing data from various sources, these platforms generate valuable operational insights through customised reports and industry-specific dashboards that are tailored to different technologies.

Central to these platforms are web-based interfaces that collect and organise machine data from an array of sources, including websites, applications, sensors and systems. By deploying agents at data collection points, they transmit data in its native format for in-depth analysis, allowing patterns, anomalies or irregularities to be pinpointed. Furthermore, establishing alerts and thresholds facilitates continuous monitoring and disaster recovery, protecting critical data.

Nonetheless, data analysis platforms can pose challenges. As data volume grows, storage and licensing costs also rise, potentially leading to mounting expenses. This dilemma forces businesses to make difficult budgetary choices, possibly impeding data analysis adoption and negatively impacting efficiency.

Investing in data analysis platforms presents both pros and cons, and being conscious of both enables businesses to take pre-emptive measures to optimise the potential of data analysis platforms, enhancing efficiency and promoting growth.               


  • Real-time data. Send data from source in ‘real-time’, enabling it to be made accessible where and when needed and analysed instantly.
  • Range of apps available. Access to hundreds of apps that feature ready-made reports and dashboards built for specific technologies or use cases.
  • Ability to reduce data spend. Data storage and licence costs can be reduced by cutting down on the amount of data being processed.
  • Monitoring 24×7. Uses monitoring tools to identify patterns, anomalies and exceptions.
  • Machine Learning Toolkit. Adds platform-supported tooling for machine learning capabilities.
  • Alerts and thresholds. Criteria can be used to look for specific data events at data points.
  • Additional features. Fail-over and disaster recovery are built-in to ensure data is protected.


  • Size of investment. Initial investment can be high. The business will need to buy an enterprise licence, pay AWS storage costs or other cloud storage costs.
  • Demonstrating ROI. It’s possible to generate ROI by simplifying management, streamlining data ingestion, speeding up analysis and helping the business meet its compliance demands. However, if you don’t take these steps, it becomes difficult to continue to justify the investment as data management costs escalate.
  • Need to control spending. Storage and licence costs will increase over time, which can see costs grow exponentially depending on the number of gigabytes being processed daily.
  • Vendor lock-in. As all data analysis is carried out within the platform’s applications, the business is tied into one provider. This means the business can’t send data to other processing environments and may find it difficult to utilise other best-of-breed cloud native technologies.

While there are clear advantages to using a data analytics platform – such as increased access to insights and versatility – these have to be considered alongside the downsides of escalating costs. Thankfully this can now be countered by applying technology to reduce the amount of data being processed and stored.

An observability pipeline tool can allow you to determine which data gets sent to the data analysis platform – and in what format – and can help you to reduce the amount of data by filtering it inflight. This can substantially reduce storing and licensing costs. For further help and guidance, you might also like to read these blogs:

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