topic 3- dq- 2-6
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Nov 24, 2024
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When choosing a data analytics platform, three key considerations should be evaluated: Features
and functionality, Scalability and Performance, and usability. First, the business must define the
requirements and capabilities of an analytics platform. Then, align the analytics platform features
and functionality with those requirements, including assessing its ability to handle large datasets,
perform complex analyses, and generate meaningful insights (Londhe & Rao, 2017).
Additionally, the software should meet the scalability and performance expectations of the
business as it grows. A robust and scalable data analytics platform should be capable of scaling
with the anticipated increase in the size or complexity of the dataset. Organizations need a
platform to handle increasing data demands as data volumes grow exponentially. The platform
should be able to run computational tasks efficiently and support the capacity to handle large-
scale data processing and analysis. Finally, evaluators should consider the platform’s ability to
handle increasing workloads and efficiently perform complex queries and calculations.
Usability and Ease of Use are also key for adoption by the user population such that they can
quickly and easily access and analyze the data without being hindered by a complicated
interface. The platform should be user-friendly and easy to navigate to increase adoption beyond
analysts and data scientists to the general users. Businesses should consider how analytics
applies to different organizational roles and which users need simplified solutions to support
decision-making (Chatterjee et al., 2021). Usability is critical in decision-making, given that
multiple departments and skill levels will require intuitive and user-friendly features to maximize
adoption and productivity.
Understanding that the discussion question asked for the top three considerations, it is important
to mention integration and compatibility with existing systems. It is critical to ensure a smooth
data flow and exchange between different platforms, ensuring compatibility and efficiency in the
overall data analytics process. Integration considerations include the availability of an already
available Application Programming Interface (API) or custom-built APIs to connect/integrate the
platform to other data sources, applications, and tools. Existing APIs simplify the integration,
whereas custom API development can be costly.
References
Chatterjee, S., Rana, N. P., & Dwivedi, Y. K. (2021). How does business analytics contribute to
organizational performance and business value? A resource-based view.
Information Technology
& People
.
https://doi.org/10.1108/itp-08-2020-0603
Londhe, A., & Rao, P. P. (2017). Platforms for big data analytics: Trend towards hybrid era.
2017
International Conference on Energy, Communication, Data Analytics and Soft Computing
(ICECDS)
.
https://doi.org/10.1109/icecds.2017.8390056
Sahu, S. K., Jacintha, M. M., & Singh, A. P. (2017). Comparative study of tools for big data
analytics: An analytical study.
2017 International Conference on Computing, Communication
and Automation (ICCCA).
https://doi.org/10.1109/ccaa.2017.8229827
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